Indooria-A Platform for Proactive Indoor Location-based Services

Positioning mobile terminals, persons and assets inside buildings opens several new possibilities for service providers and their users. Many different indoor positioning systems have been developed in the past, which differ e.g. in the underlying infrastructure, accuracy, energy consumption, or frequencies used. But sophisticated indoor location-based services (I-LBS) require not only knowledge about the targets’ positions but also detailed information about the topology of a building. A building topology comprises shapes of rooms together with their interconnections and other meta data like escape routes or entrance restrictions. That allows e.g. to calculate walking distances, to determine the accessibility for a certain room or to define topological zones in the building. However, bringing together indoor positioning systems and building topology information raises a couple of problems. In this paper we present an approach that combines real time position data with building topologies. Requirements and a classification for future indoor LBS are given and an approach for modeling the appropriate topologies as well as integrating position data from several positioning systems is presented. An open platform has been developed which offers interfaces for indoor LBS developers and providers e.g. to automatically detect proximity between mobile assets, or to calculate routes between locations in the building, which can be used by indoor navigation applications.

[1]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[2]  Andreas F. Molisch,et al.  Localization via Ultra- Wideband Radios , 2005 .

[3]  Peter Steenkiste,et al.  A Hybrid Location Model with a Computable Location Identifier for Ubiquitous Computing , 2002, UbiComp.

[4]  Ted Kremenek,et al.  A Probabilistic Room Location Service for Wireless Networked Environments , 2001, UbiComp.

[5]  Haibo Hu,et al.  Semantic location modeling for location navigation in mobile environment , 2004, IEEE International Conference on Mobile Data Management, 2004. Proceedings. 2004.

[6]  Axel Küpper Location-based Services: Fundamentals and Operation , 2005 .

[7]  Christoph Stahl,et al.  Taking Location Modelling to New Levels: A Map Modelling Toolkit for Intelligent Environments , 2006, LoCA.

[8]  Roy H. Campbell,et al.  MiddleWhere: A Middleware for Location Awareness in Ubiquitous Computing Applications , 2004, Middleware.

[9]  Axel Küpper,et al.  TraX: a device-centric middleware framework for location-based services , 2006, IEEE Communications Magazine.

[10]  Moustafa Youssef,et al.  The Horus WLAN location determination system , 2005, MobiSys '05.

[11]  Dik Lun Lee,et al.  A Lattice-Based Semantic Location Model for Indoor Navigation , 2008, The Ninth International Conference on Mobile Data Management (mdm 2008).

[12]  Fritz Hohl,et al.  Next century challenges: Nexus—an open global infrastructure for spatial-aware applications , 1999, MobiCom.

[13]  Akira Fukuda,et al.  Wireless LAN based indoor positioning system WiPS and its simulation , 2003, 2003 IEEE Pacific Rim Conference on Communications Computers and Signal Processing (PACRIM 2003) (Cat. No.03CH37490).

[14]  Jörg Roth,et al.  Detecting identifiable areas in mobile environments , 2006, SAC.

[15]  Frank Dürr,et al.  On location models for ubiquitous computing , 2004, Personal and Ubiquitous Computing.

[16]  Axel Küpper,et al.  Efficient indoor proximity and separation detection for location fingerprinting , 2008 .

[17]  Gaetano Borriello,et al.  Location Systems for Ubiquitous Computing , 2001, Computer.

[18]  Alan E. Middleditch,et al.  Convex Decomposition of Simple Polygons , 1984, TOGS.