Modeling and detecting events for sensor networks

  • Authors:
  • Wenwei Xue;Qiong Luo;Hung Keng Pung

  • Affiliations:
  • Nokia Research Center Beijing, Nokia China Campus, Building 2, No. 5 Donghuan Zhonglu, Beijing Economic and Technological Development Area, Beijing 100176, China;Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China;School of Computing, National University of Singapore, Computing 1, 13 Computing Drive, Singapore 117417, Singapore

  • Venue:
  • Information Fusion
  • Year:
  • 2011

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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.