Congestion Aware Routing in Nonlinear Elastic Optical Networks

22/10/201902/07/2019 by admin

Sensor networks are composed of small sensing devices that have the capability to take various measurements of their environment such as temperature, sound, light etc. These devices are equipped with a processor and wireless communication antenna and are powered with a battery. Upon deployment in a field, they form an ad hoc network and communicate with each other and with data processing centers. The routing protocol in such networks has an important effect on congestion, especially with increasing sizes of the deployments. Congestion becomes worse when a particular area is generating most of the data. This may occur in some deployments when sensors in one area of interest are requested to gather and transmit data at a higher rate than others.

We believe that all data generated in a sensor network may not be equally important; some may have a low priority while others have a higher priority and hence differentiated service must be provided to these data. In such a scenario, routing dynamics can lead to congestion on specific paths. Since congestion is a self-compounding problem, these paths are usually close to each other which lead to an entire zone in the network facing congestion. We refer to this zone as the congestion zone or conzone.

Congestion can adversely affect the network in two ways.

First, it can lead to indiscriminate dropping of data, i.e. some packets of high priority might be dropped while others of less priority are delivered. This happens because sensor nodes are very simple devices and do not have the capability to differentiate packets (i.e. they do not have multiple queues for different priority levels). Second, congestion can cause an increase in energy consumption as links become saturated. This can lead to depletion of the limited energy available in the sensor nodes in the congested area.

In this paper, we examine data delivery issues in the presence of congestion in wireless sensor networks. We propose the use of data prioritization and a simple priority aware routing protocol, Congestion Aware Routing (CAR). CAR does not use multiple priority queues, a QoS aware MAC layer or specialized scheduling algorithms. The first step in this protocol is to dynamically discover the conzone. The second step is to enforce differentiated routing; high priority packets are routed in the conzone. Low priority packets generated outside the conzone stay outside while those generated within the conzone are routed out. In effect, conzone nodes are dedicated to serving high priority data which will enable them to provide better service and lengthen their lifetime.

Our extensive simulations show that CAR leads to a significant increase in the successful packet delivery ratio of high priority data to the sink, and a clear decrease in the average delay to CAR also provides low jitter which makes it able to support real-time multimedia applications. It also reduces the energy consumed in the nodes that lie on the conzone which leads to an increase in connectivity lifetime. We now consider the network formation process. Once the sink node discovers its surrounding neighbors, it broadcasts a “Build Mesh” message asking all nodes in the network to organize as a mesh. In that message the sink provides its ID and zero as its depth. Once a neighboring node hears this message it will check if it has already joined the routing network (i.e. if it knows its depth); if not then it sets its depth to one plus the depth in the message received and sets the source of the message as a parent.

Each node then rebroadcasts the Build Mesh message, with its own ID and depth to its neighbors. If a node is already a member of the network, then it will check the depth in the message, and if that depth is less than its own, then the source of the message is added as a parent. In that case, the message is not rebroadcast. In this fashion, the Build Mesh message is sent down the network until all nodes become part of this routing structure. Similar to TAG, the Build Mesh message can be periodically broadcast to maintain the topology and adapt to changes caused by the failure, addition or mobility of nodes.

1.3 SCOPE OF THE PROJECT:

Design goals of the congestion aware routing (CAR) protocol for sensor networks are to provide high priority data with better service quality compared to other routing schemes. These include higher delivery ratios, lower delays and lower jitter to support real-time data. We also aim at decreasing energy consumption which will lengthen the lifetime of the network. To achieve these goals, CAR divides the network into two regions; the congestion zone (conzone) and the remaining part of the network. While high priority data is routed through the conzone, low priority data is routed using the other nodes. Low priority data that originates outside the conzone is routed exclusively on off-conzone nodes using regular routing protocols such as low priority data that originate inside the conzone are efficiently routed out of the conzone.

LITRATURE SURVEY

ELASTIC OPTICAL NETWORKING: A NEW DAWN FOR THE OPTICAL LAYER?

PUBLICATION: O. Gerstel, M. Jinno, A. Lord, and S. J. B. Yoo, IEEE Commun. Mag., vol. 50, no. 2, pp. s12–s20, Feb. 2012.

Optical networks are undergoing significant changes, fueled by the exponential growth of traffic due to multimedia services and by the increased uncertainty in predicting the sources of this traffic due to the ever changing models of content providers over the Internet. The change has already begun: simple on-off modulation of signals, which was adequate for bit rates up to 10 Gb/s, has given way to much more sophisticated modulation schemes for 100 Gb/s and beyond. The next bottleneck is the 10-year-old division of the optical spectrum into a fixed “wavelength grid,” which will no longer work for 400 Gb/s and above, heralding the need for a more flexible grid. Once both transceivers and switches become flexible, a whole new elastic optical networking paradigm is born. In this article we describe the drivers, building blocks, architecture, and enabling technologies for this new paradigm, as well as early standardization efforts.

MODELING THE ROUTING AND SPECTRUM ALLOCATION PROBLEM FOR FLEXGRID OPTICAL NETWORKS

PUBLICATION: L. Velasco, M. Klinkowski, M. Ruiz, and J. Comellas, Photon. Netw. Commun., vol. 24, no. 3, pp. 177–186, 2012.

Flexgrid optical networks are attracting huge interest due to their higher spectrum efficiency and flexibility in comparison with traditional wavelength switched optical networks based on the wavelength division multiplexing technology. To properly analyze, design, plan, and operate flexible and elastic networks, efficient methods are required for the routing and spectrum allocation (RSA) problem. Specifically, the allocated spectral resources must be, in absence of spectrum converters, the same along the links in the route (the continuity constraint) and contiguous in the spectrum (the contiguity constraint). In light of the fact that the contiguity constraint adds huge complexity to the RSA problem, we introduce the concept of channels for the representation of contiguous spectral resources. In this paper, we show that the use of a pre-computed set of channels allows considerably reducing the problem complexity. In our study, we address an off-line RSA problem in which enough spectrum needs to be allocated for each demand of a given traffic matrix. To this end, we present novel integer lineal programming (ILP) formulations of RSA that are based on the assignment of channels. The evaluation results reveal that the proposed approach allows solving the RSA problem much more efficiently than previously proposed ILP-based methods and it can be applied even for realistic problem instances, contrary to previous ILP formulations.

DISTANCE-ADAPTIVE SPECTRUM RESOURCE ALLOCATION IN SPECTRUM-SLICED ELASTIC OPTICAL PATH NETWORK

PUBLICATION: M. Jinno et al., “,” IEEE Commun. Mag., vol. 48, no. 8, pp. 138–145, Aug. 2010.

The rigid nature of current wavelength-routed optical networks brings limitations on network utilization efficiency. One limitation originates from mismatch of granularities between the client layer and the wavelength layer. The recently proposed spectrum-sliced elastic optical path network (SLICE) is expected to mitigate this problem by adaptively allocating spectral resources according to client traffic demands. This article discusses another limitation of the current optical networks associated with worst case design in terms of transmission performance. In order to address this problem, we present a concept of a novel adaptation scheme in SLICE called distance-adaptive spectrum resource allocation. In the presented scheme the minimum necessary spectral resource is adaptively allocated according to the end-to-end physical condition of an optical path. Modulation format and optical filter width are used as parameters to determine the necessary spectral resources to be allocated for an optical path. Evaluation of network utilization efficiency shows that distance-adaptive SLICE can save more than 45 percent of required spectrum resources for a 12-node ring network. Finally, we introduce the concept of a frequency slot to extend the current frequency grid standard, and discuss possible spectral resource designation schemes.

QOT PREDICTION FOR CORE NETWORKS WITH UNCOMPENSATED COHERENT TRANSMISSION

PUBLICATION: M. Angelou, P. N. Ji, I. Tomkos, and T. Wang, in Proc. OECC/PS Jul. 2013, pp. 1–2, paper TuQ3-4.

We propose a comprehensive QoT prediction tool based on fast analytical modeling for on-the-fly signal assessments in networks with uncompensated coherent systems and confirm its superiority in reducing over-engineering compared to system-reach methods.

CHAPTER 2

2.0 SYSTEM ANALYSIS

2.1 EXISTING SYSTEM:

The Problem of Existing Solutions in these scenario nodes in the network sends all high priority data to a single sink, tree-based routing is the most appropriate. In this routing scheme, a spanning tree is built with the high priority sink as its root. The setup of such a tree uses controlled flooding from the sink to all nodes in the network. Low priority data, on the other hand, do not need to follow the same routing scheme. This is true because there may be multiple low priority sinks and a node might send data to any of them. For example, temperature readings might be forwarded to one sink while the motion detection measurements go to another sink, and tree based routing schemes suffer from congestion, especially if the number of messages generated in the leaves is high.

This problem becomes worse when we have a mixture of high priority and low priority traffic traveling through the network. This is because low priority messages will cross the tree that is formed to route high priority data in order to reach their destinations. Therefore even when the rate of high priority data is relatively low, the background noise created by low priority traffic will create a congestion zone that spans the deployment from the critical area to the high priority sink. Nodes in this zone become overwhelmed and indiscriminately drop high and low priority messages. These nodes also consume more energy compared to other nodes in the network and hence die sooner. This will lead to only sub-optimal paths being available to route high priority data, or a total loss of connectivity from critical area to the sink even though other nodes outside a single routing scheme is used to route both types of traffic.

2.1.1 DISADVANTAGES:

In such a scenario, routing dynamics can lead to congestion on specific paths. Since congestion is a self-compounding problem, these paths are usually close to each other which lead to an entire zone in the network facing congestion. Congestion can adversely affect the network in two ways. First, it can lead to indiscriminate dropping of data, i.e. some packets of high priority might be dropped while others of less priority are delivered. This happens because sensor nodes are very simple devices and do not have the capability to differentiate packets (i.e. they do not have multiple queues for different priority levels). Second, congestion can cause an increase in energy consumption as links become saturated. This can lead to depletion of the limited energy available in the sensor nodes in the congested area.

2.2 PROPOSED SYSTEM:

We proposed Congestion Aware Routing (CAR) which is a simple routing protocol that uses data prioritization and treats packets according to their priorities. We defined a conzone as the set of sensors that will be required to route high priority packets from the data sources to the sink.

We presented algorithms to build a high priority routing mesh, dynamically discover and configure conzones, and perform differentiated routing. Our solutions do not require active queue management, maintenance of multiple queues or scheduling algorithms, or the use of specialized MAC protocols.

The proposed algorithm for RMSA in a nonlinear elastic network utilizing Nyquist pulse shaping is as follows:

Determine the optimum signal power spectral density given the fiber and amplifier parameters.
For a pair of nodes, select the shortest path that avoids the link with the highest spectral usage (determined by measuring the total optical power which is proportional to spectral usage).
For this path determine the total number of amplifier spans (100 km herein) in order to determine the received signal to noise ratio (SNR).
For this SNR, determine the maximum net spectral efficiency (NSE) based on known relationship between SNR and NSE for a range of polarization division multiplexed formats with Nyquist spectra where variable rate FEC is also included.
Finally determine the gross symbol rate and assign spectrum to serve the demand between the two nodes. We showed that with the inclusion of small playout buffers at the sink, the CAR-based routing is suitable for delivering real-time traffic, such as video, over a wide range of conditions.

2.2.1 ADVANTAGES:

High priority data delivery is assured without loss
Conzone (congestion zone discovery) is an overhead.
Low priority data is often dropped
Low priority data delivery is also assured along with high priority data. The channel is virtually divided for both priorities.
Still low priority data is often dropped
Low Priority data delivery is assured to maximum extent.
The burden on intermediate nodes is decreased for discovering
The request and acknowledgements traffic is reduced in this method.
The Low Priority data has to travel in long path which has less congestion
In the long path all the sensor nodes has to be in active position which increases battery consumption

2.3 HARDWARE & SOFTWARE REQUIREMENTS:

2.3.1 HARDWARE REQUIREMENT:

v Processor – Pentium –IV

Speed – 1.1 GHz
- RAM – 256 MB (min)
- Hard Disk – 20 GB
- Floppy Drive – 1.44 MB
- Key Board – Standard Windows Keyboard
- Mouse – Two or Three Button Mouse
- Monitor – SVGA

2.3.2 SOFTWARE REQUIREMENTS:

Operating System : Windows XP
Front End : Microsoft Visual Studio .NET 2008
Document : MS-Office 2007

CHAPTER 3

3.0 SYSTEM DESIGN

ARCHITECUTRE DIAGRAM / UML DIAGRAM / DATA FLOW DIAGRAM:

The DFD is also called as bubble chart. It is a simple graphical formalism that can be used to represent a system in terms of the input data to the system, various processing carried out on these data, and the output data is generated by the system

The data flow diagram (DFD) is one of the most important modeling tools. It is used to model the system components. These components are the system process, the data used by the process, an external entity that interacts with the system and the information flows in the system.

DFD shows how the information moves through the system and how it is modified by a series of transformations. It is a graphical technique that depicts information flow and the transformations that are applied as data moves from input to output.

DFD is also known as bubble chart. A DFD may be used to represent a system at any level of abstraction. DFD may be partitioned into levels that represent increasing information flow and functional detail.

NOTATION:

SOURCE OR DESTINATION OF DATA:

External sources or destinations, which may be people or organizations or other entities

DATA SOURCE:

Here the data referenced by a process is stored and retrieved.

PROCESS:

People, procedures or devices that produce data in the physical component is not identified.

DATA FLOW:

Data moves in a specific direction from an origin to a destination. The data flow is a “packet” of data.

MODELING RULES:

There are several common modeling rules when creating DFDs:

All processes must have at least one data flow in and one data flow out.
All processes should modify the incoming data, producing new forms of outgoing data.
Each data store must be involved with at least one data flow.
Each external entity must be involved with at least one data flow.
A data flow must be attached to at least one process.

3.1 ARCHITECTURE DIAGRAM:

CHAPTER 4

4.0 IMPLEMENTATION:

4.1 ALGORITHM

4.2 MODULES:

SERVER CLIENT MODULE:

FIBER NONLINEARITIES:

DISCOVERY FROM SINK:

NETWORK PROBABILITY (NBP):

ROUTING ALGORITHMS (CAR):

4.3 MODUL DISCRIPTION:

CHAPTER 5

5.0 SYSTEM STUDY:

5.1 FEASIBILITY STUDY:

The feasibility of the project is analyzed in this phase and business proposal is put forth with a very general plan for the project and some cost estimates. During system analysis the feasibility study of the proposed system is to be carried out. This is to ensure that the proposed system is not a burden to the company. For feasibility analysis, some understanding of the major requirements for the system is essential.

Three key considerations involved in the feasibility analysis are

ECONOMICAL FEASIBILITY
TECHNICAL FEASIBILITY
SOCIAL FEASIBILITY

5.1.1 ECONOMICAL FEASIBILITY:

This study is carried out to check the economic impact that the system will have on the organization. The amount of fund that the company can pour into the research and development of the system is limited. The expenditures must be justified. Thus the developed system as well within the budget and this was achieved because most of the technologies used are freely available. Only the customized products had to be purchased.

5.1.2 TECHNICAL FEASIBILITY:

This study is carried out to check the technical feasibility, that is, the technical requirements of the system. Any system developed must not have a high demand on the available technical resources. This will lead to high demands on the available technical resources. This will lead to high demands being placed on the client. The developed system must have a modest requirement, as only minimal or null changes are required for implementing this system.

5.1.3 SOCIAL FEASIBILITY:

The aspect of study is to check the level of acceptance of the system by the user. This includes the process of training the user to use the system efficiently. The user must not feel threatened by the system, instead must accept it as a necessity. The level of acceptance by the users solely depends on the methods that are employed to educate the user about the system and to make him familiar with it. His level of confidence must be raised so that he is also able to make some constructive criticism, which is welcomed, as he is the final user of the system.

5.2 SYSTEM TESTING:

Testing is a process of checking whether the developed system is working according to the original objectives and requirements. It is a set of activities that can be planned in advance and conducted systematically. Testing is vital to the success of the system. System testing makes a logical assumption that if all the parts of the system are correct, the global will be successfully achieved. In adequate testing if not testing leads to errors that may not appear even many months. This creates two problems, the time lag between the cause and the appearance of the problem and the effect of the system errors on the files and records within the system. A small system error can conceivably explode into a much larger Problem. Effective testing early in the purpose translates directly into long term cost savings from a reduced number of errors. Another reason for system testing is its utility, as a user-oriented vehicle before implementation. The best programs are worthless if it produces the correct outputs.

5.2.1 UNIT TESTING:

A program represents the logical elements of a system. For a program to run satisfactorily, it must compile and test data correctly and tie in properly with other programs. Achieving an error free program is the responsibility of the programmer. Program testing checks for two types of errors: syntax and logical. Syntax error is a program statement that violates one or more rules of the language in which it is written. An improperly defined field dimension or omitted keywords are common syntax errors. These errors are shown through error message generated by the computer. For Logic errors the programmer must examine the output carefully.

UNIT TESTING:

Description	Expected result
Test for application window properties.	All the properties of the windows are to be properly aligned and displayed.
Test for mouse operations.	All the mouse operations like click, drag, etc. must perform the necessary operations without any exceptions.

5.1.3 FUNCTIONAL TESTING:

Functional testing of an application is used to prove the application delivers correct results, using enough inputs to give an adequate level of confidence that will work correctly for all sets of inputs. The functional testing will need to prove that the application works for each client type and that personalization function work correctly.When a program is tested, the actual output is compared with the expected output. When there is a discrepancy the sequence of instructions must be traced to determine the problem. The process is facilitated by breaking the program into self-contained portions, each of which can be checked at certain key points. The idea is to compare program values against desk-calculated values to isolate the problems.

FUNCTIONAL TESTING:

Description	Expected result
Test for all modules.	All peers should communicate in the group.
Test for various peer in a distributed network framework as it display all users available in the group.	The result after execution should give the accurate result.

5.1. 4 NON-FUNCTIONAL TESTING:

The Non Functional software testing encompasses a rich spectrum of testing strategies, describing the expected results for every test case. It uses symbolic analysis techniques. This testing used to check that an application will work in the operational environment. Non-functional testing includes:

Load testing
Performance testing
Usability testing
Reliability testing
Security testing

5.1.5 LOAD TESTING:

An important tool for implementing system tests is a Load generator. A Load generator is essential for testing quality requirements such as performance and stress. A load can be a real load, that is, the system can be put under test to real usage by having actual telephone users connected to it. They will generate test input data for system test.

Load Testing

Description	Expected result
It is necessary to ascertain that the application behaves correctly under loads when ‘Server busy’ response is received.	Should designate another active node as a Server.

5.1.5 PERFORMANCE TESTING:

Performance tests are utilized in order to determine the widely defined performance of the software system such as execution time associated with various parts of the code, response time and device utilization. The intent of this testing is to identify weak points of the software system and quantify its shortcomings.

PERFORMANCE TESTING:

Description	Expected result
This is required to assure that an application perforce adequately, having the capability to handle many peers, delivering its results in expected time and using an acceptable level of resource and it is an aspect of operational management.	Should handle large input values, and produce accurate result in a expected time.

5.1.6 RELIABILITY TESTING:

The software reliability is the ability of a system or component to perform its required functions under stated conditions for a specified period of time and it is being ensured in this testing. Reliability can be expressed as the ability of the software to reveal defects under testing conditions, according to the specified requirements. It the portability that a software system will operate without failure under given conditions for a given time interval and it focuses on the behavior of the software element. It forms a part of the software quality control team.

RELIABILITY TESTING:

Description	Expected result
This is to check that the server is rugged and reliable and can handle the failure of any of the components involved in provide the application.	In case of failure of the server an alternate server should take over the job.

5.1.7 SECURITY TESTING:

Security testing evaluates system characteristics that relate to the availability, integrity and confidentiality of the system data and services. Users/Clients should be encouraged to make sure their security needs are very clearly known at requirements time, so that the security issues can be addressed by the designers and testers.

SECURITY TESTING:

Description	Expected result
Checking that the user identification is authenticated.	In case failure it should not be connected in the framework.
Check whether group keys in a tree are shared by all peers.	The peers should know group key in the same group.

5.1.7 WHITE BOX TESTING:

White box testing, sometimes called glass-box testing is a test case design method that uses the control structure of the procedural design to derive test cases. Using white box testing method, the software engineer can derive test cases. The White box testing focuses on the inner structure of the software structure to be tested.

5.1.8 WHITE BOX TESTING:

Description	Expected result
Exercise all logical decisions on their true and false sides.	All the logical decisions must be valid.
Execute all loops at their boundaries and within their operational bounds.	All the loops must be finite.
Exercise internal data structures to ensure their validity.	All the data structures must be valid.

5.1.9 BLACK BOX TESTING:

Black box testing, also called behavioral testing, focuses on the functional requirements of the software. That is, black testing enables the software engineer to derive sets of input conditions that will fully exercise all functional requirements for a program. Black box testing is not alternative to white box techniques. Rather it is a complementary approach that is likely to uncover a different class of errors than white box methods. Black box testing attempts to find errors which focuses on inputs, outputs, and principle function of a software module. The starting point of the black box testing is either a specification or code. The contents of the box are hidden and the stimulated software should produce the desired results.

5.1.10 BLACK BOX TESTING:

Description	Expected result
To check for incorrect or missing functions.	All the functions must be valid.
To check for interface errors.	The entire interface must function normally.
To check for errors in a data structures or external data base access.	The database updation and retrieval must be done.
To check for initialization and termination errors.	All the functions and data structures must be initialized properly and terminated normally.

All the above system testing strategies are carried out in as the development, documentation and institutionalization of the proposed goals and related policies is essential.

CHAPTER 6

6.0 SOFTWARE SPECIFICATION:

6.1 FEATURES OF .NET:

Microsoft .NET is a set of Microsoft software technologies for rapidly building and integrating XML Web services, Microsoft Windows-based applications, and Web solutions. The .NET Framework is a language-neutral platform for writing programs that can easily and securely interoperate. There’s no language barrier with .NET: there are numerous languages available to the developer including Managed C++, C#, Visual Basic and Java Script.

The .NET framework provides the foundation for components to interact seamlessly, whether locally or remotely on different platforms. It standardizes common data types and communications protocols so that components created in different languages can easily interoperate.

“.NET” is also the collective name given to various software components built upon the .NET platform. These will be both products (Visual Studio.NET and Windows.NET Server, for instance) and services (like Passport, .NET My Services, and so on).

6.2 THE .NET FRAMEWORK

The .NET Framework has two main parts:

1. The Common Language Runtime (CLR).

2. A hierarchical set of class libraries.

The CLR is described as the “execution engine” of .NET. It provides the environment within which programs run. The most important features are

Conversion from a low-level assembler-style language, called Intermediate Language (IL), into code native to the platform being executed on.
Memory management, notably including garbage collection.
Checking and enforcing security restrictions on the running code.
Loading and executing programs, with version control and other such features.
The following features of the .NET framework are also worth description:

Managed Code

The code that targets .NET, and which contains certain extra Information – “metadata” – to describe itself. Whilst both managed and unmanaged code can run in the runtime, only managed code contains the information that allows the CLR to guarantee, for instance, safe execution and interoperability.

Managed Data

With Managed Code comes Managed Data. CLR provides memory allocation and Deal location facilities, and garbage collection. Some .NET languages use Managed Data by default, such as C#, Visual Basic.NET and JScript.NET, whereas others, namely C++, do not. Targeting CLR can, depending on the language you’re using, impose certain constraints on the features available. As with managed and unmanaged code, one can have both managed and unmanaged data in .NET applications – data that doesn’t get garbage collected but instead is looked after by unmanaged code.

Common Type System

The CLR uses something called the Common Type System (CTS) to strictly enforce type-safety. This ensures that all classes are compatible with each other, by describing types in a common way. CTS define how types work within the runtime, which enables types in one language to interoperate with types in another language, including cross-language exception handling. As well as ensuring that types are only used in appropriate ways, the runtime also ensures that code doesn’t attempt to access memory that hasn’t been allocated to it.

Common Language Specification

The CLR provides built-in support for language interoperability. To ensure that you can develop managed code that can be fully used by developers using any programming language, a set of language features and rules for using them called the Common Language Specification (CLS) has been defined. Components that follow these rules and expose only CLS features are considered CLS-compliant.

6.3 THE CLASS LIBRARY

.NET provides a single-rooted hierarchy of classes, containing over 7000 types. The root of the namespace is called System; this contains basic types like Byte, Double, Boolean, and String, as well as Object. All objects derive from System. Object. As well as objects, there are value types. Value types can be allocated on the stack, which can provide useful flexibility. There are also efficient means of converting value types to object types if and when necessary.

The set of classes is pretty comprehensive, providing collections, file, screen, and network I/O, threading, and so on, as well as XML and database connectivity.

The class library is subdivided into a number of sets (or namespaces), each providing distinct areas of functionality, with dependencies between the namespaces kept to a minimum.

6.4 LANGUAGES SUPPORTED BY .NET

The multi-language capability of the .NET Framework and Visual Studio .NET enables developers to use their existing programming skills to build all types of applications and XML Web services. The .NET framework supports new versions of Microsoft’s old favorites Visual Basic and C++ (as VB.NET and Managed C++), but there are also a number of new additions to the family.

Visual Basic .NET has been updated to include many new and improved language features that make it a powerful object-oriented programming language. These features include inheritance, interfaces, and overloading, among others. Visual Basic also now supports structured exception handling, custom attributes and also supports multi-threading.

Visual Basic .NET is also CLS compliant, which means that any CLS-compliant language can use the classes, objects, and components you create in Visual Basic .NET.

Managed Extensions for C++ and attributed programming are just some of the enhancements made to the C++ language. Managed Extensions simplify the task of migrating existing C++ applications to the new .NET Framework.

C# is Microsoft’s new language. It’s a C-style language that is essentially “C++ for Rapid Application Development”. Unlike other languages, its specification is just the grammar of the language. It has no standard library of its own, and instead has been designed with the intention of using the .NET libraries as its own.

Microsoft Visual J# .NET provides the easiest transition for Java-language developers into the world of XML Web Services and dramatically improves the interoperability of Java-language programs with existing software written in a variety of other programming languages.

Active State has created Visual Perl and Visual Python, which enable .NET-aware applications to be built in either Perl or Python. Both products can be integrated into the Visual Studio .NET environment. Visual Perl includes support for Active State’s Perl Dev Kit.

