• DocumentCode
    169094
  • Title

    Demonstration abstract: Crowdsourced indoor localization and navigation with Anyplace

  • Author

    Petrou, Lambros ; Larkou, George ; Laoudias, Christos ; Zeinalipour-Yazti, Demetrios ; Panayiotou, Christos G.

  • Author_Institution
    Dept. of Comput. Sci., Univ. of Cyprus, Nicosia, Cyprus
  • fYear
    2014
  • fDate
    15-17 April 2014
  • Firstpage
    331
  • Lastpage
    332
  • Abstract
    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.
  • Keywords
    Big Data; indoor radio; information filtering; radionavigation; smart phones; wireless LAN; Big Data architecture; WiFi data; anyplace system; crowdsourced indoor localization; crowdsourcing-supportive mechanisms; heterogeneous mobile devices; indoor geolocation information reliability; indoor navigation; localization data retrieval; mapping data retrieval; scalable storage; sensory data; smartphones; Buildings; Computer architecture; Crowdsourcing; IEEE 802.11 Standards; Navigation; Servers; Smart phones; Crowdsourcing; indoor localization; navigation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Processing in Sensor Networks, IPSN-14 Proceedings of the 13th International Symposium on
  • Conference_Location
    Berlin
  • Print_ISBN
    978-1-4799-3146-0
  • Type

    conf

  • DOI
    10.1109/IPSN.2014.6846788
  • Filename
    6846788