Demonstration abstract: Crowdsourced indoor localization and navigation with Anyplace
Zeinalipour-Yazdi, Constantinos D.
Panayiotou, Christos G.
PublisherIEEE Computer Society
SourceIPSN 2014 - Proceedings of the 13th International Symposium on Information Processing in Sensor Networks (Part of CPS Week)
13th IEEE/ACM International Conference on Information Processing in Sensor Networks, IPSN 2014
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In this demonstration paper, we present the Anyplace system that relies on the abundance of sensory data on smartphones (e.g., WiFi signal strength and inertial measurements) to deliver reliable indoor geolocation information. Our system features two highly desirable properties, namely crowdsourcing and scalability. Anyplace implements a set of crowdsourcing-supportive mechanisms to handle the enormous amount of crowdsensed data, filter incorrect user contributions and exploit WiFi data from heterogeneous mobile devices. Moreover, Anyplace follows a big-data architecture for efficient and scalable storage and retrieval of localization and mapping data. © 2014 IEEE.