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

Abstract

This paper presents an open platform for studying and analyzing indoor positioning algorithms. While other such platforms exist, this one features novelties related to the collection and use of additional context data. The platform features a mobile client side, currently implemented on Android. It enables manual collection of radiomaps—i.e. fingerprints of WiFi 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 experiment with early results was carried out to justify its applicability and relevance.