Situation-Aware Indoor Tracking with high-density, large-scale Wireless Sensor Networks

In this paper we propose an innovative approach to the problem of indoor position estimation that aims at extending tracking to a new level of “awareness” bringing to bear new ambient data and opening the possibility of “reasoning” not only on simple positioning but also on the situation at hand. In order to validate the approach, we implemented a positioning system called Situation-Aware Indoor Tracking (SAIT). The comparison of SAIT with several commercial systems highlights a promising behaviour, showing that exploiting the movement data (e.g. the users' heading and speed) for updating the PF motion models used in the tracking engine together with situation assessment techniques can improve the accuracy of tracking up to 42% in comparison with a Wi-Fi based system.

[1]  Wolfram Burgard,et al.  Robust Monte Carlo localization for mobile robots , 2001, Artif. Intell..

[2]  Davide Merico,et al.  Tracking with high-density, large-scale wireless sensor networks , 2010, J. Ambient Intell. Smart Environ..

[3]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[4]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[5]  Mani B. Srivastava,et al.  Dynamic fine-grained localization in Ad-Hoc networks of sensors , 2001, MobiCom '01.

[6]  Hu Han-ying,et al.  An Improved LEACH Protocol for Data Gathering and Aggregation in Wireless Sensor Networks , 2008, 2008 International Conference on Computer and Electrical Engineering.

[7]  Wendi Heinzelman,et al.  Energy-efficient communication protocol for wireless microsensor networks , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[8]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[9]  A. El-Rabbany.  Introduction to GPS: The Global Positioning System , 2002 .

[10]  Marina Ruggieri,et al.  Applied Satellite Navigation Using GPS, GALILEO, and Augmentation Systems , 2005 .

[11]  Gyula Simon,et al.  The flooding time synchronization protocol , 2004, SenSys '04.

[12]  Alessandra Mileo,et al.  Support for Context-aware Monitoring in Home Healthcare , 2009, Intelligent Environments.

[13]  Joseph A. Paradiso,et al.  Localization on the pushpin computing sensor network using spectral graph drawing and mesh relaxation , 2006, MOCO.

[14]  Mica R. Endsley,et al.  Toward a Theory of Situation Awareness in Dynamic Systems , 1995, Hum. Factors.

[15]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

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

[17]  James H. Aylor,et al.  Computer for the 21st Century , 1999, Computer.