Spatial-Temporal Correlative Fault Detection in Wireless Sensor Networks

Wireless sensor networks (WSNs) have been used extensively in a range of applications to facilitate real-time critical decision-making and situation monitoring. Accurate data analysis and decision-making rely on the quality of the WSN data that have been gathered. However, sensor nodes are prone to faults and are often unreliable because of their intrinsic natures or the harsh environments in which they are used. Using dust data from faulty sensors not only has negative effects on the analysis results and the decisions made but also shortens the network lifetime and can waste huge amounts of limited valuable resources. In this paper, the quality of a WSN service is assessed, focusing on abnormal data derived from faulty sensors. The aim was to develop an effective strategy for locating faulty sensor nodes in WSNs. The proposed fault detection strategy is decentralized, coordinate-free, and node-based, and it uses time series analysis and spatial correlations in the collected data. Experiments using a real dataset from the Intel Berkeley Research Laboratory showed that the algorithm can give a high level of accuracy and a low false alarm rate when detecting faults even when there are many faulty sensors.