Cluster Heads Distribution of Wireless Sensor Networks via Adaptive Particle Swarm Optimization

Wireless sensor networks consists of hundreds or thousands of sensor nodes supported by small capacity battery. For environmental monitoring purposes, sensor nodes must have high endurance capabilities. Therefore, selecting suitable cluster heads (CH) location becomes a challenging issue. In this work, cluster heads distribution based on adaptive particle swarm (PSO) is proposed. PSO is one of the swarm intelligence methods designed to find optimum solution by mimicking the behavior of bird flocking and fish schooling. Adaptive cognitive and social learning factor can achieve better convergence speed and particles reselection mechanism can reduce the chances of getting trapped in local maximum. The performance of the proposed method is compared with low energy adaptive cluster hierarchical (LEACH). Simulation result shows that proposed method outperforms LEACH in terms of first node die (FND) round, total data received by base station and energy consume per round.