MoteTrack: a robust, decentralized approach to RF-based location tracking

In this paper, we present a robust, decentralized approach to RF-based location tracking. Our system, called MoteTrack, is based on low-power radio transceivers coupled with a modest amount of computation and storage capabilities. MoteTrack does not rely upon any back-end server or network infrastructure: the location of each mobile node is computed using a received radio signal strength signature from numerous beacon nodes to a database of signatures that is replicated across the beacon nodes themselves. This design allows the system to function despite significant failures of the radio beacon infrastructure. In our deployment of MoteTrack, consisting of 23 beacon nodes distributed across our Computer Science building, we achieve a 50th percentile and 80th percentile location-tracking accuracy of 0.9 and 1.6 m respectively. In addition, MoteTrack can tolerate the failure of up to 60% of the beacon nodes without severely degrading accuracy, making the system suitable for deployment in highly volatile conditions. We present a detailed analysis of MoteTrack’s performance under a wide range of conditions, including variance in the number of obstructions, beacon node failure, radio signature perturbations, receiver sensitivity, and beacon node density.

[1]  P. A. Godwin,et al.  A prison guard Duress alarm location system , 1993, 1993 Proceedings of IEEE International Carnahan Conference on Security Technology.

[2]  Matt Welsh,et al.  Sensor networks for emergency response: challenges and opportunities , 2004, IEEE Pervasive Computing.

[3]  Seth J. Teller,et al.  The cricket compass for context-aware mobile applications , 2001, MobiCom '01.

[4]  Gaetano Borriello,et al.  SpotON: An Indoor 3D Location Sensing Technology Based on RF Signal Strength , 2000 .

[5]  V. Padmanabhan,et al.  Enhancements to the RADAR User Location and Tracking System , 2000 .

[6]  Ravi Jain,et al.  Indoor location estimation using multiple wireless technologies , 2003, 14th IEEE Proceedings on Personal, Indoor and Mobile Radio Communications, 2003. PIMRC 2003..

[7]  Andy Hopper,et al.  The active badge location system , 1992, TOIS.

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

[9]  Daniel P. Siewiorek,et al.  Determining User Location For Context Aware Computing Through the Use of a Wireless LAN Infrastructure , 2000 .

[10]  Henry Tirri,et al.  A Probabilistic Approach to WLAN User Location Estimation , 2002, Int. J. Wirel. Inf. Networks.

[11]  Robert Szewczyk,et al.  System architecture directions for networked sensors , 2000, ASPLOS IX.

[12]  John Platt,et al.  Minimizing Calibration Effort for an Indoor 802.11 Device Location Measurement System , 2003 .

[13]  John Krumm,et al.  SmartMoveX on a Graph - An Inexpensive Active Badge Tracker , 2002, UbiComp.

[14]  Matt Welsh,et al.  MoteTrack: A Robust, Decentralized Approach to RF-Based Location Tracking , 2005, LoCA.

[15]  Moustafa Youssef,et al.  WLAN location determination via clustering and probability distributions , 2003, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[16]  David Starobinski,et al.  Robust location detection with sensor networks , 2004, IEEE Journal on Selected Areas in Communications.

[17]  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).

[18]  Hari Balakrishnan,et al.  6th ACM/IEEE International Conference on on Mobile Computing and Networking (ACM MOBICOM ’00) The Cricket Location-Support System , 2022 .

[19]  Andy Hopper,et al.  A new location technique for the active office , 1997, IEEE Wirel. Commun..