Data Discrimination in Fault-Prone Sensor Networks

While sensor networks have been used in various applications because of the automatic sensing capability and ad-hoc organization of sensor nodes, the fault-prone characteristic of sensor networks has challenged the event detection and the anomaly detection which, to some extent, have neglected the importance of discriminating events and errors. Considering data uncertainty, in this paper, the authors present the problem of data discrimination in fault-prone sensor networks, analyze the similarities and the differences between events and errors, and design a multi-level systematic discrimination framework.