Meta Heuristic Kha – Cuckoo Search Optimization Algorithm For Energy Efficient Wireless Sensor Network

Abstract

Wireless Sensor network (WSN) is a complex distributed network which encompass huge amount of nodes for data sensing particular region. In WSN energy efficiency is the major constraints since it operates on battery power including individual node in the network. To achieve energy efficiency in WSN existing approaches adopt clustering through which among group of node individual node is selected as cluster head (CH) for whole cluster. The selected CH performs the task of processing and computation process of entire cluster which in turns reduces the energy consumption of the whole cluster. In WSN clustering has been implemented through data aggregation scheme for balancing energy consumption of particular hub in the senor system for productive data transmission. The existing WSN uses various techniques for achieving energy efficiency like Harmony Search Algorithm (HAS), Particle Swarm Optimization (PSO) and LEACH (Low Energy Adaptive Clustering Hierarchy) algorithm. Those existing algorithm exhibits local search and trade-off in exploration – exploration constraints in the WSN individually. To combine the advantage of existing approaches and to achieve faster convergence speed hybrid optimization technique has been evolved. In this paper proposed a novel hybrid optimization algorithm for achieving energy efficiency in the WSN. The proposed meta- heuristic hybrid optimization algorithm combines Krill-Heard Algorithm (KHA) and Cuckoo Search Algorithm (CSA) for achieving energy efficiency. Energy efficiency of the network is achieved by selecting appropriate cluster head in the network through fitness function of the proposed hybrid approach. The proposed hybrid optimization algorithm is implemented in LEACH protocol for energy efficient cluster head selection. The simulated results of proposed hybrid KHA – CSA algorithm exhibits an improved performance than the existing approaches.