Particle Swarm Optimization Protocol for Clustering in Wireless Sensor Networks: A Realistic Approach

In Wireless Sensor Network (WSN), Clustering sensor nodes is an efficient topology control method to reduce energy consumption of the sensor nodes. Many link quality-based clustering techniques have been proposed in the literature. However, they assumed that each sensor node is equipped with a self-locating hardware such as GPS. Though this is a simple solution, the resulting cost renders that solution inefficient and unrealistic. Furthermore, several studies has shown that link quality in WSN is not correlated with distance. In addition to that, they used an energy model that is fundamentally flawed for modelling radio power consumption in sensor networks. They ignore the listening energy consumption, which is known to be the largest contributor to expended energy in WSN. Clustering is a Non-deterministic Polynomial (NP)-hard problem for a WSN. Particle Swarm Optimization (PSO) is a swarm intelligent approach that can be applied for finding fast and efficient solutions of such problem. In this paper, a PSO-based protocol is used to find the optimal set of cluster heads that maximize the network coverage, energy efficiency and link quality. The effect of using a realistic network and energy consumption model in cluster-based communication for WSN was investigated. Numerical simulations demonstrate the effectiveness of the proposed protocol.