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A Comparative Study of RSSI-Based Localization Methods: RSSI Variation Caused by Human Presence and Movement

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Abstract

In a received signal strength indicator (RSSI) based localization system, the presence or movement of humans is one of the major effects causing RSSI variation. Using RSSI data during such a situation to estimate the target position can give large errors. Regarding this problem, in this paper, a comparison of several RSSI-based localization methods with and without human presence and movement were investigated experimentally. The major contribution of this work is that the well-known and widely used RSSI-based localization methods presented in the literature, including the min–max, the trilateration, the weighted centroid localization (WCL), and the relative span exponential weighted localization (REWL) methods, were tested. Thus, how human presence or absence influences the accuracy of these methods, and which methods show the best estimates while tolerating human movement effects can be investigated. The experiments were carried out in a laboratory and in a parking building. The results demonstrate that, without human movement effects, all methods perform very similarly. In contrast, human movements significantly increased estimation errors.Here, the maximum distance errors of the min–max, the trilateration, the WCL, and the REWL are 1.34 m, 4.09 m, 1.25 m, and 1.24 m, respectively. Obviously, the min–max, the WCL (with an optimal parameter), and the REWL (with the optimal parameter) can well tolerate the RSSI variations caused by human movements and provide significantly better accuracy than the trilateration method. Based on these findings, all the mentioned localization methods should be further improved to deal with the human movement problem.

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References

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

    Article  Google Scholar 

  2. Redondi, A., Chirico, M. Borsani, Cesana, M., & Tagliasacchi, M. (2013). An integrated system based on wireless senor networks for patient monitoring. Localization and tracking. Ad-Hoc Networks, 11, 29–53.

    Article  Google Scholar 

  3. Huang, H., Zhao, H., Li, X., Ding, S., Zhao, L., & Li, Z. (2018). An accurate and efficient device-free localization approach based on sparse coding in subspace. IEEE Access, 6, 61782–61799.

    Article  Google Scholar 

  4. Booranawong, A., Jindapetch, N., & Saito, H. (2018). A system for detection and tracking of human movements using RSSI signals. IEEE Sensors Journal, 18(6), 2531–2544.

    Article  Google Scholar 

  5. Booranawong, A., Jindapetch, N., & Saito, H. (2019). Adaptive filtering methods for RSSI signals in a device-free human detection and tracking system. IEEE Systems Journal, 13(3), 2998–3009.

    Article  Google Scholar 

  6. Zhao, L., Su, C., Dai, Z., Huang, H., Ding, S., Huang, X., & Han, Z. (2019). Indoor device-free passive localization with DCNN for location-based services. The Journal of Supercomputing. https://doi.org/10.1007/s11227-019-03110-2.

  7. Zhao, L., Huang, H., Li, X., Ding, S., Zhao, H., & Han, Z. (2019). An accurate and robust approach of device-free localization with convolutional autoencoder. IEEE Internet of Things Journal, 6(3), 5825–5840.

    Article  Google Scholar 

  8. Pei, Z., Deng, Z., Xu, S., & Xu, X. (2009). Anchor-free localization method for mobile targets in coal mine wireless sensor networks. Sensors, 9(4), 2836–2850.

    Article  Google Scholar 

  9. Wang, Y., Huang, L., & Yang, W. (2010). A novel real-time coal miner localization and tracking system based on self-organized sensor networks. EURASIP Journal on Wireless Communications and Networking, 2010, 142092. https://doi.org/10.1155/2010/142092

  10. Woo, S., Jeong, S., Mok, E., Xia, L., Choi, C., Pyeon, M., et al. (2011). Application of WiFi-based indoor positioning system for labor tracking at construction sites: A case study in Guangzhou MTR. Automation in Construction, 20(1), 3–13.

    Article  Google Scholar 

  11. Luo, X., Brien, W. J., & Julien, C. L. (2011). Comparative evaluation of received signal-strength index (RSSI) based indoor localization techniques for construction jobsites. Advanced Engineering Informatics, 5(2), 355–363.

    Article  Google Scholar 

  12. Lin, P., Li, Q., Fan, Q., Gao, X., & Hu, S. (2014). A real-time location-based services system using WiFi fingerprinting algorithm for safety risk assessment of workers in tunnels. Mathematical Problems in Engineering, 2014, 1–10. https://doi.org/10.1155/2014/371456.

