Clustering Routing Algorithm for WSNs Based on Fuzzy Logic Optimized by Whale Optimization Algorithm

From the perspective of improving the longevity of networks, we design a clustering routing algorithm for WSNs based on fuzzy logic optimized by the whale optimization algorithm. First, a clustering threshold is designed based on residual energy and distance to select candidate cluster heads to improve the quality of candidate cluster heads. Second, when using fuzzy logic for cluster head selection, the number of fuzzy rule combinations in the Mamdani inference model is huge, and the fuzzy rules set based on empirical knowledge are far from optimal rules. Therefore, the fuzzy rules are encoded into the whale algorithm for optimization, and three independent linguistic variables are designed for fuzzy logic input to make the cluster head's energy, position, and density reasonable. In addition, the competitive radius is used to perform unequal clustering to balance the energy consumption of cluster heads and optimize the clustering strategy of nodes to improve the energy utilization efficiency of nodes around the base station. Experiments show that in the given network model, the proposed algorithm can effectively balance the load of nodes and improve the network life to a greater extent than the three algorithms of LEACH, FBECS, and UCMF.