Online Distributed Distance-based Outlier Clearance Approaches for Wireless Sensor Networks

One key challenge for sensor networks is to provide real-time high reliable sensor data with the minimum resource consumption. Outlier clearance in sensor networks can ensure the quality of sensor data and dependable monitoring. In this paper, we propose two online distributed outlier clearance approaches with low computational complexity and memory usage that can identify and remove the spurious sensor data. The proposed approaches are operated locally and thus save communication overhead as well as possess good scalability. The evaluation performance of proposed approaches and existing widely used methods on synthetic and real-life dataset illustrates that our Adaptive Top-n WAD approach achieves remarkable outlier clearance performance as compared to these methods.