Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cyberspace

Research Article

Heterogeneous wireless sensor network routing protocol for an adaptive gray wolf optimizer

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  • @INPROCEEDINGS{10.4108/eai.27-8-2020.2295028,
        author={Chao  Cheng and QingShan  Han and Guoli  Cheng and Shuang  Zhai},
        title={Heterogeneous wireless sensor network routing protocol for an adaptive gray wolf optimizer},
        proceedings={Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cyberspace},
        publisher={EAI},
        proceedings_a={MOBIMEDIA},
        year={2020},
        month={11},
        keywords={energy efficient; gray wolf optimizer (gwo); balanced cluster structure; wireless sensor network},
        doi={10.4108/eai.27-8-2020.2295028}
    }
    
  • Chao Cheng
    QingShan Han
    Guoli Cheng
    Shuang Zhai
    Year: 2020
    Heterogeneous wireless sensor network routing protocol for an adaptive gray wolf optimizer
    MOBIMEDIA
    EAI
    DOI: 10.4108/eai.27-8-2020.2295028
Chao Cheng1,*, QingShan Han1, Guoli Cheng2, Shuang Zhai1
  • 1: Changchun University of Technology
  • 2: National Engineering Laboratory, CRRC Changchun Railway Vehicles Co., Ltd.
*Contact email: chengchao@ccut.edu.cn

Abstract

Wireless sensor network (WSN), plays an increasingly important role in information collection. In this paper, firstly, in order to adapt to the actual conditions, the communication process of the nodes energy is limited, and a three-stage energy heterogeneous network model is designed. Secondly, for the convergence node frequent task forwarding and complex cluster first-round energy consumption, by combining the optimal number of cluster heads with the gray wolf optimization algorithm, a new fitness function is designed that integrates the remaining energy of the nodes and the distance from the nodes to the base station. In addition, an improved iterative factor is introduced to enhance the ability of local search in cluster head selection, so as to improve the accuracy of cluster head search. Finally, the simulation results show that the proposed method extends the lifetime of the network 50%, reduces the process of energy consumption, and improves the throughput of network data 30%.