Improved Cluster Head Selection for Efficient Data Aggregation in Sensor Networks

Abstract

Large-scale Wireless Sensor Networks (WSN) is the focus of recent research and development efforts. Due to their benefits in monitoring physical environments, WSN find diverse applications from military usage to agriculture and scientific works. To maximize WSN’s network life, data transfer paths are selected so that total energy consumed on the path is minimal. To ensure high scalability and improved data aggregation, sensor nodes are grouped into disjoint, non-overlapping subsets known as clusters. This study proposes improved Cluster Head (CH) selection for efficient sensor networks’ data aggregation. The suggested hybrid algorithm is based on Bacterial Foraging Optimization (BFO) and Gravitational Search Algorithm (GSA). The proposed hybrid BFO is incorporated in Lower Energy Adaptive Clustering Hierarchy (LEACH).