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Energy efficient protocol in wireless sensor network: optimized cluster head selection model

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  • Turki Ali Alghamdi

    (Umm Al-Qura University)

Abstract

Energy efficiency has become a primary issue in wireless sensor networks (WSN). The sensor networks are powered by battery and thus they turn out to be dead after a particular interval. Hence, enhancing the data dissipation in energy efficient manner remains to be more challenging for increasing the life span of sensor devices. It has been already proved that the clustering method could improve or enhance the life span of WSNs. In the clustering model, the selection of cluster head (CH) in each cluster regards as the capable method for energy efficient routing, which minimizes the transmission delay in WSN. However, the main problem dealt with the selection of optimal CH that makes the network service prompt. Till now, more research works have been processing on solving this issue by considering different constraints. Under this scenario, this paper attempts to develop a new clustering model with optimal cluster head selection by considering four major criteria like energy, delay, distance, and security. Further, for selecting the optimal CHs, this paper proposes a new hybrid algorithm that hybridizes the concept of dragon fly and firefly algorithm algorithms, termed fire fly replaced position update in dragonfly. Finally, the performance of the proposed work is carried out by comparing with other conventional models in terms of number of alive nodes, network energy, delay and risk probability.

Suggested Citation

  • Turki Ali Alghamdi, 2020. "Energy efficient protocol in wireless sensor network: optimized cluster head selection model," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 74(3), pages 331-345, July.
  • Handle: RePEc:spr:telsys:v:74:y:2020:i:3:d:10.1007_s11235-020-00659-9
    DOI: 10.1007/s11235-020-00659-9
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    References listed on IDEAS

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    1. Hintsch, Timo & Irnich, Stefan, 2018. "Large multiple neighborhood search for the clustered vehicle-routing problem," European Journal of Operational Research, Elsevier, vol. 270(1), pages 118-131.
    2. Zhao, Bin & Ren, Yi & Gao, Diankui & Xu, Lizhi & Zhang, Yuanyuan, 2019. "Energy utilization efficiency evaluation model of refining unit Based on Contourlet neural network optimized by improved grey optimization algorithm," Energy, Elsevier, vol. 185(C), pages 1032-1044.
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    Cited by:

    1. Jinhai Song & Zhiyong Zhang & Kejing Zhao & Qinhai Xue & Brij B. Gupta, 2023. "A Novel CNN-LSTM Fusion-Based Intrusion Detection Method for Industrial Internet," International Journal of Information Security and Privacy (IJISP), IGI Global, vol. 17(1), pages 1-18, January.
    2. Shaha Al-Otaibi & Venkatesan Cherappa & Thamaraimanalan Thangarajan & Ramalingam Shanmugam & Prithiviraj Ananth & Sivaramakrishnan Arulswamy, 2023. "Hybrid K-Medoids with Energy-Efficient Sunflower Optimization Algorithm for Wireless Sensor Networks," Sustainability, MDPI, vol. 15(7), pages 1-16, March.
    3. Han-Dong Jia & Shu-Chuan Chu & Pei Hu & LingPing Kong & XiaoPeng Wang & Václav Snášel & Tong-Bang Jiang & Jeng-Shyang Pan, 2022. "Hybrid algorithm optimization for coverage problem in wireless sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 80(1), pages 105-121, May.

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