Skip to main content

Advertisement

Springer Nature Link
Log in

CRAMStrack: Enhanced Nonlinear RSSI Tracking by Using Circular Multi-Sectors

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

Indoor localization using a Received Signal Strength Indicator (namely, RSSI localization) has been considered a poor measurement for target tracking. The main cause of this inaccurate measurement is that RSSI’s behaviors heavily depend on environmental factors. That is, one significant challenge to localization using RSSI is that the strength of a signal varies with the environment confounding wireless communications power and signal control. In this paper, we propose Circular RSSI And Multi-Sector tracking (CRAMStrack), a novel approach to reducing the uncertainty of RSSI localization by modifying the relationship of RSSI-to-Distance (RtD), based on the sectors of a circle and the position of the tracked target. Traditional RSSI tracking uses one uniform RtD relationship to locate a target whereas CRAMStrack utilizes multiple RtD responses for each wireless sensor. The paper examines CRAMStrack’s tracking ability in a Euclidean space with estimation techniques. Real-world experiments demonstrate CRAMStrack in a testbed environment to locate targets in both stationary, linear, and non-linear movement patterns with single and group-based formations. The track accuracy was about 1.46m for moving targets, while CRAMStrack had a 40% reduction in Root Mean Square Error (RMSE) over Uni-RtD using neighboring sensor information.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16

Similar content being viewed by others

Notes

  1. This relationship will be labeled as “Uni-RtD” throughout the paper as referring to the Uniform RtD tracking relationship.

References

  1. Ahn, H.S., & Yu, W. (2009). Environmental-adaptive RSSI-based indoor localization. IEEE Transactions on Automation Science and Engineering, 6(4), 626–633.

    Article  Google Scholar 

  2. Alaybeyoglu, A., Erciyes, K., Kantarci, A., & Dagdeviren, O. (2010). Tracking fast moving targets in wireless sensor networks. IETE Technical Review, 27(1), 46–53.

    Article  Google Scholar 

  3. Alippi, C., & Vanini, G. (2006). A RSSI-based and calibrated centralized localization technique for wireless sensor networks. In Proc. IEEE Int. conference on pervasive computing and communications workshops (PERCOMW) (pp. 301–306).

  4. Anisetti, M., Ardagna, C.A., Bellandi, V., Damiani, E., & Reale, S. (2011). Map-based location and tracking in multipath outdoor mobile networks. Transactions on Wireless Communications, 10(3), 814–824. https://doi.org/10.1109/TWC.2011.011811.100025.

    Article  Google Scholar 

  5. Baronti, P., Pillai, P., Chook, V.W.C., Chessa, S., Gotta, A., & Hu, Y.F. (2007). Wireless sensor networks: a survey on the state of the art and the 802.15.4 and zigbee standards. Computer Communications, 30(7), 1655–1695. https://doi.org/10.1016/j.comcom.2006.12.020.

    Article  Google Scholar 

  6. Barsocchi, P., Lenzi, S., Chessa, S., & Giunta, G. (2009). A novel approach to indoor RSSI localization by automatic calibration of the wireless propagation model. In IEEE 69th vehicular technology conference (pp. 1–5): IEEE.

  7. Benkic, K., Malajner, M., Planinsic, P., & Cucej, Z. (2008). Using rssi value for distance estimation in wireless sensor networks based on zigbee. In 15th International conference on systems, signals and image processing, 2008. IWSSIP 2008 (pp. 303–306): IEEE.

  8. Berman, M., Chase, J.S., Landweber, L., Nakao, A., Ott, M., Raychaudhuri, D., Ricci, R., & Seskar, I. (2014). GENI: a federated testbed for innovative network experiments. Computer Networks.

  9. Blasch, E., & Connare, T. (2001). Improving track maintenance through group tracking. In Proc of the workshop on estimation, tracking, and fusion; a tribute to Yaakov Bar Shalom (pp. 360–371).

  10. Blumrosen, G., Hod, B., Anker, T., Dolev, D., & Rubinsky, B. (2013). Enhanced calibration technique for RSSI-based ranging in body area networks. Ad hoc Networks, 11(1), 555–569.

    Article  Google Scholar 

  11. Blumrosen, G., Hod, B., Anker, T., Dolev, D., & Rubinsky, B. (2013). Enhancing RSSI-based tracking accuracy in wireless sensor networks. ACM Transactions on Sensor Networks, 9(3), 29:1–29:28. https://doi.org/10.1145/2480730.2480732.

    Article  Google Scholar 

  12. Bouckaert, S., Vandenberghe, W., Jooris, B., Moerman, I., & Demeester, P. (2011). The w-ilab. t testbed. In Testbeds and research infrastructures. Development of networks and communities (pp. 145–154): Springer.

  13. Chen, X., Rowe, N.C., Wu, J., & Xiong, K. (2012). Improving the localization accuracy of targets by using their spatial–temporal relationships in wireless sensor networks. Journal of Parallel and Distributed Computing, 72(8), 1008–1018.

