Energy aware Load Balanced Clustering using Golden Eagle Optimization in MS based LR-WPAN

Abstract

Escalation in the real world applications using Internet of Things (IoT) now demands low cost and minimum power consuming devices having sensing, communicating, and computing capabilities. Low-rate Wireless Personal Area Network (LR-WPAN) is an emerging solution which can provide these capabilities with low data rate and less complexity. The longevity and connectivity of the network is a critical issue during the data collection in LR-WPAN. An Energy aware Load Balanced Clustering (ELBC) approach is proposed in this paper that enhances the network life time by minimizing the energy consumption. Clustering is done using the Golden Eagle Optimization (GEO) and by taking connectivity, Average energy of cluster, balancing factor and cohesion as parameters for optimal cluster head selection. Mobile Sink based data collection strategy using Gravitational Search Algorithm (GSA) is used to collect data from clusters. The simulation is done in network simulator-3 and the result shows that, the performance of ELBC-GEO is better in terms of network lifetime, average energy of nodes, and data collection delay.