Wireless Networks

, Volume 20, Issue 5, pp 1157–1168 | Cite as

Cooperative network solution and implementation for emergency applications with enhanced position estimation capability

  • Meenakshi Rawat
  • Karun Rawat
  • Ramzi Darraji
  • Fermin Esparza Alfaro
  • Seyed Aidin Bassam
  • Mohamed Helaoui
  • Fadhel M. Ghannouchi
  • Michel Fattouche
  • Francisco Falcone
Article
  • 262 Downloads

Abstract

This paper proposes a cooperative network topology for emergency applications which comprises of incident scene networks (ISN) and external area networks. Both base stations and rescuers in ISN are modeled as nodes with the capabilities of software defined radio and signal processing. A worldwide interoperability for microwave access-based emergency protocol is proposed with which rescuers can estimate their geo-locations via time difference of arrival based on more than four known base stations coordinates. A comparative study of state-of-the-art position estimation methods have been carried out for the proposed cooperative network topology to select the most robust method. Hardware results for the most robust position estimation method without/with multipath mitigation have been implemented and presented to estimate the location of the rescuer.

Keywords

Positioning system Software defined radio (SDR) Public safety Emergency applications Position estimation 

Notes

Acknowledgments

The authors would like to thank the team of the iRadio laboratory, The University of Calgary.

