While each of the products mentioned in the previous section provides an interesting product that simplifies whether Electronics or programming, all of them tend to be very specific. They focus whether on HW only or SW only. Even if a HW product comes with its developing environment, it still requires advanced programming knowledge. The objective in this paper is to link the features of existing HW & SW products and combine them to get a better platform that offers the user the possibility to create hardware prototypes and program them quickly without needing technical knowledge especially in programming. An electrical board named IOIO is used as the hardware part, while the App Inventor platform is used as the software platform
IOIO is an electrical board that adds hardware I/O features to a PC or android application [14]. The board can be controlled from a distance using Bluetooth, so it is possible to program and control it via a Smartphone mobile App. The IOIO board has a java library and is compatible with Android OS, therefore, it is possible to create Android apps that control the board’s different pins. Which leads to the next point: programming Android is complex, so it would be great if this task were easier. That is the role of the App Inventor platform, which is an online platform that allows the user to create Android Apps just by interconnecting graphical blocks. mobile based college student communication portal android app source code The concept is based on the Blockly interface [15]. There is no Java code to write, no need to consider Android constraints, and the design and script interface are simple and intuitive. With this platform, even kids are capable of creating interesting projects. The existing features makes interfacing complex components easier, such as reading smartphone’s sensors, camera, microphone etc. The downside with App Inventor, however, is that it cannot control the IOIO board, the dedicated SW component does not exist in the platform. Therefore, the contribution of this paper is to bring support for this electrical board to App Inventor. This way, it will be possible to easily create Android Apps that control the IOIO board without technical knowledge. Making it possible for kids and beginners to initiate to the electronics and programming fields. The targeted audience is quite large especially that Android market share has reached 86.1% [16].
The final objective of IoT is to leverage abstraction as much as possible, with electronics and programming considered as main disciplines of IoT, several efforts are made to make them accessible for everyone. This paper presents an initiative that facilitates the process of creating Android Apps capable of controlling electrical systems. IOIO and App Inventor already exist, but do not coexist, the idea then is to develop an Android service that links the two existing solutions and get the best of both worlds. This has added an abstraction level to program the IOIO board, allowing its control via App Inventor’s graphical blocks, thus simplifying the programming side. https://codeshoppy.com/android-project-with-source-code-students.html For the HW side, dealing with electronics can be simpler by using existing HW modules, so it is actually possible to start exploring the embedded systems field with the proposed tools. The developed Android IOIO service supports so far the DigitalWrite command. The service is still under development and other commands such as Digital & Analog and PWM will be added, making it possible to interface additional electrical components and read data from sensors connected to the IOIO board.
Earlier methods used were: First, the use of an onboardcontrol system, which requires a human to control therobot/ machine by using onboard commands/buttons. Second,wired control system, which requires handling of bulkywires and limits the control length.Third, wireless dedicates remote system, requiredifferent remotes for various machines, prone todisturbance and can be hacked easily as there is notprotected transmission of signals, anyone with thefrequency can send commands.
Android application with robotics is an exciting areafor human computer interface that is used to assisthumans to control the mechanical system via software.Here, we designed an android application to control robot/machines wirelessly using a secured connection overBluetooth
The basic considerations for proposed segments are:Interfacing the robot system with communicationmodule Bluetooth (Embedded Hardware Model) Bluetooth Module with android application(Software Model). mca mini project topics 2019 The input from android application is processed usingcommand and understanding algorithms to parse intocontroller that will control the robot. A communicationprotocol that effectively translate the selected commandinto maneuverable tasks to any control board.
Driving MotorsA combination of four 100RPM motors (12V, 1A) with spurgearbox system are used to drive four wheels of the robot. 2.Arduino MegaArduino Mega 2560 based on ATmega 2560 is used as main brain of the hardware. It takes input from the sensors mounted on chassis. Suitable decision is made by Arduinoand actuators are governed accordingly for motionand implementing different selected operations. 3.Voltage Divider Voltage divider used here provides two voltage distribution one is 5V (for logic operations) and other is 12V (for motors). 4.TyresTread block tyres are used with 4.5” diameter for better gripping and push on landapplications. 5.Bluetooth Bluetooth gives connectivitybetween two devices using particular MACaddress. 6.Motor Driver (L298N based) Motor driver board based on L298N is used to drive the motors for motion.
