Skip to main content
Log in

Joint access point fuzzy rough set reduction and multisource information fusion for indoor Wi-Fi positioning

  • S.I: Cognitive-inspired Computing and Applications
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

With the increasing maturity and popularity of wireless network techniques, indoor Wi-Fi positioning will inevitably become a significant application in indoor location-based services. In this circumstance, there is normally no control over the number of access points (APs) and the diversity of the Wi-Fi signal distribution, which may significantly deteriorate the positioning effectiveness as well as the system efficiency. To address this issue, we first adopt the fuzzy information entropy-based fuzzy rough set to conduct redundant APs reduction. Second, we calculate the Wasserstein distance between the signal distribution at the target position and the one at each Reference Point (RP) by the Wasserstein distance method. Third, the multisource information fusion method based on the Dempster–Shafer evidence theory is exerted to construct the matching RPs set. Finally, the abundant experiments and results in a realistic indoor Wi-Fi environment testify that the proposed method is able to preserve satisfactory localization performance as well as reduce the computation overhead of localization.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. The fingerprint database is recognized as an information system.

  2. The area division is based on the functionality of each sub-area [28].

  3. Because different types of the kernel function have little effect on the result, the Epanechnikov kernel function in [31] is chosen in our paper.

References

  1. Demircioglu E, Gulgonul S, Yagli AF, Ertok HH (2015) GNSS augmentation and regional positioning systems over Turksat communication satellites. In: Signal processing and communications applications conference. pp 1680–1683

  2. Lymberopoulos D, Liu J (2017) The Microsoft indoor localization competition. IEEE Signal Process Mag 34(5):125–140

    Article  Google Scholar 

  3. Zhou M, Wang Y, Tian Z et al (2019) Calibrated data simplification for energy-efficient location sensing in internet of things. IEEE Internet Things J 6(4):6125–6133

    Article  Google Scholar 

  4. Zhang X, Jin Y, Tan H et al (2019) CIMLoc: a crowdsourcing indoor digital map construction system for localization. In: IEEE International conference on intelligent sensors, sensor networks and information processing. pp 1–9

  5. Torres-Sospedra J, Montoliu R, Martłnez-Us A et al (2014) UJIIndoorLoc: a new multi-building and multi-flfloor database for WLAN fingerprint-based indoor positioning problems. In: International conference on indoor positioning and indoor navigation. pp. 261–270

  6. Blum A, Langley P (1997) Selection of relevant features and examples in machine learning. In: Artificial intelligence. pp 245–271

  7. Jia B, Huang B, Gao H et al (2019) Selecting critical Wi-Fi APs for indoor localization based on a theoretical error analysis. IEEE Access 7:36312–36321

    Article  Google Scholar 

  8. Liu H, Motoda H, Yu L (2002) Feature selection with selective sampling. In: International conference on machine learning. pp 395–402

  9. Yang J, Zhao X, Li Z (2019) Crowd-sourcing indoor positioning by light-weight automatic fingerprint updating via ensemble learning. IEEE Access 7:26255–26267

    Article  Google Scholar 

  10. Deng Z, Xu Y, Ma L (2012) Joint access point selection and local discriminant embedding for energy efficient and accurate Wi-Fi positioning. KSII Trans Internet InfSyst 6(3):794–811

    Google Scholar 

  11. Chen Q, Wang B, Deng X et al (2013) Placement of access points for indoor wireless coverage and fingerprint-based localization. In: IEEE international conference on high performance computing and communications. IEEE, Hunan, pp 2253–2257

  12. Hamamoto R, Takano C, Obata H et al (2014) Characteristics analysis of an AP selection method based on coordination moving both users and Aps. In: International symposium on computing and networking. pp 243–248

  13. Shi P, Xu F, Wang Z (2005) A maximum-likelihood indoor location algorithm based on indoor propagation loss model. Signal Process 21(5):502–506

    Google Scholar 

  14. Sun B, Gong Z (2008) Rough fuzzy sets in generalized approximation space. In: Conference on fuzzy systems and knowledge discovery. pp 416–420

  15. Zalewski J (1996) Rough sets: theoretical aspects of reasoning about data. Control EngPract 4(5):741–742

    Google Scholar 

  16. Sun B, Gong Z (2008) Rough fuzzy sets in generalized approximation space. In: International conference on fuzzy systems and knowledge discovery. pp 416–420

  17. Chen C, Lee C, Lo C (2016) Vehicle localization and velocity estimation based on mobile phone sensing. IEEE Access 4(1):803–817

    Article  Google Scholar 

  18. Gao Y, Chen H, Li Y et al (2017) Autonomous Wi-Fi relay placement with mobile robots. IEEE ASME Trans Mechatron 22(6):2532–2542

    Article  Google Scholar 

  19. Shu Y, Huang Y, Zhang J et al (2016) Gradient-based fingerprinting for indoor localization and tracking. IEEE Trans Industr Electron 63(4):2424–2433

    Article  Google Scholar 

  20. Su J, Xu R, Yu S, Wang B, Wang J (2020) Idle slots skipped mechanism based tag identification algorithm with enhanced collision detection. KSII Trans Internet InfSyst 14(5):2294–2309

    Google Scholar 

  21. Su J, Xu R, Yu S, Wang B, Wang J (2020) Redundant rule detection for software-defined networking. KSII Trans Internet InfSyst 14(6):2735–2751

    Google Scholar 

  22. Su J, Sheng Z, Huang Z, Liu AX, Chen Y (2020) From M-ary query to bit query: a new strategy for efficient large-scale RFID identification. IEEE Trans Commun 68(4):2381–2393