Other languages for which .NET compilers are available include

FORTRAN
COBOL
Eiffel

ASP.NET XML WEB SERVICES	Windows Forms
Base Class Libraries
Common Language Runtime
Operating System

Fig1 .Net Framework

C#.NET is also compliant with CLS (Common Language Specification) and supports structured exception handling. CLS is set of rules and constructs that are supported by the CLR (Common Language Runtime). CLR is the runtime environment provided by the .NET Framework; it manages the execution of the code and also makes the development process easier by providing services.

C#.NET is a CLS-compliant language. Any objects, classes, or components that created in C#.NET can be used in any other CLS-compliant language. In addition, we can use objects, classes, and components created in other CLS-compliant languages in C#.NET .The use of CLS ensures complete interoperability among applications, regardless of the languages used to create the application.

CONSTRUCTORS AND DESTRUCTORS:

Constructors are used to initialize objects, whereas destructors are used to destroy them. In other words, destructors are used to release the resources allocated to the object. In C#.NET the sub finalize procedure is available. The sub finalize procedure is used to complete the tasks that must be performed when an object is destroyed. The sub finalize procedure is called automatically when an object is destroyed. In addition, the sub finalize procedure can be called only from the class it belongs to or from derived classes.

GARBAGE COLLECTION

Garbage Collection is another new feature in C#.NET. The .NET Framework monitors allocated resources, such as objects and variables. In addition, the .NET Framework automatically releases memory for reuse by destroying objects that are no longer in use.

In C#.NET, the garbage collector checks for the objects that are not currently in use by applications. When the garbage collector comes across an object that is marked for garbage collection, it releases the memory occupied by the object.

OVERLOADING

Overloading is another feature in C#. Overloading enables us to define multiple procedures with the same name, where each procedure has a different set of arguments. Besides using overloading for procedures, we can use it for constructors and properties in a class.

MULTITHREADING:

C#.NET also supports multithreading. An application that supports multithreading can handle multiple tasks simultaneously, we can use multithreading to decrease the time taken by an application to respond to user interaction.

STRUCTURED EXCEPTION HANDLING

C#.NET supports structured handling, which enables us to detect and remove errors at runtime. In C#.NET, we need to use Try…Catch…Finally statements to create exception handlers. Using Try…Catch…Finally statements, we can create robust and effective exception handlers to improve the performance of our application.

6.5 THE .NET FRAMEWORK

The .NET Framework is a new computing platform that simplifies application development in the highly distributed environment of the Internet.

OBJECTIVES OF .NET FRAMEWORK

1. To provide a consistent object-oriented programming environment whether object codes is stored and executed locally on Internet-distributed, or executed remotely.

2. To provide a code-execution environment to minimizes software deployment and guarantees safe execution of code.

3. Eliminates the performance problems.

There are different types of application, such as Windows-based applications and Web-based applications.

6.6 FEATURES OF SQL-SERVER

The OLAP Services feature available in SQL Server version 7.0 is now called SQL Server 2000 Analysis Services. The term OLAP Services has been replaced with the term Analysis Services. Analysis Services also includes a new data mining component. The Repository component available in SQL Server version 7.0 is now called Microsoft SQL Server 2000 Meta Data Services. References to the component now use the term Meta Data Services. The term repository is used only in reference to the repository engine within Meta Data Services

SQL-SERVER database consist of six type of objects,

They are,

1. TABLE

2. QUERY

3. FORM

4. REPORT

5. MACRO

TABLE:

A database is a collection of data about a specific topic.

VIEWS OF TABLE:

We can work with a table in two types,

1. Design View

2. Datasheet View

Design View

To build or modify the structure of a table we work in the table design view. We can specify what kind of data will be hold.

Datasheet View

To add, edit or analyses the data itself we work in tables datasheet view mode.

QUERY:

A query is a question that has to be asked the data. Access gathers data that answers the question from one or more table. The data that make up the answer is either dynaset (if you edit it) or a snapshot (it cannot be edited).Each time we run query, we get latest information in the dynaset. Access either displays the dynaset or snapshot for us to view or perform an action on it, such as deleting or updating.

CHAPTER 7

APPENDIX

7.1 SAMPLE SOURCE CODE

7.2 SAMPLE OUTPUT

CHAPTER 8

8.0 CONCLUSION:

Congestion aware routing has been investigated in nonlinear elastic optical networks and shown to be effective for the reference NSFNET topology. We observe that the network blocking probability (NBP) follows a generalized extreme value distribution, allowing robust estimates of the load for a given NBP to be obtained. When NSFNET is sequentially loaded with 100 GbE demands the proposed algorithm with a flexgrid, allows the network to support 1744 demands compared to 328 demands using a fixed 50 GHz grid with shortest path routing for NBP = 1%. The congestion aware routing algorithms investigated resulted in longer average paths, with 5% of all routes exceeding the maximum shortest path in order to increase the overall network capacity.

COMIC Cost Optimization for Internet Content Multi homing

22/10/201902/07/2019 by admin

Behavior Rule Specification-Based Intrusion Detection for Safety Critical Medical Cyber Physical Syst

22/10/201902/07/2019 by admin

We propose and analyze a behavior-rule specification-based technique for intrusion detection of medical devices embedded in a medical cyber physical system (MCPS) in which the patient’s safety is of the utmost importance. We propose a methodology to transform behavior rules to a state machine, so that a device that is being monitored for its behavior can easily be checked against the transformed state machine for deviation from its behavior specification. Using vital sign monitor medical devices as an example; we demonstrate that our intrusion detection technique can effectively trade false positives off for a high detection probability to cope with more sophisticated and hidden attackers to support ultra safe and secure MCPS applications. Moreover, through a comparative analysis, we demonstrate that our behavior-rule specification based IDS technique outperforms two existing anomaly-based techniques for detecting abnormal patient behaviors in pervasive healthcare applications.

INTRODUCTION

The most prominent characteristic of a medical cyber physical system (MCPS) is its feedback loop that acts on the physical environment. In other words, the physical environment provides data to the MCPS sensors whose data feed the MCPS control algorithms that drive the actuators which change the physical environment. MCPSs are often characterized by sophisticated patient treatment algorithms interacting with the physical environment including the patient. In this paper, we are concerned with intrusion detection mechanisms for detecting compromised sensors or actuators embedded in an MCPS for supporting safe and secure MCPS applications upon which patients and healthcare personnel can depend with high confidence.

Intrusion detection system (IDS) design for cyber physical systems (CPSs) has attracted considerable attention because of the dire consequence of CPS failure. However, IDS techniques for MCPSs is still in its infancy with very little work reported. Intrusion detection techniques in general can be classified into four types: signature, anomaly, trust, and specification-based techniques. In this paper, we consider specification rather than signature-based detection to deal with unknown attacker patterns. We consider specification rather than anomaly based techniques to avoid using resource constrained sensors or actuators in an MCPS for profiling anomaly patterns (e.g., through learning) and to avoid high false positives. We consider specification rather than trust based techniques to avoid delay due to trust aggregation and propagation to promptly react to malicious behaviors in safety critical MCPSs.

To accommodate resource-constrained sensors and actuators in an MCPS, we propose behavior-rule specification-based intrusion detection (BSID) which uses the notion of behavior rules for specifying acceptable behaviors of medical devices in an MCPS. Rule-based intrusion detection thus far has been applied only in the context of communication networks which have no concern of physical environments and the closed-loop control structure as in an MCPS. For example, Da Silva et al. propose an IDS that applies seven types of traffic-based rules to detect intruders: interval, retransmission, integrity, delay, repetition, radio transmission range and jamming. Ioannis et al. propose a multi trust IDS with traffic-based collection that audits the forwarding behavior of suspects to detect black hole and grey hole attacks launched by captured devices based on the rate of specification violations.

Our contribution relative to prior work cited above is that we specifically consider behavior rules for MCPS actuators controlling patient treatment algorithms as well as for physiological sensors providing information concerning the physical environment. Further, we propose a methodology to transform behavior rules to a state machine, so that a device that is being monitored for its behavior can easily be checked against the transformed state machine for deviation from its behavior specification. Existing work only considered specification-based state machines for intrusion detection of communication protocol misbehaving patterns.

Untreated in the literature, in this paper we also investigate the impact of attacker behaviors on the effectiveness of MCPS intrusion detection. We demonstrate that our specification based IDS technique can effectively trade higher false positives off for lower false negatives to cope with more sophisticated and hidden attackers. We show results for a range of configurations to illustrate this trade. Because the key motivation in MCPS is safety, our solution is deployed in a configuration yielding a high detection rate without compromising the false positive probability. Our approach is monitoring-based relying on the use of peer devices to monitor and measure the compliance degree of a trustee device connected to the monitoring node by the CPS network. The rules comparing monitor and trustee physiology (blood pressure, oxygen saturation, pulse, respiration and temperature) exceeds protection possible by considering devices in isolation.

The fundamental difference in designing IDSs for safety critical CPSs versus for other brands of systems is that the intrusion detection is closely tied with the physical components of the CPS, so the detection is less about communication protocol compliance but more about behavior compliance specific to the physical components to be controlled in the CPS. Thus, instead of monitoring packet routing or packet loss data for misbehavior detection of communication protocol compliance during packet transmission, IDSs for MCPSs may test medical sensor measurements and actuator settings for misbehavior detection of physical properties manifested because of attacks. For example, a patient requesting analgesic must have a pulse greater than some threshold, otherwise it may cause an overdose of analgesic delivered. Thus, if a patient requests analgesic while having a pulse below the threshold then an intruder may be involved. The behavior rules proposed in our work specifically address the expected behavior of individual physical components in the MCPS. The compliance threshold proposed in this paper specifically measures the goodness of a physical component. A challenge is to provide a high detection rate without introducing high false positives. We demonstrate that our IDS design based on the compliance threshold can effectively distinguish benign abnormalities from malicious attacks. To the best of our knowledge, there is no prior work discussing the difference between CPS intrusion detection and communication systems intrusion detection.

It is necessary to build an IDS per CPS domain/application since the behavior rules for specifying the behaviors of physical components/devices in a CPS are inherently domain/application specific. In the literature, ISML and T-Rex are also specification-based approaches for intrusion detection in CPSs. However, none of them considered MCPSs. In the field of intrusion detection for MCPSs or healthcare systems, Asfaw et al. studied an anomaly-based IDS for MCPSs. The authors focus on attacks that violate privacy of an MCPS; in contrast, our investigation focuses on attacks that violate the integrity of an MCPS. They use an anomaly-based approach while we use a specification-based approach. Asfaw et al. do not provide numerical results in the form of false negatives or positives which are the critical metrics for this research area; our investigation does provide these results.

Venkatasubramanian and Gupta survey security solutions for pervasive healthcare applications. Like , the authors focus on attacks on a passive pervasive healthcare system that violate patient privacy while our investigation considers integrity attacks on an MCPS that harm a patient. Their countermeasures focus on encryption and authentication/access control.

Yang and Hwang investigated an approach to fraud and abuse detection in healthcare applications. In contrast, our investigation focuses on the treatment, rather than the administrative, domain of healthcare. The authors use an anomaly-based approach while we use a specification-based approach. They provide numerical results that measure internal validity (the effectiveness of the data mining implementation) but do not provide externally valid metrics like Receiver Operating Characteristic(ROC) which can reveal the tradeoff between the detection rate vs. the false positive probability Porras and Neumann study a hierarchical multi trust behavior-based IDS called Event Monitoring Enabling Responses to Anomalous Live Disturbances (EMERALD) using complementary signature based and anomaly-based analysis. The authors identify a signature-based analysis trade between the state space created/runtime burden imposed by rich rule sets and the increased false negatives that stem from a less expressive rule set.

Porras and Neumann highlight two specific anomaly-based techniques using statistical analysis: one studies user sessions (to detect live intruders), and the other studies the runtime behavior of programs (to detect malicious code). EMERALD provides a generic analysis framework that is flexible enough to allow anomaly detectors to run with different scopes of multi trust data (service, domain or enterprise). However, Porras and Neumann did not report false positive or false negative probability data. While EMERALD pursues a domain-independent CPS security solution combining anomaly and signature-based analysis, our investigation focuses on one that is relevant for MCPSs using specification-based analysis. Park et al.propose a semi-supervised anomaly-based IDS targeted for assisted living environments. Their design is behavior-based and audits series of events which they call episodes. The authors’ events are 3-tuples comprising sensor ID, start time and duration. Park et al. test data sets using four similarity functions based on: LCS, count of common events not in LCS, event start times and event durations. They control episode length and similarity function as independent variables. The authors provide excellent ROC data which we use for a comparative analysis.

Tsang and Kwong propose a multi trust IDS called Multi-agent System (MAS) that includes an analysis function called Ant Colony Clustering Model (ACCM). The authors intend for ACCM to reduce the characteristically high false positive rate of anomaly-based approaches while minimizing the training period by using an unsupervised approach to machine learning. MAS is hierarchical and contains a large number of roles: monitor agents collect audit data, decision agents perform analysis, action agents effect responses, coordination agents manage multi trust communication, user interface agents interact with human operators and registration agents manage agent appearance and disappearance. Their results indicate ACCM slightly outperforms the detection rates and significantly outperforms the false positive rates of k means and expectation-maximization approaches. Like, MAS pursues a domain-independent CPS security solution using anomaly-based analysis; our investigation focuses on MCPS-specific IDS using specification-based analysis. We will use Park et al. and Tsang and Kwong as base schemes against which BSID will be compared because no others provide meaningful pfp/pfn data for a comparative analysis.

Our study of IDS warrants distinct treatment for medical versus generic CPSs because the behavior rule set we propose is application specific. CPSs in other domains will not have temperature sensors, medication dispensers or actuators supporting cardiac function. Furthermore, each CPS domain will have a unique environment: For example, while the population in an MCPS may be around 1000 based on the number of beds in a hospital, the population for a smart grid CPS may be in the millions. Also, while the geography of a MCPS may span a single square kilometer based on the size of a medical campus, the area of operation for a unmanned air vehicle (UAV) may be thousands of km2.

1.3 LITRATURE SURVEY

REDUNDANCY MANAGEMENT OF MULTIPATH ROUTING FOR INTRUSION TOLERANCE IN HETEROGENEOUS WIRELESS SENSOR NETWORKS.

PUBLICATION: H. Al-Hamadi and I. R. Chen. IEEE Transactions on Network and Service Management, 10(2):189–203, 2013.

In this paper we propose redundancy management of heterogeneous wireless sensor networks (HWSNs), utilizing multipath routing to answer user queries in the presence of unreliable and malicious nodes. The key concept of our redundancy management is to exploit the tradeoff between energy consumption vs. the gain in reliability, timeliness, and security to maximize the system useful lifetime. We formulate the tradeoff as an optimization problem for dynamically determining the best redundancy level to apply to multipath routing for intrusion tolerance so that the query response success probability is maximized while prolonging the useful lifetime. Furthermore, we consider this optimization problem for the case in which a voting-based distributed intrusion detection algorithm is applied to detect and evict malicious nodes in a HWSN. We develop a novel probability model to analyze the best redundancy level in terms of path redundancy and source redundancy, as well as the best intrusion detection settings in terms of the number of voters and the intrusion invocation interval under which the lifetime of a HWSN is maximized. We then apply the analysis results obtained to the design of a dynamic redundancy management algorithm to identify and apply the best design parameter settings at runtime in response to environment changes, to maximize the HWSN lifetime.

TELECOMMUNICATIONS DEMAND AND PRICING STRUCTURE: AN ECONOMETRIC ANALYSIS.

PUBLICATION: M. Aldebert, M. Ivaldi, and C. Roucolle. Telecommunication Systems, 25:89–115, 2004.

The main objective of this paper is to analyse residential demand by traffic destination, using a translogarithmic indirect utility function. We focus on five traffic directions, in order to construct a model adapted to evaluate the characteristics of telecommunications demand in a competitive market. The resulting price elasticities express high reactivity to own price changes for the main traffic directions, as well as little interactions between the different types of traffic. Moreover the high values of income elasticities confirm the importance of income effects when analysing residential telecommunications demand. This model shows useful for welfare analysis. The computation of customers’ income equivalent variation shows, on average, a higher willingness to pay for some traffic directions than the bill actually paid. Finally we show that the optimal prices for the operator, in a cost minimisation point of view, are higher than the observed prices for local and national traffic directions. This emphasises the existence of important cross-subsidies among the different segments of customers.

SECURITY CHALLENGES IN NEXT GENERATION CYBER PHYSICAL SYSTEMS.

PUBLICATION: M. Anand, E. Cronin, M. Sherr, M. Blaze, Z. Ives, and I. Lee. Beyond SCADA: Networked Embedded Control for Cyber Physical Systems, 2006.

The advent of low-powered wireless networks of embedded sensors has spurred the development of new applications at the interface between the real world and its digital manifestation. Following this trend, the next generation Supervisory Control And Data Acquisition (SCADA) system is expected to replace traditional data gathering – a distributed network of Remote Terminal Units (RTU) or Programmable Logic Controllers (PLC), with devices such as the wireless sensing devices. Before these intelligent systems can be deployed in critical infrastructure such as emergency rooms and power plants, the security properties of sensors must be fully understood. Existing wisdom has been to apply the traditional security models and techniques to sensor networks: as in conventional computing environments, the goal has been to protect physical entities: devices, packets, links, and ultimately networks. Sensors have unique characteristics that warrant novel security considerations: the geographic distribution of the devices allows an attacker to physically capture nodes and learn secret key material, or to intercept or inject messages; the hierarchical nature of sensor networks and their route maintenance protocols permit the attacker to determine where the root node is placed. Perhaps most importantly, most sensor networks rely on redundancy (followed by aggregation) to accurately capture environmental information even with poorly calibrated and unreliable devices. This results in a fundamental distinction between a physical message in a sensor network and a logical unit of sensed information: a message with a single sensor reading may reveal very little information about the real environment, whereas a message containing an aggregate or collection of readings may reveal a great deal more.

HOST-BASED ANOMALY DETECTION FOR PERVASIVE MEDICAL SYSTEMS.

PUBLICATION: B. Asfaw, D. Bekele, B. Eshete, A. Villafiorita, and K. Weldemariam. In Fifth International Conference on Risks and Security of Internet and Systems, pages 1–8, October 2010.

Intrusion detection systems are deployed on hosts in a computing infrastructure to tackle undesired events in the course of usage of the systems. One of the promising domains of applying intrusion detection is the healthcare domain. A typical healthcare scenario is characterized by high degree of mobility, frequent interruptions and above all demands access to sensitive medical records by concerned stakeholders. Migrating this set of concerns in pervasive healthcare environments where the traditional characteristics are more intensiﬁed in terms of uncertainty, one ends up with more challenges on security due to nature of pervasive devices and wireless communication media along with classic security problems for desktop based systems. Despite evolution of automated healthcare services and sophistication of attacks against such services, there is a reasonable lack of techniques, tools and experimental setups for protecting hosts against intrusive actions. This paper presents a contribution to provide a host-based, anomaly modeling and detection approach based on data mining techniques for pervasive healthcare systems. The technique maintains normal usage proﬁle of pervasive healthcare applications and inspects current work ﬂow against normal usage proﬁle so as to classify it as anomalous or normal. The technique is implemented as a prototype with sample data set and the results obtained revealed that the technique is able to perform classiﬁcation of anomalous activities.

CHAPTER 2

2.0 SYSTEM ANALYSIS

2.1 EXISTING SYSTEM:

Existing work only considered specification-based state machines for intrusion detection of communication protocol misbehaving patterns. Before that not using trust based techniques to avoid delay due to trust aggregation and propagation to promptly react to malicious behaviors in safety critical MCPSs.

2.1.1 DISADVANTAGES:

2.2 PROPOSED SYSTEM:

We propose a methodology to transform behavior rules to a state machine, so that a device that is being monitored for its behavior can easily be checked against the transformed state machine for deviation from its behavior specification. We also investigate the impact of attacker behaviors on the effectiveness of MCPS intrusion detection. We demonstrate that our specification based IDS technique can effectively trade higher false positives off for lower false negatives to cope with more sophisticated and hidden attackers. We show results for a range of configurations to illustrate this trade. Because the key motivation in MCPS is safety, our solution is deployed in a configuration yielding a high detection rate without compromising the false positive probability. Our approach is monitoring-based relying on the use of peer devices to monitor and measure the compliance degree of a trustee device connected to the monitoring node by the CPS network. The rules comparing monitor and trustee physiology (blood pressure, oxygen saturation, pulse, respiration and temperature) exceeds protection possible by considering devices in isolation.

2.2.1 ADVANTAGES:

2.3 HARDWARE & SOFTWARE REQUIREMENTS:

2.3.1 HARDWARE REQUIREMENT:

v Processor – Pentium –IV

Speed – 1.1 GHz
- RAM – 256 MB (min)
- Hard Disk – 20 GB
- Floppy Drive – 1.44 MB
- Key Board – Standard Windows Keyboard
- Mouse – Two or Three Button Mouse
- Monitor – SVGA

2.3.2 SOFTWARE REQUIREMENTS:

Operating System : Windows XP
Front End : Microsoft Visual Studio .NET 2008
Back End : MS-SQL Server 2005
Document : MS-Office 2007

CHAPTER 3

3.0 SYSTEM DESIGN:

Data Flow Diagram / Use Case Diagram / Flow Diagram:

The DFD is also called as bubble chart. It is a simple graphical formalism that can be used to represent a system in terms of the input data to the system, various processing carried out on these data, and the output data is generated by the system

The data flow diagram (DFD) is one of the most important modeling tools. It is used to model the system components. These components are the system process, the data used by the process, an external entity that interacts with the system and the information flows in the system.

DFD shows how the information moves through the system and how it is modified by a series of transformations. It is a graphical technique that depicts information flow and the transformations that are applied as data moves from input to output.

DFD is also known as bubble chart. A DFD may be used to represent a system at any level of abstraction. DFD may be partitioned into levels that represent increasing information flow and functional detail.

NOTATION:

SOURCE OR DESTINATION OF DATA:

External sources or destinations, which may be people or organizations or other entities

DATA SOURCE:

Here the data referenced by a process is stored and retrieved.

PROCESS:

People, procedures or devices that produce data. The physical component is not identified.

DATA FLOW:

Data moves in a specific direction from an origin to a destination. The data flow is a “packet” of data.

MODELING RULES:

There are several common modeling rules when creating DFDs:

All processes must have at least one data flow in and one data flow out.
All processes should modify the incoming data, producing new forms of outgoing data.
Each data store must be involved with at least one data flow.
Each external entity must be involved with at least one data flow.
A data flow must be attached to at least one process.

3.1 BLOCK DIAGRAM

3.2 DATAFLOW DIAGRAM

UML DIAGRAMS:

3.2 USE CASE DIAGRAM:

3.3 CLASS DIAGRAM:

3.4 SEQUENCE DIAGRAM:

3.5 ACTIVITY DIAGRAM:

CHAPTER 4

4.0 IMPLEMENTATION:

4.1 ALGORITHM

4.2 MODULES:

The system is proposed to have the following modules along with functional requirements.

THREAT MODEL
ATTACKER ARCHETYPES
BEHAVIOR RULES
INTRUSION DETECTION SYSTEM

4.3 MODULE DESCRIPTION:

1. THREAT MODEL

We focus on defeating inside attackers that violate the integrity of the MCPS with the objective to disable the MCPS functionality. Our design is also effective against attacks such as subtle manipulations that change medical doses slightly to cause long term harm to patients or medical or billing record exfiltrations which violate privacy. There are two distinct stages in an attack: before a node is compromised and after a node is compromised. Before a node is compromised, the adversary focuses on the tactical goal of achieving a foothold on the target system.

2. ATTACKER ARCHETYPES

We differentiate two attacker archetypes: reckless, random and opportunistic. A reckless attacker performs attacks whenever it has a chance to impair the MCPS functionality as soon as possible. A random attacker, on the other hand, performs attacks only randomly to avoid detection. It is thus insidious and hidden with the objective to cripple the MCPS functionality. We model the attacker behavior by a random attack probability pa. When pa = 1 the attacker is a reckless adversary. Random attacks are typically implemented with on off attacks in real-world scenarios, so pa is not a random variable drawn from uniform distribution U(0, 1) but rather a probability that a malicious node is performing attacks at any time with this on-off attack behavior. An opportunistic attacker is the third archetype. An opportunistic attacker exploits ambient noise modeled by perr (probability of mis-monitoring)to perform attacks.

3. BEHAVIOR RULES

Behavior rules for a device are specified during the design and testing phase of an MCPS. Our intrusion detection protocol takes a set of behavior rules for a device as input and detects if a device’s behavior deviates from the expected behavior specified by the set of behavior rules. Since the intrusion detection activity is performed in the background, it allows behavior rules to be changed if incomplete or imprecise specifications are discovered during the operational phase

Without disrupting the MCPS operation. Our IDS design for the reference MCPS model relies on

The use of lightweight specification-based behavior rules for each sensor or actuator medical device.

4. INTRUSION DETECTION SYSTEM

Intrusion detection system (IDS) design for cyber physical systems (CPSs) has attracted considerable because of the dire consequence of CPS failure. In this paper, we consider specification rather than signature-based detection to deal with unknown attacker patterns. We consider specification rather than anomaly based techniques to avoid using resource constrained

Sensors or actuators in an MCPS for profiling anomaly patterns (e.g., through learning) and to avoid high false positives. We consider specification rather than trust based techniques to avoid delay due to trust aggregation and propagation to promptly react to malicious behaviors in Safety critical MCPSs.

CHAPTER 5

5.0 SYSTEM STUDY:

5.1 FEASIBILITY STUDY:

Three key considerations involved in the feasibility analysis are

ECONOMICAL FEASIBILITY
TECHNICAL FEASIBILITY
SOCIAL FEASIBILITY

5.1.1 ECONOMICAL FEASIBILITY:

5.1.2 TECHNICAL FEASIBILITY:

This study is carried out to check the technical feasibility, that is, the technical requirements of the system. Any system developed must not have a high demand on the available technical resources. This will lead to high demands on the available technical resources. This will lead to high demands being placed on the client. The developed system must have a modest requirement, as only minimal or null changes are required for implementing this system.

5.1.3 SOCIAL FEASIBILITY:

5.2 SYSTEM TESTING:

5.2.1 UNIT TESTING:

UNIT TESTING:

Description	Expected result
Test for application window properties.	All the properties of the windows are to be properly aligned and displayed.
Test for mouse operations.	All the mouse operations like click, drag, etc. must perform the necessary operations without any exceptions.

5.1.3 FUNCTIONAL TESTING:

FUNCTIONAL TESTING:

Description	Expected result
Test for all modules.	All peers should communicate in the group.
Test for various peer in a distributed network framework as it display all users available in the group.	The result after execution should give the accurate result.

