sis 18: e5

Research Article

HHO-LPWSN: Harris Hawks Optimization Algorithm for Sensor Nodes Localization Problem in Wireless Sensor Networks

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  • @ARTICLE{10.4108/eai.25-2-2021.168807,
        author={Ravi Sharma and Shiva Prakash},
        title={HHO-LPWSN: Harris Hawks Optimization Algorithm for Sensor Nodes Localization Problem in Wireless Sensor Networks},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2021},
        month={2},
        keywords={Wireless Sensor Networks, Sensor Nodes, Localization Error, Computational Intelligence, Anchor Nodes, Location Optimization},
        doi={10.4108/eai.25-2-2021.168807}
    }
    
  • Ravi Sharma
    Shiva Prakash
    Year: 2021
    HHO-LPWSN: Harris Hawks Optimization Algorithm for Sensor Nodes Localization Problem in Wireless Sensor Networks
    SIS
    EAI
    DOI: 10.4108/eai.25-2-2021.168807
Ravi Sharma1,*, Shiva Prakash2
  • 1: Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology – Gorakhpur, India
  • 2: Department of Information Technology and Computer Application, Madan Mohan Malaviya University of Technology – Gorakhpur, India
*Contact email: ravi.cs.0904@gmail.com

Abstract

Wireless sensor network (WSN) is a prominent technology for remote area monitoring with the assimilation of the Internet of Things (IoT). Over the past decades, sensor node localization has become an essential challenge of WSNs. The sensor indicates localization challenges related to NP-hard problems. Nature-inspired computational intelligence algorithms are used to solve NP-hard problems efficiently for sensor node localization. After the rigorous advanced search in reputable research journals, efficient newly designed Harris Hawks Optimization (HHO) algorithm has not been used to sensor nodes localization until now. Therefore, this paper does and compares the proposed work from the recently available well-known optimization algorithms such as the Salp Swarm Algorithm (SSA), Equilibrium Optimizer (EO), and Grey Wolf Optimizer (GWO). The simulation results of the proposed work showed that it can outperform in terms of average localization error, the number of localized sensor nodes, and computational cost compared to other computational intelligence algorithms.