Design of a Practical WSN Based Fingerprint Localization System


Fingerprint positioning technology is among the most promising choices for seamless localization and is anticipated to be the future of seamless-locating services. The convenience of deployment and the high density signal source of wireless sensor networks (WSN) make them an ideal infrastructure for fingerprint positioning. In related researches, most WSN based fingerprint positioning systems are experimental demos that focus on the algorithm effectiveness and ignore the system reliability. This work proposes a practical WSN based fingerprint localization system. The system covers both indoor and outdoor scenarios and fulfills the demand for seamless localization. This paper work presents four measures that improve fault tolerance and system efficiency: a traffic regulation based radiomap (TRRM) establishing method, a full-overlapping clustering strategy, an adaptive feature space (AFS) algorithm, and a praxeological tracking algorithm. The proposed system is verified by hardware experiments on smart phones. Positioning accuracy is within 5 m in pedestrian tests and 10 m in driving tests.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18


  1. 1.

    Deng Z, Yu Y, Yuan X, Wan N (2013) Situation and development tendency of indoor positioning. China Communications 10(3):42–55

    Article  Google Scholar 

  2. 2.

    Liu H, Darabi H, Banerjee P, Liu J (2007) Survey of wireless indoor positioning techniques and systems. IEEE Trans Syst Man Cybern Part C Appl Rev 37(6):1067–1080

    Article  Google Scholar 

  3. 3.

    Sun G, Chen J, Guo W, Liu KJR (2005) Signal processing techniques in network-aided positioning: a survey of state-of-the-art positioning designs. IEEE Signal Proc Mag 22(4):12– 23

    Article  Google Scholar 

  4. 4.

    Wang X, Yang C, Mao S (2018) DeepML: Deep LSTM for indoor localization with smartphone magnetic and light sensors, IEEE ICC 2018, Kansas City, MO, 1-6

  5. 5.

    Wang X, Wang X, Mao S Deep convolutional neural networks for indoor localization with CSI images, IEEE Transactions on Network Science and Engineering, to appear

  6. 6.

    Wang X, Gao L, Mao S (2017) BiLoc: Bi-modality deep learning for indoor localization with 5GHz commodity Wi-Fi. IEEE Access Journal 5(1):4209–4220

    Article  Google Scholar 

  7. 7.

    Wang X, Gao L, Mao S (2016) CSI phase fingerprinting for indoor localization with a deep learning approach. IEEE Internet Things J 3(6):1113–1123

    Article  Google Scholar 

  8. 8.

    Cheng L, Li Y, Zhang M, Wang C (2018) A fingerprint localization method based on weighted KNN algorithm. In: 2018 IEEE 18th International Conference on Communication Technology (ICCT), Chongqing, China, pp 1271–1275

  9. 9.

    de Omena RALV, Silva JJ, da Rocha Neto JS (2018) WSN integrated to a virtual instrument for partial discharges detection and localization. In: 2018 IEEE International Instrumentation and Measurement Technology Conference (i2MTC), Houston

  10. 10.

    Luo J, Zhang ZY, Liu C, Luo HB (2018) Reliable and cooperative target tracking based on WSN and WiFi in indoor wireless networks. IEEE Access 6:24846–24855

    Article  Google Scholar 

  11. 11.

    Mao Kj, Fang K, Dai GY, Xu H, Chen QZ (2016) Localization in wireless sensor networks using multi-dimensional vector fingerprint based on kriging. Journal of Chinese Computer Systems 37(11):2514–2519

    Google Scholar 

  12. 12.

    Fang XM, Nan L, Jiang ZH, Chen LJ (2017) Multi-channel fingerprint localisation algorithm for wireless sensor network in multipath environment. IET Communications 11(15):2253– 2260

    Article  Google Scholar 

  13. 13.

    Baccar N, Jridi M, Bouallegue R (2017) Adaptive Neuro-Fuzzy location indicator in wireless sensor networks. Wirel Pers Commun 97(2):3165–3181

    Article  Google Scholar 

  14. 14.

    Fang Sh, Lin Tn, Lee Kc (2008) A novel algorithm for multipath fingerprinting in indoor WLAN environments. IEEE Trans Wirel Commun 7(9):3579–3588

    Article  Google Scholar 

  15. 15.

    Cherntanomwong P, Sooraksa P (2018) Soft-clustering Technique for Fingerprint-based localization. Sensors and Materials 30(10):2221–2233

    Article  Google Scholar 

  16. 16.

    Fang XM, Jiang ZH, Nan L, Chen LJ (2018) Optimal weighted K-nearest neighbour algorithm for wireless sensor network fingerprint localisation in noisy environment. IET Communications 12(10):1171–1177

    Article  Google Scholar 

  17. 17.

    Fang XM, Nan L, Jiang ZH, Chen LJ (2017) Noise-aware fingerprint localization algorithm for wireless sensor network based on adaptive fingerprint Kalman filter. Comput Netw 124:97–107

    Article  Google Scholar 

  18. 18.

    Nicoli M, Morelli C, Rampa V (2008) A jump markov particle filter for localization of moving terminals in multipath indoor scenarios. IEEE Trans Signal Process 56(8):3801–3809

    MathSciNet  Article  Google Scholar 

  19. 19.

    Zampella F, Jiménez Ruiz AR, Seco Granja F (2015) Indoor positioning using efficient map matching, RSS measurements, and an improved motion model. IEEE Trans Veh Technol 64(4):1304–1317

    Article  Google Scholar 

  20. 20.

