Growing an organic indoor location system
Author(s)
Park, Jun-geun; Charrow, Ben; Curtis, Dorothy; Battat, Jonathan; Minkov, Einat; Hicks, Jamey; Teller, Seth; Ledlie, Jonathan; ... Show more Show less
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Most current methods for 802.11-based indoor localization depend on surveys conducted by experts or skilled technicians. Some recent systems have incorporated surveying by users. Structuring localization systems "organically," however, introduces its own set of challenges: conveying uncertainty, determining when user input is actually required, and discounting erroneous and stale data. Through deployment of an organic location system in our nine-story building, which contains nearly 1,400 distinct spaces, we evaluate new algorithms for addressing these challenges. We describe the use of Voronoi regions for conveying uncertainty and reasoning about gaps in coverage, and a clustering method for identifying potentially erroneous user data. Our algorithms facilitate rapid coverage while maintaining positioning accuracy comparable to that achievable with survey-driven indoor deployments.
Date issued
2010-06Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Aeronautics and AstronauticsJournal
Proceedings of the 8th International Conference on Mobile systems, Applications, and Services, MobiSys '10
Publisher
Association for Computing Machinery
Citation
Park, Jun-geun et al. "Growing an organic indoor location system." In Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services (MobiSys '10). ACM, New York, NY, USA, 271-284.
Version: Author's final manuscript
ISBN
978-1-60558-985-5