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- Fu, Zhang,1982Chalmers tekniska högskola,Chalmers University of Technology (författare)
Online Temporal-Spatial Analysis for Detection of Critical Events in Cyber-Physical Systems
- Artikel/kapitelEngelska2014
Förlag, utgivningsår, omfång ...
- 2014
- electronicrdacarrier
Nummerbeteckningar
- LIBRIS-ID:oai:research.chalmers.se:f63daf71-dbc6-4c5a-909c-9838ed62a35e
- https://doi.org/10.1109/BigData.2014.7004221DOI
- https://research.chalmers.se/publication/206490URI
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- Språk:engelska
- Sammanfattning på:engelska
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- 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.
Ämnesord och genrebeteckningar
Biuppslag (personer, institutioner, konferenser, titlar ...)
- Almgren, Magnus,1972Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)almgren (författare)
- Landsiedel, Olaf,1979Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)olafl (författare)
- Papatriantafilou, Marina,1966Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)ptrianta (författare)
- Chalmers tekniska högskola (creator_code:org_t)
Sammanhörande titlar
- Ingår i:2014 IEEE International Conference on Big Data (IEEE BigData 2014), s. 129-134
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