An Open Platform for Studying and Testing Context-Aware Indoor Positioning Algorithms

This paper presents an open platform for studying and analyzing indoor positioning algorithms. While other such platforms exist, our proposal features novelties related to the collection and use of additional context data. The platform is realized in the form of a mobile client, currently implemented on Android. It enables manual collection of radio-maps—i.e. fingerprints of Wi-Fi signals—while also allowing for amending the fingerprints with various context data which could help improve the accuracy of positioning algorithms. While this is a research-in-progress platform, an initial experiment was carried out and its results were used to justify its applicability and relevance