Continuous Monitoring and Distributed Anomaly Detection for Ambient Factors

Considering the diverse application scenarios involving wireless sensor networks (WSNs), accurate continuous monitoring requires a solution to the essential task of estimating unmeasured locations in the monitored space. In this paper, we utilize Epsilon-Smooth Support Vector Regression (Epsilon-SSVR) to report monitoring information of environment, furthermore we combine spatial and temporal correlation to strengthen monitoring accuracy. However if our sensors are too sparsely deployed, the resulting coverage holes problem will adversely impact the monitoring result. Therefore, we utilize Uniform Design and different local interpolation methods to assist Epsilon-SSVR to mitigate the coverage holes problem. In our experiment, we compare our method with different methods applied to different sensors deployments. Epsilon-SSVR has better accuracy and computation speed than others. Besides continuous monitoring, we also propose a distributed anomaly detection mechanism to report anomaly information, in order to provide a reliable and real time anomaly monitoring system.