An Automated Application-Independent Approach to Anomaly Detection in Wireless Sensor Networks

As Wireless Sensor Networks (WSN) gain momentum in what concerns applications and deployment, monitoring is becoming crucial in order to guarantee that anomalies are promptly detected. Unfortunately, current WSN monitoring solutions have several limitations, such as being tailored for specific applications, requiring dedicated or specific hardware, consuming precious energy and/or processing resources, or relying on manual or offline intervention. In this paper we propose an approach to anomaly detection in WSNs that addresses these limitations. The approach is based on two very simple metrics, a logging tool, and a data-mining algorithm, thus leading to the following key characteristics: very low resource consumption, application independency, very good potential for multi-WSN monitoring, and automation and simplification of the detection process. The proposed approach was validated by implementation, which showed that it is quite effective in detecting several typical anomalies.