ew 18: e38

Research Article

A Hybrid Elephant Optimization Algorithm-based Cluster Head Selection to Extend Network Lifetime in Wireless Sensor Networks (WSNs)

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  • @ARTICLE{10.4108/eai.1-7-2020.165677,
        author={S. Prahadeeshwaran and G. Maria Priscilla},
        title={A Hybrid Elephant Optimization Algorithm-based Cluster Head Selection to Extend Network Lifetime in Wireless Sensor Networks (WSNs)},
        journal={EAI Endorsed Transactions on Energy Web: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={EW},
        year={2020},
        month={7},
        keywords={Elephant Herding Optimization, Novel Individual Updating Strategies, Cluster Head Selection, Wireless Sensor Networks (WSNs), Network Lifetime},
        doi={10.4108/eai.1-7-2020.165677}
    }
    
  • S. Prahadeeshwaran
    G. Maria Priscilla
    Year: 2020
    A Hybrid Elephant Optimization Algorithm-based Cluster Head Selection to Extend Network Lifetime in Wireless Sensor Networks (WSNs)
    EW
    EAI
    DOI: 10.4108/eai.1-7-2020.165677
S. Prahadeeshwaran1,*, G. Maria Priscilla2
  • 1: Assistant Professor, Department of IT, Sri Ramakrishna College of Arts and Science, Coimbatore, Tamilnadu, India
  • 2: Professor, Department of Computer Science, Sri Ramakrishna College of Arts and Science, Coimbatore, Tamilnadu, India
*Contact email: prof.praha@gmail.com

Abstract

Wireless Sensor Networks (WSNs) comprise of a number of sensor nodes that are capable of sensing and aggregating the data from the monitoring environment. However, the process of recharging the limited energy sensor node batteries are highly difficult during adverse situations. This limitation of sensor nodes greatly crumbles the network lifetime to a maximum degree and degrades the level of reliable data dissemination. In this paper, a Novel Individual Updating Strategies-based Hybrid Elephant Herding Optimization Algorithm (NIUS-HEHOA) is planned for facilitating energy balanced cluster head selection for the objective of extending the network lifetime. It included energy-aware optimization process during the clustering schemes, since it is considered as a solution to the significant NP complete optimization problem. It is propounded as a swarm intelligent algorithms are identified to be the most applicable candidate for energy optimization that leads to significant improvement in network lifetime. It is contributed to maintain the deviation between exploitation and exploration such that least potential sensor nodes are prevented from being chosen as cluster heads. The simulation experiments confirmed that the proposed NIUS-HEHOA scheme is better than the benchmarked schemes in terms of alive nodes, dead nodes, residual energy, network lifetime and throughput.