Indiana University-Purdue University Indianapolis (IUPUI) / Wireless Sensor Networks (WSN) is being used in various applications including environmental monitoring, site inspection and military. WSN is a distributed network of sensor devices that can be used to monitor temperature, humidity, light and other important metrics. The software that runs on the sensor devices define how the device should operate. In real world WSN deployment, device software update is required to maintain optimal operation. In this thesis, we propose a novel idea of updating the software of the sensor nodes using a mobile device running on Android Operating System. Our implementation builds upon Mobile Deluge with few enhancement which is a method of re-programming WSN with laptop computer. We have evaluated our application performance by lab experiments and in real world deployments of WSN and found the application stable and battery efficient.

Indiana University-Purdue University Indianapolis (IUPUI) / With the large number of Android apps available in app stores such as Google Play, it has become increasingly challenging to choose among the apps. The users generally select the apps based on the ratings and reviews of other users, or the recommendations from the app store. But it is very important to take the security into consideration while choosing an app with the increasing security and privacy concerns with mobile apps. This thesis proposes different ranking schemes for Android apps based on security apps evaluated from the static code analysis tools that are available. It proposes the ranking schemes based on the categories of evidences reported by the tools, based on the frequency of each category, and based on the severity of each evidence. The evidences are gathered, and rankings are generated based on the theory of Subjective Logic. In addition to these ranking schemes, the tools are themselves evaluated against the Ghera benchmark. Finally, this work proposes two additional schemes to combine the evidences from difference tools to provide a combined ranking.

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Background: These days when developing applications for mobile platforms, therehave been two main branches to pick between as a developer. The first option– denoted native development – targets only one specific mobile operating system(OS). The other is cross-platform development which can target multiple platformssimultaneously. A common concern with cross-platform development, however, is itsperformance when compared to native frameworks. Objectives: In this thesis, the objective is to compare the performance of applica-tions created for Android using the native framework and the cross-platform .NETMAUI framework by Microsoft. Methods: The method used in this thesis was a combination of a literature (map-ping) study and an experiment. The purpose of the mapping study was to identifyrelevant performance metrics. The experiment then observed those how those met-rics, CPU and memory (RAM) usage, differed between the applications created usingboth frameworks. Results: Overall, the .NET MAUI framework was significantly worse than the na-tive framework on both measured performance metrics in 9 out of 12 tasks tested.The .NET MAUI framework was significantly better in 1 task, while 2 other tasksended up with differences that were statistically insignificant. Conclusions: An experiment was conducted to compare CPU usage in % andmemory (RAM) usage in MB between the native framework and the .NET MAUIframework for Android development on a variety of tasks. The outcome of the ex-periment significantly favored the native framework statistically. However, due to ofthe small sample size in selected tasks as well as specific design choices it is unclearhow real-world use would compare.

Mobile devices are increasingly becoming a greater crutch for all generations. All the while, these users are garnering a greater desire for privacy and style. Apple presents a device that is known for its security, but lacks major user customization. On the other hand, Google has developed a device that is keen to customization with Android, but can be susceptible to security flaws. This thesis strives to discuss the security models, app store protections, and best practices of both mobile operating systems. In addition, multiple experiments were conducted to demonstrate how an Android device could be more easily compromised after altering few settings, as well as to demonstrate the privileges, both good and bad, that could be gained by jailbreaking an iOS device.

With the increasing ubiquity of smartphones, tablets, and large pixel-rich displays, there are many exciting new possibilities for using these devices for collaborative work. While there already exist hardware and software that support communication and interaction between mobile devices and shared displays, application sharing in these scenarios is still limited and inflexible. We present a new method of application sharing which allows collaborators to download clips or snapshots of each other's applications. These snapshots can be used to re-launch and resume the shared application back to the state it was in when it was shared. We have built a system that supports sharing, annotating, organizing, and downloading these applications to and from a LearnSpace server. We have built an application framework which allows Android applications to be built for this system while only requiring minimal changes to the program. We also describe solutions for extending our solution to new types of collaborative displays and to other application platforms.

<p>  </p> <p>Data is the foundation of digital agriculture. Data from a wide variety of sensors in the soil, in machinery, or from remote sensing can inform decisions including site-specific land and crop management but capitalizing on these data requires metadata that captures the full story related to production. Answers to metadata questions such as who, what, where, when, and how are often unavailable when aggregated data are analyzed. These metadata are crucial for making accurate operation and management decisions and certainly for developing AI models. Since farmers and researchers exhibit human behavior of forgetting to take notes or entering incorrect information, even with digital means, missing and erroneous records are common. To address this issue, a metadata collection Android app – Meta Ag for agricultural activities was created that automatically appends the operator’s name, time, and space information to an in-field event, and provides a user-friendly interface to gather information with more details describing which activity was done and how. Meta Ag has six main modules, including user registration, geofence construction, accessed geofence recognition, an infobot for extensive activity data collection, setting options for Infobot and data access. By design, manual data input, with automatic validation, when possible, was used for information collection. To achieve this, Meta Ag uses dynamically constructed, interactive option lists for fewer data entry errors. The collected data were stored in a Google Firebase database as central storage for multiple users. To facilitate data interoperability, stored data were made accessible in CSV and JSON format. The Android app collects extensive metadata from database interactive option lists and the infobot as a data collection wizard provides a dynamic environment for data collection in a short time with minimal manual input. The app was also able to reduce missing data as it automatically records the accessed fields and activity time in that field. The Meta-Ag app can work both as a standalone tool and an integration into any other farm information management system.</p>

