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Genetic Programming for Lifetime Maximization in Wireless Sensor Networks with a Mobile Sink

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Book cover Simulated Evolution and Learning (SEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10593))

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Abstract

Maximizing the lifetime of Wireless Sensor Network (WSN) with a mobile sink is a challenging and important problem that has attracted increasing research attentions. In the literature, heuristic based approaches have been proposed to solve the problem, such as the Greedy Maximum Residual Energy (GMRE) based method. However, existing heuristic based approaches highly rely on expert knowledge, which makes them inconvenient for practical applications. Taking this cue, in this paper, we propose an automatic method to construct heuristic for sink routing based on Genetic Programming (GP) approach. Empirical study shows that the proposed method can generate promising heuristics that achieve superior performance against existing methods with respect to the global lifetime of WSN.

The first author and the second author contributed equally to this work.

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Acknowledgment

This work is partially supported under the National Natural Science Foundation of China (Grant Nos. 61602181, 61603064), Fundamental Research Funds for the Central Universities (Grant No. 2017ZD053), Frontier Interdisciplinary Research Fund for the Central Universities (Grant No. 106112017CDJQJ188828).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

    Ying Li, Zhixing Huang & Jinghui Zhong

  2. College of Computer Science, Chongqing University, Chongqing, China

    Liang Feng

Authors
  1. Ying Li
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  2. Zhixing Huang
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  3. Jinghui Zhong
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  4. Liang Feng
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Corresponding author

Correspondence to Jinghui Zhong .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  2. City University of Hong Kong, Hong Kong, Kowloon, Hong Kong

    Kay Chen Tan

  3. Victoria University of Wellington, Wellington, Wellington, New Zealand

    Mengjie Zhang

  4. Southern University of Science and Technology, Shenzhen, China

    Ke Tang

  5. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

  6. City University of Hong Kong, Kowloon Tong, Hong Kong

    Qingfu Zhang

  7. Peking University, Beijing, China

    Ying Tan

  8. University of Leipzig, Leipzig, Germany

    Martin Middendorf

  9. University of Surrey, Guildford, Surrey, United Kingdom

    Yaochu Jin

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Li, Y., Huang, Z., Zhong, J., Feng, L. (2017). Genetic Programming for Lifetime Maximization in Wireless Sensor Networks with a Mobile Sink. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_63

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  • DOI: https://doi.org/10.1007/978-3-319-68759-9_63

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68758-2

  • Online ISBN: 978-3-319-68759-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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