Title: Research on abnormal node detection in a wireless sensor network based on random matrix theory

Authors: Jibao Hu

Addresses: Department of Mathematics and Physics, Anqing Normal University, Anqing, 246133, China

Abstract: Because the traditional detection methods have the problems of low recall and precision and long detection time, this paper studies a method of abnormal node detection in a wireless sensor network (WSN) based on random matrix theory. This method uses particle swarm optimisation to improve DV-Hop, and uses the improved DV-Hop method to locate WSN nodes. According to the spatiotemporal characteristics of WSN data, a data matrix is built, and the dimensionality of the data matrix is reduced by using a random matrix. The node attributes are judged according to the element correlation between multiple matrices to realise abnormal node detection in a WSN. The test results show that the average recall rate and recall rate of this method are 97.0% and 97.2% respectively, and the detection time is always less than 0.5s, so the practical application effect is good.

Keywords: random matrix theory; WSN; wireless sensor network; abnormal nodes detection; DV-hop; particle swarm optimisation.

DOI: 10.1504/IJSNET.2021.119488

International Journal of Sensor Networks, 2021 Vol.37 No.4, pp.265 - 270

Received: 29 Apr 2021
Accepted: 29 Apr 2021
Published online: 07 Dec 2021 *

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