5.1. 4 NON-FUNCTIONAL TESTING:

Load testing
Performance testing
Usability testing
Reliability testing
Security testing

5.1.5 LOAD TESTING:

Load Testing

Description	Expected result
It is necessary to ascertain that the application behaves correctly under loads when ‘Server busy’ response is received.	Should designate another active node as a Server.

5.1.5 PERFORMANCE TESTING:

PERFORMANCE TESTING:

Description	Expected result
This is required to assure that an application perforce adequately, having the capability to handle many peers, delivering its results in expected time and using an acceptable level of resource and it is an aspect of operational management.	Should handle large input values, and produce accurate result in a expected time.

5.1.6 RELIABILITY TESTING:

RELIABILITY TESTING:

Description	Expected result
This is to check that the server is rugged and reliable and can handle the failure of any of the components involved in provide the application.	In case of failure of the server an alternate server should take over the job.

5.1.7 SECURITY TESTING:

SECURITY TESTING:

Description	Expected result
Checking that the user identification is authenticated.	In case failure it should not be connected in the framework.
Check whether group keys in a tree are shared by all peers.	The peers should know group key in the same group.

5.1.7 WHITE BOX TESTING:

5.1.8 WHITE BOX TESTING:

Description	Expected result
Exercise all logical decisions on their true and false sides.	All the logical decisions must be valid.
Execute all loops at their boundaries and within their operational bounds.	All the loops must be finite.
Exercise internal data structures to ensure their validity.	All the data structures must be valid.

5.1.9 BLACK BOX TESTING:

5.1.10 BLACK BOX TESTING:

Description	Expected result
To check for incorrect or missing functions.	All the functions must be valid.
To check for interface errors.	The entire interface must function normally.
To check for errors in a data structures or external data base access.	The database updation and retrieval must be done.
To check for initialization and termination errors.	All the functions and data structures must be initialized properly and terminated normally.

All the above system testing strategies are carried out in as the development, documentation and institutionalization of the proposed goals and related policies is essential.

CHAPTER 6

6.0 SOFTWARE SPECIFICATION:

6.1 FEATURES OF .NET:

6.2 THE .NET FRAMEWORK

The .NET Framework has two main parts:

1. The Common Language Runtime (CLR).

2. A hierarchical set of class libraries.

The CLR is described as the “execution engine” of .NET. It provides the environment within which programs run. The most important features are

Conversion from a low-level assembler-style language, called Intermediate Language (IL), into code native to the platform being executed on.
Memory management, notably including garbage collection.
Checking and enforcing security restrictions on the running code.
Loading and executing programs, with version control and other such features.
The following features of the .NET framework are also worth description:

Managed Code

Managed Data

Common Type System

Common Language Specification

6.3 THE CLASS LIBRARY

The set of classes is pretty comprehensive, providing collections, file, screen, and network I/O, threading, and so on, as well as XML and database connectivity.

The class library is subdivided into a number of sets (or namespaces), each providing distinct areas of functionality, with dependencies between the namespaces kept to a minimum.

6.4 LANGUAGES SUPPORTED BY .NET

Visual Basic .NET is also CLS compliant, which means that any CLS-compliant language can use the classes, objects, and components you create in Visual Basic .NET.

Other languages for which .NET compilers are available include

FORTRAN
COBOL
Eiffel

ASP.NET XML WEB SERVICES	Windows Forms
Base Class Libraries
Common Language Runtime
Operating System

Fig1 .Net Framework

CONSTRUCTORS AND DESTRUCTORS:

GARBAGE COLLECTION

OVERLOADING

MULTITHREADING:

STRUCTURED EXCEPTION HANDLING

6.5 THE .NET FRAMEWORK

The .NET Framework is a new computing platform that simplifies application development in the highly distributed environment of the Internet.

OBJECTIVES OF .NET FRAMEWORK

1. To provide a consistent object-oriented programming environment whether object codes is stored and executed locally on Internet-distributed, or executed remotely.

2. To provide a code-execution environment to minimizes software deployment and guarantees safe execution of code.

3. Eliminates the performance problems.

There are different types of application, such as Windows-based applications and Web-based applications.

6.6 FEATURES OF SQL-SERVER

SQL-SERVER database consist of six type of objects,

They are,

1. TABLE

2. QUERY

3. FORM

4. REPORT

5. MACRO

TABLE:

A database is a collection of data about a specific topic.

VIEWS OF TABLE:

We can work with a table in two types,

1. Design View

2. Datasheet View

Design View

To build or modify the structure of a table we work in the table design view. We can specify what kind of data will be hold.

Datasheet View

To add, edit or analyses the data itself we work in tables datasheet view mode.

QUERY:

CHAPTER 7

APPENDIX

7.1 SAMPLE SOURCE CODE

7.2 SAMPLE OUTPUT

CHAPTER 8

8.1 CONCLUSION For safety-critical MCPSs, being able to detect attackers while limiting the false alarm probability to protect the welfare of patients is of utmost importance. In this paper we proposed a behavior-rule specification-based IDS technique for intrusion detection of medical devices embedded in a MCPS. We exemplified the utility with VSMs and demonstrated that the detection probability of the medical device approaches one (that is, we can always catch the attacker without false negatives) while bounding the false alarm probability to below 5% for reckless attackers and below 25% for random and opportunistic attackers over a wide range of environment noise levels. Through a comparative analysis, we demonstrated that our behavior-rule specification-based IDS technique outperforms existing techniques based on anomaly intrusion detection. In future work, we plan to analyze the overheads of our detection techniques such as the various distance-based methods in comparison with contemporary approaches. We also plan to deepen adversary modeling research based on stochastic Petri net techniques such that the system can dynamically adjust CT to maximize intrusion detection performance in response to changing attacker behaviors at runtime.

A System for Automatic Notiﬁcation and Severity Estimation of Automotive Accidents

22/10/201902/07/2019 by admin

New communication technologies integrated into modern vehicles offer an opportunity for better assistance to people injured in traffic accidents. Recent studies show how communication capabilities should be supported by artificial intelligence systems capable of automating many of the decisions to be taken by emergency services, thereby adapting the rescue resources to the severity of the accident and reducing assistance time. To improve the overall rescue process, a fast and accurate estimation of the severity of the accident represent a key point to help emergency services better estimate the required resources.

This paper proposes a novel intelligent system which is able to automatically detect road accidents, notify them through vehicular networks, and estimate their severity based on the concept of data mining and knowledge inference. Our system considers the most relevant variables that can characterize the severity of the accidents (variables such as the vehicle speed, the type of vehicles involved, the impact speed, and the status of the airbag).

Results show that a complete Knowledge Discovery in Databases (KDD) process, with an adequate selection of relevant features, allows generating estimation models that can predict the severity of new accidents. We develop a prototype of our system based on off-the-shelf devices and validate it at the Applus+ IDIADA Automotive Research Corporation facilities, showing that our system can notably reduce the time needed to alert and deploy emergency services after an accident takes place.

1.2 INTRODUCTION

1.3 LITRATURE SURVEY

CHAPTER 2

2.0 SYSTEM ANALYSIS

2.1 EXISTING SYSTEM:

Most ITS applications, such as road safety, fleet management, and navigation, will rely on data exchanged between the vehicle and the roadside infrastructure (V2I), or even directly between vehicles (V2V). The integration of sensoring capabilities on-board of vehicles, along with peer-to-peer mobile communication among vehicles, forecast significant improvements for failure. Existing V2V architecture, the transportation network is broken into zones in which a single vehicle is known as the super vehicle. Only super vehicles are able to communicate with the central infrastructure or with other Super Vehicles, and all other vehicles can only communicate with the super vehicle responsible for the zone in which they are previously traversing in describe the super vehicle detection (SVD) algorithm for how a vehicle can find or become a super vehicle of a zone and how super vehicles can aggregate the speed and location data from all of the vehicles within their zone to still ensure an accurate representation of the network.

2.1.1 DISADVANTAGES:

Zero accident objectives on the long term, a fast and efficient rescue operation during the hour following a traffic accident significantly increase the probability of survival of the injured, and reduce the injury severity.

Communication systems between vehicles, the infrastructure should be supported by intelligent systems capable of estimating the severity of accidents, and automatically deploying the actions required, thereby reducing the time needed to assist injured passengers.

Many of the manual decisions taken nowadays by emergency services are based on incomplete or inaccurate data, which may be replaced by automatic systems that adapt to the specific characteristics of each accident.

2.2 PROPOSED SYSTEM:

The proposed system consists of several components with different functions. Firstly, vehicles should incorporate an On-Board unit (OBU) responsible for: (i) detecting when there has been a potentially dangerous impact for the occupants, (ii) collecting available information coming from sensors in the vehicle, and (iii) communicating the situation to a Control Unit (CU) that will accordingly address the handling of the warning notification. Next, the notification of the detected accidents is made through a combination of both V2V and V2I communications. Finally, the destination of all the collected information is the Control Unit; it will handle the warning notification, estimating the severity of the accident, and communicating the incident to the appropriate emergency services.

Our proposed architecture provides: (i) direct communication between the vehicles involved in the accident, (ii) automatic sending of a data file containing important information about the accident to the Control Unit, and (iii) a preliminary and automatic assessment of the damage of the vehicle and its occupants, based on the information coming from the involved vehicles, and a database of accident reports. According to the reported information and the preliminary accident estimation, the system will alert the required rescue resources to optimize the accident assistance.

2.2.1 ADVANTAGES:

In-vehicle sensors: They are required to detect accidents and provide information about its causes. Accessing the data from in-vehicle sensors is possible nowadays using the On-Board Diagnostics (OBD) standard interface, which serves as the entry point to the vehicles.

Data Acquisition Unit (DAU): This device is responsible for periodically collecting data from the sensors available in the vehicle (airbag triggers, speed, fuel levels, etc.), converting them to a common format, and providing the collected data set to the OBU Processing Unit.

OBU Processing Unit: It is in charge of processing the data coming from sensors, determining whether an accident occurred, and notifying dangerous situations to nearby vehicles, or directly to the Control Unit.

The information from the DAU is gathered, interpreted and used to determine the vehicle’s current status. This unit must also have access to a positioning device (such as a GPS receiver), and to different wireless interfaces, thereby enabling communication between the vehicle and the remote control center.

2.3 HARDWARE & SOFTWARE REQUIREMENTS:

2.3.1 HARDWARE REQUIREMENT:

v Processor – Pentium –IV

Speed – 1.1 GHz
- RAM – 256 MB (min)
- Hard Disk – 20 GB
- Floppy Drive – 1.44 MB
- Key Board – Standard Windows Keyboard
- Mouse – Two or Three Button Mouse
- Monitor – SVGA

2.3.2 SOFTWARE REQUIREMENTS:

Operating System : Windows XP or Win7
Front End : Microsoft Visual Studio .NET 2008
Script : C# Script
Back End : MS-SQL Server 2005
Document : MS-Office 2007

CHAPTER 3

3.0 SYSTEM DESIGN:

Data Flow Diagram / Use Case Diagram / Flow Diagram:

The DFD is also called as bubble chart. It is a simple graphical formalism that can be used to represent a system in terms of the input data to the system, various processing carried out on these data, and the output data is generated by the system

The data flow diagram (DFD) is one of the most important modeling tools. It is used to model the system components. These components are the system process, the data used by the process, an external entity that interacts with the system and the information flows in the system.

DFD shows how the information moves through the system and how it is modified by a series of transformations. It is a graphical technique that depicts information flow and the transformations that are applied as data moves from input to output.

DFD is also known as bubble chart. A DFD may be used to represent a system at any level of abstraction. DFD may be partitioned into levels that represent increasing information flow and functional detail.

NOTATION:

SOURCE OR DESTINATION OF DATA:

External sources or destinations, which may be people or organizations or other entities

DATA SOURCE:

Here the data referenced by a process is stored and retrieved.

PROCESS:

People, procedures or devices that produce data’s in the physical component is not identified.

DATA FLOW:

Data moves in a specific direction from an origin to a destination. The data flow is a “packet” of data.

MODELING RULES:

There are several common modeling rules when creating DFDs:

All processes must have at least one data flow in and one data flow out.
All processes should modify the incoming data, producing new forms of outgoing data.
Each data store must be involved with at least one data flow.
Each external entity must be involved with at least one data flow.
A data flow must be attached to at least one process.

3.1 ARCHITECTURE DIAGRAM

3.2 DATAFLOW DIAGRAM

UML DIAGRAMS:

3.2 USE CASE DIAGRAM:

3.3 CLASS DIAGRAM:

3.4 SEQUENCE DIAGRAM:

3.5 ACTIVITY DIAGRAM:

CHAPTER 4

4.0 IMPLEMENTATION:

The KDD approach can be defined as the nontrivial process of identifying valid, novel, potentially useful, and understandable patterns from KDD process begins with the understanding of the application specific domain and the necessary prior knowledge. After the acquisition of initial data, a series of phases are performed:

1) Selection: This phase determines the information sources that may be useful, and then it transforms the data into a common format.

2) Preprocessing: In this stage, the selected data must be cleaned (noise reduction or modeling) and preprocessed (missing data handling).

3) Transformation: This phase is in charge of performing a reduction and projection of the data to find relevant features that represent the data depending on the purpose of the task.

4) Data mining: This phase basically selects mining algorithms and selection methods which will be used to find patterns in data.

5) Interpretation/Evaluation: Finally, the extracted patterns must be interpreted. This step may also include displaying the patterns and models, or displaying the data taking into account such models.

4.1 ALGORITHM

We propose to develop a complete KDD process, starting by selecting a useful data source containing instances of previous accidents. The data collected will be structured and preprocessed to ease the work to be done in the transformation and data mining phases. The final step will consist on interpreting the results, and assessing their utility for the specific task of estimating the severity of road accidents. The phases from the KDD process will be performed using the open-source Weka collection, which is a set of machine learning algorithms.

Weka is open source software issued under the GNU General Public License which contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. We will deal with road accidents in two dimensions: (i) damage on the vehicle (indicating the possibility of traffic problems or the need of cranes in the area of the accident), and (ii) passenger injuries. These two dimensions seem to be related, since heavily damaged vehicles are usually associated with low survival possibilities of the occupants.

We will use the estimations obtained with our system about the damage on the vehicle to help in the prediction of the occupants’ injuries. Finally, our system will benefit from additional knowledge to improve its accuracy, grouping accidents according to their degree of similarity. We can use the criteria used in numerous studies about accidents in which crashes are divided and analyzed separately depending on the main direction of the impact registered due to the collision. The following sections contain the results of the different phases of our KDD proposal.

4.2 MODULES:

USER MODULES:

VEHICULAR NETWORKS (ITS):

OBU AND CU STRUCTURE:

DATA ACQUISITION:

KDD MACHINE LEARNING:

4.3 MODULE DESCRIPTION:

USER MODULES:

VEHICULAR NETWORKS (ITS):

OBU AND CU STRUCTURE:

DATA ACQUISITION:

KDD MACHINE LEARNING:

CHAPTER 8

8.1 CONCLUSION:

The new communication technologies integrated into the automotive sector offer an opportunity for better assistance to people injured in traffic accidents, reducing the response time of emergency services, and increasing the information they have about the incident just before starting the rescue process. To this end, we designed and implemented a prototype for automatic accident notification and assistance based on V2V and V2I communications.

However, the effectiveness of this technology can be improved with the support of intelligent systems which can automate the decision making process associated with an accident. A preliminary assessment of the severity of an accident is needed to adapt resources accordingly. This estimation can be done by using historical data from previous accidents using a Knowledge Discovery in Databases process.

We showed that the vehicle speed is a crucial factor in front crashes, but the type of vehicle involved and the speed of the striking vehicle are more important than speed itself in side and rear-end collisions. The status of the airbag is also very useful in the estimation, since situations where it was not necessary to deploy the airbag rarely produce serious injuries to the passengers.

We developed a prototype that shows how inter-vehicle communications can make accessible the information about the different vehicles involved in an accident. Moreover, the positive results achieved on the real tests indicates that the accident detection and severity estimation algorithms are robust enough to allow a mass deployment of the proposed system.

A Stochastic Model to Investigate Data Center Performance and QoS in IaaS Cloud Computing Systems

22/10/201902/07/2019 by admin

Cloud data center management is a key problem due to the numerous and heterogeneous strategies that can be applied, ranging from the VM placement to the federation with other clouds. Performance evaluation of Cloud Computing infrastructures is required to predict and quantify the cost-benefit of a strategy portfolio and the corresponding Quality of Service (QoS) experienced by users. Such analyses are not feasible by simulation or on-the-field experimentation, due to the great number of parameters that have to be investigated.

In this paper, we present an analytical model, based on Stochastic Reward Nets (SRNs), that is both scalable to model systems composed of thousands of resources and flexible to represent different policies and cloud-specific strategies. Several performance metrics are defined and evaluated to analyze the behavior of a Cloud data center: utilization, availability, waiting time, and responsiveness. A resiliency analysis is also provided to take into account load bursts. Finally, a general approach is presented that, starting from the concept of system capacity, can help system managers to opportunely set the data center parameters under different working conditions.

EXISTING SYSTEM:

In order to integrate business requirements and application level needs, in terms of Quality of Service (QoS), cloud service provisioning is regulated by Service Level Agreements (SLAs): contracts between clients and providers that express the price for a service, the QoS levels required during the service provisioning, and the penalties associated with the SLA violations. In such a context, performance evaluation plays a key role allowing system managers to evaluate the effects of different resource management strategies on the data center functioning and to predict the corresponding costs/benefits.

Cloud systems differ from traditional distributed systems. First of all, they are characterized by a very large number of resources that can span different administrative domains. Moreover, the high level of resource abstraction allows implementing particular resource management techniques such as VM multiplexing or VM live migrations that, even if transparent to final users, have to be considered in the design of performance models in order to accurately understand the system behavior.

Finally, different clouds, belonging to the same or to different organizations, can dynamically join each other to achieve a common goal, usually represented by the optimization of resources utilization. This mechanism, referred to as cloud federation, allows providing and releasing resources on demand thus providing elastic capabilities to the whole infrastructure.

DISADVANTAGES:

On-the-field experiments are mainly focused on the offered QoS, they are based on a black box approach that makes difficult to correlate obtained data to the internal resource management strategies implemented by the system provider.
Simulation does not allow conducting comprehensive analyses of the system performance due to the great number of parameters that have to be investigated.

PROPOSED SYSTEM:

In this paper, we present a stochastic model, based on Stochastic Reward Nets (SRNs), that exhibits the above mentioned features allowing capturing the key concepts of an IaaS cloud system. The proposed model is scalable enough to represent systems composed of thousands of resources and it makes possible to represent both physical and virtual resources exploiting cloud specific concepts such as the infrastructure elasticity.

We present work is that a generic and comprehensive view of a cloud system is presented. Low level details, such as VM multiplexing, are easily integrated with cloud based actions such as federation, allowing investigating different mixed strategies. An exhaustive set of performance metrics are defined regarding both the system provider (e.g., utilization) and the final users (e.g., responsiveness).

ADVANTAGES:

To provide a fair comparison among different resource management strategies, also taking into account the system elasticity, a performance evaluation approach is described. Such an approach, based on the concept of system capacity, presents a holistic view of a cloud system and it allows system managers to study the better solution with respect to an established goal and to opportunely set the system parameters.

Our analytical techniques represent a good candidate, thanks to the limited solution cost of their associated models. However, to accurately represent a cloud system, an analytical model has to be:

. Scalable: To deal with very large systems composed of hundreds or thousands of resources.

. Flexible: Allowing us to easily implement different strategies and policies and to represent different working conditions.

HARDWARE & SOFTWARE REQUIREMENTS:

HARDWARE REQUIREMENT:

v Processor – Pentium –IV

Speed – 1.1 GHz
- RAM – 256 MB (min)
- Hard Disk – 20 GB
- Floppy Drive – 1.44 MB
- Key Board – Standard Windows Keyboard
- Mouse – Two or Three Button Mouse
- Monitor – SVGA

SOFTWARE REQUIREMENTS:

Operating System : Windows XP or Win 7
Front End : Microsoft Visual Studio .NET 2008
Back End : MSSQL Server
Script Coding : C# Script
Server : ASP .NET Web Server
Document : MS-Office 2007

SYSTEM DESIGN:

ARCHITECTURE DIAGRAM / UML DIAGRAMS / DAT FLOW DIAGRAM:

The DFD is also called as bubble chart. It is a simple graphical formalism that can be used to represent a system in terms of the input data to the system, various processing carried out on these data, and the output data is generated by the system

The data flow diagram (DFD) is one of the most important modeling tools. It is used to model the system components. These components are the system process, the data used by the process, an external entity that interacts with the system and the information flows in the system.

DFD shows how the information moves through the system and how it is modified by a series of transformations. It is a graphical technique that depicts information flow and the transformations that are applied as data moves from input to output.

DFD is also known as bubble chart. A DFD may be used to represent a system at any level of abstraction. DFD may be partitioned into levels that represent increasing information flow and functional detail.

NOTATION:

SOURCE OR DESTINATION OF DATA:

External sources or destinations, which may be people or organizations or other entities

DATA SOURCE:

Here the data referenced by a process is stored and retrieved.

PROCESS:

People, procedures or devices that produce data. The physical component is not identified.

DATA FLOW:

Data moves in a specific direction from an origin to a destination. The data flow is a “packet” of data.

MODELING RULES:

There are several common modeling rules when creating DFDs:

All processes must have at least one data flow in and one data flow out.
All processes should modify the incoming data, producing new forms of outgoing data.
Each data store must be involved with at least one data flow.
Each external entity must be involved with at least one data flow.
A data flow must be attached to at least one process.

SYSTEM ARCHITECTURE:

IMPLEMENTATION:

SRNs allow us to define reward functions that can be associated to a particular state of the model to evaluate the performance level reached by the system during the sojourn in that state.

In the following, we are interested in performance metrics able to characterize the system behavior from both the provider and the user point of views. Such metrics will help system designer to size and manage the cloud data center and they will also be determinant in the SLA definitions.

Responsiveness It is the steady-state probability R that the system is able to accept a request within a given time deadline _. The computation of such a parameter requires the knowledge of the waiting time cumulative distribution function (CDF). To this end, it is possible to apply the tagged customer technique by modifying the SRN model to isolate the behavior of a single user request u and to observe its movements through the system. In the tagged customer model shown in Fig. 3, the system queue is modeled through two places. Place Pcustomer contains a single token that represents the arrival of request u. The P tokens initially present in place Pqueue represent the number of requests still waiting in the queue when u arrives, while the M1 and M2 tokens initially present in places Pres and Prun represent the corresponding system status.

MODULES:

USER MODULE:

ADMIN:
USER:

IAAS CLOUD SYSTEM:

ANALYTICAL MODEL:

CLOUD FEDERATION:

MODELING VM MULTIPLEXING:

RESILIENCY ANALYSIS:

MODULES DESCRIPTION:

USER MODULE:

ADMIN:

In this module is used to help the server to view details and upload files with the security. Admin upload the data’s to database. Also view the subscriber details and user details. Admin find the redistribute details. Also who send the data and receive the data’s.

USER:

In this module, Users are having authentication and security to access the detail which is presented in the ontology system. Before accessing or searching the details user should have the account in that otherwise they should register first user can register their details like name, password, gender, age, and then. We develop this module, where the cloud storage can be made secure.

IAAS CLOUD SYSTEM:

Cloud computing is a promising technology able to strongly modify the way computing and storage resources will be accessed in the near future in the provision of on-demand access to virtual resources available on the Internet, cloud systems offer services at three different levels: infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). In particular, IaaS clouds provide users with computational resources in the form of virtual machine (VM) instances deployed in the provider data center, while PaaS and SaaS clouds offer services in terms of specific solution stacks and application software suites, respectively.

Our business requirements and application-level needs, in terms of quality of service (QoS), cloud service provisioning is regulated by service-level agreements (SLAs): contracts between clients and providers that express the price for a service, the QoS levels required during the service provisioning, and the penalties associated with the SLA violations. In such a context, performance evaluation plays a key role allowing system managers to evaluate the effects of different resource management strategies on the data center.

ANALYTICAL MODEL:

IaaS cloud system composed of N physical resources job requests (in terms of VM instantiation requests) are enqueued in the system queue. Such a queue has a finite size Q; once its limit is reached, further requests are rejected. The system queue is managed according to a FIFO scheduling policy. When a resource is available, a job is accepted and the corresponding VM is instantiated.

We assume that the instantiation time is negligible and that the service time (i.e., the time needed to execute a job) is exponentially distributed with mean 1=_. According to the VM multiplexing technique in the cloud system can provide a number M of logical resources greater than N. In this case, multiple VMs can be allocated in the same physical machine (PM), for example, a core in a multicore architecture.

Multiple VMs sharing the same PM can incur in a reduction of the performance mainly due to I/O interference between VMs. We define the degradation factor d (_ 0) as the percentage increase in the expected service time experienced by a VM when multiplexed with another VM. The performance degradation of multiplexed VMs depends on the multiplexing technique.

CLOUD FEDERATION:

Cloud federation allows the system to use, in particular situations, the resources offered by other public cloud systems through a sharing and paying model. In this way, elastic capabilities can be exploited to respond to particular load conditions. Job requests can be redirected to other clouds by transferring the corresponding VM disk images through the network. With respect to the federation technique, we make the following assumptions:

Finally, with respect to the arrival process, we will investigate three different scenarios. In the first one (constant arrival process), we assume the arrival process be a homogeneous Poisson process with rate _. However, largescale distributed systems with thousands of users, such as cloud systems, could exhibit self-similarity/long-range dependence with respect to the arrival process. For these reasons, to take into account the dependences of the job arrival rate on both the days of a week and the hours of a day, in the second scenario (Periodic arrival process), we also choose to model the job arrival process as a Markov Modulated Poisson Process (MMPP).

MODELING VM MULTIPLEXING:

The proposed model is scalable enough to represent systems composed of thousands of resources and it makes possible to represent both physical and virtual resources exploiting cloud-specific concepts such as the infrastructure elasticity. With respect to the existing literature, the innovative aspect of the present work is that a generic and comprehensive view of a cloud system is presented. Low-level details, such as VM multiplexing, are easily integrated with cloud-based actions such as federation, allowing us to investigate different mixed strategies. An exhaustive set of performance metrics is defined regarding both the system provider (e.g., utilization) and the final users (e.g., responsiveness).

Moreover, different working conditions are investigated and a resiliency analysis is provided to take into account the effects of load bursts. Finally, to provide a fair comparison among different resource management strategies, also taking into account the system elasticity, a performance evaluation approach is described. Such an approach, based on the concept of system capacity, presents a holistic view of a cloud system and it allows system managers to study the better solution with respect to an established goal and to opportunely set the system parameters.