    Fang SH, Lin TN (2010) Cooperative Multi-Radio localization in heterogeneous wireless networks. IEEE Trans Wirel Commun 9(5):1547–1551

    Article  Google Scholar 

  21. 21.

    Kushki A, Plataniotis KN, Venetsanopoulos AN (2007) Kernel-Based Positioning in wireless local area networks. IEEE Trans Mob Comput 6(6):689–705

    Article  Google Scholar 

  22. 22.

    Fang SH, Lin TN (2008) Indoor location system based on Discriminant-Adaptive neural network in IEEE 802.11 environments. IEEE Trans Neural Netw 19(11):1973–1978

    Article  Google Scholar 

  23. 23.

    Benaissa B, Hendrichovsky F, Yishida K, Koppen M, Sincak P (2018) Phone application for indoor localization based on Ble signal fingerprint. In: 2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS), Paris, France, pp 1–5

  24. 24.

    Mazuelas S, et al. (2009) Robust indoor positioning provided by Real-Time RSSI values in unmodified WLAN networks. IEEE J Sel Top Sign Proces 3(5):821–831

    MathSciNet  Article  Google Scholar 

  25. 25.

    Cho SY (2010) Localization of the arbitrary deployed APs for indoor wireless location-based applications. IEEE Trans Consum Electron 56(2):532–539

    Article  Google Scholar 

  26. 26.

    Chang N, Rashidzadeh R, Ahmadi M (2010) Robust indoor positioning using differential Wi-Fi access points. IEEE Trans Consum Electron 56(3):1860–1867

    Article  Google Scholar 

  27. 27.

    Fang XM, Nan L, Jiang ZH, Chen LJ (2016) Fingerprint localisation algorithm for noisy wireless sensor network based on multi-objective evolutionary model. IET Communications 11(8):1297–1304

    Article  Google Scholar 

  28. 28.

    Zhao W, Han S, Meng W, Zou D (2016) A testbed of performance evaluation for fingerprint based wlan positioning system. KSII Trans Internet Inf Syst 10(6):2583–2605

    Google Scholar 

  29. 29.

    Feng C, Au WSA, Valaee S, Tan Z (2009) Orientation-aware indoor localization using affinity propagation and compressive sensing. In: 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Aruba, Dutch Antilles, pp 261–264

  30. 30.

    Qiu C, Mutka MW (2015) Cooperation among smartphones to improve indoor position information. In: 2015 IEEE 16th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), Boston, MA, pp 1–9

  31. 31.

    He S, Chan SHG (2016) Wi-Fi Fingerprint-Based Indoor positioning: recent advances and comparisons. IEEE Commun Surv Tutorials 18(1):466–490

    Article  Google Scholar 

  32. 32.

    Chen LH, Wu EHK, Jin MH, Chen GH (2014) Intelligent fusion of Wi-Fi and inertial Sensor-Based positioning systems for indoor pedestrian navigation. IEEE Sensors J 14(11):4034– 4042

    Article  Google Scholar 

  33. 33.

    Wang X, Gao L, Mao S, Pandey S (2017) CSI-based fingerprinting for indoor localization: a deep learning approach. IEEE Trans Veh Technol 66(1):763–776

    Google Scholar 

  34. 34.

    Ding G, Chen P, Tian J, Zhao Q (2016) Power delay profile based indoor fingerprinting localization system. In: 2016 18th International Conference on Advanced Communication Technology (ICACT), Pyeongchang, pp 324–329

  35. 35.

    Chen K, Mi Y, Shen Y, Hong Y, Chen A, Lu M (2017) Sparseloc: indoor localization using sparse representation. IEEE Access 5:20171–20182

    Article  Google Scholar 

  36. 36.

    Tian X, et al. (2018) Improve accuracy of fingerprinting localization with temporal correlation of the RSS. IEEE Trans Mob Comput 17(1):113–126

    Article  Google Scholar 

  37. 37.

    Wang M, Zhang Z, Tian X, Wang X (2016) Temporal correlation of the RSS improves accuracy of fingerprinting localization. In: IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications, San Francisco, CA, pp 1–9

  38. 38.

    Cui W et al Received-Signal-Strength based Indoor Positioning Using Random Vector Functional Link Network, IEEE Transactions on Industrial, (Early Access )

  39. 39.

    Di Felice M, Bocanegra C, Chowdhury KR (2018) WI-LO: Wireless Indoor localization through multi-source radio fingerprinting. In: 2018 10th International Conference on Communication Systems & Networks (COMSNETS), Bengaluru, India, pp 305– 311

  40. 40.

    Zou D, Meng W, Han S, He K, Zhang Z (2016) Toward ubiquitous LBS: multi-radio localization and seamless positioning. IEEE Wirel Commun 23(6):107–113

    Article  Google Scholar 

Download references


Many thanks to Ziqing Jia of the 205 institute of norinco group and Meng Liu of zhongxing telecommunication equipment corporation. Their previous work makes this research work possible.

Author information



Corresponding author

Correspondence to Shuai Han.

Additional information

Publisher’s Note

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

This research work is supported by the National Natural Science Foundation of China #61701072

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zou, D., Chen, S., Han, S. et al. Design of a Practical WSN Based Fingerprint Localization System. Mobile Netw Appl 25, 806–818 (2020).

Download citation


  • Smart phone
  • Fingerprint positioning
  • Seamless positioning
  • Robustness
  • WSN