Crowdsourcing Mobile Data for A Passive Indoor Positioning System - The MAA Case Study

Abstract

Crowdsourcing radio signal fingerprints to build a radio map for indoor positioning system is an emerging alternative to conventional labour-costly manual survey. However, existing crowdsourced systems heavily rely on ground-truth location inputs or unrealistic constraints on the contributors, deterring a wider adaption of crowdsourced systems. Our work exploits three generic constraints of mobile data to retrieve the locations of the crowdsourced fingerprints and builds a completely passive indoor positioning system that assumes no manual intervention or unnatural constraints on the contributors. The proposed system was further evaluated in the Museum of Archaeology and Anthropology (MAA) with passively crowd- sourced data contributed by actual visitors while visitors can behave naturally without catering to crowdsourcing. Results show that the proposed system can achieve positioning accuracy comparable to traditional manual survey-based system with essentially no extra manual effort.