Abstract
Wireless Sensor Networks (WSNs) are a crucial component in the fabric of the Internet of Things (IoT) ecosystem, enabling a myriad of applications ranging from environmental monitoring to precision agriculture and smart cities. However, these sensors are constrained in terms of energy, computing power, and storage which makes reliable communication a critical research challenge. To address these challenges, unequal clustering has emerged as a promising solution where clusters are intentionally formed with varying sizes to accommodate heterogeneous capabilities and energy demands across the network. In this paper, we introduce a novel Multi-Objective and Randomly Distributed Fuzzy Logic-based Unequal Clustering (MORF-UC) scheme to address the challenge of energy management and hotspot issues in WSNs. By leveraging fuzzy logic to account for variables such as distance to the base station (BS), residual energy, node concentration, and data forwarding ratio of nodes, this scheme aims to extend network lifetime, energy use, and data transmission reliability while mitigating the hot spot issues. Simulation results demonstrate that the proposed methodology outperforms existing methods such as TTDFP and MOUOC in the energy conservation, network lifetime extension, and throughput enhancement, thereby offering a significant advancement in the field of WSN optimization.
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Adnan, M., Ahmad, T., Rafi, S., Abdullah, Vurity, A. (2024). Multi-objective and Randomly Distributed Fuzzy Logic-Based Unequal Clustering in Heterogeneous Wireless Sensor Networks. In: Nguyen, N.T., et al. Computational Collective Intelligence. ICCCI 2024. Lecture Notes in Computer Science(), vol 14810. Springer, Cham. https://doi.org/10.1007/978-3-031-70816-9_26
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DOI: https://doi.org/10.1007/978-3-031-70816-9_26
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