A disaster usually severely harms human health and property. After a disaster, great amount of information of a disaster area is needed urgently. The information not only indicates the severity of the disaster, but also is crucial for an efficient search and rescue process. In order to quickly and accurately collect real time information in a disaster scenario, a mobile platform is developed for an outdoor scenario and a localization and navigation system for responders is introduced for an indoor scenario. The mobile platform has been integrated to the DIORAMA system. It is built with a 6-wheel robot chassis along with an Arduino microcontroller. Controlled by a mounted Android smartphone, the mobile platform can receive commands from incident commanders and quickly respond to the commands. While patrolling in a disaster area, a constant RFID signal is collected to improve the localization accuracy of victims. Pictures and videos are also captured in order to enhance the situational awareness of rescuers. The design of the indoor information collection is focused on the responder side. During a disaster scenario, it is hard to track responders’ locations in an indoor environment. In this thesis, an indoor localization and navigation system based on Bluetooth low energy and Android is developed for helping responders report current location and quickly find the right path in the environment. Different localization algorithms are investigated and implemented. A navigation system based on A­* is also proposed.

Nowadays, prolonged sitting among office workers is a widespread problem, which is highly related to several health problems. Many proposals have been reported and evaluated to address this issue. However, motivating and engaging workers to change health behavior to a healthier working life is still a challenge. In this project, a specific application has been deployed for real-time monitoring and alerting office workers for prolonged sitting. The proposed system consists of three distinct parts: The first one is an android smartwatch, which was used to collect sensor data e.g., accelerometer and gyro data, with a custom android wear app. The second one is an android application, which was developed to act as a gateway for receiving the smartwatch’s data and sending them to IBM Bluemix cloud with MQTT protocol. The final part is a Node-Red cloud application, which was deployed for storing, analyzing and processing of the sensor data for activity detection i.e., sitting or walking/standing. The main purpose of the last one was to return relevant feedback to the user, while combining elements from gaming contexts (gamification methods), for motivating and engaging office workers to a healthier behavior. The system was firstly tested for defining appropriate accelerometer thresholds to five participants (control group), and then evaluated with five different participants (treatment group), in order to analyze its reliability for prolonged sitting detection. The results showed a good precession for the detection. No confusing between sitting and walking/standing was noticed. Communication, storage and analysis of the data was successfully done, while the push notifications to the participants, for alerting or rewarding them, were always accurate and delivered on time. Every useful information was presented to the user to a web-based dashboard accessed through a smartphone, tablet or a PC.     The proposed system can easily be implemented at a real-life scenario with office workers. Certainly, there is a lot space for improvement, considering mostly the type of data registered at the system, the method for sitting detection, and the user interface for presenting relevant information. / Numera är förlängt sittande bland kontorsarbetare ett utbrett problem som är väldigt relaterat till flera hälsoproblem. Många förslag har rapporterats och utvärderas för att ta itu med denna fråga. Tydligen är det fortfarande en utmaning att motivera och engagera arbetstagare för att förändra deras hälsobeteende till hälsosammare arbetsliv. I detta projekt har en särskild applikation använts för realtidsövervakning och varnar kontorsarbetare för förlängt sittande. Det föreslagna systemet består av tre olika delar: Den första är en android smartwatch, som användes för att samla sensordata t.ex. accelerometer och gyrodata, med en anpassad android wear app. Den andra är en en androidapplikation som fungerade som en gateway för att ta emot smartwatchens data och skickar datan till IBM Bluemix-Cloud med MQTT-protokollet. Den sista delen är en Node-RED Cloud-Applikation som användes för lagring, analysering och behandling av sensordata för aktivitetsdetektering. Detta innebär sittande eller gå/stående med det huvudsakliga ändamålet att returnera relevant återkoppling till användaren, samtidigt som man kombinerar element från spelkontekster (gamification metoder), för att motivera och engagera arbetarna till ett hälsosammare beteende. Systemet testades först för att definiera lämpliga accelerometertrösklar till fem deltagare (kontroll grupp) och utvärderades sedan med fem olika deltagare (behandingsgrupp) för att analysera dess tillförlitlighet för långvarig sittdetektering. Resultaten visade en bra precession för detektionen. Ingen förvirring mellan att sitta och gå / stående märktes. Kommunikation, lagring och analys av data gjordes framgångsrikt, medan push-meddelandena till deltagarna, för att varna eller belöna dem, var alltid korrekta och levererade i tid. All användbar information presenterades för användaren på en webbaserad dashboard som nås via en smartphone surfplatta eller en dator. Det föreslagna systemet kan enkelt implementeras i ett verkligt scenario med kontorsarbetare. Visst finns det mycket utrymme för förbättring om man tänker på majoriteten av data som registrerats i systemet, metoden för sittande detektion och användargränssnittet för presentering av relevant information.