Wireless Sensor Network Coverage Optimization based on Whale Group Algorithm

Lei Wang1, Weihua Wu1, Junyan Qi1 and Zongpu Jia1

  1. School of Computer Science and Technology, Henan Polytechnic University
    454003 Jiaozuo, Henan, China
    {wang_leiqjy, wwh02070726}@163.com, {qjywl, jiazp}@hpu.edu.cn

Abstract

For all of types of applications in wireless sensor networks (WSNs), coverage is a fundamental and hot topic research issue. To monitor the interest field and obtain the valid data, the paper proposes a wireless sensor network coverage optimization model based on improved whale algorithm. The mathematic model of node coverage in wireless sensor networks is developed to achieve full coverage for the interest area. For the model, the idea of reverse learning is introduced into the original whale swarm optimization algorithm to optimize the initial distribution of the population. This method enhances the node search capability and speeds up the global search. The experiment shows that this algorithm can effectively improve the coverage of nodes in wireless sensor networks and optimize the network performance.

Key words

wireless sensor network, coverage optimization, whale swarm algorithm, reverse learning

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS180103023W

Publication information

Volume 15, Issue 3 (October 2018)
Year of Publication: 2018
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Wang, L., Wu, W., Qi, J., Jia, Z.: Wireless Sensor Network Coverage Optimization based on Whale Group Algorithm. Computer Science and Information Systems, Vol. 15, No. 3, 569–583. (2018), https://doi.org/10.2298/CSIS180103023W