Mining lines in the sand: On trajectory discovery from untrustworthy data in cyber-physical system

Lu An Tang, Xiao Yu, Quanquan Gu, Jiawei Han, Alice Leung, Thomas La Porta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

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 sys-Tem 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 un-Trustworthy sensor data. LiSM constructs a watching net- work from sensor data and computes the locations of intruder appearances based on the link information of the network. The system retrieves a cone-model 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.

Original languageEnglish (US)
Title of host publicationKDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
EditorsRajesh Parekh, Jingrui He, Dhillon S. Inderjit, Paul Bradley, Yehuda Koren, Rayid Ghani, Ted E. Senator, Robert L. Grossman, Ramasamy Uthurusamy
PublisherAssociation for Computing Machinery
Pages410-418
Number of pages9
ISBN (Electronic)9781450321747
DOIs
StatePublished - Aug 11 2013
Event19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013 - Chicago, United States
Duration: Aug 11 2013Aug 14 2013

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
VolumePart F128815

Other

Other19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013
CountryUnited States
CityChicago
Period8/11/138/14/13

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

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  • Cite this

    Tang, L. A., Yu, X., Gu, Q., Han, J., Leung, A., & La Porta, T. (2013). Mining lines in the sand: On trajectory discovery from untrustworthy data in cyber-physical system. In R. Parekh, J. He, D. S. Inderjit, P. Bradley, Y. Koren, R. Ghani, T. E. Senator, R. L. Grossman, & R. Uthurusamy (Eds.), KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 410-418). [2487585] (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. Part F128815). Association for Computing Machinery. https://doi.org/10.1145/2487575.2487585