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A Fast Offline Database Construction Mechanism for Wi-Fi Fingerprint Based Localization Using Ultra-Wideband Technology

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Wireless Sensor Networks (CWSN 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1101))

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Abstract

With the ever-increasing demand on location-based services (LBS), fingerprint-based methods have attracted more and more attention in indoor localization. However, the considerable overhead of fingerprint is still a problem which hinders the practicability of such technology. Due to the prevalent of Wi-Fi access points (APs) and the high location accuracy of Ultra-Wideband (UWB), in this paper, we propose a hybrid system which utilizes UWB and Wi-Fi technologies to alleviate the offline overhead and improve the localization accuracy. Specifically, we employ UWB to determine the coordinate of each reference point (RP) instead of traditional manual measurement. Meanwhile, the Received Signal Strength Indicator (RSSI) of Wi-Fi is collected by a customized software installed in the mobile device. Then, a timestamp matching scheme is proposed to fuse these data coming from different devices and construct the offline fingerprint database. Besides, in order to better map the online data with offline database, an AP weight assignment scheme is proposed, which allocates APs with different weights based on the RSSI characteristic in each RP. We implement the system in real-world environment and the experimental results demonstrate the effectiveness of the proposed method.

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References

  1. Shu, Y., Bo, C., Shen, G., Zhao, C., Li, L., Zhao, F.: Magicol: indoor localization using pervasive magnetic field and opportunistic WiFi sensing. IEEE J. Sel. Areas Commun. 33(7), 1443–1457 (2015)

    Article  Google Scholar 

  2. Zhuang, Y., Yang, J., Li, Y., Qi, L., El-Sheimy, N.: Smartphone-based indoor localization with bluetooth low energy beacons. IEEE Sens. 16(5), 596 (2016)

    Article  Google Scholar 

  3. Fang, Y., Cho, Y.K., Zhang, S., Perez, E.: Case study of BIM and cloud-enabled real-time RFID indoor localization for construction management applications. J. Constr. Eng. Manag. 142(7), 05016003 (2016)

    Article  Google Scholar 

  4. Wang, K., Nirmalathas, A., Lim, C., Alameh, K., Li, H., Skafidas, E.: Indoor infrared optical wireless localization system with background light power estimation capability. Opt. Express 25(19), 22923–22931 (2017)

    Article  Google Scholar 

  5. Yayan, U., Yucel, H.: A low cost ultrasonic based positioning system for the indoor navigation of mobile robots. J. Intell. Rob. Syst. 78(3–4), 541–552 (2015)

    Article  Google Scholar 

  6. Coluccia, A., Fascista, A.: On the hybrid TOA/RSS range estimation in wireless sensor networks. IEEE Trans. Wirel. Commun. 17(1), 361–371 (2017)

    Article  Google Scholar 

  7. Yang, C., Shao, H.-R.: WiFi-based indoor positioning. IEEE Commun. Mag. 53(3), 150–157 (2015)

    Article  Google Scholar 

  8. Yang, Z., Wu, C., Liu, Y.: Locating in fingerprint space: wireless indoor localization with little human intervention. In: Proceedings of the 18th Annual International Conference on Mobile Computing and Networking, pp. 269–280. ACM, New York (2012)

    Google Scholar 

  9. Jun, J., et al.: Low-overhead wifi fingerprinting. IEEE Trans. Mob. Comput. 17(3), 590–603 (2017)

    Article  MathSciNet  Google Scholar 

  10. Shu, Y., et al.: Gradient-based fingerprinting for indoor localization and tracking. IEEE Trans. Ind. Electron. 63(4), 2424–2433 (2015)

    Article  Google Scholar 

  11. Elbakly, R., Youssef, M.: A robust zero-calibration RF-based localization system for realistic environments. In: 2016 13th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), pp. 1–9. IEEE, London (2016)

    Google Scholar 

  12. Machaj, J., Brida, P., Piché, R.: Rank based fingerprinting algorithm for indoor positioning. In: 2011 International Conference on Indoor Positioning and Indoor Navigation, pp. 1–6. IEEE, Guimaraes (2011)

    Google Scholar 

  13. Han S., Zhao C., Meng W., Li, C.: Cosine similarity based fingerprinting algorithm in WLAN indoor positioning against device diversity. In: 2015 IEEE International Conference on Communications (ICC), pp. 2710–2714. IEEE, London (2015)