VM multiplexing technique in the cloud system can provide a number M of logical resources greater than N. In this case, multiple VMs can be allocated in the same physical machine (PM), for example, a core in a multicore architecture. Multiple VMs sharing the same PM can incur in a reduction of the performance mainly due to I/O interference between VMs. We define the degradation factor d (_ 0) as the percentage increase in the expected service time experienced by a VM when multiplexed with another VM. The performance degradation of multiplexed VMs depends on the multiplexing technique and on the VM placement strategy. We assume that, to reduce the degradation and to obtain a fair distribution of VMs, the system is able to optimally balance the load among the PMs with respect to the resources required by VMs (e.g., trying to multiplex CPU-bound VMs only with I/O-bound VMs), thus reaching a homogeneous degradation factor. Then, indicating with T ¼ 1=_ the expected service time of a VM in isolation, we can derive the expected time needed to execute two multiplexed VMs as T2 ¼ T _ ð1 þ dÞ. In general, we can express the expected execution time of I multiplexed VMs

RESILIENCY ANALYSIS:

Through a transient solution of the cloud performance model of it is possible to investigate the trend over time of some performance metrics. Such an analysis is straightforward to assess the resiliency of the cloud infrastructure, in particular when the load is characterized by bursts. In fact, even if the infrastructure is optimally sized with respect to the expected load, during a load burst, users can experience a degradation of the perceived QoS with corresponding violations of SLAs. For this reason, it is needed to predict the effects of a particular load condition to study the ability of the system to react to an overload situation. To study the system resiliency, we highlight the arrival of a single burst taking into account a bursty arrival process characterized by the following behavior:

The bursty arrival process is modeled by opportunely changing the exponentially distributed firing time of the transition Tarr in the cloud performance model through the adoption of the technique described in of all; we can identify three temporal phases:

In each phase, the model is solved in transitory by setting the firing rate of Tarr with the corresponding mean value: _n for the regular load, _b for the load burst. Moreover, at the beginning of each phase (i.e., before the change on the firing rate is applied), the initial state probabilities of the model.

CHAPTER 5

5.0 SYSTEM STUDY:

5.1 FEASIBILITY STUDY:

Three key considerations involved in the feasibility analysis are

ECONOMICAL FEASIBILITY
TECHNICAL FEASIBILITY
SOCIAL FEASIBILITY

5.1.1 ECONOMICAL FEASIBILITY:

5.1.2 TECHNICAL FEASIBILITY:

This study is carried out to check the technical feasibility, that is, the technical requirements of the system. Any system developed must not have a high demand on the available technical resources. This will lead to high demands on the available technical resources. This will lead to high demands being placed on the client. The developed system must have a modest requirement, as only minimal or null changes are required for implementing this system.

5.1.3 SOCIAL FEASIBILITY:

5.2 SYSTEM TESTING:

5.2.1 UNIT TESTING:

UNIT TESTING:

Description	Expected result
Test for application window properties.	All the properties of the windows are to be properly aligned and displayed.
Test for mouse operations.	All the mouse operations like click, drag, etc. must perform the necessary operations without any exceptions.

5.1.3 FUNCTIONAL TESTING:

FUNCTIONAL TESTING:

Description	Expected result
Test for all modules.	All peers should communicate in the group.
Test for various peer in a distributed network framework as it display all users available in the group.	The result after execution should give the accurate result.

5.1. 4 NON-FUNCTIONAL TESTING:

Load testing
Performance testing
Usability testing
Reliability testing
Security testing

5.1.5 LOAD TESTING:

Load Testing

Description	Expected result
It is necessary to ascertain that the application behaves correctly under loads when ‘Server busy’ response is received.	Should designate another active node as a Server.

5.1.5 PERFORMANCE TESTING:

PERFORMANCE TESTING:

Description	Expected result
This is required to assure that an application perforce adequately, having the capability to handle many peers, delivering its results in expected time and using an acceptable level of resource and it is an aspect of operational management.	Should handle large input values, and produce accurate result in a expected time.

5.1.6 RELIABILITY TESTING:

RELIABILITY TESTING:

Description	Expected result
This is to check that the server is rugged and reliable and can handle the failure of any of the components involved in provide the application.	In case of failure of the server an alternate server should take over the job.

5.1.7 SECURITY TESTING:

SECURITY TESTING:

Description	Expected result
Checking that the user identification is authenticated.	In case failure it should not be connected in the framework.
Check whether group keys in a tree are shared by all peers.	The peers should know group key in the same group.

5.1.7 WHITE BOX TESTING:

5.1.8 WHITE BOX TESTING:

Description	Expected result
Exercise all logical decisions on their true and false sides.	All the logical decisions must be valid.
Execute all loops at their boundaries and within their operational bounds.	All the loops must be finite.
Exercise internal data structures to ensure their validity.	All the data structures must be valid.

5.1.9 BLACK BOX TESTING:

5.1.10 BLACK BOX TESTING:

Description	Expected result
To check for incorrect or missing functions.	All the functions must be valid.
To check for interface errors.	The entire interface must function normally.
To check for errors in a data structures or external data base access.	The database updation and retrieval must be done.
To check for initialization and termination errors.	All the functions and data structures must be initialized properly and terminated normally.

All the above system testing strategies are carried out in as the development, documentation and institutionalization of the proposed goals and related policies is essential.

CHAPTER 7

7.0 SOFTWARE SPECIFICATION:

7.1 FEATURES OF .NET:

7.2 THE .NET FRAMEWORK

The .NET Framework has two main parts:

1. The Common Language Runtime (CLR).

2. A hierarchical set of class libraries.

The CLR is described as the “execution engine” of .NET. It provides the environment within which programs run. The most important features are

Conversion from a low-level assembler-style language, called Intermediate Language (IL), into code native to the platform being executed on.
Memory management, notably including garbage collection.
Checking and enforcing security restrictions on the running code.
Loading and executing programs, with version control and other such features.
The following features of the .NET framework are also worth description:

Managed Code

Managed Data

Common Type System

Common Language Specification

7.3 THE CLASS LIBRARY

The set of classes is pretty comprehensive, providing collections, file, screen, and network I/O, threading, and so on, as well as XML and database connectivity.

The class library is subdivided into a number of sets (or namespaces), each providing distinct areas of functionality, with dependencies between the namespaces kept to a minimum.

7.4 LANGUAGES SUPPORTED BY .NET

Visual Basic .NET is also CLS compliant, which means that any CLS-compliant language can use the classes, objects, and components you create in Visual Basic .NET.

Other languages for which .NET compilers are available include

FORTRAN
COBOL
Eiffel

ASP.NET XML WEB SERVICES	Windows Forms
Base Class Libraries
Common Language Runtime
Operating System

Fig1 .Net Framework

CONSTRUCTORS AND DESTRUCTORS:

GARBAGE COLLECTION

OVERLOADING

MULTITHREADING:

STRUCTURED EXCEPTION HANDLING

7.5 THE .NET FRAMEWORK

The .NET Framework is a new computing platform that simplifies application development in the highly distributed environment of the Internet.

OBJECTIVES OF .NET FRAMEWORK

1. To provide a consistent object-oriented programming environment whether object codes is stored and executed locally on Internet-distributed, or executed remotely.

2. To provide a code-execution environment to minimizes software deployment and guarantees safe execution of code.

3. Eliminates the performance problems.

There are different types of application, such as Windows-based applications and Web-based applications.

7.6 FEATURES OF SQL-SERVER

SQL-SERVER database consist of six type of objects,

They are,

1. TABLE

2. QUERY

3. FORM

4. REPORT

5. MACRO

7.7 TABLE:

A database is a collection of data about a specific topic.

VIEWS OF TABLE:

We can work with a table in two types,

1. Design View

2. Datasheet View

Design View

To build or modify the structure of a table we work in the table design view. We can specify what kind of data will be hold.

Datasheet View

To add, edit or analyses the data itself we work in tables datasheet view mode.

QUERY:

CHAPTER 7

APPENDIX

7.1 SAMPLE SOURCE CODE

7.2 SAMPLE OUTPUT

CHAPTER 8

8.1 CONCLUSION:

In this paper, we have presented a stochastic model to evaluate the performance of an IaaS cloud system. Several performance metrics have been defined, such as availability, utilization, and responsiveness, allowing us to investigate the impact of different strategies on both provider and user point of views. In a market-oriented area, such as the cloud computing, an accurate evaluation of these parameters is required to quantify the offered QoS and opportunely manage SLAs.

We present an analytical model, based on Stochastic Reward Nets (SRNs), that is both scalable to model systems composed of thousands of resources and flexible to represent different policies and cloud-specific strategies. Several performance metrics are defined and evaluated to analyze the behavior of a Cloud data center: utilization, availability, waiting time, and responsiveness. A resiliency analysis is also provided to take into account load bursts. Finally, a general approach is presented that, starting from the concept of system capacity, can help system managers to opportunely set the data center parameters under different working conditions.

8.2 FUTURE ENHANCEMENT:

Future works will include the analysis of autonomic techniques able to change on-the-fly the system configuration to react to a change on the working conditions. We will also extend the model to represent PaaS and SaaS cloud systems and to integrate the mechanisms needed to capture VM migration and data center consolidation aspects that cover a crucial role in energy saving policies.

A Secure Information Transmission Scheme With a Secret Key Based on Polar Coding

22/10/201902/07/2019 by admin

A Geometric Deployment and Routing Scheme for Directional Wireless Mesh Networks

22/10/201902/07/2019 by admin

Web-Based Traffic Sentiment Analysis Methods and Applications

22/10/201902/07/2019 by admin

In the recent of social media, sentiment analysis has developed rapidly in recent years. However, only a few studies focused on the field of transportation, which failed to meet the stringent requirements of safety, efficiency, and information exchange of intelligent transportation systems (ITSs). We propose the traffic sentiment analysis (TSA) as a new tool to tackle this problem, which provides a new prospective for modern ITSs.

Our methods and models in TSA are proposed in this paper, and the advantages and disadvantages of rule- and learning-based approaches are analyzed based on web data. Practically, we applied the rule-based approach to deal with real problems, presented an architectural design, constructed related bases, demonstrated the process, and discussed the online data collection.

1.2 INTRODUCTION

Transportation systems serve the people in essence, but the modern intelligent transportation systems (ITSs) failed to concern about the public opinions. For the completeness of ITS space, it is necessary to collect and analyze the public wisdom and opinions. With the remarkable advancement of Web 2.0 in the last decade, communication platforms, such as blogs, wikis, online forums, and social-networking groups, have become a rich data-mining source for the detection of public opinions. Therefore, we propose traffic sentiment analysis (TSA) for processing traffic information from websites. As taking consideration of human affection, TSA will enrich the performance of the current ITS space.

TSA is a subfield of sentiment analysis, which concerns about the issues of traffic in particular. Due to the field sensitivity of sentiment analysis, it is necessary to discuss the TSA problems and construct TSA systems specifically. The TSA treats the traffic problems in a new angle, and it supplements the capabilities of current ITS systems. Fig. 1 illustrates the modules of ITS and exhibits that the TSA plays the role of sensing, computing, and supporting the decision making in ITSs.

The functions of the TSA system can be illustrated as follows.

1) Investigation: It is more economical and efficient than the public poll to collect the public opinion through the TSA system.

2) Evaluation: The computational production of the TSA system can be used to evaluate the performance of traffic services and policies.

3) Prediction: The TSA system can be further developed to predict the trends of some social events. For example, to predict whether a cancelled flight would bring chaos, we can analyze the emotion of passengers on their words published on Twitter or Weibo through TSA systems.

In addition, specific parts of the TSA system can be viewed as another form of “social sensors” compared with traditional sensor systems; it can detect the situation from a new humanized perspective. The TSA system is independent of current systems, which is particularly useful in an emergency when other systems were ruined. For example, in 2009, the volcano ash from Iceland caused the malfunction of many cameras in several European countries. In this paper, by constructing a specific TSA system, we addressed the issues and methods in this field and illustrated two cases to demonstrate the value of this research.

Our contribution in this paper can be addressed as follows.

1) We proposed TSA to view the traffic problems in a new perspective.

2) The main issues of TSA applications on web data were discussed based on the web data.

3) The key problems of TSA were addressed, including the design of architecture, the improved rule-based approach, and the construction of related bases.

1.3 LITRATURE SURVEY

CHINESE WORD SEGMENTATION FOR TERRORISM-RELATED CONTENTS

PUBLICATION: D. Zeng, D. Wei, M. Chau, and F. Wang, Intelligence and Security Informatics.New York, NY, USA: Springer-Verlag, 2008, pp. 1–13.

EXPLANATION:

In order to analyze security and terrorism related content in Chinese, it is important to perform word segmentation on Chinese documents. There are many previous studies on Chinese word segmentation. The two major approaches are statistic-based and dictionary-based approaches. The pure statistic methods have lower precision, while the pure dictionary-based method cannot deal with new words and are restricted to the coverage of the dictionary. In this paper, we propose a hybrid method that avoids the limitations of both approaches. Through the use of suffix tree and mutual information (MI) with the dictionary, our segmenter, called IASeg, achieves a high accuracy in word segmentation when domain training is available. It can identify new words through MI-based token merging and dictionary update. In addition, with the Improved Bigram method it can also process N-grams. To evaluate the performance of our segmenter, we compare it with the Hylanda segmenter and the ICTCLAS segmenter using a terrorism-related corpus. The experiment results show that IASeg performs better than the two benchmarks in both precision and recall.

AGENT-BASED CONTROL FOR NETWORKED TRAFFIC MANAGEMENT SYSTEMS

PUBLICATION: F.-Y. Wang, IEEE Intell. Syst., vol. 20, no. 5, pp. 92–96, Sep./Oct. 2005.

EXPLANATION:

Agent or multiagent systems have evolved and diversified rapidly since their inception around the mid 1980s as the key concept and method in distributed artificial intelligence. They have become an established, promising research and application field drawing on and bringing together results and concepts from many disciplines, including AI, computer science, sociology, economics, organization and management science, and philosophy. However, multiagent systems have yet to achieve widespread use for controlling traffic management systems. Most research focuses on developing hierarchical structures, analytical modeling, and optimized algorithms that are effective for real-time traffic applications, as you can see from well-known traffic control systems such as CRONOS, OPAC, SCOOT, SCAT, PRODYN, and RHODES. Although those functional-decomposition-based systems are useful and successful for many traffic management problems, costs and difficulties associated with their development, operation, maintenance, expansion, and upgrading are often prohibitive and sometimes unnecessary, especially in the rapidly arriving age of connectivity. We need to rethink control systems and reinvestigate the use of simple task-oriented agents for traffic control and management of transportation systems.

OPINION FEATURE EXTRACTION USING CLASS SEQUENTIAL RULES

PUBLICATION: M. Hu and B. Liu, presented at the AAAI Spring Symposium Computational

Approaches Analyzing Weblogs, Palo Alto, CA, USA, 2006, Paper AAAI-CAAW-06.

EXPLANATION:

The paper studies the problem of analyzing user comments and reviews of products sold online. Analyzing such reviews and producing a summary of them is very useful to both potential customers and product manufacturers. By analyzing reviews, we mean to extract features of products (also called opinion features) that have been commented by reviewers and determine whether the opinions are positive or negative. This paper focuses on extracting opinion features from Pros and Cons, which typically consist of short phrases or incomplete sentences. We propose a language pattern based approach for this purpose. The language patterns are generated from Class Sequential Rules (CSR). A CSR is different from a classic sequential pattern because a CSR has a fixed class (or target). We propose an algorithm to mine CSR from a set of labeled training sequences. To perform extraction, the mined CSRs are transformed into language patterns, which are used to match Pros and Cons to extract opinion features. Experimental results show that the proposed approach is very effective.

CHAPTER 2

2.0 SYSTEM ANALYSIS

2.1 EXISTING SYSTEM:

Existing approaches to sentiment analysis can be categorized into rule- and learning based approaches. Rule-based approaches often require an expert-defined dictionary of subjective words; this approach predicts the polarity of a sentence or document by analyzing the occurring patterns of such words in text. For example, Wiebe et al. provided a lexicon source of subjectivity clues, such as verbs, adjectives, and nouns, with their polarity (i.e., positive, negative, or neutral) and strength (i.e., strong or weak) annotated. However, this lexicon is able to define the original polarity of a word only, and the actual polarity of a word may be modified by its context in a sentence. Several approaches that consider the context of words have been proposed to determine the sentiment orientation of words.

Previous studies, the data set contains several subjective texts that could not be easily analyzed by the rules. The most typical phenomenon is the ironic sentiment sentences. For instance, in posts regarding fuel prices, the thread title used was “the fuel price will rise,” to which one user replied, “go to sell the car.” Such a reply apparently carries an ironic tone; thus, all annotators manually labeled the reply as “negative.” However, given that the computer cannot detect from the given text any word expressing a negative sentiment, the methods cannot recognize the sentiment polarity. Therefore, numerous problems remain unsolved.

2.1.1 DISADVANTAGES:

Rule-based approach, the disadvantage is that the sentiment polarity results cannot be as precise as expected if the context of the texts is not considered. Nevertheless, for handling web data, this type of approach has the following advantages.

The precision of the rule-based approach is independent of the sizes of the clauses. Second, the syntax rule of a certain language is basic and static despite the differences in the stylistic features of various users. The thought process and word choice basically remain unchanged.

Existing the rules of the rule-based approach is relatively static in the rule-based approach can be easily extended by simply updating the sentiment lexicon, although new sentimental words rapidly emerge and the sentiment of several words may be changed with words.

2.2 PROPOSED SYSTEM:

We propose traffic sentiment analysis (TSA) for processing traffic information from websites. As taking consideration of human affection, TSA will enrich the performance of the current ITS space. TSA is a subfield of sentiment analysis, which concerns about the issues of traffic in articular. Due to the field sensitivity of sentiment analysis, it is necessary to discuss the TSA problems and construct TSA systems specifically.

The TSA treats the traffic problems in a new angle, and it supplements the capabilities of current ITS systems in the modules of ITS and exhibits that the TSA plays the role of sensing, computing, and supporting the decision making in ITSs. The functions of the TSA system can be illustrated as follows. 1) Investigation: It is more economical and efficient than the public poll to collect the public opinion through the TSA system. 2) Evaluation: The computational production of the TSA system can be used to evaluate the performance of traffic services and policies. 3) Prediction: The TSA system can be further developed to predict the trends of some social events.

For example, to predict whether a cancelled flight would bring chaos, we can analyze the emotion of passengers on their words published on Twitter or Weibo through TSA systems. In addition, specific parts of the TSA system can be viewed as another form of “social sensors” compared with traditional sensor systems; it can detect the situation from a new humanized perspective.

2.2.1 ADVANTAGES:

We approach is adopted here to address the distinct challenges posed by the web data set illustrated the architecture of TSA; the architecture is based on the tackling process; and its main components, including 1) web data collection, 2) preprocessing, 3) extraction of subjects and objects, 4) extraction of sentiment properties, 5) sentiment calculation and classification, 6) evaluation or applications, and 7) feed-back, improve the construction of the sentiment, rule, and TSA object bases.

Data collection: We gathered data from several websites, such ensuring that the conclusions are definitely based on public opinion or, at least, represent part of the public opinion.

Preprocessing: As previously mentioned, web documents must be processed additionally because that segment words by spaces in sentences. In the preprocessing, the following steps are included: 1) the segmentation of text, 2) the labeling of words, and 3) the replacement of synonymous expressions.

2.3 HARDWARE & SOFTWARE REQUIREMENTS:

2.3.1 HARDWARE REQUIREMENT:

v Processor – Pentium –IV

Speed – 1.1 GHz
- RAM – 256 MB (min)
- Hard Disk – 20 GB
- Floppy Drive – 1.44 MB
- Key Board – Standard Windows Keyboard
- Mouse – Two or Three Button Mouse
- Monitor – SVGA

2.3.2 SOFTWARE REQUIREMENTS:

Operating System : Windows XP or Win7
Front End : JAVA JDK 1.7
Back End : MS ACCESS 2007
Tools : Netbeans 7
Document : MS-Office 2007

CHAPTER 3

3.0 SYSTEM DESIGN:

Data Flow Diagram / Use Case Diagram / Flow Diagram:

The DFD is also called as bubble chart. It is a simple graphical formalism that can be used to represent a system in terms of the input data to the system, various processing carried out on these data, and the output data is generated by the system

The data flow diagram (DFD) is one of the most important modeling tools. It is used to model the system components. These components are the system process, the data used by the process, an external entity that interacts with the system and the information flows in the system.

DFD shows how the information moves through the system and how it is modified by a series of transformations. It is a graphical technique that depicts information flow and the transformations that are applied as data moves from input to output.

DFD is also known as bubble chart. A DFD may be used to represent a system at any level of abstraction. DFD may be partitioned into levels that represent increasing information flow and functional detail.

NOTATION:

SOURCE OR DESTINATION OF DATA:

External sources or destinations, which may be people or organizations or other entities

DATA SOURCE:

Here the data referenced by a process is stored and retrieved.

PROCESS:

People, procedures or devices that produce data’s in the physical component is not identified.

DATA FLOW:

Data moves in a specific direction from an origin to a destination. The data flow is a “packet” of data.

MODELING RULES:

There are several common modeling rules when creating DFDs:

All processes must have at least one data flow in and one data flow out.
All processes should modify the incoming data, producing new forms of outgoing data.
Each data store must be involved with at least one data flow.
Each external entity must be involved with at least one data flow.
A data flow must be attached to at least one process.

3.1 ARCHITECTURE DIAGRAM

3.2 DATAFLOW DIAGRAM

UML DIAGRAMS:

3.2 USE CASE DIAGRAM:

3.3 CLASS DIAGRAM:

3.4 SEQUENCE DIAGRAM:

3.5 ACTIVITY DIAGRAM:

CHAPTER 4

4.0 IMPLEMENTATION:

TSA ARCHITECTURE

Previous studies on Chinese texts have devoted considerable efforts on architectural design. Che et al. designed the architecture of the language technology platform (LTP), an integrated Chinese processing platform including a suite of high-performance natural language processing (NLP) modules and relevant corpora. They achieved plausible results in several relevant evaluations, particularly for syntactic and semantic parsing modules. Li et al. designed the architecture of sentiment analysis application in the financial domain on the basis of morphemes. A rule-based approach is adopted here to address the distinct challenges posed by the Chinese data set. Fig. 2 illustrated the architecture of TSA; the architecture is based on the tackling process; and its main components, including 1) web data collection, 2) preprocessing, 3) extraction of subjects and objects, 4) extraction of sentiment properties, 5) sentiment calculation and classification, 6) evaluation or applications, and 7) feed- back, improve the construction of the sentiment, rule, and TSA object bases.

Data collection: To address the problem, we gathered data from several websites, such as Sina Weibo, Tencent Weibo, Tianya, and autohome (the upper block in Fig. 2), ensuring that the conclusions are definitely based on public opinion or, at least, represent part of the public opinion details of data collection are discussed in Section V.

Preprocessing: As previously mentioned, Chinese documents must be processed additionally because that Chinese language does not segment words by spaces in sentences. In the preprocessing, the following steps are included: 1) the segmentation of text, 2) the labeling of words, and 3) the replacement of synonymous expressions. The first two steps are done by a Chinese segmentation tool; we employ the Chinese Lexical Analysis System 3 launched by the social media, various expressions denote the same meaning. For example, several users commonly use “d,” which represents the Chinese character “ ” (support), to express agreement with others. Therefore, the replacement of synonymous expressions (step 3) is necessary to reduce the complexity and increase the precision of following processes.

Word segmentation optimization: To avoid unnecessary disturbances and improve precision, preprocessing should be conducted according to the material and the demand of the algorithms. However, in practice, the result of word segmentation in Chinese is far from expected. In some cases, this step may even reduce the precision. For example, “” is separated as ( /n). In fact, “ ” is an abbreviation of a company name, which represents one of the two Chinese oil giants. Therefore, it is necessary to improve the performance of the Chinese segmentation. In this paper, we propose to construct the “sentiment base” in the application of TSA. In practice, the “sentiment base” consists of the TSA sentiment base and HowNet (subsection B).

Extraction of subjects and objects: Subjects and objects are mainly extracted by context mining and document analysis. In TSA, appropriate models should be designed in context mining according to different data sets and resources. Context mining should obtain results as efficiently as possible to provide the necessary background knowledge for the subsequent steps. In practice, context mining includes conservation extraction and coreference analysis. Conservation extraction refers to handling the text, such as “citation, @.” In addition, coreference analysis refers to mining the object represented by other words. For example, the address in Sina Weibo is usually represented by a hyperlink.

4.1 ALGORITHM

In this paper, we propose to construct the sentiment, modifier, object, and rule bases. Assume that the sentiment polarity of a word is determined by its morphemes. If the morphemes of a word appear in the positive lexicon more frequently than they do in the negative lexicon, the word is positive; otherwise, the word is negative. To measure the positive and negative tendencies of the morpheme q, we assign positive and negative weights to the morphemes as follows:

In formula (3), the polarity Sci depends on morphemes Ci, and the absolute value of Sci is the degree of tendency of morphemes Ci. The steps for calculating the sentiment polarity of words are as follows. Scan the positive and negative word lexicons; if the word w appears in the positive word lexicon, Sw = 1; if the word appears in the negative word lexicon, Sw = −1. Otherwise, the sentiment polarity is computed using morphemes by

Where Sw represents the sentiment polarity of the word w, which consists of c1, c2, . . . , cp. If Sw > 0, the sentiment polarity of the word is positive; otherwise, the sentiment polarity of the word is negative. If the value obtained is close to zero, the word can be considered neutral.

4.2 MODULES:

DATA COLLECTION TSA:

IT’S TRANSPORT SYSTEMS:

RULE BASED APPROACH:

TSA ANALYTICAL TECHNIQUE:

4.3 MODULE DESCRIPTION:

DATA COLLECTION TSA:

Information regarding traffic on the Web can be classified

into three categories. The first category consists of news, expert

commentaries, announcements, etc., from the traffic website.

The second includes posts from the transport sector in forums.

