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A fingerprint-based coarse-to-fine algorithm for indoor positioning system using Bluetooth Low Energy

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Abstract

In this research, we propose a fingerprint-based fine-tuning algorithm to increase the accuracy of indoor positioning system using Bluetooth Low Energy fingerprint. In the fine-tuning step, Delta rule is used to update the coordinates of reference points based on the wireless environment represented in the training dataset. The combination of the coarse estimation and the proposed fine-tuning makes the coarse-to-fine algorithm. The algorithms used for coarse estimation are weighted sum and k-nearest neighbour, with three weight calculation algorithms that are compared in this experiment: Minkowski distance, k-means Gaussian mixture model, and autoencoder. Two subject rooms are used in order to measure the performance of the algorithms. The experiment showed that the fine-tuning algorithm improves the accuracy of the positioning throughout all combination of methods in both rooms, which shows its versatility. It reduces the mean positioning error by up to 11.3% and depends on what algorithm used in the weight calculation. Another benefit from the fine-tuning model is that it does not increase the complexity of the algorithm in the online phase. Overall, the best result is achieved by the combination of weighted sum and k-nearest neighbour with Minkowski distance weight calculation, together with the proposed fine-tuning. Its mean positioning error is 0.8740 m for Room 1 and 1.5385 for Room 2. The average computing time for a single online query is 0.4 ms for Room 1 and 0.7 ms for Room 2.

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References

  1. Anahid B, Elena Simona L, Terry M, Adam W, Pekka P, Chris H, Pouria A, Pedro S (2017) Indoor location based services challenges, requirements and usability of current solutions. Comput Sci Rev 24:1–12

    Article  Google Scholar 

  2. Li K, Du TC (2012) Building a targeted mobile advertising system for location-based services. Decis Support Syst 54(1):1–8

    Article  Google Scholar 

  3. Maghdid H, Lami I, Ghafoor K, Lloret J (2016) Seamless outdoors-indoors localization solutions on smartphones: implementation and challenges. ACM Comput Surv 48(4):1–34

    Article  Google Scholar 

  4. Medina C, Segura JC, Torre Ál (2013) Ultrasound indoor positioning system based on a low-power wireless sensor network providing sub-centimeter accuracy. Sensors 13(3):3501–3526

    Article  Google Scholar 

  5. Lee C (2004) Indoor positioning system based on incident angles of infrared emitters. In: 30th annual conference of IEEE industrial electronics society, 2004. IECON 2004, Busan, South Korea

  6. Gomez C, Oller J, Paradells J (2012) Overview and evaluation of bluetooth low energy: an emerging low-power wireless technology. Sensors 12(9):11734–11753

    Article  Google Scholar 

  7. Rida ME, Liu F, Jadi Y, Algawhari AAA, Askourih A (2015) Indoor location position based on Bluetooth Signal Strength. In: 2015 2nd international conference on information science and control engineering

  8. Wang Y, Yang X, Zhao Y, Liu Y, Cuthbert L (2013) Bluetooth positioning using RSSI and triangulation methods. In: 2013 IEEE 10th consumer communications and networking conference (CCNC), Las Vegas

  9. Faragher R, Harle R (2014) An analysis of the accuracy of bluetooth low energy for indoor positioning applications. In: Proceedings of the 27th international technical meeting of the satellite division of the Institute Of Navigation (ION GNSS + 2014)

  10. Zhao X, Xiao Z, Markham A, Trigoni N, Ren Y (2014) Does BTLE measure up against WiFi? A comparison of indoor location performance. In: European wireless 2014; 20th European wireless conference

  11. Lie MMK, Kusuma GP, Alexander I. Indoor positioning system using Gaussian mixture model on Bluetooth Fingerprint to combat signal fluctuation from BLE. Unpublished

  12. Yang Z, Wu C, Liu Y (2012) Locating in fingerprint space: wireless indoor localization with little human intervention. In: Proceedings of the 18th annual international conference on Mobile computing and networking, Istanbul

  13. Rappaport TS (2002) Wireless communications: principles and practice, 2nd edn. Prentice Hall, Upper Saddle River

    MATH  Google Scholar 

  14. Huang CH, Lee LH, Ho CC, Wu LL (2015) Lai ZH (2015) Real-time RFID indoor positioning system based on Kalman-filter drift removal and Heron-Bilateration location estimation. IEEE Trans Instrum Meas 64(3):728–739

    Article  Google Scholar 

  15. Kaemarungsi K, Krishnamurthy P (2004) Modeling of indoor positioning systems based on location fingerprinting. IEEE Infocom 2:1012–1022

    Google Scholar 

  16. Wang X, Gao L, Mao S, Pandey S (2015) DeepFi: deep learning for indoor fingerprinting using channel state information. In: 2015 IEEE wireless communications and networking conference (WCNC), New Orleans

  17. Wang X, Gao L, Mao S (2017) CSI phase fingerprinting for indoor localization. IEEE Internet Things J 3(6):1113–1123

    Article  Google Scholar 

  18. Wu K, Xiao J, Yi Y, Chen D, Luo X, Ni LM (2013) CSI-based indoor localization. IEEE Trans Parallel Distrib Syst 24(7):1300–1309

