Abstract
Wireless Sensor Networks (WSNs) play an important role in the advancement of today’s internet of things (IoT) solutions. It allows the possibility to overcome different classical challenges in the telecommunication domain with the latest modern solutions. Thus, allowing a smooth technological transformation with unprecedented new use cases. Therefore, fields such as healthcare, environment, and industrial use-cases are the most demanding areas for implementing such technology. However, WSN comes with several problems, limitations, and constraints impacting its optimized deployment. The most popular dilemma are data privacy, energy efficiency, and computation capabilities. In this paper, we address the energy performance challenge through the design of an enhanced algorithmic approach. We propose a multi-stage and energy-aware clustering algorithm to enhance the energetic performance of wireless networks. The idea behind the proposed algorithm relies on the continuous on-boarding of wireless sensor nodes in different lifetime phases for the progressive construction of a network. Throughout the phases, we apply a k-medoids and LEACH protocols with a trade-off principle for best network clustering. We compare the algorithm results to LEACH protocol and our previous contributions. The extensive simulations have shown a good energetic improvement in different metrics, such as energy dissipation trends, first dead node, last dead node, network lifetime, and energetic dissipation. The results show an improvement of 379% compared to LEACH and 166% compared to K-medoids in terms of the first dead node, while the network performance was enhanced by 379% compared to LEACH and 166% compared to IHEE and 115% compared EACA.
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Acknowledgment
This work has been conducted within the framework of the Meteorological Station Project funded by the Moroccan Ministry of Higher Education and Scientific Research, and the National Centre for Scientific and Technical Research (Meteorological Station Platform-PPR2).
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Faid, A., Sadik, M., Sabir, E. (2023). A Green and Scalable Clustering for Massive IoT Sensors with Selective Deactivation. In: Sabir, E., Elbiaze, H., Falcone, F., Ajib, W., Sadik, M. (eds) Ubiquitous Networking. UNet 2022. Lecture Notes in Computer Science, vol 13853. Springer, Cham. https://doi.org/10.1007/978-3-031-29419-8_17
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DOI: https://doi.org/10.1007/978-3-031-29419-8_17
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