These forums provide a platform through which users

can exchange information about social topics, such as traffic

congestions and transportation policies. The third includes realtime

information about traffic in microblogging, which can be

found from the social media, such as weibo.com. The sentiment

polarity of the first category is not easily distinguished, but its

content is true and meaningful. The sentiment polarity of the

second category is clear, and usually, a discussion on certain

events or topics may be highly valuable for tracking public

opinion. The third category, which includes real-time traffic

information, may not have a fixed topic but often located in a

certain place. Such information bears significance for obtaining

real-time information of travelers and creating a backup sensor

network system. Data from the specific websites can be collected by the open

application programming interface or correspondent crawler,

such as the first and third categories of information. However,

collecting a data set on a specific topic is more difficult. In most

forums, the information-publishing platform can be divided

into a series of boards containing various categories or topics. In

a predefined subject board, the topics are designed for specific

events, providing a relatively better framework for the readers

and commenters. Nevertheless, the categorization is too simple

and indistinct for analysis and research because of the following

reasons: 1) not all topics can be mapped to a single board; 2) the

contents of the post are not strictly related to the object topics;

and 3) a board of forum often contains more than one topic.

Therefore, to precisely collect a topic line and gather the

information to one post, we first design a special crawler by

using depth retrieval. Traffic-related terms are adopted to build

the key ontological vocabulary used for the built-in search

engine of the website.

IT’S TRANSPORT SYSTEMS:

The advances in cloud computing and internet of things (IoT) have provided a promising opportunity to further address the increasing transportation issues, such as heavy traffic, congestion, and vehicle safety. In the past few years, researchers have proposed a few models that use cloud computing for implementing intelligent transportation systems (ITSs). For example, a new vehicular cloud architecture called ITS-Cloud was proposed to improve vehicle-to-vehicle communication and road safety A cloud-based urban traffic control system was proposed to optimize traffic control a service-oriented architecture (SOA), this system uses a number of software services (SaaS), such as intersection control services, area management service, cloud service discovery service, and sensor service, to perform different tasks.

These services also interact with each other to exchange information and provide a solid basis for building a collaborative traffic control and processing system in a distributed cloud environment. As an emerging technology caused by rapid advances in modern wireless telecommunication, IoT has received a lot of attention and is expected to bring benefits to numerous application areas including health care, manufacturing, and transportation. Currently, the use of IoT in transportation is still in its early stage and most research on ITSs has not leveraged the IoT technology as a solution or an enabling infrastructure.

We propose to use both cloud computing and IoT as an enabling infrastructure for developing a vehicular data cloud platform where transportation-related information, such as traffic control and management, car location tracking and monitoring, road condition, car warranty, and maintenance information, can be intelligently connected and made available to drivers, automakers, part-manufacturer, vehicle quality controller, safety authorities, and regional transportation division. An experiment of using data mining models to analyze vehicular data clouds in the IoT environment was also conducted to demonstrate the feasibility of vehicular data mining service.

RULE BASED APPROACH:

Rule-based approach is needed, e.g., whether a noun that could represent the sentiment of the texts exists. As emphasized in previous studies, the data set contains several subjective texts that could not be easily analyzed by the rules. The most typical phenomenon is the ironic sentiment sentences. For instance, in posts regarding fuel prices, the thread title used was “the fuel price will rise,” to which one user replied, “go to sell the car.” Such a reply apparently carries an ironic tone; thus, all annotators manually labeled the reply as “negative.” However, given that the computer cannot detect from the given text any word expressing a negative sentiment, the methods cannot recognize the sentiment polarity. Therefore, numerous problems remain unsolved. For the limitations of the existing lexicons, an improved lexicon should be developed, which requires long-term and arduous efforts. We proposed the construction of ITSs under the architecture of artificial, computational, and parallel (ACP) methods, with the TSA system as one of the data sources.

TSA ANALYTICAL TECHNIQUE:

Text sentiment calculation can be categorized into three levels, namely, word, sentence, and document levels. The calculation of the sentiment polarity of words is a basic step in the construction of the sentiment word base. In practice, we consider the words or phrases as another form of sentence. Therefore, text processing includes two main parts, the polarity calculation of the sentence- and document-level text. Fig. 3 shows the overall process involved in the proposed approach. The method includes two major steps, i.e., the sentence sentiment analysis and document sentiment aggregation. Considering the subtlety of Chinese expression, we first decompose a document into constituting sentences and determine the sentiment polarity of each sentence. In contrast to early document-level analytical approaches we regard sentences as atomic units for semantic analysis. The polarity scores of all the sentences are subsequently synthesized to compute for the overall polarity of the entire document. The sentiment polarity of a sentence is defined as ps. ps is determined to extract the SND patterns and calculate the sentiment polarity score according to the SND patterns identified in the text.

CHAPTER 5

5.0 SYSTEM STUDY:

5.1 FEASIBILITY STUDY:

Three key considerations involved in the feasibility analysis are

ECONOMICAL FEASIBILITY
TECHNICAL FEASIBILITY
SOCIAL FEASIBILITY

5.1.1 ECONOMICAL FEASIBILITY:

5.1.2 TECHNICAL FEASIBILITY

5.1.3 SOCIAL FEASIBILITY:

5.2 SYSTEM TESTING:

This creates two problems, the time lag between the cause and the appearance of the problem and the effect of the system errors on the files and records within the system. A small system error can conceivably explode into a much larger Problem. Effective testing early in the purpose translates directly into long term cost savings from a reduced number of errors. Another reason for system testing is its utility, as a user-oriented vehicle before implementation. The best programs are worthless if it produces the correct outputs.

5.2.1 UNIT TESTING:

Description	Expected result
Test for application window properties.	All the properties of the windows are to be properly aligned and displayed.
Test for mouse operations.	All the mouse operations like click, drag, etc. must perform the necessary operations without any exceptions.

5.1.2 FUNCTIONAL TESTING:

Description	Expected result
Test for all modules.	All peers should communicate in the group.
Test for various peer in a distributed network framework as it display all users available in the group.	The result after execution should give the accurate result.

5.1. 3 NON-FUNCTIONAL TESTING:

Load testing
Performance testing
Usability testing
Reliability testing
Security testing

5.1.4 LOAD TESTING:

Description	Expected result
It is necessary to ascertain that the application behaves correctly under loads when ‘Server busy’ response is received.	Should designate another active node as a Server.

5.1.5 PERFORMANCE TESTING:

Description	Expected result
This is required to assure that an application perforce adequately, having the capability to handle many peers, delivering its results in expected time and using an acceptable level of resource and it is an aspect of operational management.	Should handle large input values, and produce accurate result in a expected time.

5.1.6 RELIABILITY TESTING:

Description	Expected result
This is to check that the server is rugged and reliable and can handle the failure of any of the components involved in provide the application.	In case of failure of the server an alternate server should take over the job.

5.1.7 SECURITY TESTING:

Description	Expected result
Checking that the user identification is authenticated.	In case failure it should not be connected in the framework.
Check whether group keys in a tree are shared by all peers.	The peers should know group key in the same group.

5.1.8 WHITE BOX TESTING:

Description	Expected result
Exercise all logical decisions on their true and false sides.	All the logical decisions must be valid.
Execute all loops at their boundaries and within their operational bounds.	All the loops must be finite.
Exercise internal data structures to ensure their validity.	All the data structures must be valid.

5.1.9 BLACK BOX TESTING:

Description	Expected result
To check for incorrect or missing functions.	All the functions must be valid.
To check for interface errors.	The entire interface must function normally.
To check for errors in a data structures or external data base access.	The database updation and retrieval must be done.
To check for initialization and termination errors.	All the functions and data structures must be initialized properly and terminated normally.

All the above system testing strategies are carried out in as the development, documentation and institutionalization of the proposed goals and related policies is essential.

CHAPTER 6

6.0 SOFTWARE DESCRIPTION:

6.1 JAVA TECHNOLOGY:

Java technology is both a programming language and a platform.

The Java Programming Language

The Java programming language is a high-level language that can be characterized by all of the following buzzwords:

Simple
- Architecture neutral
- Object oriented
- Portable
- Distributed
- High performance
- Interpreted
- Multithreaded
- Robust
- Dynamic
- Secure

With most programming languages, you either compile or interpret a program so that you can run it on your computer. The Java programming language is unusual in that a program is both compiled and interpreted. With the compiler, first you translate a program into an intermediate language called Java byte codes —the platform-independent codes interpreted by the interpreter on the Java platform. The interpreter parses and runs each Java byte code instruction on the computer. Compilation happens just once; interpretation occurs each time the program is executed. The following figure illustrates how this works.

You can think of Java byte codes as the machine code instructions for the Java Virtual Machine (Java VM). Every Java interpreter, whether it’s a development tool or a Web browser that can run applets, is an implementation of the Java VM. Java byte codes help make “write once, run anywhere” possible. You can compile your program into byte codes on any platform that has a Java compiler. The byte codes can then be run on any implementation of the Java VM. That means that as long as a computer has a Java VM, the same program written in the Java programming language can run on Windows 2000, a Solaris workstation, or on an iMac.

6.2 THE JAVA PLATFORM:

A platform is the hardware or software environment in which a program runs. We’ve already mentioned some of the most popular platforms like Windows 2000, Linux, Solaris, and MacOS. Most platforms can be described as a combination of the operating system and hardware. The Java platform differs from most other platforms in that it’s a software-only platform that runs on top of other hardware-based platforms.

The Java platform has two components:

The Java Virtual Machine (Java VM)
The Java Application Programming Interface (Java API)

You’ve already been introduced to the Java VM. It’s the base for the Java platform and is ported onto various hardware-based platforms.

The Java API is a large collection of ready-made software components that provide many useful capabilities, such as graphical user interface (GUI) widgets. The Java API is grouped into libraries of related classes and interfaces; these libraries are known as packages. The next section, What Can Java Technology Do? Highlights what functionality some of the packages in the Java API provide.

The following figure depicts a program that’s running on the Java platform. As the figure shows, the Java API and the virtual machine insulate the program from the hardware.

Native code is code that after you compile it, the compiled code runs on a specific hardware platform. As a platform-independent environment, the Java platform can be a bit slower than native code. However, smart compilers, well-tuned interpreters, and just-in-time byte code compilers can bring performance close to that of native code without threatening portability.

6.3 WHAT CAN JAVA TECHNOLOGY DO?

The most common types of programs written in the Java programming language are applets and applications. If you’ve surfed the Web, you’re probably already familiar with applets. An applet is a program that adheres to certain conventions that allow it to run within a Java-enabled browser.

However, the Java programming language is not just for writing cute, entertaining applets for the Web. The general-purpose, high-level Java programming language is also a powerful software platform. Using the generous API, you can write many types of programs.

An application is a standalone program that runs directly on the Java platform. A special kind of application known as a server serves and supports clients on a network. Examples of servers are Web servers, proxy servers, mail servers, and print servers. Another specialized program is a servlet.

A servlet can almost be thought of as an applet that runs on the server side. Java Servlets are a popular choice for building interactive web applications, replacing the use of CGI scripts. Servlets are similar to applets in that they are runtime extensions of applications. Instead of working in browsers, though, servlets run within Java Web servers, configuring or tailoring the server.

How does the API support all these kinds of programs? It does so with packages of software components that provides a wide range of functionality. Every full implementation of the Java platform gives you the following features:

The essentials: Objects, strings, threads, numbers, input and output, data structures, system properties, date and time, and so on.
Applets: The set of conventions used by applets.
Networking: URLs, TCP (Transmission Control Protocol), UDP (User Data gram Protocol) sockets, and IP (Internet Protocol) addresses.
Internationalization: Help for writing programs that can be localized for users worldwide. Programs can automatically adapt to specific locales and be displayed in the appropriate language.
Security: Both low level and high level, including electronic signatures, public and private key management, access control, and certificates.
Software components: Known as JavaBeans^TM, can plug into existing component architectures.
Object serialization: Allows lightweight persistence and communication via Remote Method Invocation (RMI).
Java Database Connectivity (JDBC^TM): Provides uniform access to a wide range of relational databases.

The Java platform also has APIs for 2D and 3D graphics, accessibility, servers, collaboration, telephony, speech, animation, and more. The following figure depicts what is included in the Java 2 SDK.

6.4 HOW WILL JAVA TECHNOLOGY CHANGE MY LIFE?

We can’t promise you fame, fortune, or even a job if you learn the Java programming language. Still, it is likely to make your programs better and requires less effort than other languages. We believe that Java technology will help you do the following:

Get started quickly: Although the Java programming language is a powerful object-oriented language, it’s easy to learn, especially for programmers already familiar with C or C++.
Write less code: Comparisons of program metrics (class counts, method counts, and so on) suggest that a program written in the Java programming language can be four times smaller than the same program in C++.
Write better code: The Java programming language encourages good coding practices, and its garbage collection helps you avoid memory leaks. Its object orientation, its JavaBeans component architecture, and its wide-ranging, easily extendible API let you reuse other people’s tested code and introduce fewer bugs.
Develop programs more quickly: Your development time may be as much as twice as fast versus writing the same program in C++. Why? You write fewer lines of code and it is a simpler programming language than C++.
Avoid platform dependencies with 100% Pure Java: You can keep your program portable by avoiding the use of libraries written in other languages. The 100% Pure Java^TMProduct Certification Program has a repository of historical process manuals, white papers, brochures, and similar materials online.
Write once, run anywhere: Because 100% Pure Java programs are compiled into machine-independent byte codes, they run consistently on any Java platform.
Distribute software more easily: You can upgrade applets easily from a central server. Applets take advantage of the feature of allowing new classes to be loaded “on the fly,” without recompiling the entire program.

6.5 ODBC:

Microsoft Open Database Connectivity (ODBC) is a standard programming interface for application developers and database systems providers. Before ODBC became a de facto standard for Windows programs to interface with database systems, programmers had to use proprietary languages for each database they wanted to connect to. Now, ODBC has made the choice of the database system almost irrelevant from a coding perspective, which is as it should be. Application developers have much more important things to worry about than the syntax that is needed to port their program from one database to another when business needs suddenly change.

Through the ODBC Administrator in Control Panel, you can specify the particular database that is associated with a data source that an ODBC application program is written to use. Think of an ODBC data source as a door with a name on it. Each door will lead you to a particular database. For example, the data source named Sales Figures might be a SQL Server database, whereas the Accounts Payable data source could refer to an Access database. The physical database referred to by a data source can reside anywhere on the LAN.

The ODBC system files are not installed on your system by Windows 95. Rather, they are installed when you setup a separate database application, such as SQL Server Client or Visual Basic 4.0. When the ODBC icon is installed in Control Panel, it uses a file called ODBCINST.DLL. It is also possible to administer your ODBC data sources through a stand-alone program called ODBCADM.EXE. There is a 16-bit and a 32-bit version of this program and each maintains a separate list of ODBC data sources.

From a programming perspective, the beauty of ODBC is that the application can be written to use the same set of function calls to interface with any data source, regardless of the database vendor. The source code of the application doesn’t change whether it talks to Oracle or SQL Server. We only mention these two as an example. There are ODBC drivers available for several dozen popular database systems. Even Excel spreadsheets and plain text files can be turned into data sources. The operating system uses the Registry information written by ODBC Administrator to determine which low-level ODBC drivers are needed to talk to the data source (such as the interface to Oracle or SQL Server). The loading of the ODBC drivers is transparent to the ODBC application program. In a client/server environment, the ODBC API even handles many of the network issues for the application programmer.

The advantages of this scheme are so numerous that you are probably thinking there must be some catch. The only disadvantage of ODBC is that it isn’t as efficient as talking directly to the native database interface. ODBC has had many detractors make the charge that it is too slow. Microsoft has always claimed that the critical factor in performance is the quality of the driver software that is used. In our humble opinion, this is true. The availability of good ODBC drivers has improved a great deal recently. And anyway, the criticism about performance is somewhat analogous to those who said that compilers would never match the speed of pure assembly language. Maybe not, but the compiler (or ODBC) gives you the opportunity to write cleaner programs, which means you finish sooner. Meanwhile, computers get faster every year.

6.6 JDBC:

In an effort to set an independent database standard API for Java; Sun Microsystems developed Java Database Connectivity, or JDBC. JDBC offers a generic SQL database access mechanism that provides a consistent interface to a variety of RDBMSs. This consistent interface is achieved through the use of “plug-in” database connectivity modules, or drivers. If a database vendor wishes to have JDBC support, he or she must provide the driver for each platform that the database and Java run on.

To gain a wider acceptance of JDBC, Sun based JDBC’s framework on ODBC. As you discovered earlier in this chapter, ODBC has widespread support on a variety of platforms. Basing JDBC on ODBC will allow vendors to bring JDBC drivers to market much faster than developing a completely new connectivity solution.

JDBC was announced in March of 1996. It was released for a 90 day public review that ended June 8, 1996. Because of user input, the final JDBC v1.0 specification was released soon after.

The remainder of this section will cover enough information about JDBC for you to know what it is about and how to use it effectively. This is by no means a complete overview of JDBC. That would fill an entire book.

6.7 JDBC Goals:

Few software packages are designed without goals in mind. JDBC is one that, because of its many goals, drove the development of the API. These goals, in conjunction with early reviewer feedback, have finalized the JDBC class library into a solid framework for building database applications in Java.

The goals that were set for JDBC are important. They will give you some insight as to why certain classes and functionalities behave the way they do. The eight design goals for JDBC are as follows:

SQL Level API

The designers felt that their main goal was to define a SQL interface for Java. Although not the lowest database interface level possible, it is at a low enough level for higher-level tools and APIs to be created. Conversely, it is at a high enough level for application programmers to use it confidently. Attaining this goal allows for future tool vendors to “generate” JDBC code and to hide many of JDBC’s complexities from the end user.

SQL Conformance

SQL syntax varies as you move from database vendor to database vendor. In an effort to support a wide variety of vendors, JDBC will allow any query statement to be passed through it to the underlying database driver. This allows the connectivity module to handle non-standard functionality in a manner that is suitable for its users.

JDBC must be implemental on top of common database interfaces

The JDBC SQL API must “sit” on top of other common SQL level APIs. This goal allows JDBC to use existing ODBC level drivers by the use of a software interface. This interface would translate JDBC calls to ODBC and vice versa.

Provide a Java interface that is consistent with the rest of the Java system

Because of Java’s acceptance in the user community thus far, the designers feel that they should not stray from the current design of the core Java system.

Keep it simple

This goal probably appears in all software design goal listings. JDBC is no exception. Sun felt that the design of JDBC should be very simple, allowing for only one method of completing a task per mechanism. Allowing duplicate functionality only serves to confuse the users of the API.

Use strong, static typing wherever possible

Strong typing allows for more error checking to be done at compile time; also, less error appear at runtime.

Keep the common cases simple

Because more often than not, the usual SQL calls used by the programmer are simple SELECT’s, INSERT’s, DELETE’s and UPDATE’s, these queries should be simple to perform with JDBC. However, more complex SQL statements should also be possible.

Finally we decided to precede the implementation using Java Networking.

And for dynamically updating the cache table we go for MS Access database.

Java ha two things: a programming language and a platform.

Java is a high-level programming language that is all of the following

Simple Architecture-neutral

Object-oriented Portable

Distributed High-performance

Interpreted Multithreaded

Robust Dynamic Secure

Java is also unusual in that each Java program is both compiled and interpreted. With a compile you translate a Java program into an intermediate language called Java byte codes the platform-independent code instruction is passed and run on the computer.

Compilation happens just once; interpretation occurs each time the program is executed. The figure illustrates how this works.

6.7 NETWORKING TCP/IP STACK:

The TCP/IP stack is shorter than the OSI one:

TCP is a connection-oriented protocol; UDP (User Datagram Protocol) is a connectionless protocol.

IP datagram’s:

The IP layer provides a connectionless and unreliable delivery system. It considers each datagram independently of the others. Any association between datagram must be supplied by the higher layers. The IP layer supplies a checksum that includes its own header. The header includes the source and destination addresses. The IP layer handles routing through an Internet. It is also responsible for breaking up large datagram into smaller ones for transmission and reassembling them at the other end.

UDP:

UDP is also connectionless and unreliable. What it adds to IP is a checksum for the contents of the datagram and port numbers. These are used to give a client/server model – see later.

TCP:

TCP supplies logic to give a reliable connection-oriented protocol above IP. It provides a virtual circuit that two processes can use to communicate.

Internet addresses

In order to use a service, you must be able to find it. The Internet uses an address scheme for machines so that they can be located. The address is a 32 bit integer which gives the IP address.

Network address:

Class A uses 8 bits for the network address with 24 bits left over for other addressing. Class B uses 16 bit network addressing. Class C uses 24 bit network addressing and class D uses all 32.

Subnet address:

Internally, the UNIX network is divided into sub networks. Building 11 is currently on one sub network and uses 10-bit addressing, allowing 1024 different hosts.

Host address:

8 bits are finally used for host addresses within our subnet. This places a limit of 256 machines that can be on the subnet.

Total address:

The 32 bit address is usually written as 4 integers separated by dots.

Port addresses

A service exists on a host, and is identified by its port. This is a 16 bit number. To send a message to a server, you send it to the port for that service of the host that it is running on. This is not location transparency! Certain of these ports are “well known”.

Sockets:

A socket is a data structure maintained by the system to handle network connections. A socket is created using the call socket. It returns an integer that is like a file descriptor. In fact, under Windows, this handle can be used with Read File and Write File functions.

#include <sys/types.h>

#include <sys/socket.h>

int socket(int family, int type, int protocol);

Here “family” will be AF_INET for IP communications, protocol will be zero, and type will depend on whether TCP or UDP is used. Two processes wishing to communicate over a network create a socket each. These are similar to two ends of a pipe – but the actual pipe does not yet exist.

6.8 JFREE CHART:

JFreeChart is a free 100% Java chart library that makes it easy for developers to display professional quality charts in their applications. JFreeChart’s extensive feature set includes:

A consistent and well-documented API, supporting a wide range of chart types;

A flexible design that is easy to extend, and targets both server-side and client-side applications;

Support for many output types, including Swing components, image files (including PNG and JPEG), and vector graphics file formats (including PDF, EPS and SVG);

JFreeChart is “open source” or, more specifically, free software. It is distributed under the terms of the GNU Lesser General Public Licence (LGPL), which permits use in proprietary applications.

6.8.1. Map Visualizations:

Charts showing values that relate to geographical areas. Some examples include: (a) population density in each state of the United States, (b) income per capita for each country in Europe, (c) life expectancy in each country of the world. The tasks in this project include: Sourcing freely redistributable vector outlines for the countries of the world, states/provinces in particular countries (USA in particular, but also other areas);

Creating an appropriate dataset interface (plus default implementation), a rendered, and integrating this with the existing XYPlot class in JFreeChart; Testing, documenting, testing some more, documenting some more.

6.8.2. Time Series Chart Interactivity

Implement a new (to JFreeChart) feature for interactive time series charts — to display a separate control that shows a small version of ALL the time series data, with a sliding “view” rectangle that allows you to select the subset of the time series data to display in the main chart.

6.8.3. Dashboards

There is currently a lot of interest in dashboard displays. Create a flexible dashboard mechanism that supports a subset of JFreeChart chart types (dials, pies, thermometers, bars, and lines/time series) that can be delivered easily via both Java Web Start and an applet.

6.8.4. Property Editors

The property editor mechanism in JFreeChart only handles a small subset of the properties that can be set for charts. Extend (or reimplement) this mechanism to provide greater end-user control over the appearance of the charts.

CHAPTER 7

APPENDIX

7.1 SAMPLE SOURCE CODE

7.2 SAMPLE OUTPUT

CHAPTER 8

8.1 CONCLUSION

We have proposed Web-based TSA to analyze the traffic problems in a humanizer way. To the best of our knowledge, this is the first attempt to apply sentiment analysis on the area of traffic. The study of TSA will provide us a new perspective when facing with traffic problems.

Our work can be concluded as the following five folds: 1) designing the application architecture of TSA; 2) constructing the related bases for the TSA system; 3) comparing the advantages and disadvantages of both rule- and learning-based approaches based on the characters of web data; 4) proposing an algorithm for the sentiment polarity calculation based on the rule-based approach; and 5) taking consideration of the modifying relationships of sentence patterns and locations in the sentiment polarity calculations.

The task to implement the TSA system into existing ITSs is also critically important, and it does need further research. We suggested that take the policy evaluation part to support decision making of managers and view the evaluation results related to specific location as sensor information. The keynote of implementation is jointly accommodating the traveler’s best interest and reasonable workload. Since TSA is still in its infancy, we anticipate that more techniques will be developed for the joint performance of ITS with the TSA system in the future.

Video Dissemination over Hybrid Cellular and Ad Hoc Networks

22/10/201902/07/2019 by admin

We study the problem of disseminating videos to mobile users by using a hybrid cellular and ad hoc network. In particular, we formulate the problem of optimally choosing the mobile devices that will serve as gateways from the cellular to the ad hoc network, the ad hoc routes from the gateways to individual devices, and the layers to deliver on these ad hoc routes.

We develop a Mixed Integer Linear Program (MILP)-based algorithm, called POPT, to solve this optimization problem. Pocket delivers the highest possible video quality and optimization problem that determines:

1) The mobile devices that will serve as gateways and relay video data from the cellular network to the ad hoc network,

2) The multihop ad hoc routes for disseminating video data

3) The subsets of video data each mobile device relays to the next hops under capacity constraints. We formulate the optimization problem into a Mixed Integer Linear Program (MILP), and propose an MILP-based algorithm, called POPT, to optimally solve the problem.

We recommend the THS algorithm for video streaming over hybrid cellular and ad hoc networks. Last, we also build a real video dissemination system among multiple Android smart phones over a live cellular network. Via actual experiments, we demonstrate the practicality and efficiency of the proposed THS algorithm.

We call it Tree-Based Heuristic Scheduling (THS) algorithm, and it works as follows: We first sort all the transmission units in the W-segment scheduling window in descending order of importance, by layer, segment, and video. We then go through these WL units, and sequentially schedule the transmissions to all mobile devices.

1.2 INTRODUCTION

Mobile devices, such as smart phones and tablets, are getting increasingly popular, and continue to generate record-high amount of mobile data traffic. For example, a Cisco report indicates that mobile data traffic will increase 39 times by 2015. Sixty six percent of the increase is due to video traffic. Unfortunately, existing cellular networks were designed for unicast voice services, and do not natively support multicast and broadcast. Therefore, cellular networks are not suitable for large-scale video dissemination. This was validated by a measurement study, which shows that each HSDPA cell can only support up to six mobile video users at 256 kbps. Thus, disseminating videos to many mobile users over cellular networks could lead to network congestion and degraded user experience.

This network capacity issue may be partially addressed by deploying more cellular base stations, installing dedicated broadcast networks (such as Digital Video Broadcast- Handheld, DVB-H), or upgrading the cellular base stations to support Multimedia Broadcast Multicast Service (MBMS). However, these approaches all result in additional costs for new network infrastructure, and might not be fully compatible with existing mobile devices. Hence, a better way to disseminate videos to many mobile users is critical to the profitability of cellular service providers.

