ARIEL: automatic wi-fi based room fingerprinting for indoor localization

  • Authors:

  • Yifei Jiang;Xin Pan;Kun Li;Qin Lv;Robert P. Dick;Michael Hannigan;Li Shang

  • Affiliations:

  • University of Colorado Boulder, CO;University of Colorado Boulder, CO;University of Colorado Boulder, CO;University of Colorado Boulder, CO;University of Michigan, Ann Arbor, MI;University of Colorado Boulder, CO;University of Colorado Boulder, CO

  • Venue:
  • Proceedings of the 2012 ACM Conference on Ubiquitous Computing
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

People spend the majority of their time indoors, and human indoor activities are strongly correlated with the rooms they are in. Room localization, which identifies the room a person or mobile phone is in, provides a powerful tool for characterizing human indoor activities and helping address challenges in public health, productivity, building management, etc. Existing room localization methods, however, require labor-intensive manual annotation of individual rooms. We present ARIEL, a room localization system that automatically learns room fingerprints based on occupants' indoor movements. ARIEL consists of (1) a zone-based clustering algorithm that accurately identifies in-room occupancy "hotspot(s)" using Wi-Fi signatures; (2) a motion-based clustering algorithm to identify inter-zone correlation, thereby distinguishing different rooms; and (3) an energy-efficient motion detection algorithm to minimize the noise of Wi-Fi signatures. ARIEL has been implemented and deployed for real-world testing with 21 users over a 10-month period. Our studies show that it supports room localization with higher than 95% accuracy without requiring labor-intensive manual annotation.