Shuffled Particle Swarm Optimization for Energy Efficiency Using Novel Fitness Function in WSN
Clustering of sensor nodes in Wireless Sensor Networks (WSN) is a critical task in order to minimize energy consumption as well as prolonging the lifetime of the network. In cluster based WSN, the gateway from the cluster collects, aggregates and sends data to the base station. To perform these operations, the energy consumption depends on two significant factors; load on the gateway and the data transmission distance. The improper clustering of sensor nodes may consume more energy, and the network may die soon. Therefore, load balancing of gateways according to the transmission distance is necessary to increase the network lifetime. We applied the shuffling strategy from Shuffled Frog Leaping Algorithm (SFLA) to Particle Swarm Optimization (PSO) to implement an algorithm called shuffled PSO (SPSO) for improving energy efficiency in WSN. The quality of the solution is measured by the novel fitness function concerning the lifetime of gateways and average cluster distance. The extensive simulations are performed with some of the existing algorithms. From the experimental analysis, it is observed that the proposed clustering approach gives effective results.
KeywordsClustering Energy efficiency Particle swarm optimization Shuffled frog leaping algorithm Wireless sensor networks
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