Biogeography-Based Krill Herd algorithm for energy efficient clustering in wireless sensor networks for structural health monitoring application

Civil buildings are prone to various kinds of damages. The detection of damages caused in a building at an early stage is essential in order to save the invaluable human life and significant belongings. Wireless sensor networks (WSN) help to detect damages caused to a building by sensing different factors, which affect civil structures. Energy efficiency of sensor nodes and network congestion are quite common issues in wireless sensor networks that affect the network performance. In this research work, the formation of energy efficient clusters mitigates congestion by considering the buffer occupancy level and fairness index of flows to improve the network lifetime. The proposed method uses Biogeography-Based Krill Herd (BBKH) algorithm for cluster head selection. BBKH based congestion mitigation outperforms other classical evolutionary optimizations and swarm intelligence algorithms like Genetic Algorithm, Particle Swarm Optimization (PSO) and Symbiotic Organisms Search (SOS). Compared with PSO, the network throughput has increased by 26.18% using BBKH. The network lifetime has increased by 42.11% using the proposed BBKH, compared to PSO. The extended lifetime of the network helps damage detection in civil structures for extensive periods.