Modeling and detecting events for sensor networks

• Published in: Information fusion 2011-07, Vol.12 (3), p.176-186
• Main Authors: Xue, Wenwei, Luo, Qiong, Pung, Hung Keng
• Format: Article
• Language: English
• Subjects: Event detection; Region matching; Regression; Sensor networks; Spatial relationships
• ISSN: 1566-2535; 1872-6305
• DOI: 10.1016/j.inffus.2010.11.001

► Model an event using the spatio-temporal sensor data distribution it generates. ► Regression modeling of sensor data distributions of events in spatial regions. ► Absolute and relative regions with timestamps for event modeling. ► Region matching at the sensor network gateway for event detection. ► Good detection accuracy and small response time upon parameter defect or data loss. 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.