Skip to main content
SpringerLink
Log in

Practical approximate indoor nearest neighbour locating with crowdsourced RSSIs

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

In the indoor space, finding the nearest neighbour is of great importance in location-based services. Received Signal Strength Indication (RSSI) has received much attention due to its simplicity and compatibility with existing hardware, which has been widely used for indoor localization. Existing indoor nearest neighbour search methods are based on the real walking distance, which need ground survey and much labor work to measure many real distances. Crowdsourcing is a low-cost and efficient way to collect the RSSI of indoor space without expert surveyors and designated coordinates for RSSI collection points. The crowdsourced RSSIs can reflect the location of indoor objects and RSSI-based localization method is the simplistic method as it needs low hardware requirements, low deployment cost and no survey indoor distance. So we study how to search the nearest neighbour of indoor objects with crowdsourced RSSIs. To address this problem, we propose a graph with interval weights, called I-graph, which can connect the RSSIs and represent the topology of indoor space. We also construct a search tree index D-tree, which can index the graph with interval weights and search the nearest neighbour objects efficiently. We also propose a novel distance metric for RSSI and study the relationship between the RSSI distance and the indoor distance. To locate nearest neighbour of indoor objects with crowdsourced RSSIs, we devise efficient search algorithms and pruning strategies for computing the nearest neighbour query. We demonstrate the efficiency and effectiveness of the proposed solution through extensive experiments with two real data sets.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19

Similar content being viewed by others

References

  1. Bahl, P., Padmanabhan, V.N.: Radar: an in-building rf based user location and tracking system. IEEE 2, 775–784 (2000)

    Google Scholar 

  2. Chen, Y., Kobayashi, H.: Signal strength based indoor geolocation. In: IEEE International Conference on Communications (2002)

  3. Elhamshary, M.M., Alzantot, M.F., Youssef, M.: Justwalk: A crowdsourcing approach for the automatic construction of indoor floorplans. IEEE Trans. Mob. Comput.: 1–1 (2018)

  4. Fernando, S., Christian, P., Jimenez, A.R., Wolfram, B.: Improving rfid-based indoor positioning accuracy using gaussian processes. In: International Conference on Indoor Positioning and Indoor Navigation (IPIN), vol. 1, pp 1–8. IEEE (2010)

  5. Hasani, M., Talvitie, J., Sydanheimo, L., Lohan, E.S., Ukkonen, L.: Hybrid wlan-rfid indoor localization solution utilizing textile tag. IEEE Antennas Wirel. Propag. Lett. 14, 1358–1361 (2015)

    Article  Google Scholar 

  6. Hightower, J., Borriello, G., Want, R.: Spoton: An indoor 3d location sensing technology based on rf signal strength. UW CSE Technical Report (2000)

  7. Hua, L., Xin, C., Jensen, C.: A foundation for efficient indoor distance-aware query processing. In: ICDE, vol. 41, pp. 438–449 (2012)

  8. Jekabsons, G., Zuravlyov, V.: Refining wi-fi based indoor positioning. AICT :87–95 (2010)

  9. Jensen, C., Kolar, J., Pedersen, T.B., Timko, I.: Nearest neighbor queries in road networks. In: Proceedings of the 11th ACM International Symposium on Advances in Geographic Information Systems, pp. 1–8. ACM (2003)

  10. Kiers, M., Krajnc, E., Dornhofer, M., Bischof, W.: Evaluation and improvements of an rfid based indoor navigation system for visually impaired and blind people. In: International Conference on Indoor Positioning and Indoor Navigation (2011)

  11. Kolahdouzan, M., Shahabi, C.: Voronoi-based k nearest neighbor search for spatial network databases. In: VLDB, vol. 30, pp. 840–851 (2004)

  12. Luo, C., Hong, H., Chan, M.C.: Piloc: A self-calibrating participatory indoor localization system. In: IPSN, pp. 143–153. IEEE (2014)

  13. Ming, J., Hu, N., Niu, X., Zhang, Y.: Study on the personnel localization algorithm of the underground mine based on rssi technology. In: IEEE International Conference on Communication Software and Networks, pp. 408–411 (2017)

  14. Ni, LM, Liu, Y, Lau, YC, Patil, AP: Landmarc: indoor location sensing using active rfid. Wirel. Netw. 10(6), 701–710 (2004)

    Article  Google Scholar 

  15. Mu, Z., Liu, Y., Wei, N., Xie, L., Tian, Z.: Secure mobile crowdsourcing for wlan indoor localization. In: IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (2018)

  16. Niu, J., Long, C., Wang, B., Rodriguees, J.J.P.C.: Wicloc:an indoor localization system based on wifi fingerprints and crowdsourcing. In: IEEE International Conference on Communications (2015)

