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Cognitively Inspired Artificial Bee Colony Clustering for Cognitive Wireless Sensor Networks

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Abstract

The swarm cognitive behavior of bees readily translates to swarm intelligence with “social cognition,” thus giving rise to the rapid promotion of survival skills and resource allocation. This paper presents a novel cognitively inspired artificial bee colony clustering (ABCC) algorithm with a clustering evaluation model to manage the energy consumption in cognitive wireless sensor networks (CWSNs). The ABCC algorithm can optimally align with the dynamics of the sensor nodes and cluster heads in CWSNs. These sensor nodes and cluster heads adapt to topological changes in the network graph over time. One of the major challenges with employing CWSNs is to maximize the lifetime of the networks. The ABCC algorithm is able to reduce and balance the energy consumption of nodes across the networks. Artificial bee colony (ABC) optimization is attractive for this application as the cognitive behaviors of artificial bees match perfectly with the intrinsic dynamics in cognitive wireless sensor networks. Additionally, it employs fewer control parameters compared to other heuristic algorithms, making identification of optimal parameter settings easier. Simulation results illustrate that the ABCC algorithm outperforms particle swarm optimisation (PSO), group search optimization (GSO), low-energy adaptive clustering hierarchy (LEACH), LEACH-centralized (LEACH-C), and hybrid energy-efficient distributed clustering (HEED) for energy management in CWSNs. Our proposed algorithm is increasingly superior to these other approaches as the number of nodes in the network grows.

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Acknowledgements

The authors sincerely thank the editors and anonymous reviewers for their helpful suggestions on how to improve the presentation of our paper. This study is supported by the 2016 Research Grant from the Kangwon National University (Grant No. 520160235) and the Program for New Century Excellent Talents in University (Grant No. NCET-11-0861).

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Authors and Affiliations

  1. Department of System and Management Engineering, Kangwon National University, Chunchon, 200701, Korea

    Sung-Soo Kim

  2. School of Electronics, Electrical Engineering and Computer Science at Queen’s University Belfast, Belfast, Northern Ireland, BT9 5AH, UK

    Sean McLoone

  3. Kaiem Co., LTD., Seoul, 152780, Korea

    Ji-Hwan Byeon

  4. School of Industrial Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Seokcheon Lee

  5. Swartz Center for Computational Neuroscience, University of California San Diego, La Jolla, CA, 92093, USA

    Hongbo Liu

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  1. Sung-Soo Kim
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  2. Sean McLoone
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  3. Ji-Hwan Byeon
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  4. Seokcheon Lee
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  5. Hongbo Liu
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Correspondence to Hongbo Liu.

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All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Declaration of Helsinki 1975, as revised in 2008 (5). Additional informed consent was obtained from all patients for which identifying information is included in this article.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Informed Consent

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Declaration of Helsinki 1975, as revised in 2008 (5). Additional informed consent was obtained from all patients for which identifying information is included in this article.

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Kim, SS., McLoone, S., Byeon, JH. et al. Cognitively Inspired Artificial Bee Colony Clustering for Cognitive Wireless Sensor Networks. Cogn Comput 9, 207–224 (2017). https://doi.org/10.1007/s12559-016-9447-z

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  • DOI: https://doi.org/10.1007/s12559-016-9447-z

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