The demand of Indoor Location Based Services LBS has increased over the past years as smart phone market expands. As a result, there's a growing interest in developing efficient and reliable indoor positioning systems for mobile devices. Wi-Fi signal strength fingerprint-based approaches attract more and more attention due to the wide deployment of Wi-Fi access points. Indoor positioning problem using Wi-Fi signal fingerprints can be viewed as a machine learning task to be solved mathematically. This thesis proposes an efficient and reliable Wi-Fi real-time indoor positioning system using machine learning algorithms. The proposed positioning system, together with a location server equipped with the same algorithms, are tested and evaluated in several indoor scenarios. Simulation and testing results show that the proposed system is a feasible LBS solution.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/43382 |
| Date | 12 December 2013 |
| Creators | Yu, Yibo |
| Contributors | Valaee, Shahrokh |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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