PSO-based clustering for the optimization of energy consumption in wireless sensor network

Wireless sensors (nodes) are devices with built-in batteries, sensors and communication units. Wireless sensor networks (WSNs) are structures that are formed by multiple nodes coming together to transmit the data they collect from each other to the base station. Significant work has been done on WSNs in recent years. One of the important issues that these studies have focused on is increasing the energy efficiency of the nodes forming the network and ensuring their survival for a longer time. In this paper, two-dimensional particle swarm optimization (PSO) is proposed to solve the problem of clustering in WSNs by inspiration from PSO modified by Fan to solve discrete problems such as the traveling salesman problem. The proposed algorithm was analyzed comparatively with the low-energy adaptive clustering hierarchy (Leach) protocol. As a result, an improvement of 4% compared with Leach was achieved in terms of the amount of energy left in the network. The data packets sent in 20 rounds increased by 2000 packets compared to Leach, and a 27% improvement was achieved. In addition, the number of surviving nodes increased by 22%.