Energy‐aware cluster‐based routing optimization for WSNs in the livestock industry

Abstract

AbstractThe core objective of the wireless sensor networks (WSNs) in the livestock industry is monitoring the state and activities of different animals for their well‐being. Being energy‐constrained, the consumption of these tiny devices needs to be optimized. Several techniques have been developed for making clusters in WSNs. The sensor nodes attached to the animals are mobile and dynamic in nature. Cluster heads (CHs) are selected to manage communication within a cluster. For efficient working of a network, the lifetime of clusters should be longer and there must always be a minimum number of clusters to make a network operational. This paper presents a metaheuristic artificial intelligence technique based on the social behavior of gray wolves to reduce the energy consumption of WSNs in the livestock industry. This nature‐inspired clustering algorithm provides the robust and smooth communication for WSNs in the livestock industry. The grid size, energy level, direction, and transmission range are the key parameters used to measure the performance of algorithm. Results are compared with other well‐known similar nature‐inspired optimization algorithms such as comprehensive learning particle swarm optimization (CLPSO) and ant colony optimization–based clustering (CACONET). The simulation results exhibit the superiority of grey wolf optimizer in energy efficiency, cost effectiveness, and CH selection than CACONET and CLPSO.