EECHS-ARO: Energy-efficient cluster head selection mechanism for livestock industry using artificial rabbits optimization and wireless sensor networks

Abstract

In the livestock industry, wireless sensor networks (WSNs) play a significant role in monitoring many fauna health statuses and behaviors. Energy preservation in WSNs is considered one of the critical, complicated tasks since the sensors are coupled to constrained resources. Therefore, the clustering approach has proved its efficacy in preserving energy in WSNs. In recent studies, various clustering approaches have been introduced that use optimization techniques to improve the network lifespan by decreasing energy depletion. Yet, they take longer to converge and choose the optimal cluster heads in the network. In addition, the energy is exhausted quickly in the network. This paper introduces a novel optimization technique, i.e., an artificial rabbits optimization algorithm-based energy efficient cluster formation (EECHS-ARO) approach in a WSN, to extend the network lifetime by minimizing the energy consumption rate. The EECHS-ARO technique balances the search process in terms of enriched exploration and exploitation while selecting the optimal cluster heads. The experimentation was carried out on a MATLAB 2021a platform with varying sensor nodes. The obtained results of EECHS-ARO are contrasted with other existing approaches via teaching–learning based optimization algorithm (TLBO), ant lion optimizer (ALO) and quasi oppositional butterfly optimization algorithm (QOBOA). The proposed EECHS-ARO enriches the network lifespan by ~15% and improves the packet delivery ratio by ~5%.

Keywords

• artificial rabbits optimization
• clustering approach
• wireless sensor networks
• environment monitoring
• livestock industry
• network lifetime