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Solving the Energy Efficient Coverage Problem in Wireless Sensor Networks: A Distributed Genetic Algorithm Approach with Hierarchical Fitness Evaluation

Author

Listed:
  • Zi-Jia Wang

    (School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China)

  • Zhi-Hui Zhan

    (Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information, School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China)

  • Jun Zhang

    (Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information, School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China)

Abstract

This paper proposed a distributed genetic algorithm (DGA) to solve the energy efficient coverage (EEC) problem in the wireless sensor networks (WSN). Due to the fact that the EEC problem is Non-deterministic Polynomial-Complete (NPC) and time-consuming, it is wise to use a nature-inspired meta-heuristic DGA approach to tackle this problem. The novelties and advantages in designing our approach and in modeling the EEC problems are as the following two aspects. Firstly, in the algorithm design, we realized DGA in the multi-processor distributed environment, where a set of processors run distributed to evaluate the fitness values in parallel to reduce the computational cost. Secondly, when we evaluate a chromosome, different from the traditional model of EEC problem in WSN that only calculates the number of disjoint sets, we proposed a hierarchical fitness evaluation and constructed a two-level fitness function to count the number of disjoint sets and the coverage performance of all the disjoint sets. Therefore, not only do we have the innovations in algorithm, but also have the contributions on the model of EEC problem in WSN. The experimental results show that our proposed DGA performs better than other state-of-the-art approaches in maximizing the number of disjoin sets.

Suggested Citation

  • Zi-Jia Wang & Zhi-Hui Zhan & Jun Zhang, 2018. "Solving the Energy Efficient Coverage Problem in Wireless Sensor Networks: A Distributed Genetic Algorithm Approach with Hierarchical Fitness Evaluation," Energies, MDPI, vol. 11(12), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3526-:d:191428
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    References listed on IDEAS

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    5. Nikos Kampelis & Elisavet Tsekeri & Dionysia Kolokotsa & Kostas Kalaitzakis & Daniela Isidori & Cristina Cristalli, 2018. "Development of Demand Response Energy Management Optimization at Building and District Levels Using Genetic Algorithm and Artificial Neural Network Modelling Power Predictions," Energies, MDPI, vol. 11(11), pages 1-22, November.
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    Cited by:

    1. Miltiadis D. Lytras & Kwok Tai Chui, 2019. "The Recent Development of Artificial Intelligence for Smart and Sustainable Energy Systems and Applications," Energies, MDPI, vol. 12(16), pages 1-7, August.
    2. Jun-Ho Huh & Jimin Hwa & Yeong-Seok Seo, 2020. "Hierarchical System Decomposition Using Genetic Algorithm for Future Sustainable Computing," Sustainability, MDPI, vol. 12(6), pages 1-32, March.

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