An Innovative Fuzzy-Enhanced Black Widow Spider Optimization for Energy-Efficient Cluster Communication Through the Selection of the Ideal Cluster Head - Km Anjuman Bano; Sushil Kumar Sharma (International Journal of Wireless Security and Networks, 2024)
This paper presents a novel approach to enhance energy efficiency in cluster-based wireless sensor networks (WSNs) by integrating fuzzy logic with the black widow spider optimization (BWSO) algorithm. The focus of the suggested fuzzy-enhanced BWSO approach is on cluster head selection, which is essential for lowering energy consumption and enhancing network performance. The program uses fuzzy logic to assess a number of factors, including network density, node proximity, and energy levels, guaranteeing a more flexible and astute decision-making process. Simulation results show that in terms of energy efficiency, network longevity, and communication overhead, the suggested approach performs noticeably better than conventional clustering strategies. This creative strategy opens the door for more sustainable communication solutions in WSNs while also addressing the shortcomings of current techniques. Interest in studying WSNs has significantly increased as a result of the advancement of wireless communications. Numerous sensor nodes that are digitally attached to one another comprise a WSN. Sensor node components are responsible for observing their environment, processing the information they get, and interacting with different sensor nodes. These sensor nodes collaborate systematically to accomplish a common goal. This suggests using the fuzzy-enhanced black widow spider (FEBWS) algorithm to improve WSN energy efficiency. The effectiveness of the proposed approach is evaluated using the NS-2 simulator. The suggested strategy performs better when the ideal parameter values are predicted using the hyper-parameter tuning technique. Performance is assessed using a number of parameters, including energy efficiency, packet loss, end-to-end latency, network longevity, and energy usage. For the sake of comparative study, the ACI-GSO algorithm, MWCSGA, RNN-LSTM approach, and Particle Swarm Optimization (PSO) algorithm are the other approaches that are contrasted with the recommended FEBWS algorithm.
Keywords: Cluster Head; Clustering; Wireless sensor network; energy efficiency