Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.inffus.2010.11.001
Title: Modeling and detecting events for sensor networks
Authors: Xue, W.
Luo, Q.
Pung, H.K. 
Keywords: Event detection
Region matching
Regression
Sensor networks
Spatial relationships
Issue Date: 2011
Source: Xue, W.,Luo, Q.,Pung, H.K. (2011). Modeling and detecting events for sensor networks. Information Fusion 12 (3) : 176-186. ScholarBank@NUS Repository. https://doi.org/10.1016/j.inffus.2010.11.001
Abstract: Event detection is an essential element for various sensor network applications, such as disaster alarm and object tracking. In this paper, we propose a novel approach to model and detect events of interest in sensor networks. Our approach models an event using the kind of spatio-temporal sensor data distribution it generates, and specifies such distribution as a number of regression models over spatial regions within the network coverage at discrete points in time. The event is detected by matching the modeled distribution with the real-time sensor data collected at a gateway. Because the construction of a regression model is computation-intensive, we utilize the temporal data correlation in a region as well as the spatial relationships of multiple regions to maintain the models over these regions incrementally. Our evaluation results based on both real-world and synthetic data sets demonstrate the effectiveness and efficiency of our approach. © 2010 Elsevier B.V. All rights reserved.
Source Title: Information Fusion
URI: http://scholarbank.nus.edu.sg/handle/10635/39697
ISSN: 15662535
DOI: 10.1016/j.inffus.2010.11.001
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