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

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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.

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Authors and Affiliations

  1. Department of Computer Engineering and Information Technology, Qazvin Branch, Islamic Azad University, Qazvin, Iran

    Seyed Mahdi Jameii

  2. Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran

    Karim Faez

  3. Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran

    Mehdi Dehghan

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  1. Seyed Mahdi Jameii
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  2. Karim Faez
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Correspondence to Seyed Mahdi Jameii.

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Jameii, S.M., Faez, K. & Dehghan, M. AMOF: adaptive multi-objective optimization framework for coverage and topology control in heterogeneous wireless sensor networks. Telecommun Syst 61, 515–530 (2016). https://doi.org/10.1007/s11235-015-0009-6

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  • DOI: https://doi.org/10.1007/s11235-015-0009-6

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