  13. Bondavalli, A., Ceccarelli, A., Gogaj, F., Seminatore, A., & Vadursi, M. (2013). Experimental assessment of low-cost GPS-based localization in railway worksite-like scenarios. Measurement, 46(1), 456–466.

    Article  Google Scholar 

  14. Bjorkbom, M., Nethi, S., Eriksson, L. M., & Jantti, R. (2011). Wireless control system design and co-simulation. Control Engineering Practice., 19(9), 1075–1086.

    Article  Google Scholar 

  15. Booranawong, A., Teerapabkajorndet, W., & Limsakul, C. (2013). Energy consumption and control response evaluations of AODV routing in WSANs for building-temperature control. Sensors, 13(7), 8303–8330.

    Article  Google Scholar 

  16. Yeh, L. W., Lu, C. Y., Kou, C. W., Tseng, Y. C., & Yi, C. W. (2010). Autonomous light control by wireless sensor and actuator networks. IEEE Sensor Journal, 10(6), 1029–1041.

    Article  Google Scholar 

  17. Yassin, A., Nasser, Y., Awad, M., Al-Dubai, A., Liu, R., Yuen, C., et al. (2016). Recent advances in indoor localization: A survey on theoretical approaches and applications. IEEE Communications Surveys & Tutorials, 19(2), 1327–1346.

    Article  Google Scholar 

  18. Yang, B., Guo, L., Guo, R., Zhao, M., & Zhao, T. (2020). A novel trilateration algorithm for RSSI-based indoor localization. IEEE Sensors Journal.

  19. Goldoni, E., Prando, L., Vizziello, A., Savazzi, P., & Gamba, P. (2019). Experimental data set analysis of RSSI-based indoor and outdoor localization in LoRa networks. Internet Technology Letters, 2(1), e75.

    Article  Google Scholar 

  20. Kwasme, H., & Ekin, S. (2019). RSSI-based localization using LoRaWAN technology. IEEE Access, 7, 99856–99866.

    Article  Google Scholar 

  21. Yang, B., Qiu, Q., Han, Q. L., & Yang, F. (2020). Received signal strength indicator-based indoor localization using distributed set-membership filtering. IEEE Transactions on Cybernetics.

  22. Lam, K. H., Cheung, C. C., & Lee, W. C. (2019). RSSI-based LoRa localization systems for large-scale indoor and outdoor environments. IEEE Transactions on Vehicular Technology, 68(12), 11778–11791.

    Article  Google Scholar 

  23. 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 

  24. Zanca, G., Zorzi, F., Zanella, A., & Zorzi, M. (2008). Experimental comparison of RSSI-based localization algorithms for indoor wireless sensor networks. In Proceedings of the workshop on real-word wireless sensor networks (pp. 1–5).

  25. Pahtma, R., Preden, J., Ager, R., & Pikk, P. (2009). Utilization of received signal strength indication by embedded nodes. Elektronika ir Elektrotechnika., 5(93), 39–43.

    Google Scholar 

  26. Chapre, Y., Mohapatra, P., Jha, S., & Seneviratne, A. (2013). Received signal strength indicator and its analysis in a typical WLAN system. In Proceedings of the 38th IEEE conference on local computer networks (pp. 304–307).

  27. Goldoni, E., Savioli, A., Risi, M., & Gamba, P. (2010). Experimental analysis of RSSI-based indoor localization with IEEE 802.15.4. In Proceedings of the European wireless conference (pp. 71–77).

  28. Rattanalert, B., Jindamaneepon, W., Sengchuai, K., Booranawong, A., & Jindapetch, N. (2015). Problem investigation of min-max method for RSSI based indoor localization. In Proceedings of the 12th international conference on electrical engineering/electronics, computer, telecommunications and information technology (pp. 1–5).

  29. Naghdi, S., & O’Keefe, K. (2020). Detecting and correcting for human obstacles in BLE Trilateration using artificial intelligence. Sensors, 20(5), 1350.

    Article  Google Scholar 

  30. Liu, J., Teng, G., & Hong, F. (2020). Human activity sensing with wireless signals: a survey. Sensors, 20(4), 1210.

    Article  Google Scholar 

  31. Hamida, E. B., & Chaliue, G. (2010). Investigating the impact of human activity on the performance of wireless networks-an experimental approach. In Proceedings of the IEEE international symposium on a world of wireless mobile and multimedia networks (WoWMoM2010) (pp. 1–8).