  17. Roussopoulos, N., Kelley, S., Vincent, F.: Nearest neighbor queries. In: SIGMOD, vol. 24, pp. 71–79. ACM (1995)

  18. Roy, W., Andy, H., Veronica, F., Jonathan, G.: The active badge location system. ACM Trans. Inf. Syst. (TOIS) 10(1), 91–102 (1992)

    Article  Google Scholar 

  19. Siddhartha, S., Kamalika, C., Dheeraj, S., Pravin, B.: Location determination of a mobile device using ieee 802.11 b access point signals. In: Wireless Communications and Networking, 2003. WCNC 2003. 2003 IEEE, vol. 3, pp. 1987–1992. IEEE (2003)

  20. Sun, J., Yang, X., Wang, B.: Crowdsourced indoor localization for diverse devices with rssi sequences. In: WISA, pp. 614–625 (2019)

  21. Thomas, K., Stephan, K., Thomas, H., Christian, L., Wolfgang, E.: Compass: A probabilistic indoor positioning system based on 802.11 and digital compasses. In: Proceedings of the 1st International Workshop on Wireless Network Testbeds, Experimental Evaluation And Characterization, pp. 34–40. ACM (2006)

  22. Wu, C., Yang, Z., Liu, Y.: Smartphones based crowdsourcing for indoor localization. J. Locat. Based Serv. 14(2), 444–457 (2015)

    Google Scholar 

  23. Xie, X., Lu, H., Pedersena, T.B.: Efficient distance-aware query evaluation on indoor moving objects. In: IEEE International Conference on Data Engineering, pp. 434–445 (2013)

  24. Xue, W., Qiu, W., Hua, X., Yu, K.: Improved wi-fi rssi measurement for indoor localization. IEEE Sens. J. PP(99), 1–1 (2017)

    Google Scholar 

  25. Yang, B., Lu, H., Jensen, C.S.: Probabilistic threshold k nearest neighbor queries over moving objects in symbolic indoor space. In: International Conference on Extending Database Technology, pp. 335–346 (2010)

  26. Yang, Q., Pan, S.J., Zheng, V.W.: Estimating location using wi-fi. In: IEEE 2007 ICDM Contest, pp. 8–13 (2008)

  27. Yang, S., Dessai, P., Verma, M., Gerla, M.: Freeloc: Calibration-free crowdsourced indoor localization. In: INFOCOM, 2013 Proceedings IEEE (2013)

  28. Yiu, M.L., Mamoulis, N., Papadias, D.: Aggregate nearest neighbor queries in road networks. IEEE Trans. Knowl. Data Eng. 17(6), 820–833 (2005)

    Article  Google Scholar 

  29. Yiu, S., Dashti, M., Claussen, H., Perez-Cruz, F.: Wireless rssi fingerprinting localization. Signal Process. 131, 235–244 (2017)

    Article  Google Scholar 

  30. Zhao, Y., Wong, W.C., Feng, T., Garg, H.K.: Calibration-free indoor positioning using crowdsourced data and multidimensional scaling. IEEE Trans. Wirel. Commun. 19(3), 1770–1785 (2020)

    Article  Google Scholar 

  31. Zhong, R., Li, G., Tan, K.L., Zhou, L.: G-tree: An efficient index for knn search on road networks. In: CIKM, pp. 39–48 (2013)

  32. Zhou, S., Ogihara, A., Nishimura, S., Jin, Q.: Analyzing the changes of health condition and social capital of elderly people using wearable devices. Health Inf. Sci. Syst. 6(1), 4 (2018)

    Article  Google Scholar 

  33. Zhu, M., Zhang, H.: Research on model of indoor distance measurement based on receiving signal strength. In: International Conference on Computer Design and Applications (ICCDA), vol. 5, pp. V5–54 (2010)

Download references

Acknowledgements

The work is partially supported by the National Key Research and Development Program of China (2018YFB1700404), National Natural Science Foundation of China (Nos.U1736104, 61991404, 61532021), and the Fundamental Research Funds for the Central Universities (No.N171602003), Ten Thousand Talent Program (No.ZX20200035), Liaoning Distinguished Professor (No.XLYC1902057), CCF Huawei database innovation research program (No.CCF-HuaweiDBIR001A).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, China

    Jing Sun, Bin Wang & Xiaochun Yang

Authors
  1. Jing Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaochun Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaochun Yang.

Ethics declarations

Conflict of Interests

The authors declare no conflict of interest.

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

Sun, J., Wang, B. & Yang, X. Practical approximate indoor nearest neighbour locating with crowdsourced RSSIs. World Wide Web 24, 747–779 (2021). https://doi.org/10.1007/s11280-021-00868-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-021-00868-5

Keywords

Search

Navigation