sis 18: e12

Research Article

Eurasian Wolves-Cuckoo Search Optimizer for Localization of Mobile Sensor Nodes using Single Beacon Node in WSN

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  • @ARTICLE{10.4108/eai.13-7-2018.164553,
        author={Ravi Sharma and Shiva Prakash},
        title={Eurasian Wolves-Cuckoo Search Optimizer for Localization of Mobile Sensor Nodes using Single Beacon Node in WSN},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2020},
        month={5},
        keywords={Wireless Sensor Network, Mobile Sensor Nodes, Beacon Node, Computational Intelligence, Localization Error, Computational Cost},
        doi={10.4108/eai.13-7-2018.164553}
    }
    
  • Ravi Sharma
    Shiva Prakash
    Year: 2020
    Eurasian Wolves-Cuckoo Search Optimizer for Localization of Mobile Sensor Nodes using Single Beacon Node in WSN
    SIS
    EAI
    DOI: 10.4108/eai.13-7-2018.164553
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 Networks (WSNs) is a widely used technology for remote area monitoring in collaboration with the Internet of Things (IoT). The fundamental research challenge of mobile sensor nodes for the WSN community is localization. The sensor node localization of the WSN is related to the NP-hard problem, and because of this, determining the actual coordinate of the sensor node is quite complex. The computational intelligence approach is assisted in obtaining an optimal solution to the given NP-hard problem. Most researchers today are more concerned about three beacon-based localization approaches, but the fewest researchers are concerned about two or single beacon-based localization approaches. This paper provides a single beacon-based localization approach using the hybrid approach of the Eurasian Wolves Optimizer (EWO) and the Cuckoo Search Optimizer (CSO) algorithm called the EW-CSO computational intelligence algorithm for randomly deployed mobile sensor nodes. The simulation results of the computational intelligence algorithms show that the proposed work using EW-CSO performs better in terms of mean localization error, computational cost, and number of localized nodes from the EWO and EW- Particle Swarm Optimization (EW-PSO) algorithms. It also reduced the line of sight problem for mobile sensor nodes with efficient use of network resources.