An Efficient Wi-Fi RSS Indoor Positioning System and Its Client-Server Implementation

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.