A stochastic model for context-aware anomaly detection in indoor location traces

Chuanren Liu, Hui Xiong, Yong Ge, Wei Geng, Matt Perkins

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

14 Scopus citations

Abstract

Rapid growth in the development of real-time location system solutions has led to an increased interest in indoor location-aware services, such as hospital asset management. Although there are extensive studies in the literature on the analysis of outdoor location traces, the studies of indoor location traces are less touched and fragmented. To that end, in this paper, we provide a focused study of indoor location traces collected by the sensors attached to medical devices in a hospital environment. Along this line, we first introduce some unique properties of these indoor location traces. We show that they can capture the movement patterns of the medical devices, which are tightly coupled with the work flow in the controlled hospital environment. Based on this observation, we propose a stochastic model for context-aware anomaly detection in indoor location traces, which exploits the hospital work flow and models the movements of medical devices as transitions in finite state machines. In detail, we first develop a density-based method to identify the hotspots filled with high-level abnormal activities in the indoor environment. The discovered hotspots serve as the context for nearby trajectories. Then, we introduce an N-gram based method for measuring the degree of anomaly based on the detected hotspots, which is able to predict the missing events possibly due to the devices being stolen. Besides, to address the noisy nature of the indoor sensor networks, we also propose an iterative algorithm to estimate the transition probabilities. This algorithm allows to effectively recover the missing location records which are critical for the abnormality estimation. Finally, the experimental results on the real-world date sets validate the effectiveness of the proposed context-aware anomaly detection method for identifying abnormal events.

Original languageEnglish (US)
Title of host publicationProceedings - 12th IEEE International Conference on Data Mining, ICDM 2012
Pages449-458
Number of pages10
DOIs
StatePublished - 2012
Externally publishedYes
Event12th IEEE International Conference on Data Mining, ICDM 2012 - Brussels, Belgium
Duration: Dec 10 2012Dec 13 2012

Publication series

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

Other

Other12th IEEE International Conference on Data Mining, ICDM 2012
Country/TerritoryBelgium
CityBrussels
Period12/10/1212/13/12

Keywords

  • Anomaly detection
  • Indoor trajectory
  • Stochastic modeling

ASJC Scopus subject areas

  • Engineering(all)

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