Load Balancing Control Algorithm of Internet of Things Link Based on Non-Parametric Regression Model

Xinyan Yu ()
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Xinyan Yu: Faculty of Basic Sciences Teaching and Research, Changchun Guanghua University, Changchun, 130033, P. R. China

Journal of Information & Knowledge Management (JIKM), 2023, vol. 22, issue 03, 1-15

Abstract: In order to solve the problems of poor channel balance control ability and unable to effectively reduce the output bit error rate in the traditional Internet of things link load balance control methods, a new Internet of things (IoT) link load balance control algorithm based on non-parametric regression model is proposed in this paper. The transmission model of IoT link channel is constructed, and the sparse random cluster analysis method is used to extract the load characteristics of IoT link. According to the load feature extraction results, through the estimated regression function of known data features, a non-parametric regression model is constructed, and the fuzzy cyclic iterative control is used to realize the load balancing control of the Internet of things link. The experimental results show that this method has strong channel balance control ability, low output bit error rate, the maximum average link utilisation can reach 1, and the maximum output bit error rate is only 102, which improves the stability of the Internet of things.

Keywords: Non-parametric regression model; internet of things; link load; equilibrium control (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0219649223500041

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