In recent years, there has been a significant surge in the usage anddevelopment of apps for smartphones and tablets. Developers arewriting more apps for mobile platforms than for desktops. The com-plexity of mobile apps often lies in their graphical user interfaces(GUIs). Testing efforts of such apps mostly focus on the behaviorof graphical user interfaces.Several automated GUI testing techniques have recently beenproposed. The techniques include learning-based testing [8,29,31,32], model-based testing [1,23,45], genetic programming [27,28],fuzz testing [25,26,37], and static-analysis based approaches [4,33,34,44,49]. The goal of the majority of these techniques is to achievegood code and screen coverage (i.e. covering all distinct screens ofan app), and to find common bugs such as crashes and unrespon-siveness. Most of these techniques work by injecting sequences ofautomatically generated user inputs or actions to an app for severaltens of hours. We consider each sequence of actions injected bythese techniques to be atest case, and the set of all sequences ofactions to be atest suite.
Although automated GUI testing techniques could find bugs,they tend to generate large test suites containing thousands oftest cases. Each test case can contain tens to thousands of useractions. Such a large test suite can take several hours to execute,because the running time of a test suite is linear in the size of thetest suite.1However, regression tests should be fast so that theycan be used frequently during development. Therefore, such testsuites are difficult to use in regression testing.In this paper, we address the problem of generating a smallregression GUI test suite for an Android app. We assume that weare given a large test suite generated by an existing automatedGUI testing tool. simple mobile app project ideas for students We also assume that the test suite is replayablein the sense that if we rerun the test suite multiple times we getthe same coverage and observe the same sequence of app screens.(The evaluation section has details on how to obtain a replayable test suite from an automated GUI testing tool.) We assume thatthe test suite takes several hours to run on the app.
Our goal is tospend a reasonable amount of time, say a day, to generate a smallregression test suite for the app that runs for less than an hour andthat achieves similar code and screen coverage as the original testsuite provided as input.A couple of techniques have been proposed to minimize testsuites for GUIs. For example, Clapp et al. [7]and Hammoudi et al.[17]proposed delta-debugging [48] based algorithms. These tech-niques work well if the size of the input test suite is small, containingless than one thousand user inputs. However, they fail to scale forlarge test suites because they depend heavily on the rapid gen-eration and feasibility checking of new test cases. Unfortunately,for most real-world GUI apps, it takes few minutes to check thefeasibility of a new input sequence. Therefore, for large test suitescontaining tens of thousands of user actions, a delta-debuggingbased approach could take more than a month to effectively mini-mize a test suite. McMaster and Memon[30]proposed a GUI testsuite reduction technique for reducing the number of test cases ina test suite. However, this technique does not make any effort toreduce the size of each test case. In our experimental evaluation, weobserved that test cases generated by an automated tool can containsubsequences of redundant user actions, which can be removed toobtain smaller test suites.We propose an Android GUI test suite reduction algorithm thatcan scalably and effectively minimize large test suites. The keyinsight behind our technique is that if we can identify and removesome common forms of redundancies introduced by existing au-tomated GUI testing tools, then we can drastically lower the timerequired to minimize a test suite.
We manually analyzed test suitesgenerated by existing automated GUI testing tools and found thereare three kinds of redundancies that are common in these testsuites: 1) some test cases can be safely removed from a test suitewithout impacting code and screen coverage, 2) within a test case,certain loops can be eliminated without decreasing coverage, and3) many test cases share common subsequences of actions whoserepeated execution can be avoided by combining fragments fromdifferent action sequences. Based on these observations, we devel-oped an algorithm that removes these redundancies one-by-onewhile preserving the overall code and screen coverage of the testsuite.In order to identify redundant loops and common fragments oftest cases, we define a notion of state abstraction which enablesus to approximately determine if we are visiting the same abstractstate at least twice while executing a test case. If an abstract stateis visited twice during the execution, we have identified a loopwhich can potentially be removed. Similarly, if the executions oftwo test cases visit an identical subsequence of abstract states, weknow that fragments from the two test cases can be combined toobtain a longer test case which avoids re-executing the commonfragment. Whenever we get a new test case by removing a loopor by combining two fragments, the resulting test case may nottraverse the same abstract states as expected. In our algorithm,we check the feasibility of a newly created test case by executingit a few times and by checking if the execution visits the samesequence of abstract states every time—we call thisreplayability. https://codeshoppy.com/android-app-ideas-for-students-college-project.html We noticed that if our state abstraction is too coarse-grained ourfeasibility checks often fail, leading to longer running time.
