KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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A Learning Automata-based Algorithm for Area Coverage Problem in Directional Sensor Networks

  • Liu, Zhimin (School of Information Science and Engineering, Central South University) ;
  • Ouyang, Zhangdong (School of Mathematics and Computational Science, Hunan First Normal University)
  • Received : 2017.03.15
  • Accepted : 2017.06.27
  • Published : 2017.10.31
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Abstract

Coverage problem is a research hot spot in directional sensor networks (DSNs). However, the major problem affecting the performance of the current coverage-enhancing strategies is that they just optimize the coverage of networks, but ignore the maximum number of sleep sensors to save more energy. Aiming to find an approximate optimal method that can cover maximum area with minimum number of active sensors, in this paper, a new scheduling algorithm based on learning automata is proposed to enhance area coverage, and shut off redundant sensors as many as possible. To evaluate the performance of the proposed algorithm, several experiments are conducted. Simulation results indicate that the proposed algorithm have effective performance in terms of coverage enhancement and sleeping sensors compared to the existing algorithms.

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References

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