References

  1. 1.
    Genovese, A., Labati R. D., Piuri V. & Scotti F. (2011). Wildfire smoke detection using computational intelligence techniques. IEEE International Conference Computational Intelligence for Measurement Systems and Applications (CIMSA), 1–6, 19–21 September.Google Scholar
  2. 2.
    Amin M., Hudaya A. & Khan A. I. (2011). Spatio-temporal forest fire detection using a distributed hierarchical graph neuron within an integrated wireless sensor network-grid environment. The Second International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering 2011, (PARENG 2011), Ajaccio, Corsica, France, 12–15 April 2011.Google Scholar
  3. 3.
    Liu Y., Gu Y., Chen G., Ji Y. & Li J. (2011). A novel accurate forest fire detection system using wireless sensor networks. Seventh International Conference on Mobile Ad-hoc and Sensor Networks (MSN), 52–59, 16–18 December.Google Scholar
  4. 4.
    Klann et al. M., (2007). Life net: An ad-hoc sensor network and wearable system to provide firefighters with navigation support. Proceedings of 9th International Conference on Ubiquitous Computing, 124–127.Google Scholar
  5. 5.
    Scholz M., Riedel T., Decker C. (2010). A flexible architecture for a robust indoor navigation support device for firefighters. Seventh International Conference nn Networked Sensing Systems, (INNS 2010).Google Scholar
  6. 6.
    Chen Z., Chen L., Liu Y. & Piao Y. (2009). Application research of wireless mesh network on earthquake. International Conference on Industrial and Information Systems, 2009 (IIS ‘09), 19–22, 24–25 April 2009.Google Scholar
  7. 7.
    Ran, Y. (2011). Considerations and suggestions on improvement of communication network disaster countermeasures after the Wenchuan earthquake. IEEE Communications Magazine, 49(1), 44–47.CrossRefGoogle Scholar
  8. 8.
    Bakhtiari, S., Elmer, T. W., Cox, N. M., Gopalsami, N., Raptis, A. C., Liao, S., et al. (2012). Compact millimeter-wave sensor for remote monitoring of vital signs. IEEE Transactions on Instrumentation and Measurement, 61(3), 830–841.CrossRefGoogle Scholar
  9. 9.
    Chunlei A., Timm-Giel A. & Goerg C. (2009). Virtual sensor network lifeline for communications in fire fighting rescue scenarios. Proceedings of IEEE Vehicular Technology Conference Fall (VTC Fall), 1–5 September 2009.Google Scholar
  10. 10.
    Zeng Y., Sreenan C. J. & Sitanayah L. (2010). A real-time and robust routing protocol for building fire emergency applications using wireless sensor networks. Proceedings of IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), 358–363.Google Scholar
  11. 11.
    Del Re E., Morosi S., Jayousi S. & Sacchi C. (2009). SALICE—Satellite-assisted localization and communication systems for emergency services. 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology, (Wireless VITAE 2009), 544–548, 17–20 May 2009.Google Scholar
  12. 12.
    Sana S. and Matsumoto M. (2007). A wireless sensor network protocol for disaster management. Information, Decision and Control (IDC ‘07), 209–213.Google Scholar
  13. 13.
    Rantakokko, J., Rydell, J., Strömbäck, P., Handel, P., Callmer, J., Törnqvist, D., et al. (2011). Accurate and reliable soldier and first responder indoor positioning: Multisensor systems and cooperative localization. IEEE Wireless Communications, 18(2), 10–18.CrossRefGoogle Scholar
  14. 14.
    Rantakokko, J., Handel, P., Fredholm, M., & Marsten-Eklof, F. (2010). User requirements for localization and tracking technology: A Survey of mission-specific needs and constraints. Zurich: International Conference on Indoor Positioning and Indoor Navigation (IPIN).Google Scholar
  15. 15.
    Sana S. & Matsumoto M. (2007). A framework for data collection and wireless sensor network protocol for disaster management. The 2nd International Conference on Communication Systems Software and Middleware (COMSWARE 2007), 1–6.Google Scholar
  16. 16.
    Pawelczak P., Prasad R. V., Xia L., Niemegeers I. G. M. M. (2005). Cognitive radio emergency networks—requirements and design. Proceedings of IEEE International Conference on New Frontiers in Dynamic Spectrum Access Networks, 601–606.Google Scholar
  17. 17.
    Sayed, A. H., Tarighat, A., & Khajehnouri, N. (2005). Network-based wireless location: Challenges faced in developing techniques for accurate wireless location information. IEEE Signal Processing Magazine, 22(4), 24–40.CrossRefGoogle Scholar
  18. 18.
    Sun, G., Chen, J., Guo, W., & Liu, K. J. R. (2005). Signal processing techniques in network-aided positioning: A survey of state-of-the-art positioning designs. IEEE Signal Processing Magazine, 22(4), 12–23.CrossRefGoogle Scholar
  19. 19.
    Patwari, N., Ash, J. N., Kyperountas, S., Hero, A. O., I. I. I., Moses, R. L., & Correal, N. S. (2005). Locating the nodes: Cooperative localization in wireless sensor networks. IEEE Signal Processing Magazine, 22(4), 54–69.CrossRefGoogle Scholar
  20. 20.
    Figueiras, J., & Frattasi, S. (2010). Mobile Positioning and Tracking. From Conventional to Cooperative Techniques. New Jersey: John Wiley and Sons.CrossRefGoogle Scholar
  21. 21.
    Mauve, M., Widmer, A., & Hartenstein, H. (2001). A survey on position-based routing in mobile ad hoc networks. IEEE Network, 15(6), 30–39.CrossRefGoogle Scholar
  22. 22.
    Savarese, C., Rabaey, J. M., & Beutel, J. (2001). Locationing in distributed ad-hoc wireless sensor networks. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’01), 4, 2037–2040.CrossRefGoogle Scholar
  23. 23.
    Hanzo, L. (2003). OFDM and MC-CDMA for broadband multi-user communications, WLANs, and broadcasting. New Jersey: Wiley.CrossRefGoogle Scholar
  24. 24.
    http://focus.ti.com/general/docs/bcg/bcggencontent.tsp. Broadband Solutions: e-Newsletter, Texas Instruments.
  25. 25.
    www.cse.wustl.edu/~jain/cse574-06/ftp/WiMAX/. Metropolitan and Regional Wireless Networking: 802.16, 802.20 and 802.22.
  26. 26.
  27. 27.
    Ault A., Coyle E., Zhong X., (2005). K-nearest-neighbor analysis of received signal strength distance estimation across environments. Proceedings of the First Workshop on Wireless Network Measurements, April. 2005.Google Scholar
  28. 28.
    Wang, X., Wang, Z., & O’Dea, B. (2003). A TOA-based location algorithm reducing the errors due to the non-line-of-sight (NLOS) propagation. IEEE Transactions on Vehicular Technology, 52, 112–116.CrossRefGoogle Scholar
  29. 29.
    Venkatraman, S., Caffery, J., Jr, & You, H.-R. (2004). A novel ToA location algorithm using LoS range estimation for NLoS environments. IEEE Transactions on Vehicular Technology, 53(5), 1515–1524.CrossRefGoogle Scholar
  30. 30.
    Smith, J. O., Abel, J., & Abel, J. (1987). Closed form least-squares source location estimation from range-difference measurements. IEEE Transactions on Acoustics, Speech and Signal Processing, ASSP-35(12), 1661–1669.CrossRefGoogle Scholar
  31. 31.
    Bakhoum, E. G. (2006). Closed-form solution of hyperbolic geolocation equations. IEEE Transactions on Aerospace and Electronic Systems, 42(4), 1396–1404.CrossRefGoogle Scholar
  32. 32.
    Foy, W. H. (1976). Position location solutions by Taylor-series estimation. IEEE Transactions on Aerospace and Electronic Systems, 12(2), 187–194.CrossRefGoogle Scholar
  33. 33.
    Chan, Y. T., & Ho, K. C. (1994). A simple and efficient estimator for hyperbolic location. IEEE Transactions on Signal Processing, 42(8), 1905–1915.CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Meenakshi Rawat
    • 1
  • Karun Rawat
    • 1
  • Ramzi Darraji
    • 1
  • Fermin Esparza Alfaro
    • 1
  • Seyed Aidin Bassam
    • 1
  • Mohamed Helaoui
    • 1
  • Fadhel M. Ghannouchi
    • 1
  • Michel Fattouche
    • 1
  • Francisco Falcone
    • 1
  1. 1.UPNAPamplonaSpain

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