Implementing Real-Life Indoor Positioning Systems Using Machine Learning Approaches

Position determination in an indoor environment has become a widely discussed problem, due to the growing complexity of building layouts and the lack of any natural heuristics for orientation as compared to the case outdoors. Additionally there is no universal standard for indoor positioning, such as GPS, which however cannot be used for this purpose. Locating oneself in a building serves an increasingly vital function, especially in time-critical scenarios such as airports etc. The use of expensive hardware may assist in solving this problem, which has been studied thoroughly with different technologies being used to achieve a precision of within a few meters. Nevertheless these methods have remained in the academic realm for the most part. This is largely due to the high costs and labour of such hardware installations and the construction of software to interpret the measurements. The goal of this paper is to use existing wireless LAN access points in a building and user-provided smartphones to create a cost-effective positioning system, by omitting the labour and cost of altering building infrastructure, and at the same time simplifying the construction of classifiers for real-life use-cases. An alternative approach using image recognition techniques is presented, for a purely web-based solution.