Wireless Sensor Networks (WSNs) finds numerous applications in military, industry and civil application for event detection and object tracking. For unattended operation and long lifetime, sensor networks must operate under self configure mode without any central entity. The routing and data communication in WSN should be auto-configurable, dynamic and adaptive to the changes in the network environment. Optimization techniques for energy efficient routing, clustering and data aggregation in WSN is crucial to sustaining the network for long life and for effective utilization of limited bandwidth of the sensor network. In this paper, bio-inspired earthworm optimization algorithm (EWA) is used for optimal cluster head (CH) selection and data aggregation in the WSN and is also compared with other clustering methods like genetic algorithm(GA)-based clustering and particle swarm optimization(PSO) based clustering in terms of delay, throughput, energy and number of live nodes.
Volume 11 | 12-Special Issue