Demo: unsupervised indoor localization

  • Authors:

  • He Wang;Souvik Sen;Alexander Mariakakis;Romit Roy Choudhury;Ahmed Elgohary;Moustafa Farid;Moustafa Youssef

  • Affiliations:

  • Duke University, Durham, NC, USA;Duke University, Durham, NC, USA;Duke University, Durham, NC, USA;Duke University, Durham, NC, USA;Egypt-Japan University of Science and Technology, Alexandria, Egypt;Egypt-Japan University of Science and Technology, Alexandria, Egypt;Egypt-Japan University of Science and Technology, Alexandria, Egypt

  • Venue:
  • Proceedings of the 10th international conference on Mobile systems, applications, and services
  • Year:
  • 2012

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Abstract

We propose UnLoc [1], an unsupervised indoor localization scheme that bypasses the need for war-driving. Our key observation is that certain locations in an indoor environment present an identifiable signature on one or more sensing dimensions. An elevator, for instance, imposes a distinct pattern on a smartphone's accelerometer; a specific spot may experience an unusual magnetic fluctuation. This form of urban sensing and activity recognition has already been demonstrated in literature [2, 3], but not yet applied in pure localization applications. We hypothesize that these kind of signatures naturally exist in the environment and can be envisioned as internal landmarks of a building. Mobile devices that "sense" these landmarks can recalibrate their locations, while dead-reckoning schemes can track them between landmarks. Neither war-driving nor floorplans are necessary - the system simultaneously computes the locations of users and landmarks, in a manner so that they converge reasonably quickly. We believe this is an unconventional approach to indoor localization, holding promise for real-world deployment.