Title: Study on active sleeping node detection method in sensor network based on multi-dimensional sliding window

Authors: Jing Qiu; Feng Gao

Addresses: School of Electronics and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China ' School of Artificial Intelligence, Chongqing University of Arts and Sciences, Chongqing, 402160, China

Abstract: To overcome the problems of low coverage and detection accuracy in traditional detection methods, a multidimensional sliding window based active sleep node detection method for sensor networks is proposed. Firstly, we set up an active sleep node simulator and controller in the sensor network space to determine the active sleep range. Secondly, we design a multidimensional sliding window algorithm to determine anomalies in the transmission link by calculating the standard deviation of sensing information in the sliding window. Finally, the total length of data transmission is dimensionally transformed to achieve reliable detection of active sleep nodes. The experimental results show that the coverage rate of the detection results of this method is closer to 1, and its detection accuracy remains between 94.84% -97.32%, and the detection process time remains between 1.72 s-232 s. It has the advantages of strong reliability and high efficiency in applications.

Keywords: sensor networks; active sleep node; node detection; multi dimensional sliding window algorithm; sliding window; active sleep range; standard deviation.

DOI: 10.1504/IJNVO.2023.133867

International Journal of Networking and Virtual Organisations, 2023 Vol.28 No.2/3/4, pp.337 - 347

Received: 07 Dec 2022
Accepted: 31 May 2023
Published online: 04 Oct 2023 *

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