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In wireless sensor networks (WSNs), it has been observed that most abnormal events persist over a considerable period of time instead of being transient. As existing anomaly detection techniques usually operate in a point-based manner that handles each observation individually, they are unable to reliably and efficiently report such long-term anomalies appeared in an individual sensor node. Therefore, in this paper, we focus on a new technique for handling data in a segment-based manner. Considering a collection of neighbouring data segments as random variables, we determine those behaving abnormally by exploiting their spatial predictabilities and, motivated by spatial analysis, specifically investigate how to implement a prediction variance detector in a WSN. As the communication cost incurred in aggregating a covariance matrix is finally optimised using the Spearman&#x2019;s rank correlation coefficient and differential compression, the proposed scheme is able to efficiently detect a wide range of long-term anomalies. In theory, comparing to the regular centralised approach, it can reduce the communication cost by approximately 80 percent. Moreover, its effectiveness is demonstrated by the numerical experiments, with a real world data set collected by the Intel Berkeley ResearchLab (IBRL).

Segment-Based Anomaly Detection with Approximated Sample Covariance Matrix in Wireless Sensor Networks