Advertisement

BSLoc: Base Station ID-Based Telco Outdoor Localization

Conference paper
  • 427 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11410)

Abstract

Telecommunication (Telco) localization is an important complementary technique of Global Position System (GPS). Traditional Telco localization approaches requires radio signal strength indicator (RSSI) of mobile devices with the connected base stations (BSs). Unfortunately, many of real-world signal measurement could miss RSSI values, and Telco operators typically will not record RSSI information, e.g., due to the major departure from current operational practices of Telco operators [6]. To address this problem, we design a novel BS ID-based coarse-to-fine Telco localization model, namely BSLoc, which requires only the connected BS IDs, time and speed information of mobile devices. BSLoc consists of two layers: (1) a sequence localization model via Hidden Markov Model (HMM) to localize the mobile devices with coarse-grained locations, and (2) a machine learning regression model with engineered features to acquire the fine-grained locations of mobile devices. Our experiments verify that, on a 2G dataset, BSLoc achieves a median error 26.0 m, which is almost comparable with two state-of-art RSSI-based techniques [9] 17.0 m and [20] 20.3 m.

Notes

Acknowledgment

This work is partially supported by National Natural Science Foundation of China (Grant No. 61572365, 61503286, 61702372) and sponsored by The Fundamental Research Funds for the Central Universities.

References

  1. 1.
    Forney, G.D.: The viterbi algorithm. Proc. IEEE 61(3), 268–278 (1973)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  3. 3.
    Huang, Y., et al.: Experimental study of telco localization methods. In: 2017 18th IEEE International Conference on Mobile Data Management, MDM, pp. 299–306. IEEE (2017)Google Scholar
  4. 4.
    Ibrahim, M., Youssef, M.: CellSense: a probabilistic RSSI-based GSM positioning system. In: 2010 IEEE Global Telecommunications Conference, GLOBECOM 2010, pp. 1–5. IEEE (2010)Google Scholar
  5. 5.
    Ibrahim, M., Youssef, M.: A hidden Markov model for localization using low-end GSM cell phones. In: 2011 IEEE International Conference on Communications, ICC, pp. 1–5. IEEE (2011)Google Scholar
  6. 6.
    Leontiadis, I., Lima, A., Kwak, H., Stanojevic, R., Wetherall, D., Papagiannaki, K.: From cells to streets: estimating mobile paths with cellular-side data. In: Proceedings of the 10th ACM International on Conference on Emerging Networking Experiments and Technologies, pp. 121–132. ACM (2014)Google Scholar
  7. 7.
    Lopes, L., Viller, E., Ludden, B.: GSM standards activity on location (1999)Google Scholar
  8. 8.
    Lou, Y., Zhang, C., Zheng, Y., Xie, X., Wang, W., Huang, Y.: Map-matching for low-sampling-rate GPS trajectories. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 352–361. ACM (2009)Google Scholar
  9. 9.
    Margolies, R., et al.: Can you find me now? Evaluation of network-based localization in a 4G LTE network. In: IEEE Conference on Computer Communications, INFOCOM 2017, pp. 1–9. IEEE (2017)Google Scholar
  10. 10.
    Paek, J., Kim, K.H., Singh, J.P., Govindan, R.: Energy-efficient positioning for smartphones using Cell-ID sequence matching. In: Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services, pp. 293–306. ACM (2011)Google Scholar
  11. 11.
    Patwari, N., Ash, J.N., Kyperountas, S., Hero, A.O., Moses, R.L., Correal, N.S.: Locating the nodes: cooperative localization in wireless sensor networks. IEEE Signal Process. Mag. 22(4), 54–69 (2005)CrossRefGoogle Scholar
  12. 12.
    Perera, K., Bhattacharya, T., Kulik, L., Bailey, J.: Trajectory inference for mobile devices using connected cell towers. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, p. 23. ACM (2015)Google Scholar
  13. 13.
    Ray, A., Deb, S., Monogioudis, P.: Localization of LTE measurement records with missing information. In: The 35th Annual IEEE International Conference on Computer Communications, IEEE INFOCOM 2016, pp. 1–9. IEEE (2016)Google Scholar
  14. 14.
    Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)Google Scholar
  15. 15.
    Swales, S., Maloney, J., Stevenson, J.: Locating mobile phones and the US wireless E-911 mandate (1999)Google Scholar
  16. 16.
    Thiagarajan, A., Ravindranath, L., Balakrishnan, H., Madden, S., Girod, L.: Accurate, low-energy trajectory mapping for mobile devices (2011)Google Scholar
  17. 17.
    Vaghefi, R.M., Gholami, M.R., Ström, E.G.: RSS-based sensor localization with unknown transmit power. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, pp. 2480–2483. IEEE (2011)Google Scholar
  18. 18.
    Vo, Q.D., De, P.: A survey of fingerprint-based outdoor localization. IEEE Commun. Surv. Tutor. 18(1), 491–506 (2016)CrossRefGoogle Scholar
  19. 19.
    Zhang, Y., Rao, W., Yuan, M., Zeng, J., Yang, H.: Confidence model-based data repair for telco localization. In: 2017 18th IEEE International Conference on Mobile Data Management, MDM, pp. 186–195. IEEE (2017)Google Scholar
  20. 20.
    Zhu, F., et al.: City-scale localization with telco big data. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 439–448. ACM (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Tongji UniversityShanghaiPeople’s Republic of China
  2. 2.Huawei Noah’s Ark LabHong KongChina

Personalised recommendations