We study video dissemination in hybrid cellular and ad hoc networks in the underlying network, consisting of one or several base stations and multiple mobile devices equipped with heterogeneous network interfaces. Mobile devices not only connect to the base station over the cellular network, but also form an ad hoc network using short-range wireless protocols such as WiFi and Bluetooth. Mobile devices relay video traffic among each other using ad hoc links, leveraging such a free spectrum to alleviate bandwidth bottlenecks and cut down the expense of cellular service providers. Throughout the paper, we denote mobile devices that directly receive video data over the cellular network and relay the receiving data to other mobile devices over the ad hoc network as gateways.

1.3 SCOPE OF THE PROJECT

3G, and 4G cellular networks, and examples of ad hoc networks are WiFi ad hoc and Bluetooth networks. Mobile devices can always receive video data from the base station via cellular links. Distributing videos in a hybrid network is challenging because: Wireless networks are dynamic in terms of connectivity, latency, and capacity and video data require high throughput and low latency. To cope with these challenges, we employ layered video coding, such as H.264/MPEG4.

1.4 LITRATURE SURVEY

RATE CONTROL AND STREAM ADAPTATION FOR SCALABLE VIDEO STREAMING OVER MULTIPLE ACCESS NETWORKS

Author: C. Hsu, N. Freris, J. Singh, and X. Zhu

Publish:” Proc. Int’l Packet Video Workshop (PV ’10), pp. 1-8, Dec.2010.

In a multihomed video streaming system, a video sequence is simultaneously transmitted over multiple access networks to a client. In this paper, we formulate the rate control and a stream adaptation problem into a unified optimization problem, which determines the sending rates of individual networks, selects which video packets to transmit, and assigns each packet to an access network. We propose two heuristic algorithms with a trade-off between optimality and computational complexity. One of the proposed algorithms runs faster, while the other one results in better video quality. We propose a hybrid algorithm that demonstrates a good balance between optimality and computational complexity. We conduct extensive packet-level simulations to evaluate our algorithms using real network conditions and actual scalable video streams. We compare our algorithms against the rate control algorithms defined in the Datagram Congestion Control Protocol (DCCP) standard. The simulation results show that our algorithms significantly outperform current systems while being TCP-friendly. Our algorithms achieve at least 10 dB quality improvements over DCCP and result in up to 83% packet delivery delay reduction.

CHAPTER 2

2.0 SYSTEM ANALYSIS

2.1 EXISTING SYSTEM:

Linear Program (LP)-based algorithm called MTS, for lower time complexity generic ad hoc protocols do not work well in hybrid cellular and WiFi ad hoc networks, and may lead to:

1) degraded overall throughput, 2) unfair resource allocation, and 3) low resilience to mobility. They propose two approaches to improve the efficiency of ad hoc protocols. First, the base station can run optimization algorithms for the WiFi ad hoc network, for example, to build optimized routes. Second, mobile devices connected to other access networks can offload traffic from the cellular network to those access networks, so as to avoid network congestion around the base station.

2.1.1 DISADVANTAGES:

Existing algorithms achieve at least 10 dB quality improvements and result in up to 80% packet delivery delay reduction.

2.2 PROPOSED SYSTEM:

We propose a hybrid network, in which each multicast group is either in the cellular in the ad hoc mode. Initially, all multicast groups are in ad hoc mode, and when the bandwidth requirement of a group exceeds the ad hoc network capacity, the base station picks up that group and switches it into the cellular mode.

In the ad hoc network, a flooding routing protocol is used to discover neighbors and a heuristic is employed to forward video data. Our work differs from in several aspects: 1) we propose a unified optimization problem that jointly finds the optimal gateway mobile devices, ad hoc routes, and video adaptation, 2) we consider existing cellular base stations that may not natively support multicast, and 3) we employ Variable-Bit-Rate (VBR) streams.

More specifically, we empirically measure the mapping between the node location and link capacity several times, and use the resulting values for capacity estimation. We adopt the video traces of H.264/MPEG4 layered videos from an online video library. The mean bit rate and average video quality for each layer of the considered videos are given in Table 2. In this paper, we report sample simulation results of distributing Crew. However, the proposed formulation and solutions are general and also work for the scenarios where mobile devices watch different videos.

2.2.1 ADVANTAGES:

1. The links into mobile devices on breadth-first trees of transmission units with higher quality improvement values are given higher priorities.

2. The links with higher ad hoc link capacities are given higher priorities.

3. The links from mobile devices with higher cellular link capacities are given higher priorities.

2.3 HARDWARE & SOFTWARE REQUIREMENTS:

2.3.1 HARDWARE REQUIREMENT:

v Processor – Pentium –IV

Speed – 1.1 GHz
- RAM – 256 MB (min)
- Hard Disk – 20 GB
- Floppy Drive – 1.44 MB
- Key Board – Standard Windows Keyboard
- Mouse – Two or Three Button Mouse
- Monitor – SVGA

2.3.2 SOFTWARE REQUIREMENTS:

Operating System : Windows XP
Front End : JAVA JDK 1.7
Tool : Eclipse
Document : MS-Office 2007

CHAPTER 3

3.0 SYSTEM DESIGN:

Data Flow Diagram / Use Case Diagram / Flow Diagram:

The DFD is also called as bubble chart. It is a simple graphical formalism that can be used to represent a system in terms of the input data to the system, various processing carried out on these data, and the output data is generated by the system

The data flow diagram (DFD) is one of the most important modeling tools. It is used to model the system components. These components are the system process, the data used by the process, an external entity that interacts with the system and the information flows in the system.

DFD shows how the information moves through the system and how it is modified by a series of transformations. It is a graphical technique that depicts information flow and the transformations that are applied as data moves from input to output.

DFD is also known as bubble chart. A DFD may be used to represent a system at any level of abstraction. DFD may be partitioned into levels that represent increasing information flow and functional detail.

NOTATION:

SOURCE OR DESTINATION OF DATA:

External sources or destinations, which may be people or organizations or other entities

DATA SOURCE:

Here the data referenced by a process is stored and retrieved.

PROCESS:

People, procedures or devices that produce data. The physical component is not identified.

DATA FLOW:

Data moves in a specific direction from an origin to a destination. The data flow is a “packet” of data.

MODELING RULES:

There are several common modeling rules when creating DFDs:

All processes must have at least one data flow in and one data flow out.
All processes should modify the incoming data, producing new forms of outgoing data.
Each data store must be involved with at least one data flow.
Each external entity must be involved with at least one data flow.
A data flow must be attached to at least one process.

3.1 BLOCK DIAGRAM:

ARCHITECTURE DIAGRAM:

3.2 DATAFLOW DIAGRAM:

UML DIAGRAMS:

3.2 USE CASE DIAGRAM:

3.3 CLASS DIAGRAM:

3.4 SEQUENCE DIAGRAM:

3.5 ACTIVITY DIAGRAM:

CHAPTER 4

4.0 IMPLEMENTATION:

UNICAST DATA TRANSFER:

We design a hybrid network that uses a WiFi ad hoc network to route cellular data via other mobile devices with higher cellular data rates. Two neighbor discovery and routing protocols, proactive and on-demand, are proposed. With the former protocol, all devices proactively maintain the states of their immediate neighbors. When a device wants to discover a route to the base station, it issues a route discovery message to a neighbor with the highest cellular data rate. The message is further relayed by the neighbor to its highest rate neighbor until there is no neighbor with higher rate than the relayer or the hop count limit is reached. The final relayer is the one that receives data from the cellular network and propagates data to the original requester. With the on-demand protocol, devices do not maintain their neighbors’ states. A requester discovers a route to the base station by flooding a route discovery message to all its neighbors within a given range.

Higher data rates than that of the previous hops forward the message to the base station, which eventually selects the best path to the requester. Simulation results show that the on-demand protocol typically incurs higher traffic overhead on the cellular network, while the proactive protocol consumes more energy. Through simulations show that generic ad hoc protocols do not work well in hybrid cellular and WiFi ad hoc networks, and may lead to: 1) degraded overall throughput, 2) unfair resource allocation, and 3) low resilience to mobility. They propose two approaches to improve the efficiency of ad hoc protocols. First, the base station can run optimization algorithms for the WiFi ad hoc network, for example, to build optimized routes. Second, mobile devices connected to other access networks can offload traffic from the cellular network to those access networks, so as to avoid network congestion around the base station.

MULTICAST DATA TRANSFER:

Evaluate a hybrid network in which a cellular base station reduces its transmission range to achieve a higher data rate for mobile devices inside its range. Some mobile devices act as gateways and relay data to mobile devices outside the range via a multihop ad hoc network. The analysis and simulation results indicate that up to 70 percent downlink capacity improvement over pure cellular networks is possible. We propose a hybrid network, in which each multicast group is either in the cellular mode or in the ad hoc mode. Initially, all multicast groups are in ad hoc mode, and when the bandwidth requirement of a group exceeds the ad hoc network capacity, the base station picks up that group and switches it into the cellular mode. Park and Kasera consider the gateway node discovery problem, and model the ad hoc interference as a graph coloring problem. Solving this problem allows them to approximate the number of other mobile devices in the transmission range of a specific mobile device in the ad hoc routing problem for multicast services, and also abstract ad hoc interference as a graph. They formulate a problem of finding the relay forest to maximize the overall data rate, and they propose an approximation algorithm.

4.1 ALGORITHM:

A Tree-Based Heuristic Algorithm: THS Both POPT and MTS algorithms employ optimization problem solvers. Although commercial and open-source solvers are available, these solvers might lead to long running time in the worst-case scenarios. Hence, we next propose a greedy scheduling algorithm that does not rely on any solvers. We call it Tree-Based Heuristic Scheduling (THS) algorithm, and it works as follows: We first sort all the transmission units in the W-segment scheduling window in descending order of importance, by layer, segment, and video. We then go through these WL units, and sequentially schedule the transmissions to all mobile devices.

4.2 MODULES:

SERVER CLIENT MODULE:

RESOURCE ALLOCATION:

VIDEO STREAMING:

QUALITY OPTIMIZATION:

4.3 MODULE DESCRIPTION:

SERVER CLIENT MODULE:

Client-server computing or networking is a distributed application architecture that partitions tasks or workloads between service providers (servers) and service requesters, called clients. Often clients and servers operate over a computer network on separate hardware. A server machine is a high-performance host that is running one or more server programs which share its resources with clients. A client also shares any of its resources; Clients therefore initiate communication sessions with servers which await (listen to) incoming requests.

WIMAX RELAY NETWORKS:

WiMAX bandwidth allocation schemes in employ multiple loops to examine the performance of the different combinations of recipients, which results in extremely high computational complexity. The bandwidth allocation scheme proposed in this study applies greedy methods to achieve low computational complexity while incorporating the table-consulting mechanisms to avoid redundant bandwidth allocation scheme can efficiently allocate bandwidth while maintaining low computational complexity. WiMAX provide diverse data rates, H.264/SVC allow a video stream to be split into one base layer and multiple enhancement layers. This study assumes that a video can be split into six layers (one base layer and five enhancement layers) corresponding to the six video quality levels a user with the requirements of 64kbit/s 128 kbit/s can be satisfied by receiving the base layer and one enhancement layer.

RESOURCE ALLOCATION:

Our resource allocation model for two-hop WiMAX relay networks consists of one BS, M RSs, and N SSs. For consistency, the BS is regarded as the 0th RS and is denoted by RS0 in the following discussion, while the RSs are denoted by RS1 to RSM.An SS can associate either with the BS or with one of the RSs, and the number of SSs associated with RSm is denoted by Nm. The notation SSm;n represents the nth SS associated with RSm.

CQm represents the channel quality of the link between the BS and RSm while CQm;n represents the channel quality between RSm and SSm;n. Assume that the video streams for the links with lower channel quality should be transmitted by the modulation schemes with higher reliability.

VIDEO STREAMING:

Scalable video broadcast/multicast solutions efficiently integrates scalable video coding, 3G broadcast and ad-hoc forwarding to balance the system-wide and video quality of all viewers at 3G cell. In our solution, video is downloading into multiple layers. The base station broadcasts different layers at different rates to cover viewers at different ranges. All viewers are guaranteed to receive the base layer, and viewers closer to the base station can receive more enhancement layers. Using WiMAX Relay Networks connections, viewers far away from the base station can obtain from their neighbors closer to the base station the enhancement layers that they cannot receive directly from the base station. Our solution strikes a good balance between the average and worst-case performance for all viewers in the cell. We design multi-hop relay routing schemes to exploit the broadcast nature of ad-hoc transmissions and eliminate redundant video relays from helpers to their receivers.

QUALITY OPTIMIZATION:

Our channel qualities of these links, BSs and RSs can dynamically adapt the downlink modulation and coding schemes (MCSs) for data transmission. When RSs are deployed at appropriate locations between the BSs and SSs, the end-to-end channel qualities can be improved and the BSs and RSs can adopt high data-rate MCSs. Based on this improvement in data rate, IEEE 802.16j systems can offer higher throughput and serve more users than IEEE 802.16e systems. Based on the performance enhancements above, IEEE 802.16j has the potential to provide real-time video multicast services such as mobile IPTV, live video streaming (e.g., athletic events), and online gaming).

However, the BSs should allocate bandwidth efficiently to support such bandwidth-hungry services while guaranteeing the quality of user experience (QoE). The bandwidth allocation problems in IEEE 802.16j networks are more challenging than those in IEEE 802.16e networks because the BSs allocate bandwidth not only to the SSs, but also to the RSs. Multicasting also complicates the bandwidth allocation problems of these factors, designing an efficient bandwidth allocation scheme for video multicast services.

We have presented various bandwidth allocation approaches for video services in IEEE 802.16e networks (i.e., single-hop WiMAX systems). The approaches in and allocate bandwidth by exploiting the common technology of scalable video coding (SVC) specified in the H.264/SVC standard. The H.264/SVC standard is extended from H.264/AVC, and can further split a video stream into a base layer for providing the basic video quality and multiple enhancement layers for providing better video quality layer by-layer.

4.4 EXPRIMENTAL RESULTS

PERFORMANCE IMPROVEMENT:

We investigate the performance improvement achieved by the hybrid network compared to the cellular-only network with varied number of mobile devices U. Fig. 6a shows with 95 percent confidence intervals that, for a PSNR requirement of 30 dB, the Current* scheduler can only support 10 mobile devices. POPT, MTS, and THS algorithms all achieve that quality with any investigated number of mobile devices. Note that these schedulers provide an advantage over Current*—mobile devices receive almost equally—good PSNR. That is, the longest range of 95 percent confidential interval achieved by POPT, MTS, and THS is merely 0.30 dB, while Current* suffers from a much larger range of up to 3.78 dB.

We observe that two algorithms, MTS and THS, achieve similar PSNR, at most 2 dB lower than POPT. Fig. 6b indicates that MTS is more efficient than POPT, but MTS’ running time still increases prohibitively with the increase of device density more device network, MTS takes more than half an hour to generate a schedule. In contrast, the THS algorithm always terminates in very short time under any number of devices. This shows that the THS algorithm achieves a good tradeoff between complexity and solution quality. Because MTS and THS achieve similar PSNR, but THS runs faster than MTS, we do not consider MTS in the remaining comparisons.

CHAPTER 4

5.0 SYSTEM STUDY:

5.1 FEASIBILITY STUDY:

Three key considerations involved in the feasibility analysis are

ECONOMICAL FEASIBILITY
TECHNICAL FEASIBILITY
SOCIAL FEASIBILITY

5.1.1 ECONOMICAL FEASIBILITY:

5.1.2 TECHNICAL FEASIBILITY:

5.1.3 SOCIAL FEASIBILITY:

5.2 SYSTEM TESTING:

5.2.1 UNIT TESTING:

UNIT TESTING:

Description	Expected result
Test for application window properties.	All the properties of the windows are to be properly aligned and displayed.
Test for mouse operations.	All the mouse operations like click, drag, etc. must perform the necessary operations without any exceptions.

5.1.3 FUNCTIONAL TESTING:

FUNCTIONAL TESTING:

Description	Expected result
Test for all modules.	All peers should communicate in the group.
Test for various peer in a distributed network framework as it display all users available in the group.	The result after execution should give the accurate result.

5.1. 4 NON-FUNCTIONAL TESTING:

Load testing
Performance testing
Usability testing
Reliability testing
Security testing

5.1.5 LOAD TESTING:

Load Testing

Description	Expected result
It is necessary to ascertain that the application behaves correctly under loads when ‘Server busy’ response is received.	Should designate another active node as a Server.

5.1.5 PERFORMANCE TESTING:

PERFORMANCE TESTING:

Description	Expected result
This is required to assure that an application perforce adequately, having the capability to handle many peers, delivering its results in expected time and using an acceptable level of resource and it is an aspect of operational management.	Should handle large input values, and produce accurate result in a expected time.

5.1.6 RELIABILITY TESTING:

RELIABILITY TESTING:

Description	Expected result
This is to check that the server is rugged and reliable and can handle the failure of any of the components involved in provide the application.	In case of failure of the server an alternate server should take over the job.

5.1.7 SECURITY TESTING:

SECURITY TESTING:

Description	Expected result
Checking that the user identification is authenticated.	In case failure it should not be connected in the framework.
Check whether group keys in a tree are shared by all peers.	The peers should know group key in the same group.

5.1.7 WHITE BOX TESTING:

5.1.8 WHITE BOX TESTING:

Description	Expected result
Exercise all logical decisions on their true and false sides.	All the logical decisions must be valid.
Execute all loops at their boundaries and within their operational bounds.	All the loops must be finite.
Exercise internal data structures to ensure their validity.	All the data structures must be valid.

5.1.9 BLACK BOX TESTING:

5.1.10 BLACK BOX TESTING:

Description	Expected result
To check for incorrect or missing functions.	All the functions must be valid.
To check for interface errors.	The entire interface must function normally.
To check for errors in a data structures or external data base access.	The database updation and retrieval must be done.
To check for initialization and termination errors.	All the functions and data structures must be initialized properly and terminated normally.

All the above system testing strategies are carried out in as the development, documentation and institutionalization of the proposed goals and related policies is essential.

CHAPTER 7

7.0 SOFTWARE DESCRIPTION:

7.1 JAVA TECHNOLOGY:

Java technology is both a programming language and a platform.

The Java Programming Language

The Java programming language is a high-level language that can be characterized by all of the following buzzwords:

Simple
- Architecture neutral
- Object oriented
- Portable
- Distributed
- High performance
- Interpreted
- Multithreaded
- Robust
- Dynamic
- Secure

7.2 THE JAVA PLATFORM:

The Java platform has two components:

The Java Virtual Machine (Java VM)
The Java Application Programming Interface (Java API)

You’ve already been introduced to the Java VM. It’s the base for the Java platform and is ported onto various hardware-based platforms.

The following figure depicts a program that’s running on the Java platform. As the figure shows, the Java API and the virtual machine insulate the program from the hardware.

7.3 WHAT CAN JAVA TECHNOLOGY DO?

The essentials: Objects, strings, threads, numbers, input and output, data structures, system properties, date and time, and so on.
Applets: The set of conventions used by applets.
Networking: URLs, TCP (Transmission Control Protocol), UDP (User Data gram Protocol) sockets, and IP (Internet Protocol) addresses.
Internationalization: Help for writing programs that can be localized for users worldwide. Programs can automatically adapt to specific locales and be displayed in the appropriate language.
Security: Both low level and high level, including electronic signatures, public and private key management, access control, and certificates.
Software components: Known as JavaBeans^TM, can plug into existing component architectures.
Object serialization: Allows lightweight persistence and communication via Remote Method Invocation (RMI).
Java Database Connectivity (JDBC^TM): Provides uniform access to a wide range of relational databases.

The Java platform also has APIs for 2D and 3D graphics, accessibility, servers, collaboration, telephony, speech, animation, and more. The following figure depicts what is included in the Java 2 SDK.

7.4 HOW WILL JAVA TECHNOLOGY CHANGE MY LIFE?

Get started quickly: Although the Java programming language is a powerful object-oriented language, it’s easy to learn, especially for programmers already familiar with C or C++.
Write less code: Comparisons of program metrics (class counts, method counts, and so on) suggest that a program written in the Java programming language can be four times smaller than the same program in C++.
Write better code: The Java programming language encourages good coding practices, and its garbage collection helps you avoid memory leaks. Its object orientation, its JavaBeans component architecture, and its wide-ranging, easily extendible API let you reuse other people’s tested code and introduce fewer bugs.
Develop programs more quickly: Your development time may be as much as twice as fast versus writing the same program in C++. Why? You write fewer lines of code and it is a simpler programming language than C++.
Avoid platform dependencies with 100% Pure Java: You can keep your program portable by avoiding the use of libraries written in other languages. The 100% Pure Java^TMProduct Certification Program has a repository of historical process manuals, white papers, brochures, and similar materials online.
Write once, run anywhere: Because 100% Pure Java programs are compiled into machine-independent byte codes, they run consistently on any Java platform.
Distribute software more easily: You can upgrade applets easily from a central server. Applets take advantage of the feature of allowing new classes to be loaded “on the fly,” without recompiling the entire program.

7.5 ODBC:

7.6 JDBC:

JDBC was announced in March of 1996. It was released for a 90 day public review that ended June 8, 1996. Because of user input, the final JDBC v1.0 specification was released soon after.

7.7 JDBC Goals:

The goals that were set for JDBC are important. They will give you some insight as to why certain classes and functionalities behave the way they do. The eight design goals for JDBC are as follows:

SQL Level API

SQL Conformance

JDBC must be implemental on top of common database interfaces

Provide a Java interface that is consistent with the rest of the Java system

Because of Java’s acceptance in the user community thus far, the designers feel that they should not stray from the current design of the core Java system.

Keep it simple

Use strong, static typing wherever possible

Strong typing allows for more error checking to be done at compile time; also, less error appear at runtime.

Keep the common cases simple

Finally we decided to precede the implementation using Java Networking.

And for dynamically updating the cache table we go for MS Access database.

Java ha two things: a programming language and a platform.

Java is a high-level programming language that is all of the following

Simple Architecture-neutral

Object-oriented Portable

Distributed High-performance

Interpreted Multithreaded

Robust Dynamic Secure

Compilation happens just once; interpretation occurs each time the program is executed. The figure illustrates how this works.

7.7 NETWORKING TCP/IP STACK:

The TCP/IP stack is shorter than the OSI one:

TCP is a connection-oriented protocol; UDP (User Datagram Protocol) is a connectionless protocol.

IP datagram’s:

UDP:

UDP is also connectionless and unreliable. What it adds to IP is a checksum for the contents of the datagram and port numbers. These are used to give a client/server model – see later.

TCP:

TCP supplies logic to give a reliable connection-oriented protocol above IP. It provides a virtual circuit that two processes can use to communicate.

Internet addresses

In order to use a service, you must be able to find it. The Internet uses an address scheme for machines so that they can be located. The address is a 32 bit integer which gives the IP address.

Network address:

Class A uses 8 bits for the network address with 24 bits left over for other addressing. Class B uses 16 bit network addressing. Class C uses 24 bit network addressing and class D uses all 32.

Subnet address:

Internally, the UNIX network is divided into sub networks. Building 11 is currently on one sub network and uses 10-bit addressing, allowing 1024 different hosts.

Host address:

8 bits are finally used for host addresses within our subnet. This places a limit of 256 machines that can be on the subnet.

Total address:

The 32 bit address is usually written as 4 integers separated by dots.

Port addresses

Sockets:

#include <sys/types.h>

#include <sys/socket.h>

int socket(int family, int type, int protocol);

7.8 JFREE CHART:

JFreeChart is a free 100% Java chart library that makes it easy for developers to display professional quality charts in their applications. JFreeChart’s extensive feature set includes:

A consistent and well-documented API, supporting a wide range of chart types;

A flexible design that is easy to extend, and targets both server-side and client-side applications;

Support for many output types, including Swing components, image files (including PNG and JPEG), and vector graphics file formats (including PDF, EPS and SVG);

JFreeChart is “open source” or, more specifically, free software. It is distributed under the terms of the GNU Lesser General Public Licence (LGPL), which permits use in proprietary applications.

7.8.1. Map Visualizations:

7.8.2. Time Series Chart Interactivity

7.8.3. Dashboards

7.8.4. Property Editors

CHAPTER 7

APPENDIX

7.1 SAMPLE SOURCE CODE

7.2 SAMPLE OUTPUT

CHAPTER 8

8.1 CONCLUSION

We proposed algorithms: 1) an MILP-based algorithm called POPT a greedy algorithm, THS. Via packet-level simulations, we found that neither POPT nor MTS scale to large hybrid networks. This is because they both employ numerical methods to solve optimization problems. Therefore, we recommend the THS algorithm, which terminates in real time even when there are 70+ mobile devices in the hybrid network.

The experimental results from the actual testbed confirm the observations we made in Qualnet simulations: the THS algorithm clearly outperforms the Current* algorithm. Furthermore, the THS algorithm may outperform POPT in real systems, which can be attributed to the long running time of the POPT algorithm. This demonstrates that the THS algorithm is practical and efficient.

The simulation results indicate that the THS algorithm not only runs fast, but also achieves overall video quality close to the optimum: at most 2 dB difference is observed, compared to the POPT algorithm. In contrast, optimum schedules over the cellular network achieve much lower video quality compared to POPT: more than 15 dB difference is observed. We also validated the practicality and efficiency of the THS algorithm using a real testbed in a live cellular network. The experimental results confirm that the THS algorithm result in high video quality. Moreover, the THS algorithm could outperform the POPT algorithm in real systems. This is because although POPT could generate optimal schedules, its high running time may lead to many late segments, which in turn render inferior video quality.

VeDi A Vehicular Crowd-Sourced Video Social Network for VANETs

22/10/201902/07/2019 by admin

CHAPTER 1

1.1 ABSTRACT:

As one of the important members of Internet of Things (IoT), vehicles have seen steep advancement in communication technology. With the advent of Vehicular Ad-Hoc Networks (VANETs), vehicles now can evolve into social interactions to share safety, efficiency, and comfort related messages with other vehicles. In this paper, we study vehicular social network from Social Internet of Things (SIoT) perspective and propose VeDi, a vehicular crowd-sourced video social network for VANETs.When a user shares a video in the VeDi, it can be accessed by other surrounding vehicles. Any social interaction (e.g. view, comment, like) with the video on the roadway are stored in the social network cloud along with the video itself.

