Abstract
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.
Similar content being viewed by others
References
Deng Z, Yu Y, Yuan X, Wan N (2013) Situation and development tendency of indoor positioning. China Communications 10(3):42–55
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
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
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
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
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
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
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
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
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
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
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
Baccar N, Jridi M, Bouallegue R (2017) Adaptive Neuro-Fuzzy location indicator in wireless sensor networks. Wirel Pers Commun 97(2):3165–3181
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
Cherntanomwong P, Sooraksa P (2018) Soft-clustering Technique for Fingerprint-based localization. Sensors and Materials 30(10):2221–2233
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
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
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
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
Fang SH, Lin TN (2010) Cooperative Multi-Radio localization in heterogeneous wireless networks. IEEE Trans Wirel Commun 9(5):1547–1551
Kushki A, Plataniotis KN, Venetsanopoulos AN (2007) Kernel-Based Positioning in wireless local area networks. IEEE Trans Mob Comput 6(6):689–705
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
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
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
Cho SY (2010) Localization of the arbitrary deployed APs for indoor wireless location-based applications. IEEE Trans Consum Electron 56(2):532–539
Chang N, Rashidzadeh R, Ahmadi M (2010) Robust indoor positioning using differential Wi-Fi access points. IEEE Trans Consum Electron 56(3):1860–1867
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
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
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
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
He S, Chan SHG (2016) Wi-Fi Fingerprint-Based Indoor positioning: recent advances and comparisons. IEEE Commun Surv Tutorials 18(1):466–490
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
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
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
Chen K, Mi Y, Shen Y, Hong Y, Chen A, Lu M (2017) Sparseloc: indoor localization using sparse representation. IEEE Access 5:20171–20182
Tian X, et al. (2018) Improve accuracy of fingerprinting localization with temporal correlation of the RSS. IEEE Trans Mob Comput 17(1):113–126
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
Cui W et al Received-Signal-Strength based Indoor Positioning Using Random Vector Functional Link Network, IEEE Transactions on Industrial, (Early Access )
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
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
Acknowledgements
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
Authors and Affiliations
Corresponding author
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
About this article
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). https://doi.org/10.1007/s11036-019-01298-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11036-019-01298-4