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Variable-categorized clustering algorithm using fuzzy logic for Internet of things local networks

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Abstract

This paper presents a variable-categorized clustering algorithm (VCCA) using fuzzy logic for Internet of Things (IoT) local networks. The VCCA selects the cluster head (CH) that has the highest network capacity through a classification process of cluster variables in accordance with the characteristics in order to configure a clustered network, which differs for different IoT applications. To achieve this, the VCCA employs a fuzzy inference system (FIS) that calculates an outcome through rule-based variable mapping for low complexity in the CH election and high scalability of cluster variables. In addition, experimental simulations using MATLAB are conducted to evaluate the performance of the VCCA. The simulation results show that the VCCA exhibits better network performance compared to the existing algorithms in terms of throughput, end-to-end latency, network lifetime, and energy consumption.

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Acknowledgments

This research was supported in part by the Leading Human Resource Training Program of Regional Neo Industry through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2016H1D5A1910427), by Basic Science Research Program through the NRF funded by the Ministry of Education (NRF-2017R1D1A1B03031055), by NRF Grant funded by the Korean Government (NRF-2016-Fostering Core Leaders of the Future Basic Science Program/Global Ph.D. Fellowship Program) (2016H1A2A1908620), and by Hallym University Research Fund, 2017 (HRF-201702-009).

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  1. Department of Convergence Software, Hallym University, 1 Hallymdaehak-gil, Chuncheon-si, Gangwon-do, 24252, South Korea

    Jung-Hyok Kwon, Minki Cha, Sol-Bee Lee & Eui-Jik Kim

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  1. Jung-Hyok Kwon
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  2. Minki Cha
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Correspondence to Eui-Jik Kim.

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Kwon, JH., Cha, M., Lee, SB. et al. Variable-categorized clustering algorithm using fuzzy logic for Internet of things local networks. Multimed Tools Appl 78, 2963–2982 (2019). https://doi.org/10.1007/s11042-017-5176-x

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  • DOI: https://doi.org/10.1007/s11042-017-5176-x

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