    Google Scholar 

  14. Silva, B., Pang, Z., Akerberg, J., Neander, J., Hancke, G.: Experimental study of UWB-based high precision localization for industrial applications. In: 2014 IEEE International Conference on Ultra-WideBand (ICUWB), pp. 280–285. IEEE, Paris (2014)

    Google Scholar 

  15. Martin, E., Vinyals, O., Friedland, G., Bajcsy, R.: Precise indoor localization using smart phones. In: Proceedings of the 18th ACM International Conference on Multimedia, pp. 787–790. ACM, New York (2010)

    Google Scholar 

  16. Chai, E., Shin, K.G.: Low-overhead control channels in wireless networks. IEEE Trans. Mob. Comput. 14(11), 2303–2315 (2015)

    Article  Google Scholar 

  17. Toth, C.K., Jozkow, G., Koppanyi, Z., Grejner-Brzezinska, D.: Positioning slow-moving platforms by UWB technology in GPS-challenged areas. J. Surv. Eng. 143(4), 04017011 (2017)

    Article  Google Scholar 

  18. Shi, G., Ming, Y.: Survey of indoor positioning systems based on ultra-wideband (UWB) technology. In: Zeng, Q.-A. (ed.) Wireless Communications, Networking and Applications. LNEE, vol. 348, pp. 1269–1278. Springer, New Delhi (2016). https://doi.org/10.1007/978-81-322-2580-5_115

    Chapter  Google Scholar 

  19. Alarifi, A., et al.: Ultra wideband indoor positioning technologies: analysis and recent advances. Sensors 16(5), 707 (2016)

    Article  Google Scholar 

  20. Zhang, H., Liu, K., Jin, F., Feng, L., Lee, V., Ng, J.: A scalable indoor localization algorithm based on distance fitting and fingerprint mapping in Wi-Fi environments. Neural Comput. Appl. 2019, 1–15 (2019)

    Google Scholar 

  21. Zhang, H., et al.: An Annulus Local Search Based Localization (ALSL) algorithm in indoor Wi-Fi environments. In: 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced, pp. 887–892. IEEE, Guangzhou (2018)

    Google Scholar 

  22. Liu, K., et al.: Toward low-overhead fingerprint-based indoor localization via transfer learning: design, implementation, and evaluation. IEEE Trans. Ind. Inform. 14(3), 898–908 (2017)

    Article  Google Scholar 

  23. Jin, F., Liu, K., Zhang, H., Wu, W., Cao, J., Zhai, X.: A zero site-survey overhead indoor tracking system using particle filter. In: ICC 2019–2019 IEEE International Conference on Communications (ICC), pp. 1–7. IEEE, Shanghai (2019)

    Google Scholar 

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Acknowledgement

This work was supported in part by the National Natural Science Foundation of China under Grant No. 61872049; the Frontier Interdisciplinary Research Funds for the Central Universities (Project No. 2018CDQYJSJ0034); and the Venture & Innovation Support Program for Chongqing Overseas Returnees (Project No. cx2018016).

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Authors and Affiliations

  1. Department of Computer Science, Chongqing University, Chongqing, 400040, China

    Huilin Jie, Hao Zhang, Kai Liu, Feiyu Jin, Chao Chen & Chaocan Xiang

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  1. Huilin Jie
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  2. Hao Zhang
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  3. Kai Liu
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  4. Feiyu Jin
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  5. Chao Chen
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  6. Chaocan Xiang
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Corresponding author

Correspondence to Kai Liu .

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Editors and Affiliations

  1. Chongqing University, Chongqing, China

    Songtao Guo

  2. Chongqing University, Chongqing, China

    Kai Liu

  3. Chongqing University, Chongqing, China

    Chao Chen

  4. Chongqing University, Chongqing, China

    Hongyu Huang

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Jie, H., Zhang, H., Liu, K., Jin, F., Chen, C., Xiang, C. (2019). A Fast Offline Database Construction Mechanism for Wi-Fi Fingerprint Based Localization Using Ultra-Wideband Technology. In: Guo, S., Liu, K., Chen, C., Huang, H. (eds) Wireless Sensor Networks. CWSN 2019. Communications in Computer and Information Science, vol 1101. Springer, Singapore. https://doi.org/10.1007/978-981-15-1785-3_22

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  • DOI: https://doi.org/10.1007/978-981-15-1785-3_22

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1784-6

  • Online ISBN: 978-981-15-1785-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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