Anomaly detection in wireless sensor networks based on time factor

Issue title: Special Section: Fuzzy theoretical model analysis for signal processing

Guest editors: Valentina E. Balas, Jer Lang Hong, Jason Gu and Tsung-Chih Lin

Article type: Research Article

Authors: Li, Yan^{; *}

Affiliations: Ningbo Dahongying University, Ningbo, China

Correspondence: [*] Corresponding author. Yan Li, Ningbo Dahongying University, Ningbo, 315015, China. E-mail: [email protected].

Abstract: With the development and wide application of wireless sensor networks, a data detection method based on time series is proposed to solve the problem that the sampling values of sensors vary greatly in harsh environments and the detection results of events are inaccurate with the increase of fault nodes in wireless sensor networks. The median of the normal data collected by the sensor is used to establish the reference value, and then the confidence interval is constructed. Finally, a method based on calculating the difference degree of the data interval is proposed to determine the source of the anomaly. The experimental results show that the proposed method keeps the detection rate of abnormal data in sensor networks above 98% and the false alarm rate below 0.6%. It has certain reliability and practicability.

Keywords: Anomaly detection, wireless sensor network, data buffer, confidence interval, difference

DOI: 10.3233/JIFS-179298

Journal: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 4, pp. 4639-4645, 2019