A Hierarchical Signal-Space Partitioning Technique for Indoor Positioning with WLAN to Support Location-Awareness in Mobile Map Services

The precise and accurate performance of location estimation is a vital component of context-aware applications. Numerous mobile devices with built-in IEEE 802.11 Wi-Fi technology can be used to estimate a user’s location through a wireless local area network (WLAN) in indoor environments in which fixed access points are deployed. This study deals with improving the common techniques of such positioning once the acquisition of the fingerprint database in offline phase is performed. The main idea is to propose a methodology that includes two layers of classification: a concurrent hierarchical partitioning of both signal and physical space in a way that signal patterns in each part of building have the highest similarity, and a precise and independent positioning in a given part. A procedure for combining the proposed classifier with either artificial neural network (ANN) or Bayesian probabilistic model is then introduced. We also consider an alternative strategy for ANN learning by including all raw observations. The average distance error was successfully reduced in the proposed methodology by 32 % compared to the simple approach. We concluded that the physical partitioning should also consider the signal behavior. Toosi location-aware mobile system was ultimately implemented, providing different services (e.g., friend finder and nearest point of interest) based on the proposed technique via WLAN. The system benefits from the high level of interaction provided by Asynchronous JavaScript and XML (AJAX) technology. It is capable of transferring locational data and GIS map services efficiently to the mobile terminal.