2013 MiningLinesintheSandOnTrajector

• (Tang et al., 2013) ⇒ Lu-An Tang, Xiao Yu, Quanquan Gu, Jiawei Han, Alice Leung, and Thomas La Porta. (2013). “Mining Lines in the Sand: On Trajectory Discovery from Untrustworthy Data in Cyber-physical System.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487585

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Cited By

• http://scholar.google.com/scholar?q=%222013%22+Mining+Lines+in+the+Sand%3A+On+Trajectory+Discovery+from+Untrustworthy+Data+in+Cyber-physical+System
• http://dl.acm.org/citation.cfm?id=2487575.2487585&preflayout=flat#citedby

Quotes

Author Keywords

• Cyber-physical system; data mining; sensor network; trajectory

Abstract

A Cyber-Physical System (CPS) integrates physical (i.e., sensor) devices with cyber (i.e., informational) components to form a context sensitive system that responds intelligently to dynamic changes in real-world situations. The CPS has wide applications in scenarios such as environment monitoring, battlefield surveillance and traffic control. One key research problem of CPS is called " mining lines in the sand ". With a large number of sensors (sand) deployed in a designated area, the CPS is required to discover all the trajectories (lines) of passing intruders in real time. There are two crucial challenges that need to be addressed: (1) the collected sensor data are not trustworthy; (2) the intruders do not send out any identification information. The system needs to distinguish multiple intruders and track their movements. In this study, we propose a method called LiSM (Line-in-the-Sand Miner) to discover trajectories from untrustworthy sensor data. LiSM constructs a watching network from sensor data and computes the locations of intruder appearances based on the link information of the network. The system retrieves a cone-mode l from the historical trajectories and tracks multiple intruders based on this model. Finally the system validates the mining results and updates the sensor's reliability in a feedback process. Extensive experiments on big datasets demonstrate the feasibility and applicability of the proposed methods.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 MiningLinesintheSandOnTrajector	Quanquan Gu Xiao Yu Lu-An Tang Alice Leung Thomas La Porta Jiawei Han			Mining Lines in the Sand: On Trajectory Discovery from Untrustworthy Data in Cyber-physical System				10.1145/2487575.2487585		2013