On theother hand, if we use a too fine-grained state abstraction, we fail toidentify many redundancies. One contribution of this paper is todesign a good enough abstraction that works well in practice.One advantage of our algorithm over delta-debugging or otherblack-box algorithms is that we do not blindly generate all possiblenew test cases that can be constructed by dropping some actions.Rather, we use a suitable state abstraction to only drop potentiallyredundant loops. Another advantage is that we create new test casesby combining fragments from input test cases. This enables us tocome up with new, longer test cases which cannot be generatedusing delta-debugging or other test suite reduction techniques.Longer test cases are usually better than multiple shorter test casesbecause we do not have to perform a clean restart of an app. A cleanrestart of an app requires us to kill the app, erase app data, anderase SD card contents, which is very time consuming.
A longertest case in place of several shorter test cases avoids several suchexpensive restarts.We have implemented our algorithm in a prototype tool, calledDetReduce, for Android apps. The tool is publicly available at https://github.com/wtchoi/swifthand2. We applied DetReduce to severalapps and found that DetReduce could reduce a test-suite by a factorof 16.2×in size and a factor of 14.7×in running time on average. Wealso found that for a test suite generated by running SwiftHand [5]and a random testing algorithm [5] for 8 hours, DetReduce canreduce the test suite in an average of 14.6 hours. We are not awareof any existing technique that could get such huge reduction inthe size of a large GUI test suite in such a reasonable amount oftime. Note that DetReduce often runs longer than generating alltest cases; however, running DetReduce is a one-time cost. Once aregression suite has been generated, it will be run many times andeach run will take a fraction of the time required to generate alltest cases.
The Android-based IoT(Internet of Things) platform just like the existing Android provides an environment that makes it easy to utilize Google’s infrastructure services including development tools and APIs through which it helps to control the sensors of IoT devices. Applications running on the Android-based IoT platform are often UI free and are used without the user’s consent to registered permissions. It is difficult to respond to the misuse of permissions as well as to check them when they are registered indiscriminately while updating applications. This paper analyzes the versions of before and after an application the update running on the Android-based IoT platform and the collected permission lists. https://codeshoppy.com/android-projects-titles-ieee.html It aims to identify the same permissions before and after the update, and deleted and newly added permissions after the update were identified, and thereby respond to security threats that can arise from the permissions that is not needed for IoT devices to perform certain functions.
The Android-based IoT platform was first unveiled to the public as the developer preview version on December 13, 2016. The Android-based IoT platform provides the technology to develop applications that run on IoT devices based on the Android operating system. It makes it easy to develop applications while leveraging existing Android development tools, Android APIs and Google infrastructure services. Applications that run on the Android-based IoT platform have much in common with those that run on existing Android-based Smartphone. Both applications running on the IoT device and smartphone register permissions to provide users with certain functions. If an application is used differently from its original purpose or asks additional permissions rather than using given permissions to provide certain functions for the user, it can perform malicious activities such as collecting excessive information or leaking personal information [1]. For example, if an IoT device that provides temperature and humidity registered permissions such as location information, camera, package installation and deletion, etc., it would perform functions different from the original purpose through the newly registered permissions. android projects titles topics ideas This paper collects permission lists for the versions of an application running on the Android-based IoT platform before and after the update. It aims to respond to future security threats by identifying the same, deleted, and added permission information compared to the update based on the collected permission lists. The structure of this paper is as follows. Section 2 discusses the Android-based IoT platform, the AndroidManifest.xml file, and the Android permission protection level. Section 3 performs permission analysis on the application to identify permission differences before and after the update. Finally, section 4 concludes this study.
