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Online Temporal-Spatial Analysis for Detection of Critical Events in Cyber-Physical Systems

Zhang Fu (Institutionen för data- och informationsteknik, Nätverk och system (Chalmers) ) ; Magnus Almgren (Institutionen för data- och informationsteknik, Nätverk och system (Chalmers) ) ; Olaf Landsiedel (Institutionen för data- och informationsteknik, Nätverk och system (Chalmers) ) ; Marina Papatriantafilou (Institutionen för data- och informationsteknik, Nätverk och system (Chalmers) )
2014 IEEE International Conference on Big Data (IEEE BigData 2014) p. 129-134. (2014)
[Konferensbidrag, refereegranskat]

Cyber-Physical Systems (CPS) employ sensors to observe physical environments and to detect events of interest. Equipped with sensing, computing, and communication capabilities, Cyber-Physical Systems aim to make physical-systems smart(er). For example, smart electricity meters nowadays measure and report power consumption as well as critical events such as power outages. However, each day, such sensors report a variety of warnings and errors: many merely indicate transient faults or short instabilities of the physical system (environment). Thus, given the big volumes of data, the time-efficient processing of these events, especially in large-scale scenarios with hundreds of thousands of sensors, is a key challenge in CPSs. Motivated by the fact that critical events of CPSs often have temporal-spatial properties, we focus on identifying critical events by an online temporal-spatial analysis on the data stream of messages. We explicitly model the online detection problem as a single-linkage clustering on a data stream over a sliding-window, where the inherent computational complexity of the detection problem is derived. Based on this model, we propose a grid-based single-linkage clustering algorithm over a sliding-window, which is an online time-space efficient method satisfying the quick processing demand of big data streams. We analyze the performance of the proposed approach by both a series of propositions and a large, real-world data-set of deployed CPS, composing 300,000 sensors, over one year. We show that the proposed method identifies above 95% of the critical events in the data-set and save the time-space requirement by 4 orders of magnitude compared with the conventional clustering method.


Article number 7004221



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Denna post skapades 2014-11-24. Senast ändrad 2015-02-26.
CPL Pubid: 206490

 

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