Software Development for Mobile Devices, the Internet-of-Things, and Cyber-Physical Systems

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Now showing 1 - 7 of 7
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    Machine Learning-Based Power Consumption Prediction for Unmanned Aerial Vehicles in Dynamic Environments
    ( 2023-01-03) Gatscher, Julian ; Breitenbach, Johannes ; Buettner, Ricardo
    Unmanned aerial vehicles are becoming integrated into a wide range of modern IoT and CPS environments for various industrial, military, and entertainment applications. With growing estimations for this market in the future, the problem of energy consumption and its prediction is becoming increasingly important for optimal battery-saving, as well as the safety of the application and thus protection of surrounding persons near the drone flight. This paper presents a machine learning-based approach for the prediction of the power consumption of unmanned aerial vehicles at certain times of the flight. Instead of predicting the power consumption in prescribed environments with complex, time-consuming measurement techniques, our approach is fast, easy to implement, and predicts real-world power consumption in five classes, with a balanced accuracy of 66.7 percent.
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    Effect of App Market Conditions on Permissions Usage by App Developers
    ( 2023-01-03) Wei, Jia ; Mallampalli, Kamesh
    The common mechanism for controlling security and privacy data on mobile platforms is through the app permissions model. Platform owners evolve the model through changes to the APIs provided to app developers. This however places increased responsibility on app developers to determine the privileges they need to deliver the app’s functionality. In this paper, we investigate the factors influencing an app developer to seek permissions for privileged access in the context of the Android mobile platform. We find that the apps facing higher competition in their category or targeted at more mature audiences seek more permissions from users. However, apps charging higher prices for downloads ask for a lower number of permissions. The findings suggest that market conditions incentivize app developers to seek more privileges whereas a revenue stream such as download price does not. Therefore, more control exercised by the platform owner on the conditions of their app market would be more beneficial to the platform from the security and privacy perspective.
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    Criteria Based Evaluation of Cross-Platform Development Frameworks
    ( 2023-01-03) El Tom, Ali ; Bogdan, Cristian ; Majchrzak, Tim A. ; Grønli, Tor-Morten
    Cross-platform development frameworks continue to play an important role in developing of mobile applications. There are a plethora of possible frameworks to choose between, and all claim to give developers an advantage in one or more aspects of the development cycle. To further shed light on this area, our research investigates the selection of mobile cross-platform frameworks by investigating criteria and methods for choosing the best suitable framework for the development context. Our study confirm previous research findings and with that anchor the categorisation for a cross-platform comparison taxonomy. Our research further contribute with a criteria check-list to apply for cross-platform framework selection and showcase its use through a comparative study of a cross-platform application implemented in two frameworks. Although showcasing novelty in the criteria checklist, further research is needed to make the checklist robust and streamlined for public use.
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    Kernel-Segregated Transpose Convolution Operation
    ( 2023-01-03) Tida, Vijay Srinivas ; Chilukoti, Sai Venkatesh ; Hsu, Sonya Hy ; Hei, Xiali
    Transpose convolution has shown prominence in many deep learning applications. However, transpose convolution layers are computationally intensive due to the increased feature map size due to adding zeros after each element in each row and column. Thus, convolution operation on the expanded input feature map leads to poor utilization of hardware resources. The main reason for unnecessary multiplication operations is zeros at predefined positions in the input feature map. We propose an algorithmic-level optimization technique for the effective transpose convolution implementation to solve these problems. Based on kernel activations, we segregated the original kernel into four sub-kernels. This scheme could reduce memory requirements and unnecessary multiplications. Our proposed method was 3.09(3.02)× faster computation using the Titan X GPU (Intel Dual Core CPU) with a flower dataset from the Kaggle website. Furthermore, the proposed optimization method can be generalized to existing devices without additional hardware requirements. A simple deep learning model containing one transpose convolution layer was used to evaluate the optimization method. It showed 2.2× faster training using the MNIST dataset with an Intel Dual-core CPU than the conventional implementation.
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    Source code protection against unauthorised copying and analysis in IoT devices
    ( 2023-01-03) Hyla, Tomasz ; Byczyk, Sebastian
    One problem for manufacturers of IoT devices is protecting intellectual rights to the software. Unprotected software can easily be copied or analysed and used on other devices. Proving another party that the source code has been illegally copied is difficult. One of the solutions is code obfuscation, i.e., modifying the code, so it works the same way, but its structure is complicated to understand and analyse. The paper presents a solution dedicated to IoT devices that combines code obfuscation techniques and uses the trusted platform module to decrypt part of the data during execution. A dedicated obfuscation method is described. Experiments show that the method increases the time needed to change the code at least several times, and some junior programmers cannot understand an obfuscated code. Test results show almost no similarity between the code in clear form and obfuscated form. The obfuscated code is more complicated but takes slightly longer to execute.
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    Codeless App Development: Evaluating A Cloud-Native Domain-Specific Functions Approach
    ( 2023-01-03) Wu, Chuhao ; Pérez-Álvarez, José Miguel ; Mos, Adrian ; Carroll, John
    Mobile applications play an important role in the economy today and there is an increasing trend for app enablement on multiple platforms. However, creating, distributing, and maintaining an application remain expert tasks. Even for software developers, the process can be error-prone and resource-consuming, especially when targeting different platforms simultaneously. Researchers have proposed several frameworks to facilitate cross-platform app development, but little attention has been paid to non-technical users. In this paper, we described the Flow framework, which takes the advantage of domain-specific languages to enable no-code specification for app modeling. The cloud-native coordination mechanism further supports non-technical users to execute, monitor, and maintain apps for any target platforms. User evaluations were conducted to assess the usability and user experience with the system. The results indicated that users can develop apps in Flow with ease, but the prototype could be optimized to reduce learning time and workload.