  32. Turner, J. S. C., Ramli, M. F., Kamarudin, L. M., Zakaria, A., Shakaff, A. Y. M., Ndzi, D. L., et al. (2013). The study of human movement effect on signal strength for indoor WSN deployment. In: Proceedings of the IEEE conference on wireless sensors (ICWiSe2013) (pp. 30–35).

  33. Lin, W. C., Seah, W. K. G., & Li, W. (2011). Exploiting radio irregularity in the internet of things for automated people counting. In: Proceedings of the 22nd IEEE international symposium on personal, indoor and mobile radio communications (PIMRC2011) (pp. 1015–1019).

  34. Alshami, I. H., Ahmad, N. A., & Sahibuddin, S. (2015). People’s presence effect on WLAN-based IPs accuracy. Journal Teknologi, 77(9), 173–178.

    Google Scholar 

  35. Booranawong, A., Sopajarn, J., Sittiruk, T., & Jindapetch, N. (2018). Reduction of RSSI variations for indoor position estimation in wireless sensor networks. Engineering and Applied Science Research., 45(3), 212–220.

    Google Scholar 

  36. Booranawong, A., Sengchuai, K., & Jindapetch, N. (2019). Implementation and test of an RSSI-based indoor target localization system: Human movement effects on the accuracy. Measurement, 133, 370–382.

    Article  Google Scholar 

  37. Sasiwat, Y., Buranapanichkit, D., Chetpattananondh, K., Sengchuai, K., Jindapetch, N., & Booranawong, A. (2020). Human movement effects on the performance of the RSSI-based trilateration method: adaptive filters for distance compensation. Journal of Reliable Intelligent Environments, 6, 67–78.

    Article  Google Scholar 

  38. Wattananawin, T., Sengchuai, K., Jindapetch, N., & Booranawong, A. (2019). Reduction of RSSI variation and position estimation error caused by human movements in an RSSI-based indoor localization system. Suranaree Journal of Science and Technology, 26(3), 266–277.

    Google Scholar 

  39. Reichenbach, F., & Timmermann, D. (2006). Indoor localization with low complexity in wireless sensor networks. In Proceedings of IEEE international conference on industrial informatics (pp. 1018–1023).

  40. Blumenthal, J., Grossmann, R., Golatowski, F., & Timmermann, D. (2007). Weighted centroid localization in ZigBee-based sensor networks. In Proceedings of IEEE international symposium on intelligent signal processing (pp. 1–6).

  41. Pivato, P., Palopoli, L., & Petri, D. (2011). Accuracy of RSS-based centroid localization algorithms in an indoor environment. IEEE Transactions on Instrumentation and Measurement, 60(10), 3451–3460.

    Article  Google Scholar 

  42. Savvides, A., Park, H., & Srivastava, M. (2002). The bits and flops of the N-hop multilateration primitive for node localization problems. In Proceedings of the first ACM international workshop on wireless sensor networks and application (pp. 112–121).

  43. Langendoen, K., & Reijers, N. (2003). Distributed localization in wireless sensor networks: a quantitative comparison. Computer Networks, 43(4), 499–518.

    Article  MATH  Google Scholar 

  44. Bulusu, N., Heidemann, J., & Estrin, D. (2000). GPS-less low cost outdoor localization for very small devices. IEEE Personal Communications Magazine, 7(5), 28–34.

    Article  Google Scholar 

  45. Laurendeau, C., & Barbeau, M. (2009, August). Relative span weighted localization of uncooperative nodes in wireless networks. In International conference on wireless algorithms, systems, and applications (pp. 358–367). Berlin: Springer.

  46. Instrument T. CC2500 datasheet. Retrieved January 1, 2020 from http://www.ti.com.cn/cn/lit/ds/swrs040c/swrs040c.pdf.

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Acknowledgements

This work was supported by Nakhon Si Thammarat Rajabhat University, and Faculty of Engineering, Prince of Songkla University.

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Correspondence to Apidet Booranawong.

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Wattananavin, T., Sengchuai, K., Jindapetch, N. et al. A Comparative Study of RSSI-Based Localization Methods: RSSI Variation Caused by Human Presence and Movement. Sens Imaging 21, 31 (2020). https://doi.org/10.1007/s11220-020-00296-1

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