    Article  Google Scholar 

  23. Su J, Sheng Z, Liu A, Han Y, Chen Y (2020) A group-based binary splitting algorithm for UHF RFID anti-collision systems. IEEE Trans Commun 68(2):998–1012

    Article  Google Scholar 

  24. Zhou M, Li X, Wang Y et al (2020) 6G multi-source information fusion based indoor positioning via Gaussian kernel density estimation. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2020.3031639

    Article  Google Scholar 

  25. Zhou M, Wang Y, Liu Y et al (2019) An information-theoretic view of WLAN localization error bound in GPS-denied environment. IEEE Trans VehTechnol 68(4):4089–4093

    Google Scholar 

  26. Achroufene A, Amirat Y, Chibani A (2019) RSS-based indoor localization using belief function theory. IEEE Trans AutomSciEng 16(3):1163–1180

    Google Scholar 

  27. Kasebzadeh P, Granados GS, Lohan ES (2014) Indoor localization via WLAN path-loss models and Dempster-Shafer combining. In: IEEE international conference on localization and GNSS. pp 1–6

  28. Yang Z, Wu C, Liu Y (2012) Locating in fingerprint space: wireless indoor localization with little human intervention. In: International conference on mobile computing and networking. pp 269–280

  29. Hu Q, Yu D, Xie Z et al (2006) Fuzzy probabilistic approximation spaces and their information measures. IEEE Trans Fuzzy Syst 14(2):191–201

    Article  Google Scholar 

  30. Hajek B (1987) Average case analysis of Greedy algorithms for Kelly’s triangle problem and the independent set problem. In: Conference on decision and control, Los Angeles, California, pp. 1455–1460

  31. Stefanski LA, Carroll RJ (1990) Deconvoluting kernel density estimators. Statistics 21(2):169–184

    Article  MathSciNet  Google Scholar 

  32. Ahmad A, Fan Y (2001) Optimal bandwidths for kernel density estimators of functions of observations. Statist ProbabLett 51(3):245–251

    MathSciNet  MATH  Google Scholar 

  33. Arabsheibani RG, Rees H (1998) On the weak vs strong version of the screening hypothesis: a re-examination of the P-test for the UK. Econ Educ Rev 17(2):189–192

    Article  Google Scholar 

  34. Fournier N, Guillin A (2015) On the rate of convergence in Wasserstein distance of the empirical measure. Probab Theory Relat Fields 162(3–4):707–738

    Article  MathSciNet  Google Scholar 

  35. Martín F, Luis M, Santiago G et al (2015) Kullback–Leibler divergence-based differential evolution Markov chain filter for global localization of mobile robots. Sensors 15(9):23431–23458

    Article  Google Scholar 

  36. Naghshvar M, Javidi T, Wigger M (2015) Extrinsic Jensen-Shannon divergence: applications to variable-length coding. IEEE Trans Inf Theory 61(4):2148–2164

    Article  MathSciNet  Google Scholar 

  37. Gangbo W, Mccann RJ (2000) Shape recognition via Wasserstein distance. Q Appl Math 58(4):705–737

    Article  MathSciNet  Google Scholar 

  38. Fan X, Ming JZ (2006) Fault diagnosis of machines based on DST. Part 1: DST and its improvement. Pattern RecognLett 27(5):366–376

    Article  Google Scholar 

  39. Yu, Liu J (2013) A KNN indoor positioning algorithm that is weighted by the membership of fuzzy set. In: 2013 IEEE international conference on green computing and communications and IEEE internet of things and IEEE cyber, physical and social computing, Beijing. pp 1899–1903

  40. Pawlak Z (1991) Rough setsłtheoreticalsspects of reasoning about data. Kluwer Academic Publishers, London

    MATH  Google Scholar 

  41. Wang G, Yu H, Yang D (2002) Decision table reduction based on conditional information entropy. Chinese Journal of Computers 25(7):1–8

    MathSciNet  Google Scholar 

  42. Hu Q, Yu D, Xie Z (2006) Information-preserving hybrid data reduction based on fuzzy-rough techniques. Pattern Recogn 27(5):414–423

    Article  Google Scholar 

  43. Stojanovic B, Neskovic A (2012) Impact of PCA based fifingerprint compression on matching performance. In: Telecommunications forum. pp 693–696

  44. Ni W, Xiao W, Toh YK et al (2010) Fingerprint-MDS based algorithm for indoor wireless localization. In: IEEE international symposium on personal, indoor and mobile radio communications. pp 1972–1977

  45. Aggarwal V, Patterh MS (2009) Quality controlled ECG compression using discrete cosine transform (DCT) and Laplacian Pyramid (LP). In: International multimedia, signal processing and communication technologies. pp 12–15

Download references

Acknowledgements

This work was supported in part by the Science and Technology Research Program of Chongqing Municipal Education (KJZD-K202000605, KJQN202000630), the Chongqing Natural Science Foundation Project (cstc2020jcyj-msxmX0842), and the National Natural Science Foundation of China (61901076, 61704015).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mu Zhou.

Ethics declarations

Conflict of interest

There was no conflict of interest in the submission of the manuscript, and the author agreed to publish it. I declare that the work described is an original study that has not been published before and is not considered elsewhere, in whole or in part.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nie, W., Liu, Z., Zhou, M. et al. Joint access point fuzzy rough set reduction and multisource information fusion for indoor Wi-Fi positioning. Neural Comput & Applic 34, 2677–2689 (2022). https://doi.org/10.1007/s00521-021-05934-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-05934-7

Keywords

Navigation