Real-time event detection with water sensor networks using a spatio-temporal model

Abstract

Event detection with the spatio-temporal correlation is one of the most popular applications of wireless sensor networks. This kind of task trends to be a difficult problem of big data analysis due to the massive data generated from large-scale sensor networks like water sensor networks, especially in the context of real-time analysis. To reduce the computational cost of abnormal event detection and improve the response time, sensor node selection is needed to cut down the amount of data for the spatio-temporal correlation analysis. In this paper, a connected dominated set (CDS) approach is introduced to select backbone nodes from the sensor network. Furthermore, a spatio-temporal model is proposed to achieve the spatio-temporal correlation analysis, where Markov chain is adopted to model the temporal dependency among the different sensor nodes, and Bayesian Network (BN) is used to model the spatial dependency. The proposed approach and model have been applied to the real-time detection of urgent events (e.g. water pollution incidents) with water sensor networks. Preliminary experimental results on simulated data indicate that our solution can achieve better performance in terms of response time and scalability, compared to the simple threshold algorithm and the BN-only algorithm.