In VeDi, every vehicle maintains a list of video related metadata (e.g. blur and shakiness) of available videos which are used to selectively retrieve quality videos by surrounding vehicles. We also present a method to determine representative quality scores for an entire video clip using blur and shakiness values. The prototype implementations and experimental results denote that the proposed system can be a viable option to create video social networks such as youtube, vine, and vimeo by employing vehicular crowd.

1.2 INTRODUCTION

State-of-the-art vehicles are equipped with advanced technologies that enable them to communicate with nearby vehicles by forming vehicular ad-hoc networks (VANETs). There has been growing interest in building a social network of vehicles that can ensure safety of the driver and passengers, and also improve travel efficiency through collaborative application. While main purpose of VANETs is safety and efficiency, there is plenty of room in the allocated bandwidth for comfort applications as well. In this work we study vehicular social network from video sharing perspective. We propose VeDi, a crowd sourced video social network over VANETs. We envision it to be integrated part of future vehicular social network and eventually Internet of Things.

The distribution of multimedia content over vehicular networks is a challenging task for several reasons such as network partitioning due to nodes mobility, and medium contention due to broadcasting nature of the technology. Therefore users cannot browse through all the videos. In VeDi, OBUs automatically calculate metadata description of video through content processing. This metadata description is shared among other OBUs through a Dedicated Short Range Communication (DSRC1) type message called tNote. Furthermore, it is difficult for the users to comprehend quality of complete video from individual frame quality. We experimentally analyse mobile recorded short video clips and find representative blur and shakiness scores for the entire video. The main contributions of the paper are two-fold: an architecture of crowd sourced video social network and quality based metadata description of videos.

1.3 LITRATURE SURVEY

AUTHOR AND PUBLICATION: N. Abbani, M. Jomaa, T. Tarhini, H. Artail, and W. El-Hajj. MANAGING SOCIAL NETWORKS IN VEHICULAR NETWORKS USING TRUST RULES. In Wireless Technology and Applications (ISWTA), 2011 IEEE Symposium on, pages 168–173, Sept 2011.

EXPLANATION:

Drivers and passengers in urban areas may spend large portion of their time waiting in their cars on the road while commuting to and from work, to school, or to the supermarket. Regularities of driving patterns in time and in space motivate the formation of communities of common backgrounds and interests. We propose a model for forming and maintaining Vehicular Social Networks (VSNs) that uses trust principles for admission to social groups, and controlling the interactions among members. This paper describes the details of the design, and proposes a simple but representative probabilistic model for deriving the probability of wrongful admissions and the probability of an agent trusting a malicious node. The experimental results, which were obtained from simulations using the network simulation software ns2, describe metrics related to the dynamics of group formation and time to form groups as well as to detecting malicious members. Our system was able to form social groups with agents of common interests and maintain an accurate trust evaluation of their behavior.

AUTHOR AND PUBLICATION: M. Asefi, J. W. Mark, and X. Shen. AN APPLICATION-CENTRIC INTER-VEHICLE ROUTING PROTOCOL FOR VIDEO STREAMING OVER MULTI-HOP URBAN VANETS. In Communications (ICC), 2011 IEEE International Conference on, pages 1–5. IEEE, 2011.

EXPLANATION:

Service-oriented vehicular networks face challenge to deliver delay-sensitive data such as video packets. Most research on video streaming consider network-centric quality of service (QoS) metrics rather than the user perceived quality. In this paper, we propose an application-centric routing framework for real-time video transmission over urban multi-hop vehicular ad-hoc network (VANET) scenarios. Queueing based mobility model, spatial traffic distribution and probability of connectivity for sparse and dense VANET scenarios are taken into consideration in designing the routing protocol. The numerical results demonstrate the gain achieved by the proposed routing protocol versus geographic greedy forwarding in terms of video frame distortion and streaming start-up delay in several urban communication scenarios for various vehicle entrance rate and traffic densities.

AUTHOR AND PUBLICATION: M. Asefi, J. W. Mark, and X. Shen. A MOBILITY-AWARE AND QUALITYDRIVEN RETRANSMISSION LIMIT ADAPTATION SCHEME FOR VIDEO STREAMING OVER VANETS. Wireless Communications, IEEE Transactions on, 11(5):1817– 1827, 2012.

EXPLANATION:

An adaptive medium access control (MAC) retransmission limit selection scheme is proposed to improve the performance of IEEE 802.11p standard MAC protocol for video streaming applications over vehicular ad-hoc networks (VANETs). A multi-objective optimization framework, which jointly minimizes the probability of playback freezes and start-up delay of the streamed video at the destination vehicle by tuning the MAC retransmission limit with respect to channel statistics as well as packet transmission rate, is applied at road side unit (RSU). Periodic channel state estimation is performed at the RSU using the information derived from the received signal strength (RSS) and Doppler shift effect. Estimates of access probability between the RSU and the destination vehicle is incorporated in the design of the adaptive MAC scheme. The adaptation parameters are embedded in the user datagram protocol (UDP) packet header. Two-hop transmission is applied in zones in which the destination vehicle is not within the transmission range of any RSU. For multi-hop scenario, we discuss two-hop joint MAC retransmission adaptation and path selection. Compared with the non-adaptive IEEE 802.11p standard MAC, numerical results show that the proposed adaptive MAC protocol exhibits significantly fewer playback freezes while introduces only a slight increase in start-up delay.

AUTHOR AND PUBLICATION: L. Atzori, A. Iera, and G. Morabito. SIOT: GIVING A SOCIAL STRUCTURE TO THE INTERNET OF THINGS. Communications Letters, IEEE, 15(11):1193–1195, 2011.

EXPLANATION:

The actual development of the Internet of Things (IoT) needs major issues related to things’ service discovery and composition to be addressed. This paper proposes a possible approach to solve such issues. We introduce a novel paradigm of “social network of intelligent objects”, namely the Social Internet of Things (SIoT), based on the notion of social relationships among objects. Following the definition of a possible social structure among objects, a preliminary architecture for the implementation of SIoT is presented. Through the SIoT paradigm, the capability of humans and devices to discover, select, and use objects with their services in the IoT is augmented. Besides, a level of trustworthiness is enabled to steer the interaction among the billions of objects which will crowd the future IoT.

CHAPTER 2

2.0 SYSTEM ANALYSIS

2.1 EXISTING SYSTEM:

2.1.1 DISADVANTAGES:

2.2 PROPOSED SYSTEM:

2.2.1 ADVANTAGES:

2.3 HARDWARE & SOFTWARE REQUIREMENTS:

2.3.1 HARDWARE REQUIREMENT:

v Processor – Pentium –IV

Speed – 1.1 GHz
- RAM – 256 MB (min)
- Hard Disk – 20 GB
- Floppy Drive – 1.44 MB
- Key Board – Standard Windows Keyboard
- Mouse – Two or Three Button Mouse
- Monitor – SVGA

2.3.2 SOFTWARE REQUIREMENTS:

JAVA

Operating System : Windows XP or Win7
Front End : JAVA JDK 1.7
Back End : MYSQL Server
Server : Apache Tomact Server
Script : JSP Script
Document : MS-Office 2007

.NET

Operating System : Windows XP or Win7
Front End : Microsoft Visual Studio .NET 2008
Script : C# Script
Back End : MS-SQL Server 2005
Document : MS-Office 2007

CHAPTER 3

3.0 SYSTEM DESIGN:

Data Flow Diagram / Use Case Diagram / Flow Diagram:

The DFD is also called as bubble chart. It is a simple graphical formalism that can be used to represent a system in terms of the input data to the system, various processing carried out on these data, and the output data is generated by the system

The data flow diagram (DFD) is one of the most important modeling tools. It is used to model the system components. These components are the system process, the data used by the process, an external entity that interacts with the system and the information flows in the system.

DFD shows how the information moves through the system and how it is modified by a series of transformations. It is a graphical technique that depicts information flow and the transformations that are applied as data moves from input to output.

DFD is also known as bubble chart. A DFD may be used to represent a system at any level of abstraction. DFD may be partitioned into levels that represent increasing information flow and functional detail.

NOTATION:

SOURCE OR DESTINATION OF DATA:

External sources or destinations, which may be people or organizations or other entities

DATA SOURCE:

Here the data referenced by a process is stored and retrieved.

PROCESS:

People, procedures or devices that produce data’s in the physical component is not identified.

DATA FLOW:

Data moves in a specific direction from an origin to a destination. The data flow is a “packet” of data.

MODELING RULES:

There are several common modeling rules when creating DFDs:

All processes must have at least one data flow in and one data flow out.
All processes should modify the incoming data, producing new forms of outgoing data.
Each data store must be involved with at least one data flow.
Each external entity must be involved with at least one data flow.
A data flow must be attached to at least one process.

SYSTEM DESIGN 🙁 user)

User Case Diagram

Class Diagram

Activity Diagram

Sequence Diagram

CHAPTER 4

4.0 IMPLEMENTATION:

4.1 ALGORITHM

4.2 MODULES:

4.3 MODULE DESCRIPTION:

CHAPTER 5

5.0 SYSTEM STUDY:

5.1 FEASIBILITY STUDY:

Three key considerations involved in the feasibility analysis are

ECONOMICAL FEASIBILITY
TECHNICAL FEASIBILITY
SOCIAL FEASIBILITY

5.1.1 ECONOMICAL FEASIBILITY:

5.1.2 TECHNICAL FEASIBILITY

5.1.3 SOCIAL FEASIBILITY:

5.2 SYSTEM TESTING:

5.2.1 UNIT TESTING:

Description	Expected result
Test for application window properties.	All the properties of the windows are to be properly aligned and displayed.
Test for mouse operations.	All the mouse operations like click, drag, etc. must perform the necessary operations without any exceptions.

5.1.2 FUNCTIONAL TESTING:

Description	Expected result
Test for all modules.	All peers should communicate in the group.
Test for various peer in a distributed network framework as it display all users available in the group.	The result after execution should give the accurate result.

5.1. 3 NON-FUNCTIONAL TESTING:

Load testing
Performance testing
Usability testing
Reliability testing
Security testing

5.1.4 LOAD TESTING:

Description	Expected result
It is necessary to ascertain that the application behaves correctly under loads when ‘Server busy’ response is received.	Should designate another active node as a Server.

5.1.5 PERFORMANCE TESTING:

Description	Expected result
This is required to assure that an application perforce adequately, having the capability to handle many peers, delivering its results in expected time and using an acceptable level of resource and it is an aspect of operational management.	Should handle large input values, and produce accurate result in a expected time.

5.1.6 RELIABILITY TESTING:

Description	Expected result
This is to check that the server is rugged and reliable and can handle the failure of any of the components involved in provide the application.	In case of failure of the server an alternate server should take over the job.

5.1.7 SECURITY TESTING:

Description	Expected result
Checking that the user identification is authenticated.	In case failure it should not be connected in the framework.
Check whether group keys in a tree are shared by all peers.	The peers should know group key in the same group.

5.1.8 WHITE BOX TESTING:

Description	Expected result
Exercise all logical decisions on their true and false sides.	All the logical decisions must be valid.
Execute all loops at their boundaries and within their operational bounds.	All the loops must be finite.
Exercise internal data structures to ensure their validity.	All the data structures must be valid.

5.1.9 BLACK BOX TESTING:

Description	Expected result
To check for incorrect or missing functions.	All the functions must be valid.
To check for interface errors.	The entire interface must function normally.
To check for errors in a data structures or external data base access.	The database updation and retrieval must be done.
To check for initialization and termination errors.	All the functions and data structures must be initialized properly and terminated normally.

All the above system testing strategies are carried out in as the development, documentation and institutionalization of the proposed goals and related policies is essential.

CHAPTER 6

6.0 SOFTWARE DESCRIPTION:

6.1 JAVA TECHNOLOGY:

Java technology is both a programming language and a platform.

The Java Programming Language

The Java programming language is a high-level language that can be characterized by all of the following buzzwords:

Simple
- Architecture neutral
- Object oriented
- Portable
- Distributed
- High performance
- Interpreted
- Multithreaded
- Robust
- Dynamic
- Secure

6.2 THE JAVA PLATFORM:

The Java platform has two components:

The Java Virtual Machine (Java VM)
The Java Application Programming Interface (Java API)

You’ve already been introduced to the Java VM. It’s the base for the Java platform and is ported onto various hardware-based platforms.

The following figure depicts a program that’s running on the Java platform. As the figure shows, the Java API and the virtual machine insulate the program from the hardware.

6.3 WHAT CAN JAVA TECHNOLOGY DO?

The essentials: Objects, strings, threads, numbers, input and output, data structures, system properties, date and time, and so on.
Applets: The set of conventions used by applets.
Networking: URLs, TCP (Transmission Control Protocol), UDP (User Data gram Protocol) sockets, and IP (Internet Protocol) addresses.
Internationalization: Help for writing programs that can be localized for users worldwide. Programs can automatically adapt to specific locales and be displayed in the appropriate language.
Security: Both low level and high level, including electronic signatures, public and private key management, access control, and certificates.
Software components: Known as JavaBeans^TM, can plug into existing component architectures.
Object serialization: Allows lightweight persistence and communication via Remote Method Invocation (RMI).
Java Database Connectivity (JDBC^TM): Provides uniform access to a wide range of relational databases.

The Java platform also has APIs for 2D and 3D graphics, accessibility, servers, collaboration, telephony, speech, animation, and more. The following figure depicts what is included in the Java 2 SDK.

6.4 HOW WILL JAVA TECHNOLOGY CHANGE MY LIFE?

Get started quickly: Although the Java programming language is a powerful object-oriented language, it’s easy to learn, especially for programmers already familiar with C or C++.
Write less code: Comparisons of program metrics (class counts, method counts, and so on) suggest that a program written in the Java programming language can be four times smaller than the same program in C++.
Write better code: The Java programming language encourages good coding practices, and its garbage collection helps you avoid memory leaks. Its object orientation, its JavaBeans component architecture, and its wide-ranging, easily extendible API let you reuse other people’s tested code and introduce fewer bugs.
Develop programs more quickly: Your development time may be as much as twice as fast versus writing the same program in C++. Why? You write fewer lines of code and it is a simpler programming language than C++.
Avoid platform dependencies with 100% Pure Java: You can keep your program portable by avoiding the use of libraries written in other languages. The 100% Pure Java^TMProduct Certification Program has a repository of historical process manuals, white papers, brochures, and similar materials online.
Write once, run anywhere: Because 100% Pure Java programs are compiled into machine-independent byte codes, they run consistently on any Java platform.
Distribute software more easily: You can upgrade applets easily from a central server. Applets take advantage of the feature of allowing new classes to be loaded “on the fly,” without recompiling the entire program.

6.5 ODBC:

6.6 JDBC:

JDBC was announced in March of 1996. It was released for a 90 day public review that ended June 8, 1996. Because of user input, the final JDBC v1.0 specification was released soon after.

6.7 JDBC Goals:

The goals that were set for JDBC are important. They will give you some insight as to why certain classes and functionalities behave the way they do. The eight design goals for JDBC are as follows:

SQL Level API

SQL Conformance

JDBC must be implemental on top of common database interfaces

Provide a Java interface that is consistent with the rest of the Java system

Because of Java’s acceptance in the user community thus far, the designers feel that they should not stray from the current design of the core Java system.

Keep it simple

Use strong, static typing wherever possible

Strong typing allows for more error checking to be done at compile time; also, less error appear at runtime.

Keep the common cases simple

Finally we decided to precede the implementation using Java Networking.

And for dynamically updating the cache table we go for MS Access database.

Java ha two things: a programming language and a platform.

Java is a high-level programming language that is all of the following

Simple Architecture-neutral

Object-oriented Portable

Distributed High-performance

Interpreted Multithreaded

Robust Dynamic Secure

Compilation happens just once; interpretation occurs each time the program is executed. The figure illustrates how this works.

6.7 NETWORKING TCP/IP STACK:

The TCP/IP stack is shorter than the OSI one:

TCP is a connection-oriented protocol; UDP (User Datagram Protocol) is a connectionless protocol.

IP datagram’s:

UDP:

UDP is also connectionless and unreliable. What it adds to IP is a checksum for the contents of the datagram and port numbers. These are used to give a client/server model – see later.

TCP:

TCP supplies logic to give a reliable connection-oriented protocol above IP. It provides a virtual circuit that two processes can use to communicate.

Internet addresses

In order to use a service, you must be able to find it. The Internet uses an address scheme for machines so that they can be located. The address is a 32 bit integer which gives the IP address.

Network address:

Class A uses 8 bits for the network address with 24 bits left over for other addressing. Class B uses 16 bit network addressing. Class C uses 24 bit network addressing and class D uses all 32.

Subnet address:

Internally, the UNIX network is divided into sub networks. Building 11 is currently on one sub network and uses 10-bit addressing, allowing 1024 different hosts.

Host address:

8 bits are finally used for host addresses within our subnet. This places a limit of 256 machines that can be on the subnet.

Total address:

The 32 bit address is usually written as 4 integers separated by dots.

Port addresses

Sockets:

#include <sys/types.h>

#include <sys/socket.h>

int socket(int family, int type, int protocol);

6.8 JFREE CHART:

JFreeChart is a free 100% Java chart library that makes it easy for developers to display professional quality charts in their applications. JFreeChart’s extensive feature set includes:

A consistent and well-documented API, supporting a wide range of chart types;

A flexible design that is easy to extend, and targets both server-side and client-side applications;

Support for many output types, including Swing components, image files (including PNG and JPEG), and vector graphics file formats (including PDF, EPS and SVG);

JFreeChart is “open source” or, more specifically, free software. It is distributed under the terms of the GNU Lesser General Public Licence (LGPL), which permits use in proprietary applications.

6.8.1. Map Visualizations:

6.8.2. Time Series Chart Interactivity

6.8.3. Dashboards

6.8.4. Property Editors

CHAPTER 7

APPENDIX

7.1 SAMPLE SOURCE CODE

7.2 SAMPLE OUTPUT

CHAPTER 8

8.1 CONCLUSION

Sharing video over VANETs is challenging due to dynamic and unpredictable topology, low bandwidth, and fleeting connections. In this paper, we have proposed a framework, VeDi, for vehicular crowd sourced video social network over VANETs. In the proposed work, vehicles share metadata based description of videos that are captured by the occupants of the vehicle and are accessible to surrounding vehicles. The metadata consists of video specifications and derived blur and shakiness measures. These metadata scores help video consumers to select the right video while on the roadway. VeDi reduces the overall bandwidth consumption as users can select most appropriate video without downloading them all. We have provided implementation technique of DSRC type tNote message and encoding size analysis at various system instances. A detail about system architecture implementation approach is also provided with various observations. In our future work, we envision presenting the modeling and simulation results of the proposed system along with scalability measurements and required optimizations.

CHAPTER 9

9.1 REFERENCES

N. Abbani, M. Jomaa, T. Tarhini, H. Artail, and W. El-Hajj. Managing social networks in vehicular networks using trust rules. In Wireless Technology and Applications (ISWTA), 2011 IEEE Symposium on, pages 168–173, Sept 2011.

M. Asefi, J. W. Mark, and X. Shen. An application-centric inter-vehicle routing protocol for video streaming over multi-hop urban vanets. In Communications (ICC), 2011 IEEE International Conference on, pages 1–5. IEEE, 2011.

M. Asefi, J. W. Mark, and X. Shen. A mobility-aware and qualitydriven retransmission limit adaptation scheme for video streaming over vanets. Wireless Communications, IEEE Transactions on, 11(5):1817– 1827, 2012.

L. Atzori, A. Iera, and G. Morabito. Siot: Giving a social structure to the internet of things. Communications Letters, IEEE, 15(11):1193–1195, 2011.

L. Atzori, A. Iera, G. Morabito, and M. Nitti. The social internet of things (siot)–when social networks meet the internet of things: Concept, architecture and network characterization. Computer Networks, 2012.

M. Campanella, H. Weda, and M. Barbieri. Edit while watching: home video editing made easy. In Proceedings of SPIE, volume 6506, 2007.

Y.-C. Chu and N.-F. Huang. Delivering of live video streaming for vehicular communication using peer-to-peer approach. In 2007 Mobile Networking for Vehicular Environments, pages 1–6. IEEE, 2007.

Supporting Privacy Protection in Personalized Web Search

22/10/201902/07/2019 by admin

Sleep Scheduling for Geographic Routing in Duty-Cycled Mobile Sensor Networks

22/10/201902/07/2019 by admin

Security Analysis of Handover Key Management in 4G LTESAE Networks

22/10/201902/07/2019 by admin

Secure De-duplication with Efficient and Reliable Convergent Key Management

22/10/201902/07/2019 by admin

Secure and Efficient Data Transmission for Cluster-Based Wireless Sensor Networks

22/10/201902/07/2019 by admin

SECURE AND EFFICIENT DATA TRANSMISSION FOR CLUSTER-BASED

WIRELESS SENSOR NETWORKS

PROJECT REPORT

Submitted to the Department of Computer Science & Engineering in the FACULTY OF ENGINEERING & TECHNOLOGY

In partial fulfillment of the requirements for the award of the degree

MASTER OF TECHNOLOGY

COMPUTER SCIENCE & ENGINEERING

APRIL 2015

BONAFIDE CERTIFICATE

Certified that this project report titled “SECURE AND EFFICIENT DATA TRANSMISSION FOR CLUSTER-BASED WIRELESS SENSOR NETWORKS SYSTEMS” is the bonafide work of Mr. _____________Who carried out the research under my supervision Certified further, that to the best of my knowledge the work reported herein does not form part of any other project report or dissertation on the basis of which a degree or award was conferred on an earlier occasion on this or any other candidate.

Signature of the Guide Signature of the H.O.D

Name Name

CHAPTER 1

ABSTRACT:

Secure data transmission is a critical issue for wireless sensor networks (WSNs).Clustering is an effective and practical way to enhance the system performance of WSNs. In this paper, we study a secure data transmission for cluster-based WSNs (CWSNs), where the clusters are formed dynamically and periodically. We propose two Secure and Efficient data Transmission (SET) protocols for CWSNs, called SET-IBS and SET-IBOOS, by using the Identity-Based digital Signature (IBS) scheme and the Identity-Based Online/Offline digital Signature (IBOOS) scheme, respectively. In SET-IBS, security relies on the hardness of the Difﬁe-Hellman problem in the pairing domain. SET-IBOOS further reduces the computational overhead for protocol security, which is crucial for WSNs, while its security relies on the hardness of the discrete logarithm problem. We show the feasibility of the SET-IBS and SET-IBOOS protocols with respect to the security requirements and security analysis against various attacks. The calculations and simulations are provided to] illustrate the efficiency of the proposed protocols. The results show that, the proposed protocols have better performance than the existing secure protocols for CWSNs, in terms of security overhead and energy consumption.

1.2 INTRODUCTION

A sensor network (WSN) is a network system comprised of spatially distributed devices using wireless sensor nodes to monitor physical or environmental conditions, such as sound, temperature, and motion. The individual nodes are capable of sensing their environments, processing the information data locally, and sending data to one or more collection points in a WSN. Eﬃcient data transmission is one of the most important issues for WSNs. Meanwhile, many WSNs are deployed in harsh, neglected and often adversarial physical environments for certain applications, such as military domains and sensing tasks with trustless surroundings. Secure and eﬃcient data transmission is thus especially necessary and is demanded in many such practical WSNs.

Cluster-based data transmission in WSNs has been investigated by researchers to achieve the network scalability and management, which maximizes node lifetime and reduce bandwidth consumption by using local collaboration among sensor nodes in a cluster-based WSN (CWSN), every cluster has a leader sensor node, regarded as cluster head (CH). A CH aggregates the data collected by the leaf nodes (non-CH sensor nodes) in its cluster, and sends the aggregation to the base station (BS). The low-energy adaptive clustering hierarchy (LEACH) protocol is a widely known and effective one to reduce and balance the total energy consumption for CWSNs. To prevent quick energy consumption of the set of CHs, LEACH randomly rotates CHs among all sensor nodes in the network, in rounds. LEACH achieves improvements in terms of network lifetime. Following the idea of LEACH, a number of protocols have been presented such as APTEEN and PEACH which use similar concepts of LEACH. In this paper, for convenience, we call this sort of cluster-based protocols as LEACH-like protocols.

Researchers have been widely studying CWSNs in the last decade in the literature. However, the implementation of the cluster-based architecture in the real world is rather complicated. Adding security to LEACH-like protocols is challenging because they dynamically, randomly, and periodically rearrange the network’s clusters and data links. Therefore, providing steady long-lasting node-to-node trust relationships and common key distributions are inadequate for LEACH-like protocols (most existing solutions are provided for distributed WSNs, but not for CWSNs). There are some secure data transmission protocols based on LEACH-like protocols, such as SecLEACH, GS-LEACH, and RLEACH.

Most of them, however, apply the symmetric key management for security, which suffers from a so-called orphan node problem occurs when a node does not share a pairwise key with others in its preloaded key ring. To mitigate the storage cost of symmetric keys, the key ring in a node is not sufficient for it to share pairwise symmetric keys with all of the nodes in a network. In such a case, it cannot participate in any cluster, and therefore, has to elect itself as a CH. Furthermore, the orphan node problem reduces the possibility of a node joining with a CH, when the number of alive nodes owning pairwise keys decreases after a long-term operation of the network.

Since the more CHs elected by them, the more overall energy consumed of the network the orphan node problem increases the overhead of transmission and system energy consumption by raising the number of CHs. Even in the case that a sensor node does share a pairwise key with a distant CH but not a nearby CH, it requires comparatively high energy to transmit data to the distant CH. The feasibility of the asymmetric key management has been shown in WSNs recently, which compensates the shortage from applying the symmetric key management for security. Digital signature is one of the most critical security services offered by cryptography in asymmetric key management systems, where the binding between the public key and the identification of the signer is obtained via a digital certificate. The identity-based digital signature (IBS) scheme, based on the difficulty of factoring integers from identity-based cryptography (IBC), is to derive an entity’s public key from its identity information, for example, from its name or ID number.

Recently, the concept of IBS has been developed as a key management in WSNs for security. Carman first combined the benefits of IBS and key predistribution set into WSNs, and some papers appeared in recent years IBOOS scheme has been proposed to reduce the computation and storage costs of signature processing. A general method for constructing online/offline signature schemes was introduced by Even et al. The IBOOS scheme could be effective for the key management in WSNs. Specifically; the offline phase can be executed on a sensor node or at the BS prior to communication, while the online phase is to be executed during communication. Some IBOOS schemes are designed for WSNs afterwards, such as [20] and [21]. The offline signature in these schemes, however, is precomputed by a third party and lacks reusability, thus they are not suitable for CWSNs.