    Article  Google Scholar 

  19. Feng C, Wain Sy Anthea A, Valaee S, Zhen Hui T (2012) Received signal strength based indoor positioning using compressive sensing. IEEE Trans Mobile Comput 11(12):1983–1993

    Article  Google Scholar 

  20. Kusuma GP, Lie MMK A review of indoor positioning system techniques using bluetooth low energy. ICIC Express Lett 13(12):1139–1147

  21. Hossain AKMM, Wee Seng S (2015) A survey of calibration-free indoor positioning systems. Computer Communications 66:1–13

    Article  Google Scholar 

  22. Alfakih M, Keche M, Benoudnine H (2015) Gaussian mixture modeling for Indoor Positioning WIFI System. In: 2015 3rd international conference on control, engineering & information technology (CEIT), Tlemcen

  23. Ke CK, Wu MY, Chan YW, Lu KC (2018) Developing a BLE Beacon-based location system using location fingerprint positioning for smart home power management. Energies 11(12):3464

    Article  Google Scholar 

  24. Pu Y, You P (2018) Indoor positioning system based on BLE location fingerprinting with classification approach. Appl Math Model 62:654–663

    Article  Google Scholar 

  25. Kriz P, Maly F, Kozel T (2016) Improving indoor localization using bluetooth low energy Beacons. Mobile Inf Syst 2016:2083094

    Google Scholar 

  26. Wang Q, Sun R, Zhang X, Sun Y, Lu X (2017) Bluetooth positioning based on weighted K-nearest neighbors and adaptive bandwidth mean shift. Int J Distrib Sens Netw 13(5):1550147717706681

    Google Scholar 

  27. Shin B, Lee H, Lee T, Kim HS (2012) Enhanced weighted K-nearest neighbor algorithm for indoor Wi-Fi positioning systems. In: 2012 8th international conference on computing technology and information management (NCM and ICNIT), vol 2, pp 574–577

  28. Paterna VC, Auge CA, Aspas JP, Bullones MAP (2017) A bluetooth low energy indoor positioning system with channel diversity, weighted trilateration and kalman filtering. Sensors 17(12):2927

    Article  Google Scholar 

  29. Yim J, Park C, Joo J, Jeong S (2008) Extended Kalman filter for wireless LAN based indoor positioning. Decis Support Syst

  30. Liu Y, Fan X, Lv C, Wu J, Li L, Ding D (2018) Relative multiplicative extended Kalman filter for observable GPS-denied navigation. Mech Syst Signal Process 100:605–616

    Article  Google Scholar 

  31. de Blasio G, Quesada-Arencibia A, García CR, Molina-Gil JM, Caballero-Gil C (2017) Study on an indoor positioning system for harsh environments based on Wi-Fi and Bluetooth Low Energy. Sensors 17(6):1299

    Article  Google Scholar 

  32. Yang A, Ganesh A, Sastry S, Ma Y (2010) Fast ℓ 1-minimization algorithms and an application in robust face recognition: a review. In: 2010 IEEE international conference on image processing

  33. Piccinni G, Avitabile G, Coviello G (2017) Narrowband distance evaluation technique for indoor positioning systems based on Zadoff-Chu sequences. In: 2017 IEEE 13th international conference on wireless and mobile computing, networking and communications (WiMob)

  34. Eisa S, Peixoto J, Meneses F, Moreira A (2013) Removing useless APs and fingerprints from WiFi indoor positioning radio maps. In: International conference on indoor positioning and indoor navigation. IEEE

  35. Farshid H, Tavakolian M (2019) Video classification using deep autoencoder network. In: Conference on complex, intelligent, and software intensive systems, Cham

  36. Hossain AM, Jin Y, Soh W-S, Van HN (2013) SSD: a robust RF location fingerprint addressing mobile devices’ heterogeneity. IEEE Trans Mobile Comput 12(1):65–77

    Article  Google Scholar 

  37. Jin J, Li M, Jin L (2015) Data normalization to accelerate training for linear neural net to predict tropical cyclone tracks. Math Probl Eng 2015:931629

    Google Scholar 

Download references

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Authors and Affiliations

  1. Computer Science Department, BINUS Graduate Program - Master of Computer Science, Bina Nusantara University, Jakarta, 11480, Indonesia

    Maximilianus Maria Kolbe Lie & Gede Putra Kusuma

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  1. Maximilianus Maria Kolbe Lie
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  2. Gede Putra Kusuma
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Correspondence to Gede Putra Kusuma.

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Lie, M.M.K., Kusuma, G.P. A fingerprint-based coarse-to-fine algorithm for indoor positioning system using Bluetooth Low Energy. Neural Comput & Applic 33, 2735–2751 (2021). https://doi.org/10.1007/s00521-020-05159-0

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  • DOI: https://doi.org/10.1007/s00521-020-05159-0

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