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

Seyed Mahdi Jameii (), Karim Faez () and Mehdi Dehghan ()
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Seyed Mahdi Jameii: Islamic Azad University
Karim Faez: Amirkabir University of Technology
Mehdi Dehghan: Amirkabir University of Technology

Telecommunication Systems: Modelling, Analysis, Design and Management, 2016, vol. 61, issue 3, No 8, 515-530

Abstract: 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.

Keywords: Wireless sensor networks; Multi-objective optimization; NSGA-II; Coverage; Connectivity; Learning automata; Network lifetime (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11235-015-0009-6

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