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

Rajakumar Ramalingam; Saleena B; Shakila Basheer; Prakash Balasubramanian; Mamoon Rashid; Gitanjali Jayaraman; Rajakumar Ramalingam; Saleena B; Shakila Basheer; Prakash Balasubramanian; Mamoon Rashid; Gitanjali Jayaraman

doi:10.3934/era.2023158

Electronic Research Archive

2023, Volume 31, Issue 6: 3123-3144. doi: 10.3934/era.2023158

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

1.
Department of CST, Madanapalle Institute of Technology & Science, Madanapalle, India
2.
School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India
3.
Department of Information Systems, College of Computer and Information Science, Princess Nourah bint Abdulrahman University, P.O. BOX 84428, Riyadh 11671, Saudi Arabia
4.
Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune 411048, India
5.
Department of Information Technology, Vellore Institute of Technology, Vellore, India

Received: 19 January 2023 Revised: 12 March 2023 Accepted: 14 March 2023 Published: 23 March 2023

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%.
- artificial rabbits optimization,
- clustering approach,
- wireless sensor networks,
- environment monitoring,
- livestock industry,
- network lifetime
Citation: Rajakumar Ramalingam, Saleena B, Shakila Basheer, Prakash Balasubramanian, Mamoon Rashid, Gitanjali Jayaraman. EECHS-ARO: Energy-efficient cluster head selection mechanism for livestock industry using artificial rabbits optimization and wireless sensor networks[J]. Electronic Research Archive, 2023, 31(6): 3123-3144. doi: 10.3934/era.2023158

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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%.

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