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 CO 2 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.
Author: Liu Xin Zhang
Shaoliang
Journal Code: jptkomputergg150160