    Article  Google Scholar 

  14. Cheng, L., Wu, C.D., & Zhang, Y.Z. (2011). Indoor robot localization based on wireless sensor networks. IEEE Transactions on Consumer Electronics, 57(3), 1099–1104.

    Article  Google Scholar 

  15. Chin, T., & Xiong, K. (2016). Mpbsd: a moving target defense approach for base station security in wireless sensor networks. In International conference on wireless algorithms, systems, and applications (pp. 487–498): Springer.

  16. Chin, T., Xiong, K., & Blasch, E. (2015). Nonlinear target tracking for threat detection using rssi and optical fusion. In 18th International conference on information fusion (Fusion) (pp. 1946–1953).

  17. Connare, T., Blasch, E., Schmitz, J., Salvatore, F., & Scarpino, F. (2001). Group imm tracking utilizing track and identification fusion. In Proc. of the workshop on estimation, tracking, and fusion; a tribute to Yaakov Bar Shalom (pp. 205–220).

  18. Dashti, M., Yiu, S., Yousefi, S., Perez-Cruz, F., & Claussen, H. (2015). RSSI localization with gaussian processes and tracking. In 2015 IEEE Globecom workshops (GC Wkshps) (pp. 1–6), DOI https://doi.org/10.1109/GLOCOMW.2015.7414106, (to appear in print).

  19. Dyo, V., & Mascolo, C. (2008). Efficient node discovery in mobile wireless sensor networks. In Proceedings of the 4th IEEE international conference on distributed computing in sensor systems, DCOSS ’08 (pp. 478–485). Berlin: Springer, DOI https://doi.org/10.1007/978-3-540-69170-9_33, (to appear in print).

  20. Galluzzi, V., & Herman, T. (2012). Survey: discovery in wireless sensor networks. IJDSN, 2012.

  21. Halder, S., & Ghosal, A. (2016). A survey on mobility-assisted localization techniques in wireless sensor networks. Journal of Network and Computer Applications, 60(C), 82–94. https://doi.org/10.1016/j.jnca.2015.11.019.

    Article  Google Scholar 

  22. Iliev, N., & Paprotny, I. (2015). Review and comparison of spatial localization methods for low-power wireless sensor networks. IEEE Sensors Journal, 15(10), 5971–5987.

    Article  Google Scholar 

  23. Ito, K., & Xiong, K. (2000). Gaussian filters for nonlinear filtering problems. IEEE Transactions on Automatic Control, 45(5).

  24. Karenos, K., & Kalogeraki, V. (2010). Traffic management in sensor networks with a mobile sink. IEEE Transactions on Parallel and Distributed Systems, 21(10), 1515–1530.

    Article  Google Scholar 

  25. Kohvakka, M., Suhonen, J., Kuorilehto, M., Kaseva, V., Hännikäinen, M., & Hämäläinen, T.D. (2009). Energy-efficient neighbor discovery protocol for mobile wireless sensor networks. Ad Hoc Networks, 7(1), 24–41. https://doi.org/10.1016/j.adhoc.2007.11.016.

    Article  Google Scholar 

  26. Lau, E.E.L., & Chung, W.Y. (2007). Enhanced RSSI-based real-time user location tracking system for indoor and outdoor environments. In International conference on convergence information technology (pp. 1213–1218): IEEE.

  27. Lee, B.G., & Chung, W.Y. (2011). Multitarget three-dimensional indoor navigation on a PDA in a wireless sensor network. IEEE Sensors Journal, 11(3), 799–807.

    Article  Google Scholar 

  28. Li, D., & Hu, Y. (2003). Energy-based collaborative source localization using acoustic micro-sensor array. EURASIP Journal on Advances in Signal Processing.

  29. Li, Y., Serrano, M., Chin, T., Xiong, K., & Lin, J. (2019). A software-defined networking-based detection and mitigation approach against krack.

  30. Mahfouz, S., Mourad-Chehade, F., Honeine, P., Farah, J., & Snoussi, H. (2014). Target tracking using machine learning and kalman filter in wireless sensor networks. IEEE Sensors Journal.

  31. Mahfouz, S., Mourad-Chehade, F., Honeine, P., Farah, J., & Snoussi, H. (2016). Non-parametric and semi-parametric RSSI/distance modeling for target tracking in wireless sensor networks. IEEE Sensors Journal, 16(7).

  32. Malajner, M., Gleich, D., & Planinšič, P. (2015). Angle of arrival measurement using multiple static monopole antennas. IEEE Sensors Journal, 15(6), 3328–3337.

    Article  Google Scholar 

  33. Malajner, M., Planinsic, P., & Gleich, D. (2012). Angle of arrival estimation using RSSI and omnidirectional rotatable antennas. IEEE Sensors Journal, 12(6), 1950–1957.

    Article  Google Scholar 

  34. Martin, E., Vinyals, O., Friedland, G., & Bajcsy, R. (2010). Precise indoor localization using smart phones. In Proceedings of the 18th ACM international conference on multimedia (pp. 787–790): ACM.

