ZigBee-Based Device-Free Wireless Localization in Internet of Things

  • Yongliang Sun
  • Xiaocheng Wang
  • Xuzhao ZhangEmail author
  • Xinggan Zhang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 251)


In recent years, localization has been one of the research hot-spots in Internet of Things (IoT). Device-Free Wireless Localization (DFWL) that extends the application range of wireless localization has been considered as a promising technology. In this paper, we propose a ZigBee-based DFWL system using Artificial Neural Networks (ANNs) in IoT. The proposed system utilizes Received Signal Strength (RSS) variations, which is caused by the obstructing of the Line of Sight (LoS) links, to estimate the location of a target using an ANN model. A nonlinear function is approximated between RSS difference information and location coordinates using the ANN model. With the ANN model, the location of the target can be estimated. The experimental results show that the proposed DFWL system is able to locate the target without any terminal device and offer a valuable reference for DFWL in IoT.


Device-free wireless localization Internet of Things Artificial neural networks ZigBee 



The authors gratefully thank the referees for the constructive and insightful comments. This work was supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant No. 16KJB510014, the Natural Science Foundation of Jiangsu Province under Grant No. BK20171023, and the National Natural Science Foundation of China under Grant No. 61701223.


  1. 1.
    Zanella, A., Bui, N., Castellani, A., Vangelista, L., Zorzi, M.: Internet of Things for smart cities. IEEE Internet Things J. 1(1), 22–32 (2014)CrossRefGoogle Scholar
  2. 2.
    Fantacci, R., Pecorella, T., Viti, R., Carlini, C.: A network architecture solution for efficient IOT WSN backhauling: challenges and opportunities. IEEE Wirel. Commun. 21(4), 113–119 (2014)CrossRefGoogle Scholar
  3. 3.
    Zhou, M., Tang, Y.X., Tian, Z.S., Xie, L.B., Geng, X.L.: Semi-supervised learning for indoor hybrid fingerprint database calibration with low effort. IEEE Access 5(1), 4388–4400 (2017)CrossRefGoogle Scholar
  4. 4.
    Sun, Y.L., Xu, Y.B.: Error estimation method for matrix correlation-based Wi-Fi indoor localization. KSII Trans. Internet Inf. Syst. 7(11), 2657–2675 (2013)CrossRefGoogle Scholar
  5. 5.
    Gu, Y.Y., Lo, A., Niemegeers, I.: A survey of indoor positioning systems for wireless personal networks. IEEE Commun. Surv. Tutor. 11(1), 13–32 (2009)CrossRefGoogle Scholar
  6. 6.
    Wilson, J., Patwari, N.: Radio tomographic imaging with wireless networks. IEEE Trans. Mobile Comput. 9(5), 621–632 (2010)CrossRefGoogle Scholar
  7. 7.
    Wilson, J., Patwari, N.: See through walls: motion tracking using variance-based radio tomography networks. IEEE Trans. Mobile Comput. 10(5), 612–621 (2011)CrossRefGoogle Scholar
  8. 8.
    Bocca, M., Kaltiokallio, O., Patwari, N., Venkatasubramanian, S.: Multiple target tracking with RF sensor networks. IEEE Trans. Mobile Comput. 13(8), 1787–1800 (2014)CrossRefGoogle Scholar
  9. 9.
    Alippi, C., Bocca, M., Boracchi, G., Patwari, N., Roveri, M.: RTI goes wild: radio tomographic imaging for outdoor people detection and localization. IEEE Trans. Mobile Comput. 15(10), 2585–2598 (2016)CrossRefGoogle Scholar
  10. 10.
    Wang, J., Gao, Q.H., Pan, M., Zhang, X., Yu, Y., Wang, H.Y.: Toward accurate device-free wireless localization with a saddle surface model. IEEE Trans. Veh. Technol. 65(8), 6665–6677 (2016)CrossRefGoogle Scholar
  11. 11.
    Wang, J., Gao, Q.H., Wang, H.Y., Cheng, P., Xin, K.F.: Device-free localization with multidimensional wireless link information. IEEE Trans. Veh. Technol. 64(1), 356–366 (2015)CrossRefGoogle Scholar
  12. 12.
    Wang, J., Gao, Q.H., Cheng, P., Wu, L., Xin, K.F., Wang, H.Y.: Lightweight robust device-free localization in wireless networks. IEEE Trans. Ind. Electron. 61(10), 5681–5689 (2014)CrossRefGoogle Scholar
  13. 13.
    Wang, J., Fang, D.Y., Yang, Z., et al.: E-HIPA: an energy-efficient framework for high-precision multi-target-adaptive device-free localization. IEEE Trans. Mobile Comput. 16(3), 716–729 (2017)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Zhang, D., Liu, Y., Ni, L.M.: RASS: a real-time, accurate and scalable system for tracking transceiver-free objects. In: 2011 9th IEEE International Conference on Pervasive Computing and Communications, pp. 197–204 (2011)Google Scholar
  15. 15.
    Youssef, M., Mah, M., and Agrawala, A.: Challenges: device-free passive localization for wireless environments. In: 13th Annual ACM International Conference on Mobile Computing and Networking, pp. 222–229 (2007)Google Scholar
  16. 16.
    Saeed, A., Kosba, A.E., Youssef, M.: Ichnaea: a low-overhead robust WLAN device-free passive localization system. IEEE J. Sel. Topics Signal Process. 8(1), 5–15 (2014)CrossRefGoogle Scholar
  17. 17.
    Xu, C.R., Firner, B.Y., Zhang, Y., Howard, R.E.: The case for efficient and robust RF-based device-free localization. IEEE Trans. Mobile Comput. 15(9), 2362–2375 (2016)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Yongliang Sun
    • 1
    • 2
  • Xiaocheng Wang
    • 2
  • Xuzhao Zhang
    • 2
    Email author
  • Xinggan Zhang
    • 1
  1. 1.School of Electronic Science and EngineeringNanjing UniversityNanjingChina
  2. 2.School of Computer Science and TechnologyNanjing Tech UniversityNanjingChina

Personalised recommendations