A novel hybrid krill herd tabu search algorithm for cluster head selection in wireless sensor network

Abstract

The Wireless Sensor Network (WSNs) contain sensor nodes that are autonomous and distributed spatially. The primary constraint of the WSN is its limited source of power in sensor nodes. In the clustering process, the network has been divided into various groups known as clusters. By schemes of clustering, a low overhead is provided and the resources are efficiently allocated thus decreasing the ultimate consumption of energy and reducing interfaces within the sensor nodes. Every cluster consists of a leader called the Cluster Head (CH) which is responsible for the collecting the data from nodes within the cluster and transmit it to the base station. The Low-Energy Adaptive Clustering Hierarchy (LEACH) is a routing algorithm based on hierarchical clustering that is employed popularly in WSN. According to various studies, an optimal solution can be obtained through evolutionary and Swarm Intelligence (SI) algorithm, pertaining to Non-deterministic Polynomial (NP)-hard problems like CH selection. There are a number of protocols which enhance the life of WSN network based on bio-inspired algorithms like Genetic Algorithm (GA), Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), and Cuckoo Search (CS). A good SI algorithm is one which shows improvement in global search and has increased convergence for global best solution. In this work, CH selection using GA, Particle Swarm Optimization (PSO), and Krill Herd (KH) algorithm is proposed. The WSNs routing protocol are designed for improving the efficiency of energy consumption of the entire network. newline