A Wireless Sensor Network (WSN) is comprised of a collection of small, autonomous devices known as sensors. Various types of physical and environmental data, including temperature, sound, vibration, pressure, and motion, are captured by these sensors at different locations. The data is then processed and transmitted to end-users. In cluster-based WSNs, the role of aggregating and forwarding data to the central sink node is played by Cluster Heads (CH). However, improper clustering can result in the overloading of certain sensor nodes and gateways, leading to premature device failure and a decrease in the overall network lifespan. To address these challenges, the implementation of cost-effective solutions is essential, with objectives focusing on load balancing, availability, reliability, energy efficiency, processing power, and memory usage. Numerous metaheuristic algorithms have been explored in the existing literature to tackle computationally demanding optimization problems. In this paper, an improved Particle Swarm Optimization (PSO) algorithm is proposed for optimizing the cluster structure. The transmission distances are minimized, and energy efficiency within the network is maximized. A concise overview of PSO and its evolution as a robust stochastic optimization technique based on swarm intelligence is provided. Its successful application in solving a wide range of search and optimization problems, inspired by natural swarm behavior, is also highlighted.

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Loadbalancing Metaheuristic algorithm Transmission distance Stochastic optimization technique Intelligence of smarms