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An energy-aware routing protocol for wireless sensor network based on genetic algorithm

Author

Listed:
  • Lingping Kong

    (Harbin Institute of Technology)

  • Jeng-Shyang Pan

    (Harbin Institute of Technology
    Fujian University of Technology)

  • Václav Snášel

    (VSB-Technical University of Ostrava)

  • Pei-Wei Tsai

    (Swinburne University of Technology)

  • Tien-Wen Sung

    (Fujian University of Technology)

Abstract

Energy saving and effective utilization are an essential issue for wireless sensor network. Most previous cluster based routing protocols only care the relationship of cluster heads and sensor nodes but ignore the huge difference costs between them. In this paper, we present a routing protocol based on genetic algorithm for a middle layer oriented network in which the network consists of several stations that are responsible for receiving data and forwarding the data to the sink. The amount of stations should be not too many and not too few. Both cases will cause either too much construction cost or extra transmission energy consumption. We implement five methods to compare the performance and test the stability of our presented methods. Experimental results demonstrate that our proposed scheme reduces the amount of stations by 36.8 and 20% compared with FF and HL in 100-node network. Furthermore, three methods are introduced to improve our proposed scheme for effective cope with the expansion of network scale problem.

Suggested Citation

  • Lingping Kong & Jeng-Shyang Pan & Václav Snášel & Pei-Wei Tsai & Tien-Wen Sung, 2018. "An energy-aware routing protocol for wireless sensor network based on genetic algorithm," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 67(3), pages 451-463, March.
    • Handle: RePEc:spr:telsys:v:67:y:2018:i:3:d:10.1007_s11235-017-0348-6
    DOI: 10.1007/s11235-017-0348-6
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    References listed on IDEAS

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    1. Dongyue Liang & Liquan Mei & James Willson & Wei Wang, 2016. "A simple greedy approximation algorithm for the minimum connected $$k$$ k -Center problem," Journal of Combinatorial Optimization, Springer, vol. 31(4), pages 1417-1429, May.
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    3. Sourour Elloumi & Martine Labbé & Yves Pochet, 2004. "A New Formulation and Resolution Method for the p-Center Problem," INFORMS Journal on Computing, INFORMS, vol. 16(1), pages 84-94, February.
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    Cited by:

    1. Alma Rodríguez & Marco Pérez-Cisneros & Julio C. Rosas-Caro & Carolina Del-Valle-Soto & Jorge Gálvez & Erik Cuevas, 2021. "Robust Clustering Routing Method for Wireless Sensor Networks Considering the Locust Search Scheme," Energies, MDPI, vol. 14(11), pages 1-19, May.
    2. Alma Rodríguez & Carolina Del-Valle-Soto & Ramiro Velázquez, 2020. "Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks Based on Yellow Saddle Goatfish Algorithm," Mathematics, MDPI, vol. 8(9), pages 1-17, September.
    3. S. Jeevanantham & B. Rebekka, 2022. "Energy-aware neuro-fuzzy routing model for WSN based-IoT," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 81(3), pages 441-459, November.
    4. Chang Zhou & Zhenghong Gu & Yu Gao & Jin Wang, 2019. "An Improved Style Transfer Algorithm Using Feedforward Neural Network for Real-Time Image Conversion," Sustainability, MDPI, vol. 11(20), pages 1-15, October.
    5. Min Zhao & Danyang Qin & Ruolin Guo & Guangchao Xu, 2019. "Multi-targets device-free localization based on sparse coding in smart city," International Journal of Distributed Sensor Networks, , vol. 15(6), pages 15501477198, June.

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