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Inter-mode Development: Android Application Transition from Mobile to IoT
- 1 Northeastern University at Qinhuangdao, Qinhuangdao, Hebei Province, China
* Author to whom correspondence should be addressed.
Abstract
The development of IoT applications based on the Android operating system is on the rise. However, the process presents greater challenges compared to traditional mobile application development, primarily due to the need to support and ensure compatibility with hardware across diverse scenarios. This complexity demands increased manpower and specialized expertise, making the promotion and reuse of Android-based IoT application development more difficult. To address these challenges, this study first reviews the existing research on IoT application development, with a particular focus on analyzing the scenarios, scope, and functional tasks associated with Android-based IoT applications, thereby identifying the current bottlenecks in the field. Secondly, it examines the typical development process, analyzing interaction methods within specific hardware scenarios to extract key coding patterns and development approaches. Furthermore, by comparing these findings with the development paradigms of Android mobile applications, the work proposes an inter-mode development paradigm for the critical development task involved in IoT application. A case study is then used to thoroughly illustrate the transformation process from mobile to IoT applications. This paper aims to provide effective strategies for reducing IoT development costs and offers recommendations for simplifying the transfer process between different devices.
Keywords
Android, IoT, Mobile app, Software development
[1]. Fahmideh, M., & Zowghi, D. (2020). An exploration of IoT platform development. Information Systems, 87, 101409.
[2]. Khan, M. A., Ahmad, I., Nordin, A. N., et al. (2022). Smart android-based home automation system using internet of things (IoT). Sustainability, 14(17), 10717.
[3]. Gargenta, M. (2011). Learning Android. O’Reilly Media.
[4]. Khanna, A., & Kaur, S. (2020). Internet of things (IoT), applications and challenges: A comprehensive review. Wireless Personal Communications, 114, 1687–1762.
[5]. Ren, Z., Wu, H., Ning, Q., et al. (2020). End-to-end malware detection for Android IoT devices using deep learning. Ad Hoc Networks, 101, 102098.
[6]. Duc, A. N., Jabangwe, R., Paul, P., et al. (2017). Security challenges in IoT development: A software engineering perspective. In Proceedings of the XP2017 Scientific Workshops (pp. 1–5). ACM.
[7]. Khan, M. A., Ahmad, I., Nordin, A. N., et al. (2022). Smart android-based home automation system using internet of things (IoT). Sustainability, 14(17), 10717.
[8]. Rana, B., Singh, Y., & Singh, P. K. (2021). A systematic survey on internet of things: Energy efficiency and interoperability perspective. Transactions on Emerging Telecommunications Technologies, 32(8), e4166.
[9]. Türk, E., & Challenger, M. (2018). An Android-based IoT system for vehicle monitoring and diagnostic. In 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1–4). IEEE.
[10]. Ismail, S., & Dawoud, D. W. (2022). Software development models for IoT. In 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0524–0530). IEEE.
[11]. Zanaj, E., Caso, G., De Nardis, L., et al. (2021). Energy efficiency in short and wide-area IoT technologies—A survey. Technologies, 9(1), 22.
[12]. Iwendi, C., Maddikunta, P. K. R., Gadekallu, T. R., et al. (2021). A metaheuristic optimization approach for energy efficiency in the IoT networks. Software: Practice and Experience, 51(12), 2558–2571.
Cite this article
Gao,Y. (2024). Inter-mode Development: Android Application Transition from Mobile to IoT. Applied and Computational Engineering,120,35-41.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 5th International Conference on Signal Processing and Machine Learning
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