Spatial-temporal anomaly detection algorithm for wireless sensor networks

Abstract

Traditional anomaly detection algorithms cannot effectively identify spatial-temporal anomalies in wireless sensor networks (WSNs), so we take the CO2 concentration obtained by WSNs as an example and propose a spatial-temporal anomaly detection algorithm for WSNs. First, we detected outliers through the adaptive threshold. Then, we extracted the eigenvalue (average) of the sliding window to be detected, constructed the spatial-temporal matrix for the relationship between neighboring nodes in the specified interval, used the fuzzy clustering method to analyze the eigenvalue of adjacent nodes in spatial-temporal correlation and classify them, and identified the abnormal leakage probability according to the results of the classification. Finally, we used real datasets to verify this algorithm and analyze the parameters selected. The results show that the algorithm has a high detection rate and a low false positive rate.