Tru-alarm: Trustworthiness analysis of sensor networks in cyber-physical systems

Lu An Tang, Xiao Yu, Sangkyum Kim, Jiawei Han, Chih Chieh Hung, Wen Chih Peng

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

Abstract

A Cyber-Physical System (CPS) integrates physical devices (e.g., sensors, cameras) with cyber (or informational) components to form a situation-integrated analytical system that responds intelligently to dynamic changes of the real-world scenarios. One key issue in CPS research is trustworthiness analysis of the observed data: Due to technology limitations and environmental influences, the CPS data are inherently noisy that may trigger many false alarms. It is highly desirable to sift meaningful information from a large volume of noisy data. In this paper, we propose a method called Tru-Alarm which finds out trustworthy alarms and increases the feasibility of CPS. Tru-Alarm estimates the locations of objects causing alarms, constructs an object-alarm graph and carries out trustworthiness inferences based on linked information in the graph. Extensive experiments show that Tru-Alarm filters out noises and false information efficiently and guarantees not missing any meaningful alarms.

Original languageEnglish (US)
Title of host publicationProceedings - 10th IEEE International Conference on Data Mining, ICDM 2010
Pages1079-1084
Number of pages6
DOIs
StatePublished - Dec 1 2010
Externally publishedYes
Event10th IEEE International Conference on Data Mining, ICDM 2010 - Sydney, NSW, Australia
Duration: Dec 14 2010Dec 17 2010

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other10th IEEE International Conference on Data Mining, ICDM 2010
CountryAustralia
CitySydney, NSW
Period12/14/1012/17/10

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Tang, L. A., Yu, X., Kim, S., Han, J., Hung, C. C., & Peng, W. C. (2010). Tru-alarm: Trustworthiness analysis of sensor networks in cyber-physical systems. In Proceedings - 10th IEEE International Conference on Data Mining, ICDM 2010 (pp. 1079-1084). [5694088] (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDM.2010.63