Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

Accurate Event Detection and Velocity Estimation in Wireless Environments

, , , ,

DOI: http://dx.doi.org/10.15439/2016F202

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 10571066 ()

Full text

Abstract. Radio signals can be used to detect the presence of a person (target) in an environment by analysing the fluctuations in the Received Signal Strength Indicator (RSSI). The velocity of the target can be estimated by examining the sequence of disturbances in consecutive radio links over a period of time. This requires knowledge of the deployment of the radio transceivers and the time when the target crosses the Line of Sight (LoS) of each radio link. However, it is not trivial to precisely estimate the exact time of the link crossing due to the broad range of RSSI fluctuations generated as the target approaches the link. In this paper, we evaluate and compare 15 techniques for estimating the velocity of the target and propose enhancements to some of the techniques. In our experiments the techniques perform with an average accuracy in the range between 13.02\% and 96.18\%, which corresponds to an average error of 0.05m/s for a moving target.

References

  1. K. Woyach, D. Puccinelli, and M. Haenggi, “Sensorless sensing in wireless networks: Implementation and measurements,” 2006 4th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks, WiOpt 2006, 2006. http://dx.doi.org/10.1109/WIOPT.2006.1666495.
  2. S. Hussain, R. Peters, and D. Silver, “Using received signal strength variation for surveillance in residential areas,” SPIE Defense, 2008. Available: http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=837114
  3. J. Wilson and N. Patwari, “Radio tomographic imaging with wireless networks,” Mobile Computing, IEEE Transactions on, vol. 9, no. 5, pp. 621–632, 2010. Available: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5374407
  4. O. Kaltiokallio and M. Bocca, “Real-Time Intrusion Detection and Tracking in Indoor Environment through Distributed RSSI Processing,” 2011 IEEE 17th International Conference on Embedded and Real-Time Computing Systems and Applications, pp. 61–70, aug 2011. http://dx.doi.org/10.1109/RTCSA.2011.38. Available: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6029830
  5. M. Kochláň, J. Miček, and P. Ševčík, “2.4ghz ism band radio frequency signal indoor propagation,” in Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, ser. Annals of Computer Science and Information Systems, M. P. M. Ganzha, L. Maciaszek, Ed., vol. 2. IEEE, 2014. http://dx.doi.org/10.15439/2014F299 pp. pages 1027–1034. Available: http://dx.doi.org/10.15439/2014F299
  6. J. Yang, Y. Ge, and H. Xiong, “Performing Joint Learning for Passive Intrusion Detection in Pervasive Wireless Environments,” 2010 Proceedings IEEE, 2010. Available: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5462148
  7. M. Bocca, O. Kaltiokallio, N. Patwari, and S. Venkatasubramanian, “Multiple target tracking with rf sensor networks,” IEEE Transactions on Mobile Computing, vol. 13, no. 8, pp. 1787–1800, 2014. http://dx.doi.org/10.1109/TMC.2013.92.
  8. F. Adib, Z. Kabelac, D. Katabi, R. C. Miller, I. Nsdi, and Z. Kabelac, “Multi-Person Localization via RF Body Reflection,” Usenix Nsdi, 2014. Available: https://www.usenix.org/conference/nsdi15/technical-sessions/presentation/adib
  9. T. Li, Y. Wang, L. Song, and H. Tan, Wireless Sensor Networks: 12th European Conference, EWSN 2015, Porto, Portugal, February 9-11, 2015. Proceedings. Cham: Springer International Publishing, 2015, ch. On Target Counting by Sequential Snapshots of Binary Proximity Sensors, pp. 19–34. ISBN 978-3-319-15582-1. http://dx.doi.org/10.1007/978-3-319-15582-1_2
  10. J. Wilson and N. Patwari, “See-through walls: Motion tracking using variance-based radio tomography networks,” Mobile Computing, IEEE Transactions on, vol. 10, no. 5, pp. 612–621, 2011. Available: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5582100
  11. O. Kaltiokallio, M. Bocca, and N. Patwari, “Follow @grandma: Long-term device-free localization for residential monitoring,” Proceedings - Conference on Local Computer Networks, LCN, pp. 991–998, 2012. http://dx.doi.org/10.1109/LCNW.2012.6424092.
  12. J. Wilson and N. Patwari, “A Fade-Level Skew-Laplace Signal Strength Model for Device-Free Localization with Wireless Networks,” IEEE Transactions on Mobile Computing, vol. 11, no. 6, pp. 947–958, jun 2012. http://dx.doi.org/10.1109/TMC.2011.102. Available: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6188339
  13. N. Kassem, A. E. Kosba, and M. Youssef, “RF-Based Vehicle Detection and Speed Estimation,” 2012 IEEE 75th Vehicular Technology Conference (VTC Spring), pp. 1–5, may 2012. http://dx.doi.org/10.1109/VETECS.2012.6240184. Available: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6240184
  14. O. Karpis, “Sensor for vehicles classification.” in FedCSIS, 2012, pp. 785–789. Available: https://fedcsis.org/proceedings/2012/pliks/215.pdf
  15. O. Kaltiokallio, “Intrusion Detection Based on Embedded Processing of Received Signal Strength Indicator,” Master’s thesis, Aalto University, 2011.
  16. D. Zhang and L. M. Ni, “Dynamic clustering for tracking multiple transceiver-free objects,” 2009 IEEE International Conference on Pervasive Computing and Communications, pp. 1–8, mar 2009. http://dx.doi.org/10.1109/PERCOM.2009.4912777. Available: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4912777
  17. R. Figura, M. Ceriotti, C.-Y. Shih, M. Mulero-Pâzmâny, S. Fu, R. Daidone, S. Jungen, J. J. Negro, and P. J. Marrôn, “Iris: Efficient visualization, data analysis and experiment management for wireless sensor networks,” EAI Endorsed Transactions on Ubiquitous Environments, vol. 14, no. 3, 11 2014. http://dx.doi.org/10.4108/ue.1.3.e4.