Skip to main content
Log in

Investigation of radio channel uncertainty in distance estimation in wireless sensor networks

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

The distance estimation between nodes is a crucial requirement for localization and object tracking. Received signal strength (RSS) measurement is one of the used methods for the distance estimation in wireless networks. Its main advantage is that there are no additional hardware requirements. This paper describes a lateration approach for localization and distance estimation using RSS. For the purpose of investigation of RSS uncertainty, several scenarios were designed for both indoor and outdoor measurements. The first set of RSS measurement scenarios was proposed with the intention of hardware independent investigation of radio channel. For the second set of measurements, we employed IRIS sensor nodes to evaluate the distance estimation with certain devices. The experiments considered also obstacles in the radio channel. The results obtained in the proposed scenarios present usability of the method under different conditions. There is also a signal propagation model constructed from measured data at a node, which subsequently serves for distance determination.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Macha, T., Martinasek, Z., & Stancik, S. (2009). Impact of scattering models on connectivity in sensor network. In 32nd international conference on telecommunications and signal processing (p. 5).

    Google Scholar 

  2. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Computer Networks, 38(4), 393–422.

    Article  Google Scholar 

  3. Simek, M., Komosny, D., Burget, R., & Sa Silva, J. (2008). Multicast routing in wireless sensor network. In Telecommunication and signal processing (pp. 54–59).

    Google Scholar 

  4. Camilo, T., Rodrigues, A., Silva, J., & Boavida, F. (2008). Management of mobility in wireless sensor networks. In IEEE international symposium on a world of wireless mobile and multimedia networks.

    Google Scholar 

  5. Hightower, J., & Borriello, G. (2001). Location systems for ubiquitous computing. Computer, 34(8), 57–66.

    Article  Google Scholar 

  6. Moravek, P., Komosny, D., Sveda, J., et al. (2009). Vivaldi and other localization methods. In 32nd international conference on telecommunications and signal processing (pp. 214–218).

    Google Scholar 

  7. IEEE Standard for Information Technology Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) specifications for Low-Rate Wireless Personal Area Networks (LR-WPANs), IEEE Std 802.15.4-2003.

  8. Stoyanova, T., Kerasiotis, F., Prayati, A., & Papadopoulos, G. (2009). A practical RF propagation model for wireless network sensors. In Third international conference on sensor technologies and applications (pp. 194–199).

    Chapter  Google Scholar 

  9. Rappaport, T. (2001). Wireless communications: principles and practice (2nd ed.). New York: Prentice Hall.

    Google Scholar 

  10. Jakes, W. C. (1974). Microwave mobile communications. New York: Wiley. Reprinted by IEEE Press in 1994.

    Google Scholar 

  11. Patwari, N., Ash, J. N., Kyperountas, S., Hero, A. O., Moses, R. L., & Correal, N. S. (2005). Locating the nodes: cooperative localization in wireless sensor networks. IEEE Signal Processing Magazine, 22(4), 54–69.

    Article  Google Scholar 

  12. Moravek, P., Girbau, G., Lazaro, A., & Komosny, D. (2010). Received signal strength uncertainty in energy-aware localization in wireless sensor networks. In 9th international conference on environment and electrical engineering EEEIC, Prague.

    Google Scholar 

  13. Cho, H., Kang, M., Park, J., Park, B., & Kim, H. (2007). Performance analysis of location estimation algorithm in zigbee networks using received signal strength. In Advanced information networking and applications workshops.

    Google Scholar 

  14. Jianwu, Z., & Lu, Z. (2009). Research on distance measurement based on RSSI of ZigBee. ISECS International Colloquium on Computing, Communication, Control, and Management, 3, 210–212.

    Article  Google Scholar 

  15. Mao, G., Anderson, B., & Fidan, B. (2007). Path loss exponent estimation for wireless sensor network localization. Computer Networks, 51(10), 2467–2483.

    Article  Google Scholar 

  16. Parameswaran, A. T., Husain, M. H., & Upadhyaya, S. (2009). Is RSSI a reliable parameter in sensor localization algorithms—an experimental study. In Field failure data analysis workshop (F2DA’09), New York.

    Google Scholar 

  17. Seybold, J. (2005). Introduction to RF propagation. New Jersey: Wiley.

    Book  Google Scholar 

  18. Chrysikos, T., & Kotsopoulos, S. (2009). Impact of channel-dependent variation of path loss exponent on wireless information-theoretic security. In Wireless telecommunications symposium (pp. 1–7).

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Patrik Moravek.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Moravek, P., Komosny, D., Simek, M. et al. Investigation of radio channel uncertainty in distance estimation in wireless sensor networks. Telecommun Syst 52, 1549–1558 (2013). https://doi.org/10.1007/s11235-011-9522-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-011-9522-4

Keywords

Navigation