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《Journal of Computer Applications》 2015-11
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Online abnormal event detection with spatio-temporal relationship in river networks

MAO Yingchi;JIE Qing;CHEN Hao;College of Computer and Information, Hohai University;College of Water Conservancy and Hydropower Engineering, Hohai University;Huaneng Lancang River Hydropower Company Limited;  
When the network abnormal event occurs, the spatial-temporal correlation of the sensor nodes is very obvious.While existing methods generally separate time and space data properties, a decentralized algorithm of spatial-temporal abnormal detection based on Probabilistic Graphical Model(PGM) was proposed. Firstly the Connected Dominating Set(CDS)algorithm was used to select part of the sensor nodes to avoid monitoring all the sensor nodes, and then Markov Chain(MC)was used to predict time exception event, at last Bayesian Network(BN) was utilized in modelling the spatial dependency of sensors, combining spatio-temporal events to predict whether the abnormal events would or would not occur. Compared with the simple threshold algorithm and BN algorithm, the experimental results demonstrate that the proposed algorithm has higher detection precision, and low delay rate, greatly reducing the communication overhead and improving the response speed.
【Fund】: 国家自然科学基金资助项目(61272543);; 国家科技支撑计划项目(2013BAB06B04);; 中央高校基本科研业务费专项资金资助项目(2015B22214);; 中国华能集团公司总部科技项目(HNKJ13-H17-04);; 云南省科技计划项目(2014GA007)
【CateGory Index】: X832;TN929.5;TP212.9
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