Residual Energy-Aware Fuzzy-Based Clustering Algorithm for Underwater Wireless Sensor Networks

In the field of underwater exploration and research, Underwater Wireless Sensor Networks (UWSNs) play a vital role in understanding the marine environment, oceanography, and marine biology. A key strategy used in UWSNs to aggregate sensor nodes and improve network performance while extending battery life through lower energy usage is clustering. However, available clustering algorithms do not specifically address all the underwater problems, viz., communication is constrained by the limited bandwidth and high latency of acoustic signals, while energy consumption is critical due to the difficulty of recharging or replacing underwater batteries. The harsh underwater environment, with varying pressure, salinity, and movement, affects sensor performance and durability. Accurate localization is difficult without GPS and relies on less precise acoustic methods. So, this paper proposes a Residual Energy-Aware Fuzzy-Based Clustering Algorithm (REAFCA) for UWSNs which presents a novel framework intended to improve network performance and address issues with energy usage. For effective data routing, the REAFCA dynamically arranges clusters based on important factors such as node rank, radius, threshold, angular velocity, and residual energy. To maximize leadership inside the clusters, the adaptive threshold method makes sure that only superior cluster heads are chosen. The algorithm also incorporates dynamic range changes for communication to adapt to changing network circumstances. This algorithm mainly focuses on clustering in an underwater environment while improving the energy efficiency and network life of the nodes. Simulation results demonstrate the superiority of the proposed algorithm over K-means, K-meansA, LEACH, PEGASIS, HEED, DB-SCAN and HEER algorithms in terms of energy efficiency and throughput while achieving comparable average delay.