Low Overhead Assignment of Symbolic Coordinates in Sensor Networks

  • Matthias Gauger
  • Pedro José Marrón
  • Daniel Kauker
  • Kurt Rothermel
Conference paper
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 248)


Approximate information on the location of nodes in a sensor network is essential to many types of sensor network applications and algorithms. In many cases, using symbolic coordinates is an attractive alternative to the use of geographic coordinates due to lower costs and lower requirements on the available location information during coordinate assignment. In this paper, we investigate different possible methods of assigning symbolic coordinates to sensor nodes. We present a method based on broadcasting coordinate messaging and filtering using sensor events. We show in the evaluation that this method allows a reliable assignment of symbolic coordinates while only generating a low overhead.


Sensor Node Wireless Sensor Network Event Threshold Ubiquitous Computing Receive Signal Strength Indication 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Langendoen, K., Reijers, N.: Distributed localization in wireless sensor networks: a quantitative comparison. Computer Networks 43(4) (2003) 499–518zbMATHCrossRefGoogle Scholar
  2. 2.
    Priyantha, N.B., Chakraborty, A., Balakrishnan, H.: The cricket location-support system. In: Proceedings of the 6th Int. Conf. on Mobile computing and networking. (2000)Google Scholar
  3. 3.
    Elnahrawy, E., Li, X., Martin, R.P.: The limits of localization using signal strength: A comparative study. In: Proceedings of The First IEEE International Conference on Sensor and Ad hoc Communications and Networks (SECON 2004). (2004)Google Scholar
  4. 4.
    Stoleru, R., He, T., Stankovic, J.A., Luebke, D.: A high-accuracy, low-cost localization system for wireless sensor networks. In: SenSys’ 05: Proceedings of the 3rd international conference on Embedded networked sensor systems. (2005)Google Scholar
  5. 5.
    Corke, P., Peterson, R., Rus, D.: Networked robots: Flying robot navigation using a sensor net. In: Proceedings of the Eleventh International Symposium of Robotics Research (ISRR). (2003)Google Scholar
  6. 6.
    Brumitt, B., Shafer, S.: Topological world modeling using semantic spaces. In: Workshop Proc. of Ubicomp: Location Modeling for Ubiquitous Computing. (2001)Google Scholar
  7. 7.
    Jiang, C, Steenkiste, P.: A hybrid location model with a computable location identifier for ubiquitous computing. In: UbiComp’ 02: Proceedings of the 4th international conference on Ubiquitous Computing, London, UK, Springer-Verlag (2002) 246–263Google Scholar
  8. 8.
    Becker, C, Dürr, F.: On location models for ubiquitous computing. Personal Ubiquitous Computing 9(1) (2005) 20–31CrossRefGoogle Scholar

Copyright information

© International Federation for Information Processing 2007

Authors and Affiliations

  • Matthias Gauger
    • 1
    • 2
  • Pedro José Marrón
    • 2
  • Daniel Kauker
    • 1
  • Kurt Rothermel
    • 1
  1. 1.IPVSUniversität StuttgartGermany
  2. 2.Universität BonnGermany

Personalised recommendations