KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Constrained Relay Node Deployment using an improved multi-objective Artificial Bee Colony in Wireless Sensor Networks

  • Yu, Wenjie (School of Mechatronics Engineering, University of Electronic Science and Technology of China) ;
  • Li, Xunbo (School of Mechatronics Engineering, University of Electronic Science and Technology of China) ;
  • Li, Xiang (School of Mechatronics Engineering, University of Electronic Science and Technology of China) ;
  • Zeng, Zhi (School of Mechatronics Engineering, University of Electronic Science and Technology of China)
  • Received : 2016.07.22
  • Accepted : 2017.03.10
  • Published : 2017.06.30
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Abstract

Wireless sensor networks (WSNs) have attracted lots of attention in recent years due to their potential for various applications. In this paper, we seek how to efficiently deploy relay nodes into traditional static WSNs with constrained locations, aiming to satisfy specific requirements of the industry, such as average energy consumption and average network reliability. This constrained relay node deployment problem (CRNDP) is known as NP-hard optimization problem in the literature. We consider addressing this multi-objective (MO) optimization problem with an improved Artificial Bee Colony (ABC) algorithm with a linear local search (MOABCLLS), which is an extension of an improved ABC and applies two strategies of MO optimization. In order to verify the effectiveness of the MOABCLLS, two versions of MO ABC, two additional standard genetic algorithms, NSGA-II and SPEA2, and two different MO trajectory algorithms are included for comparison. We employ these metaheuristics on a test data set obtained from the literature. For an in-depth analysis of the behavior of the MOABCLLS compared to traditional methodologies, a statistical procedure is utilized to analyze the results. After studying the results, it is concluded that constrained relay node deployment using the MOABCLLS outperforms the performance of the other algorithms, based on two MO quality metrics: hypervolume and coverage of two sets.

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References

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