Multidimensional Analysis of Atypical Events in Cyber-Physical Data

Tang, Lu-An; Yu, Xiao; Kim, Sangkyum; Han, Jiawei; Peng, Wen-Chih; Sun, Yizhou; González, Héctor; Seith, Sebastian

doi:10.1109/icde.2012.32

Cited by 11 publications

(4 citation statements)

References 13 publications

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“…Multi-dimension Analysis of Atypical Events. [27] considers the CPS integrates physical device with cyber components to form analysis system with the situation. It can respond and change dynamically according to the real world.…”

Section: Research Status Of Event Processing In Cpsmentioning

confidence: 99%

A Survey on Event Processing for CPS

2013

Communications in Computer and Information Science

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Abstract. Cyber-Physical System (CPS) is an important research area which promotes the progress of IT revolution and digital life in the 21st century. As a vital element in CPS, event plays an important role in transforming physical data into our semantic model. Therefore, the event processing scheme is of importance to guarantee the usability of the CPS. In this survey, we at first represent the notion, characteristics and research significance of the events in CPS. Then, we summarize event processing techniques under the context of CPS and its related research fields separately, with pointing out the deficiencies of existing technologies when to support CPS applications. Finally, we discuss the challenges of event processing in CPS as well as its future research directions.

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Section: Research Status Of Event Processing In Cpsmentioning

confidence: 99%

A Survey on Event Processing for CPS

2013

Communications in Computer and Information Science

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“…Their method is similar to the singlelinkage clustering. However, unlike our paper which provides a time-space efficient method for processing a real-time data stream, the authors in [14] mainly focused on processing data in a database and the computational complexity of their method is O(n 2 ). We refer the reader to a survey [9] for further work on processing data in CPSs.…”

Section: Related Workmentioning

confidence: 99%

Online temporal-spatial analysis for detection of critical events in Cyber-Physical Systems

Zhang

Almgren

Papatriantafilou

2014

2014 IEEE International Conference on Big Data (Big Data)

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Abstract-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 gridbased 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.

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“…In addition, some more recent publications of trustworthiness analysis of CPS can be found. A new model of atypical cluster is proposed to effectively represent the complex atypical events and efficiently retrieve them from massive data [23]. A method called LiSM (Line-in-the-Sand Miner) is proposed to discover trajectories from untrustworthy sensor data [24].…”

Section: Related Workmentioning

confidence: 99%

Practical Swarm Optimization based Fault-Tolerance Algorithm for the Internet of Things

2014

KSII TIIS

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The fault-tolerance routing problem is one of the most important issues in the application of the Internet of Things, and has been attracting growing research interests. In order to maintain the communication paths from source sensors to the macronodes, we present a hybrid routing scheme and model, in which alternate paths are created once the previous routing is broken. Then, we propose an improved efficient and intelligent fault-tolerance algorithm (IEIFTA) to provide the fast routing recovery and reconstruct the network topology for path failure in the Internet of Things. In the IEIFTA, mutation direction of the particle is determined by multi-swarm evolution equation, and its diversity is improved by the immune mechanism, which can improve the ability of global search and improve the converging rate of the algorithm. The simulation results indicate that the IEIFTA-based fault-tolerance algorithm outperforms the EARQ algorithm and the SPSOA algorithm due to its ability of fast routing recovery mechanism and prolonging the lifetime of the Internet of Things.

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Multidimensional Analysis of Atypical Events in Cyber-Physical Data

Cited by 11 publications

References 13 publications

A Survey on Event Processing for CPS

A Survey on Event Processing for CPS

Online temporal-spatial analysis for detection of critical events in Cyber-Physical Systems

Practical Swarm Optimization based Fault-Tolerance Algorithm for the Internet of Things

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