Android applications (also called Androidapps) have been proved the effective target.Google Play store has provided billions of Android apps, but unfortunately, the advance has a dark side because security cannot be ensured by many Android apps. Hence, more and more attention has been paid to Android malware. Taint flow analysis has been proved an effective approach to providing potential malicious data flows. It aims at determining whether a sensitive data flows from a source to a sink. The analysis can be executed either dynamically or statically. Dynamic taint analysis [5] relies on testing to reach a appropriate code coverage criterion. It is able to precisely pinpoint leaks, but may be incomplete in exploring all possible executing paths. In contrast, static analysis takes all the possible paths for consideration. But most of the static analyses available for Android apps [1,3] are inner-component based analysis which are unable to detect leaks across-components.Even though most of the privacy leaks happen in a single com-ponent, lots of inter-components privacy leaks have been reported. mca php project topics 2019 computer science Thus, inner-component taint analysis is not enough to detect leaks.Efforts have also been devoted to implement static analysis for An-droid [2]to supply us with a relatively satisfactory outcome. Among them, Inter-Component Communication (ICC) [4] analysis plays important roles since ICC values can facilitate a precise consequent.However, the current ICC analyses only consider ICC links be-tween components where reuse and revision of an Intent across-component are not considered. Thus, lots of potential leaks will escape from being tracked in the succeeding ICC leak detection.With this motivation, in this paper, we devote to ICC analysis on reused and revised Intents. To do so, first, ICC values are analyzed by taking reused and revised Intents into account. With this basis,target components of Intents are analyzed and ICC Graphs are built.On an ICCG, all the ICC flows, which are useful in tracking leaks across-components, are contained. This will lay a critical foundation to the succeeding taint flow analysis. The proposed approach has been implemented in a tool called ICC-Analyzer (ICCA) where IC3is integrated in for providing ICC values of the Intents which are not reused or revised.
We have implemented our approach in a tool named ICCA to analyze ICC values with ICIR and construct ICCGs of Android apps for the convenience of the succeeding ICC leak detection. The evaluation of our approach addresses the following two research questions:(1) How does ICCA perform when analyze ICC values with ICIR?and (2) As an ICC analysis tool for Android apps, how ICCA can precisely match the targets of Intents?
CC Analysis with ICIRBy experiments, we can obtain that 37 and 36revised Intents existin GooglePlay and MalGenome, respectively. We apply ICCA in analyzing ICC values of the 73revised Intents. Table 1 illustrates abird’s eye view of the whole experiment. The l.h.s of Table 1 shows the seven attributes Action,Category,Type,Data,Flag,Extra, and Component of the 37 different revised Intentsin GooglePlay. Ther.h.s illustrates the attributes of the 36revise Intentsin MalGenome.Note that in the table, ‘–’ means that the relative value is captured but not modified, and ‘√’ indicates that the revised value is successfully acquired. As shown in Table 1, ICC values of all the 73revisedIntentsare successfully captured which are unable to be obtained by all the existing ICC analysis tools
In this part, we illustrate the target components of different Intentsin GooglePlay and MalGenome matched by ICCA. The results are compared with the target components matched with IC3. https://codeshoppy.com/php-projects-titles-topics.html All the Intents are classified into three categories: explicit, implicit and reused ones. We record the numbers of Intents in different categories and numbers of the matched target components.The results on GooglePlay and MalGenomeare presented in Ta-ble 2. The first column are the sets of data; the second one shows the categories of Intents; the third one illustrates the numbers of the involved Intents in the relative category. The right-most two columns present the numbers of the target components identified by IC3 and ICCA, respectively. As shown in the experiment, both IC3 and ICCA can identify most of the explicitly defined target components of In-tents (94.8% inGooglePlay and 98.7% inMalGenome). For implicitones, a small part (1.3% inGooglePlay and 0.7% inMalGenome) of them are acquired by ICCA whereas null of them can be obtained by IC3. The success rate of ICCA is low since implicit Intents are frequently used to launch target components in other apps which cannot be acquired without the runtime environment. Thus, our re-sult is reasonable. For the reused Intents, ICCA can acquire almost all the target components while null of them are acquired by IC3. In this experiment, we compare the results of ICCA in ICC analysis only with the newest ICC analysis tool IC3 as it is an improvement of Epicc. To be best of our knowledge, IC3 and Epicc are the only ICC analysis tools publicly available
