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AMOF: adaptive multi-objective optimization framework for coverage and topology control in heterogeneous wireless sensor networks

Author

Listed:
  • Seyed Mahdi Jameii

    (Islamic Azad University)

  • Karim Faez

    (Amirkabir University of Technology)

  • Mehdi Dehghan

    (Amirkabir University of Technology)

Abstract

In this paper, we propose adaptive multi-objective optimization framework based on non-dominated sorting genetic algorithm-II and learning automata (LA) for coverage and topology control in heterogeneous wireless sensor networks. The multi-objective optimization approach of the proposed framework, called MOOCTC (multi-objective optimization coverage and topology control), can simultaneously optimize several conflicting issues such as number of active sensor nodes, coverage rate of the monitoring area and balanced energy consumption while maintaining the network connectivity. This approach incorporates problem-specific knowledge in its operators to find high-quality solutions. In addition, this approach uses LA to dynamically adapt the crossover and mutation rates without any external control to improve the behavior of the optimization algorithm. Simulation results demonstrate the efficiency of the proposed multi-objective optimization approach in terms of lifetime, coverage and connectivity.

Suggested Citation

  • Seyed Mahdi Jameii & Karim Faez & Mehdi Dehghan, 2016. "AMOF: adaptive multi-objective optimization framework for coverage and topology control in heterogeneous wireless sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 61(3), pages 515-530, March.
    • Handle: RePEc:spr:telsys:v:61:y:2016:i:3:d:10.1007_s11235-015-0009-6
    DOI: 10.1007/s11235-015-0009-6
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    Cited by:

    1. Shuxin Wang & Hairong You & Yinggao Yue & Li Cao, 2021. "A novel topology optimization of coverage-oriented strategy for wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 17(4), pages 15501477219, April.

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