  35. Narayanan, R.G.L., & Ibe, O.C. (2012). A joint network for disaster recovery and search and rescue operations. Computer Networks, 56(14), 3347–3373.

    Article  Google Scholar 

  36. Niu, R., Vempaty, A., & Varshney, P. (2018). . Received signal strength based localization in wireless sensor networks, 106(7), 1166–1182.

    Google Scholar 

  37. Papandrea, M., Giordano, S., Vanini, S., & Cremonese, P. (2010). Proximity marketing solution tailored to user needs. In IEEE International symposium on a world of wireless mobile and multimedia networks (WoWMoM) (pp. 1–3): IEEE.

  38. Paul, A.S., Wan, E., & et al. (2009). RSSI-based indoor localization and tracking using sigma-point kalman smoothers. IEEE Journal of Selected Topics in Signal Processing, 3(5), 860–873.

    Article  Google Scholar 

  39. Pu, C.C., & Chung, W.Y. (2008). Mitigation of multipath fading effects to improve indoor RSSI performance. IEEE Sensors Journal, 8(11), 1884–1886.

    Article  Google Scholar 

  40. Sheng, X., & Hu, Y. (2005). Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks. IEEE Transactions on Signal Process, 53(1), 44–53.

    Article  MathSciNet  Google Scholar 

  41. Snidaro, L., Garcia-Herrera, J., Llinas, J., & Blasch, E. (2016). Context-enhanced information fusion. Springer.

  42. Straka, O., Duník, J., Šimandl, M., & Blasch, E. (2012). Randomized unscented transform in state estimation of non-gaussian systems: algorithms and performance. In 2012 15th International conference on information fusion (FUSION) (pp. 2004–2011): IEEE.

  43. Vadlamani, S., Eksioglu, B., Medal, H., & Nandi, A. (2016). Jamming attacks on wireless networks: a taxonomic survey. International Journal of Production Economics, 172, 76–94.

    Article  Google Scholar 

  44. Wang, Z., Blasch, E., Chen, G., Shen, D., Lin, X., & Pham, K. (2015). A low-cost, near-real-time two-UAS-based UWB emitter monitoring system. IEEE Aerospace and Electronic Systems Magazine, 30(11), 4–11.

    Article  Google Scholar 

  45. Xiong, K., & Thuente, D. (2009). Dynamic localization schemes in malicious sensor networks. Journal of Networks, 4(8), 677– 686.

    Google Scholar 

  46. Yang, C., & Blasch, E. (2009). Kalman filtering with nonlinear state constraints. IEEE Transactions on Aerospace and Electronic Systems, 45(1), 70–84.

    Article  Google Scholar 

  47. Yim, J., Jeong, S., Gwon, K., & Joo, J. (2010). Improvement of kalman filters for wlan based indoor tracking. Expert Systems with Applications, 37(1), 426–433.

    Article  Google Scholar 

  48. Yuan, Y., Zhao, J., Qiu, C., & Xi, W. (2013). Estimating crowd density in an rf-based dynamic environment. IEEE Sensors Journal, 13(10), 3837–3845.

    Article  Google Scholar 

Download references

Acknowledgements

This research was started through the 2014 summer research program of the Air-Force Research Laboratory (AFRL) in Rome, NY with a Dynamic Data Driven Application Systems (DDDAS) AFOSR grant 88ABW-2014-1321. We would like to thank Pieter Becue, Brecht Vermeulen, Vincent Sercu, and Bart Jooris for their diligent work in maintaining and supporting the iMinds w-iLab.t testbed that has made possible to conduct the real-world experiments in this paper. Tommy Chin would also like to thank Dr. Bo Yuan, Dr. Sumita Mishra, Liz Zimmerman, and Megan Fritts for their help during my graduate study in the Computing Security Department at the Rochester Institute of Technology. Finally, Dr. Kaiqi Xiong’s research has been supported in part by National Science Foundation (NSF) under his grants #1633978, #1620871, #1620862, and #1620868 and by NSF/BBN under grants CNS#1125515 for project #1895 and CNS#1346688 for project #1936.

The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of Air Force Research Laboratory, National Science Foundation, or the U.S. Government.

Author information

Authors and Affiliations

  1. Rochester Institute of Technology, Rochester, NY, USA

    Tommy Chin

  2. University of South Florida, Tampa, FL, USA

    Kaiqi Xiong

  3. U.S. Air Force Research Laboratory, Wright-Patterson Air Force Base, Toledo, OH, USA

    Eric Blasch

Authors
  1. Tommy Chin
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Kaiqi Xiong
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Eric Blasch
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Tommy Chin.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chin, T., Xiong, K. & Blasch, E. CRAMStrack: Enhanced Nonlinear RSSI Tracking by Using Circular Multi-Sectors. J Sign Process Syst 93, 79–97 (2021). https://doi.org/10.1007/s11265-020-01516-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-020-01516-3

Keywords