Online data fault detection for wireless sensor networks - case study

Abstract

Sensor faults are the rule and not the exception in every WSN deployment. Sensors themselves may get stuck at a particular value or get partially disconnected and report noisy measurements. Sensor nodes may reboot unexpectedly or stop transmitting data. Software running on the sensor nodes may have bugs and may cause data loss. In this paper, we present an efficient approach for online data fault detection and its application to a case study from a real world dataset. Our approach exposes four types of data faults as they occur by locally applying five simple heuristic rules. By locally applying these rules, node will not need to exchange messages with neighboring ones and consequently to consume energy. Simulation results showed that around 19% of the total collected readings from a real world dataset were faulty.