Enhanced Grey Wolf Algorithm for Energy Efficient Wireless Sensor Networks

Wireless sensor networks have entered a period of a rapid development, due to several novel technologies which have emerged in the past few years, such as Internet of Things and cloud computing. Miniature sensor nodes are integral components of numerous complex systems. The biggest problem for any wireless sensor network, in any possible application domain, is to maximize the overall network lifetime by improving the total energy consumption of the network. A large number of clustering algorithms have been implemented in the past decade, with a main goal to balance the energy consumption of each node in the network and to increase energy efficiency - the term known in literature as load balancing. One important representative of these traditional algorithms for load balancing which is still in frequent use is LEACH. On the other hand, swarm intelligence meaheuristics have recently been successfully applied in solving a large number of NP hard problems from the wireless sensor networks domain. In this paper, we propose an improved version of grey wolf algorithm, that has been applied to improve the network lifetime optimization. Grey wolf algorithm was employed in forming the clusters and the cluster head selection process. As a part of our research, we have evaluated the performance of the proposed exploration enhanced grey wolf algorithm by comparing it to the traditional LEACH algorithm, basic grey wolf approach and particle swarm optimization, that were all tested under the same experimental conditions. Obtained results from conducted simulations have proven that our proposed metaheuristics performs better that other considered algorithms.