LITRATURE SURVEY

A SECURE ROUTING PROTOCOL FOR CLUSTER-BASED WIRELESS SENSOR NETWORKS USING ID-BASED DIGITAL SIGNATURE

AUTHOR: H. Lu, J. Li, and H. Kameda,

PUBLISH: Proc. IEEE GLOBECOM, pp. 1-5, 2010.

In this paper, we study the secure routing for cluster-based sensor networks where clusters are formed dynamically and periodically. We point out the deficiency in the secure routing protocols with symmetric key pairing. Along with the investigation of ID-based cryptography for security in WSNs, we propose a new secure routing protocol with ID-based signature scheme for cluster-based WSNs, in which the security relies on the hardness of the Diffie-Hellman problem in the random oracle model. Because of the communication overhead for security, we provide analysis and simulation results in details to illustrate how various parameters act between security and energy efficiency.

AN IDENTITY-BASED SECURITY SYSTEM FOR USER PRIVACY IN VEHICULAR AD HOC NETWORKS

AUTHOR: J. Sun et al., IEEE Trans. Parallel & Distributed Systems,

Vehicular ad hoc network (VANET) can offer various services and benefits to users and thus deserves deployment effort. Attacking and misusing such network could cause destructive consequences. It is therefore necessary to integrate security requirements into the design of VANETs and defend VANET systems against misbehavior, in order to ensure correct and smooth operations of the network. In this paper, we propose a security system for VANETs to achieve privacy desired by vehicles and traceability required by law enforcement authorities, in addition to satisfying fundamental security requirements including authentication, nonrepudiation, message integrity, and confidentiality. Moreover, we propose a privacy-preserving defense technique for network authorities to handle misbehavior in VANET access, considering the challenge that privacy provides avenue for misbehavior. The proposed system employs an identity-based cryptosystem where certificates are not needed for authentication. We show the fulfillment and feasibility of our system with respect to the security goals and efficiency.

A SECURE ROUTING PROTOCOL FOR CLUSTER-BASED WIRELESS SENSOR NETWORKS USING GROUP KEY MANAGEMENT

AUTHOR: K. Zhang, C. Wang, and C. Wang,

PUBLISH: Proc. Fourth Int’l Conf. Wireless Comm., Networking and Mobile Computing (WiCOM), pp. 1-5, 2008.

Wireless sensor networks routing protocols always neglect security problem at the designing step, while plenty of solutions of this problem exist, one of which is using key management. Researchers have proposed many key management schemes, but most of them were designed for flat wireless sensor networks, which is not fit for cluster-based wireless sensor networks (e.g. LEACH). In this paper, we investigate adding security to cluster-based routing protocols for wireless sensor networks which consisted of sensor nodes with severely limited resources, and propose a security solution for LEACH, a protocol in which the clusters are formed dynamically and periodically. Our solution uses improved random pair-wise keys (RPK) scheme, an optimized security scheme that relys on symmetric-key methods; is lightweight and preserves the core of the original LEACH. Simulations show that security of RLEACH has been improved, with less energy consumption and lighter overhead.

CHAPTER 2

2.0 SYSTEM ANALYSIS

2.1 EXISTING SYSTEM:

LEACH-like protocols is challenging because they dynamically, randomly, and periodically rearrange the network’s clusters and data links providing steady long-lasting node-to-node trust relationships and common key distributions are inadequate for LEACH-like protocols (most existing solutions are provided for distributed WSNs, but not for CWSNs). There are some secure data transmission protocols based on LEACH-like protocols, such as SecLEACH, GS-LEACH and RLEACH. Most of them, however, apply the symmetric key management for security, which suffers from a so-called orphan node problem occurs when a node does not share a pairwise key with others in its preloaded key ring. To mitigate the storage cost of symmetric keys, the key ring in a node is not sufficient for it to share pairwise symmetric keys with all of the nodes in a network. In such a case, it cannot participate in any cluster, and therefore, has to elect itself as a CH. Furthermore, the orphan node problem reduces the possibility of a node joining with a CH, when the number of alive nodes owning pairwise keysdecreases after a long-term operation of the network.

2.1.1 DISADVANTAGES:

Low-Energy Adaptive Clustering Hierarchy (or LEACH) was one of the first major improvements on conventional clustering approaches in wireless sensor networks. Conventional approaches algorithms such as MTE (Minimum-Transmission-Energy) or direct-transmission do not lead to even energy dissipation throughout a network. LEACH provides a balancing of energy usage by random rotation of clusterheads. The algorithm is also organized in such a manner that data-fusion can be used to reduce the amount of data transmission in very slowly and security issues.

2.2 PROPOSED SYSTEM:

Recently, the concept of IBS has been developed as a key management in WSNs for security. Carman first combined the benefits of IBS and key pre distribution set into WSNs, and some papers appeared in IBOOS scheme has been proposed to reduce the computation and storage costs of signature processing. A general method for constructing online/offline signature schemes was introduced IBOOS scheme could be effective for the key management in WSNs. Specifically, the offline phase can be executed on a sensor node or at the BS prior to communication, while the online phase is to be executed during communication.

We propose two Secure and Efficient data Transmission protocols for CWSNs, called SET-IBS and SET-IBOOS, by using the IBS scheme and the IBOOS scheme, respectively. The key idea of both SET-IBS and SET-IBOOS is to authenticate the encrypted sensed data, by applying digital signatures to message packets, which are efficient in communication and applying the key management for security. In the proposed protocols, secret keys and pairing parameters are distributed and preloaded in all sensor nodes by the BS initially, which overcomes the key escrow problem described in ID-based cryptosystems.

2.2.1 ADVANTAGES:

Secure communication in SET-IBS relies on the IDbased cryptography, in which, user public keys are their ID information. Thus, users can obtain the corresponding private keys without auxiliary data transmission, which is efficient in communication and saves energy.

SET-IBOOS is proposed to further reduce the computational overhead for security using the IBOOS scheme, in which security relies on the hardness of the discrete logarithmic problem. Both SET-IBS and SET-IBOOS solve the orphan node problem in the secure data transmission with a symmetric key management.

We show the feasibility of the proposed protocols with respect to the security requirements and analysis against three attack models. Moreover, we compare the proposed protocols with the existing secure protocols for efficiency by calculations and simulations, respectively, with respect to both computation and communication.

2.3 HARDWARE & SOFTWARE REQUIREMENTS:

2.3.1 HARDWARE REQUIREMENT:

v Processor – Pentium –IV

Speed – 1.1 GHz
- RAM – 256 MB (min)
- Hard Disk – 20 GB
- Floppy Drive – 1.44 MB
- Key Board – Standard Windows Keyboard
- Mouse – Two or Three Button Mouse
- Monitor – SVGA

2.3.2 SOFTWARE REQUIREMENTS:

Operating System : Windows XP or Win7
Front End : JAVA JDK 1.7
Document : MS-Office 2007

CHAPTER 3

3.0 SYSTEM DESIGN:

ARCHITECTURE DIAGRAM / UML DIAGRAMS / DAT FLOW DIAGRAM:

The DFD is also called as bubble chart. It is a simple graphical formalism that can be used to represent a system in terms of the input data to the system, various processing carried out on these data, and the output data is generated by the system

The data flow diagram (DFD) is one of the most important modeling tools. It is used to model the system components. These components are the system process, the data used by the process, an external entity that interacts with the system and the information flows in the system.

DFD shows how the information moves through the system and how it is modified by a series of transformations. It is a graphical technique that depicts information flow and the transformations that are applied as data moves from input to output.

DFD is also known as bubble chart. A DFD may be used to represent a system at any level of abstraction. DFD may be partitioned into levels that represent increasing information flow and functional detail.

NOTATION:

SOURCE OR DESTINATION OF DATA:

External sources or destinations, which may be people or organizations or other entities

DATA SOURCE:

Here the data referenced by a process is stored and retrieved.

PROCESS:

People, procedures or devices that produce data. The physical component is not identified.

DATA FLOW:

Data moves in a specific direction from an origin to a destination. The data flow is a “packet” of data.

MODELING RULES:

There are several common modeling rules when creating DFDs:

All processes must have at least one data flow in and one data flow out.
All processes should modify the incoming data, producing new forms of outgoing data.
Each data store must be involved with at least one data flow.
Each external entity must be involved with at least one data flow.
A data flow must be attached to at least one process.

3.1 ARCHITECTURE DIAGRAM

3.2 DATAFLOW DIAGRAM

UML DIAGRAMS:

3.2 USE CASE DIAGRAM:

3.3 CLASS DIAGRAM:

3.4 SEQUENCE DIAGRAM

3.5 ACTIVITY DIAGRAM:

CHAPTER 4

4.0 IMPLEMENTATION:

We assume that all sensor nodes and the BS are time synchronized with symmetric radio channels, nodes are distributed randomly, and their energy is constrained. In CWSNs, data sensing, processing, and transmission consume energy of sensor nodes. The cost of data transmission is much more expensive than that of data processing. Thus, the method that the intermediate node (e.g., a CH) aggregates data and sends it to the BS is preferred than the method that each sensor node directly sends data to the BS. A sensor node switches into sleep mode for energy saving when it does not sense or transmit data, depending on the time-division multiple access (TDMA) control used for data transmission. In this paper, the proposed SET-IBS and SET-IBOOS are both designed for the same scenarios of CWSNs above.

An IBS scheme implemented for CWSNs consists of the following operations, specifically, setup at the BS, key extraction and signature signing at the data sending nodes, and verification at the data receiving nodes:

. Setup. The BS (as a trust authority) generates a master key msk and public parameters param for the private key generator (PKG), and gives them to all sensor nodes.

. Extraction. Given an ID string, a sensor node generates a private key sekID associated with the

ID using msk.

. Signature signing. Given a message M, time stamp t and a signing key _, the sending node generates a signature SIG.

. Verification. Given the ID, M, and SIG, the receiving node outputs “accept” if SIG is valid, and outputs “reject” otherwise.

4.1 PROTOCOL AND ALGORITHM

SET-IBOOS PROTOCOL

We present the SET protocol for CWSNs by using IBOOS (SET-IBOOS) in this section. The SET-IBOOS protocol is designed with the same purpose and scenarios for CWSNs with higher efficiency. The proposed SET-IBOOS operates similarly to the previous SET-IBS, which has a protocol initialization prior to the network deployment and operates in rounds during communication. We first introduce the protocol initialization, and then describe the key management of the protocol by using the IBOOS scheme, and the protocol operations afterwards. To reduce the computation and storage costs of signature signing processing in the IBS scheme, we improve SET-IBS by introducing IBOOS for security in SET-IBOOS. The operation of the protocol initialization in SET-IBOOS is similar to that of SET-IBS; however, the operations of key predistribution are revised for IBOOS. The BS does the following operations of key predistribution in the network:

4.2 MODULES:

SERVER CLIENT MODULE:

NETWORK SECURITY:

ATTACK MODELS:

PROTOCOL CHARACTERISTICS:

SECURE DATA TRANSMISSION:

4.3 MODULES DESCRIPTION:

SERVER CLIENT MODULE:

NETWORK SECURITY:

Network-accessible resources may be deployed in a network as surveillance and early-warning tools, as the detection of attackers are not normally accessed for legitimate purposes. Techniques used by the attackers that attempt to compromise these decoy resources are studied during and after an attack to keep an eye on new exploitation techniques. Such analysis may be used to further tighten security of the actual network being protected by the data’s.

Data forwarding can also direct an attacker’s attention away from legitimate servers. A user encourages attackers to spend their time and energy on the decoy server while distracting their attention from the data on the real server. Similar to a server, a user is a network set up with intentional vulnerabilities. Its purpose is also to invite attacks so that the attacker’s methods can be studied and that information can be used to increase network security.

ATTACK MODELS:

In this paper, we group attack models into three categories according to their attacking means as follows, and study how these attacks may be applied to affect the proposed protocols:

. Passive attack on wireless channel: Passive attackers are able to perform eavesdropping at any point of the network, or even the whole communication of the network. Thus, they can undertake traffic analysis or statistical analysis based on the monitored or eavesdropped messages.

. Active attack on wireless channel: Active attackers have greater ability than passive adversaries, which can tamper with the wireless channels. Therefore, the attackers can forge, reply, and modify messages. Especially in WSNs, various types of active attacks can be triggered by attackers, such as bogus and replayed routing information attack, sinkhole and wormhole attack.

. Node compromising attack: Node compromising attackers are the most powerful adversaries against the proposed protocols as we considered. The attackers can physically compromise sensor nodes, by which they can access the secret information stored in the compromised nodes, for example, the security keys. The attackers also can change the inner state and behavior of the compromised sensor node, whose actions may be varied from the premier protocol specifications.

PROTOCOL CHARACTERISTICS:

The protocol characteristics and hierarchical clustering solutions are presented in this section. We first summarize the features of the proposed SET-IBS and SET-IBOOS protocols as follows:

Key management: The key cryptographies used in the protocol to achieve secure data transmission, which consist of symmetric and asymmetric key based security.

Neighborhood authentication: Used for secure access and data transmission to nearby sensor nodes, by authenticating with each other. Here, “limited” means the probability of neighborhood authentication, where only the nodes with the shared pairwise key can authenticate each other.

Storage cost: Represents the requirement of the security keys stored in sensor node’s memory.

Network scalability: Indicates whether a security protocol is able to scale without compromising the security requirements. Here, “comparatively low” means that, compared with SET-IBS and SET-IBOOS, in the secure data transmission with a symmetric key management, the larger network scale increases, the more orphan nodes appear in the network, and vice versa.

Communication overhead: The security overhead in the data packets during communication.

Computational overhead: The energy cost and computation efficiency on the generation and verification of the certificates or signatures for security.

Attack resilience: the types of attacks that security protocol can protect against.

SECURE DATA TRANSMISSION:

In large-scale CWSNs, multihop data transmission is used for transmission between the CHs to the BS, where the direct communication is not possible due to the distance or obstacles between them. The version of the proposed SET-IBS and SET-IBOOS protocols for CWSNs can be extended using multihop routing algorithms, to form secure data transmission protocols for hierarchical clusters.

The solutions to this extension could be achieved by applying the following two routing models:

1. The multihop planar model. A CH node transmits data to the BS by forwarding its data to its neighbor nodes, in turn the data are sent to the BS. We have proposed an energy-efficient routing algorithm for hierarchically clustered WSNs in suitable for the proposed secure data transmission protocols.

2. The cluster-based hierarchical method. The network is broken into clustered layers, and the data packages travel from a lower cluster head to a higher one, in turn to the BS.

3. Both the proposed SET-IBS and SET-IBOOS protocols provide secure data transmission for CWSNs with concrete ID-based settings, which use ID information and digital signature for authentication. Thus, both SET-IBS and SET-IBOOS fully solve the orphan-node problem from using the symmetric key management for CWSNs.

4. The proposed secure data transmission protocols are with concrete ID-based settings, which use ID information and digital signature for verification.

CHAPTER 5

5.0 SYSTEM STUDY:

5.1 FEASIBILITY STUDY:

Three key considerations involved in the feasibility analysis are

ECONOMICAL FEASIBILITY
TECHNICAL FEASIBILITY
SOCIAL FEASIBILITY

5.1.1 ECONOMICAL FEASIBILITY:

5.1.2 TECHNICAL FEASIBILITY:

5.1.3 SOCIAL FEASIBILITY:

5.2 SYSTEM TESTING:

5.2.1 UNIT TESTING:

UNIT TESTING:

Description	Expected result
Test for application window properties.	All the properties of the windows are to be properly aligned and displayed.
Test for mouse operations.	All the mouse operations like click, drag, etc. must perform the necessary operations without any exceptions.

5.1.3 FUNCTIONAL TESTING:

FUNCTIONAL TESTING:

Description	Expected result
Test for all modules.	All peers should communicate in the group.
Test for various peer in a distributed network framework as it display all users available in the group.	The result after execution should give the accurate result.

5.1. 4 NON-FUNCTIONAL TESTING:

Load testing
Performance testing
Usability testing
Reliability testing
Security testing

5.1.5 LOAD TESTING:

Load Testing

Description	Expected result
It is necessary to ascertain that the application behaves correctly under loads when ‘Server busy’ response is received.	Should designate another active node as a Server.

5.1.5 PERFORMANCE TESTING:

PERFORMANCE TESTING:

Description	Expected result
This is required to assure that an application perforce adequately, having the capability to handle many peers, delivering its results in expected time and using an acceptable level of resource and it is an aspect of operational management.	Should handle large input values, and produce accurate result in a expected time.

5.1.6 RELIABILITY TESTING:

RELIABILITY TESTING:

Description	Expected result
This is to check that the server is rugged and reliable and can handle the failure of any of the components involved in provide the application.	In case of failure of the server an alternate server should take over the job.

5.1.7 SECURITY TESTING:

SECURITY TESTING:

Description	Expected result
Checking that the user identification is authenticated.	In case failure it should not be connected in the framework.
Check whether group keys in a tree are shared by all peers.	The peers should know group key in the same group.

5.1.7 WHITE BOX TESTING:

5.1.8 WHITE BOX TESTING:

Description	Expected result
Exercise all logical decisions on their true and false sides.	All the logical decisions must be valid.
Execute all loops at their boundaries and within their operational bounds.	All the loops must be finite.
Exercise internal data structures to ensure their validity.	All the data structures must be valid.

5.1.9 BLACK BOX TESTING:

5.1.10 BLACK BOX TESTING:

Description	Expected result
To check for incorrect or missing functions.	All the functions must be valid.
To check for interface errors.	The entire interface must function normally.
To check for errors in a data structures or external data base access.	The database updation and retrieval must be done.
To check for initialization and termination errors.	All the functions and data structures must be initialized properly and terminated normally.

All the above system testing strategies are carried out in as the development, documentation and institutionalization of the proposed goals and related policies is essential.

CHAPTER 6

6.0 SOFTWARE DESCRIPTION:

6.1 JAVA TECHNOLOGY:

Java technology is both a programming language and a platform.

The Java Programming Language

The Java programming language is a high-level language that can be characterized by all of the following buzzwords:

Simple
- Architecture neutral
- Object oriented
- Portable
- Distributed
- High performance
- Interpreted
- Multithreaded
- Robust
- Dynamic
- Secure

6.2 THE JAVA PLATFORM:

The Java platform has two components:

The Java Virtual Machine (Java VM)
The Java Application Programming Interface (Java API)

You’ve already been introduced to the Java VM. It’s the base for the Java platform and is ported onto various hardware-based platforms.

The following figure depicts a program that’s running on the Java platform. As the figure shows, the Java API and the virtual machine insulate the program from the hardware.

6.3 WHAT CAN JAVA TECHNOLOGY DO?

The essentials: Objects, strings, threads, numbers, input and output, data structures, system properties, date and time, and so on.
Applets: The set of conventions used by applets.
Networking: URLs, TCP (Transmission Control Protocol), UDP (User Data gram Protocol) sockets, and IP (Internet Protocol) addresses.
Internationalization: Help for writing programs that can be localized for users worldwide. Programs can automatically adapt to specific locales and be displayed in the appropriate language.
Security: Both low level and high level, including electronic signatures, public and private key management, access control, and certificates.
Software components: Known as JavaBeans^TM, can plug into existing component architectures.
Object serialization: Allows lightweight persistence and communication via Remote Method Invocation (RMI).
Java Database Connectivity (JDBC^TM): Provides uniform access to a wide range of relational databases.

The Java platform also has APIs for 2D and 3D graphics, accessibility, servers, collaboration, telephony, speech, animation, and more. The following figure depicts what is included in the Java 2 SDK.

6.4 HOW WILL JAVA TECHNOLOGY CHANGE MY LIFE?

Get started quickly: Although the Java programming language is a powerful object-oriented language, it’s easy to learn, especially for programmers already familiar with C or C++.
Write less code: Comparisons of program metrics (class counts, method counts, and so on) suggest that a program written in the Java programming language can be four times smaller than the same program in C++.
Write better code: The Java programming language encourages good coding practices, and its garbage collection helps you avoid memory leaks. Its object orientation, its JavaBeans component architecture, and its wide-ranging, easily extendible API let you reuse other people’s tested code and introduce fewer bugs.
Develop programs more quickly: Your development time may be as much as twice as fast versus writing the same program in C++. Why? You write fewer lines of code and it is a simpler programming language than C++.
Avoid platform dependencies with 100% Pure Java: You can keep your program portable by avoiding the use of libraries written in other languages. The 100% Pure Java^TMProduct Certification Program has a repository of historical process manuals, white papers, brochures, and similar materials online.
Write once, run anywhere: Because 100% Pure Java programs are compiled into machine-independent byte codes, they run consistently on any Java platform.
Distribute software more easily: You can upgrade applets easily from a central server. Applets take advantage of the feature of allowing new classes to be loaded “on the fly,” without recompiling the entire program.

6.5 ODBC:

Microsoft Open Database Connectivity (ODBC) is a standard programming interface for application developers and database systems providers. Before ODBC became a de facto standard for Windows programs to interface with database systems, programmers had to use proprietary languages for each database they wanted to connect to. Now, ODBC has made the choice of the database system almost irrelevant from a coding perspective, which is as it should be. Application developers have much more important things to worry about than the syntax that is needed to port their program from one database to another when business needs suddenly change.

6.6 JDBC:

JDBC was announced in March of 1996. It was released for a 90 day public review that ended June 8, 1996. Because of user input, the final JDBC v1.0 specification was released soon after.

6.7 JDBC Goals:

The goals that were set for JDBC are important. They will give you some insight as to why certain classes and functionalities behave the way they do. The eight design goals for JDBC are as follows:

SQL Level API

SQL Conformance

JDBC must be implemental on top of common database interfaces

Provide a Java interface that is consistent with the rest of the Java system

Because of Java’s acceptance in the user community thus far, the designers feel that they should not stray from the current design of the core Java system.

Keep it simple

Use strong, static typing wherever possible

Strong typing allows for more error checking to be done at compile time; also, less error appear at runtime.

Keep the common cases simple

Finally we decided to precede the implementation using Java Networking.

And for dynamically updating the cache table we go for MS Access database.

Java ha two things: a programming language and a platform.

Java is a high-level programming language that is all of the following

Simple Architecture-neutral

Object-oriented Portable

Distributed High-performance

Interpreted Multithreaded

Robust Dynamic Secure

Compilation happens just once; interpretation occurs each time the program is executed. The figure illustrates how this works.

7.7 NETWORKING TCP/IP STACK:

The TCP/IP stack is shorter than the OSI one:

TCP is a connection-oriented protocol; UDP (User Datagram Protocol) is a connectionless protocol.

IP datagram’s:

UDP:

UDP is also connectionless and unreliable. What it adds to IP is a checksum for the contents of the datagram and port numbers. These are used to give a client/server model – see later.

TCP:

TCP supplies logic to give a reliable connection-oriented protocol above IP. It provides a virtual circuit that two processes can use to communicate.

Internet addresses

In order to use a service, you must be able to find it. The Internet uses an address scheme for machines so that they can be located. The address is a 32 bit integer which gives the IP address.

Network address:

Class A uses 8 bits for the network address with 24 bits left over for other addressing. Class B uses 16 bit network addressing. Class C uses 24 bit network addressing and class D uses all 32.

Subnet address:

Internally, the UNIX network is divided into sub networks. Building 11 is currently on one sub network and uses 10-bit addressing, allowing 1024 different hosts.

Host address:

8 bits are finally used for host addresses within our subnet. This places a limit of 256 machines that can be on the subnet.

Total address:

The 32 bit address is usually written as 4 integers separated by dots.

Port addresses

Sockets:

#include <sys/types.h>

#include <sys/socket.h>

int socket(int family, int type, int protocol);

6.8 JFREE CHART:

JFreeChart is a free 100% Java chart library that makes it easy for developers to display professional quality charts in their applications. JFreeChart’s extensive feature set includes:

A consistent and well-documented API, supporting a wide range of chart types;

A flexible design that is easy to extend, and targets both server-side and client-side applications;

Support for many output types, including Swing components, image files (including PNG and JPEG), and vector graphics file formats (including PDF, EPS and SVG);

JFreeChart is “open source” or, more specifically, free software. It is distributed under the terms of the GNU Lesser General Public Licence (LGPL), which permits use in proprietary applications.

6.8.1. Map Visualizations:

6.8.2. Time Series Chart Interactivity

6.8.3. Dashboards

6.8.4. Property Editors

CHAPTER 7

APPENDIX

7.1 SAMPLE SOURCE CODE

7.2 SAMPLE OUTPUT

CHAPTER 8

8.0 CONCLUSION

In this paper, we ﬁrst reviewed the data transmission issues and the security issues in CWSNs. The deﬁciency of the symmetric key management for secure data transmission has been discussed. We then presented two secure and eﬃcient data transmission protocols respectively for CWSNs, SET-IBS and SET-IBOOS. In the evaluation section, we provided feasibility of the proposed SET-IBS and SET-IBOOS with respect to the security requirements and analysis against routing attacks. SET-IBS and SET-IBOOS are eﬃcient in communication and applying the ID-based crypto-system, which achieves security requirements in CWSNs, as well as solved the orphan node problem in the secure transmission protocols with the symmetric key management. Lastly, the comparison in the calculation and simulation results show that, the proposed SET-IBS and SET-IBOOS protocols have better performance than existing secure protocols for CWSNs. With respect to both computation and communication costs, we pointed out the merits that, using SET-IBOOS with less auxiliary security overhead is preferred for secure data transmission in CWSNs.

CHAPTER 9

9.0 REFERENCES

[1] H. Lu, J. Li, and H. Kameda, “A Secure Routing Protocol for Cluster-Based Wireless Sensor Networks Using ID-Based Digital Signature,” Proc. IEEE GLOBECOM, pp. 1-5, 2010.

[2] J. Sun et al., “An Identity-Based Security System for User Privacy in Vehicular Ad Hoc Networks,” IEEE Trans. Parallel & Distributed Systems, vol. 21, no. 9, pp. 1227-1239, Sept. 2010.

[3] D. Boneh and M. Franklin, “Identity-Based Encryption from the Weil Pairing,” Proc. 21st Ann. Int’l Cryptology Conf. Advances in Cryptology (CRYPTO ’01), pp. 213-229, 2001.

[4]K. Zhang, C. Wang, and C. Wang, “A Secure Routing Protocol for Cluster-Based Wireless Sensor Networks Using Group Key Management,” Proc. Fourth Int’l Conf. Wireless Comm., Networking and Mobile Computing (WiCOM), pp. 1-5, 2008.

[5] S. Sharma and S.K. Jena, “A Survey on Secure Hierarchical Routing Protocols in Wireless Sensor Networks,” Proc. Int’l Conf. Comm., Computing & Security (ICCCS), pp. 146-151, 2011.