Clustering Routing Protocol Based on Tuna Swarm Optimization and Fuzzy Control Theory in Wireless Sensor Networks

Wireless sensor networks (WSNs) are composed of a large number of sensor nodes, typically powered by batteries. Once the energy is exhausted, the sensor nodes will stop working, which will have a serious impact on network performance. Therefore, energy consumption has always been a bottleneck in the development of WSNs. To balance the network energy and improve energy efficiency, this article proposes a clustering routing protocol based on tuna swarm optimization and fuzzy control theory (TSFC). First, tuna swarm optimization (TSO) is utilized to optimize network clustering by considering the average and standard deviation of distances from nodes to their respective cluster centers, which improves the compactness of cluster structure. Therefore, communication distance within the cluster is reduced, thereby reducing the energy consumption of communication within the cluster. Then, in the cluster head (CH) selection stage, a fuzzy controller for selecting CHs is designed based on the fuzzy control theory. The residual energy of nodes, distance to the base station (BS), and average distance to other nodes in the cluster are taken as inputs for the fuzzy controller, which outputs a fitness value of the node to become the CH. This method optimizes the CH selection, thereby reducing node energy consumption and balancing network energy consumption. Simulation results demonstrate that the proposed protocol can reduce network energy consumption, improve energy efficiency, and balance network energy. Furthermore, the network lifetime is 45.9%, 33.5%, and 10.0% longer than that of gray wolf optimization (GWO-C), energy-efficient LEACH (EE-LEACH), and enhanced fuzzy-based LEACH (E-FLEACH), respectively.