Skip to main content
SpringerLink
Log in

An efficient location reporting and indexing framework for urban road moving objects

  • Published:
Distributed and Parallel Databases Aims and scope Submit manuscript

Abstract

The tracking of moving objects consists of two critical operations: location reporting, in which moving objects (or clients) send their locations to centralized servers, and index maintenance, through which centralized servers update the locations of moving objects. In existing location reporting techniques, each moving object reports its locations to servers by utilizing long-distance links such as 3G/4G. Corresponding to this location reporting strategy, servers need to respond to all the location updating requests from individual moving objects. Such techniques suffer from very high communication cost (due to the individual reporting using long-distance links) and high index update I/Os (due to the massive amount of location updating requests). In this paper, we present a novel Group-movement based location Reporting and Indexing (GRI) framework for location reporting (at moving object side) and index maintenance (at server side). In the GRI framework, we introduce a novel location reporting strategy which allows moving objects to report their locations to servers in a group (instead of individually) by aggregating the moving objects that share similar movement patterns through wireless local links (such as WiFi). At the server side, we present a dual-index, Hash-GTPR-tree (H-GTPR), to index objects sharing similar movement patterns. Our experimental results on synthetic and real data sets demonstrate the effectiveness and efficiency of our new GRI framework, as well as the location reporting strategy and the H-GTPR tree index technique.

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

Fig. 1
Algorithm 1
Algorithm 2
Fig. 2
Algorithm 3
Algorithm 4
Algorithm 5
Algorithm 6
Algorithm 7
Algorithm 8
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31
Fig. 32
Fig. 33
Fig. 34
Fig. 35
Fig. 36
Fig. 37

Similar content being viewed by others

Notes

  1. http://www.openstreetmap.org/.

References

  1. Pesti, P., Liu, L., Bamba, B., Iyengar, A., Weber, M.: Roadtrack: scaling location updates for mobile clients on road networks with query awareness. Proc. VLDB Endow. 3(2), 1493–1504 (2010)

    Google Scholar 

  2. Hu, H., Xu, J., Lee, D.L.: A generic framework for monitoring continuous spatial queries over moving objects. In: Proc. of SIGMOD’05, pp. 479–490 (2005)

    Google Scholar 

  3. Saltenis, S., Jensen, C.S.: Indexing of moving objects for location-based services. In: Proc. of the 18th ICDE, pp. 463–472 (2002)

    Google Scholar 

  4. Pfoser, D., Jensen, C.S., Theodoridis, Y.: Novel approaches in query processing for moving object trajectories. In: Proc. of the 26th VLDB, pp. 395–406 (2000)

    Google Scholar 

  5. Xu, B., Wolfson, O., Cho, H.J.: Monitoring neighboring vehicles for safety via v2v communication. In: 2011 International Conference on Vehicular Electronics and Safety, pp. 280–285 (2011)

    Chapter  Google Scholar 

  6. Faezipour, M., Nourani, M., Saeed, A., Addepalli, S.: Progress and challenges in intelligent vehicle area networks. Commun. ACM 55(2), 90–101 (2012)

    Article  Google Scholar 

  7. Chen, S., Ooi, B.C., Zhang, Z.: An adaptive updating protocol for reducing moving object database workload. VLDB J. 21(2), 265–286 (2012)

    Article  Google Scholar 

  8. Wolfson, O., Chamberlain, S., Dao, S., Jiang, L.: Location management in moving objects databases. In: WOSBIS’97, pp. 7–13 (1997)

    Google Scholar 

  9. Jensen, C.S., Pakalnis, S.: Trax: real-world tracking of moving objects. In: Proc. of 33rd VLDB, pp. 1362–1365 (2007)

    Google Scholar 

  10. Liu, F., Hua, K.A., Xie, F.: On reducing communication cost for distributed moving query monitoring systems. In: Proc. of 9th Mobile Data Management (MDM), pp. 156–164 (2008)

    Google Scholar 

  11. Wu, W., Guo, W., Tan, K.-L.: Distributed processing of moving k-nearest-neighbor query on moving objects. In: Proc. of 23rd ICDE, pp. 1116–1125 (2007)

    Google Scholar 

  12. Gedik, B., Liu, L.: Mobieyes: distributed processing of continuously moving queries on moving objects in a mobile system. In: Proc. of the 9th EDBT, pp. 67–87 (2004)

    Google Scholar 

  13. Liu, F., Do, T.T., Hua, K.A.: Dynamic range query in spatial network environments. In: Proc. of 17th DEXA, pp. 254–265 (2006)

    Google Scholar 

  14. Liu, F., Hua, K.A., Do, T.T.: A p2p technique for continuous k-nearest-neighbor query in road networks. In: Proc. of 18th DEXA, pp. 264–276 (2007)

    Google Scholar 

  15. Cornejo, A., Gilbert, S., Newport, C.: Aggregation in dynamic networks. In: Proc. of the 2012 ACM Symposium of Distributed Computing, pp. 195–204 (2012)

    Chapter  Google Scholar 

  16. Hellerstein, J.M., Naughton, J.F., Pfeffer, A.: Generalized search trees for database systems. In: Proc. of the 21th VLDB, pp. 562–573 (1995)

    Google Scholar 

  17. Lin, B., Su, J.: Handling frequent updates of moving objects. In: Proc. of the 14th CIKM, pp. 493–500 (2005)

    Google Scholar 

  18. Cui, B., Lin, D., Tan, K.L.: Towards optimal utilization of main memory for moving object indexing. In: Proc. of the 10th DASFAA, pp. 600–611 (2005)

    Google Scholar 

  19. Biveinis, L., Saltenis, S., Jensen, C.S.: Main-memory operation buffering for efficient R-tree update. In: Proc. of the 33rd VLDB, pp. 591–602 (2007)

    Google Scholar 

  20. Nguyen, T., He, Z., Zhang, R., Ward, P.: Boosting moving object indexing through velocity partitioning. Proc. VLDB Endow. 5(9), 860–871 (2012)

    Google Scholar 

  21. Saltenis, S., Jensen, C.S., Leutenegger, S.T., Lopez, M.A.: Indexing the positions of continuously moving objects. In: SIGMOD’00, pp. 331–342 (2000)

    Google Scholar 

  22. Guttman, A.: R-trees: a dynamic index structure for spatial searching. In: Proc. of ACM, SIGMOD’84, pp. 47–57 (1984)

    Google Scholar 

  23. Tao, Y., Papadias, D.: Mv3r-tree: a spatio-temporal access method for timestamp and interval queries. In: Proc. of the 27th VLDB, pp. 431–440 (2001)

    Google Scholar 

  24. Chakka, V.P., Everspaugh, A., Patel, J.M.: Indexing large trajectory data sets with seti. In: Online Proceedings of the 1st Biennial Conference on Innovative Data Systems Research (CIDR) (2003)

    Google Scholar 

  25. Procopiuc, C.M., Agarwal, P.K., Har-Peled, S.: Star-tree: an efficient self-adjusting index for moving objects. In: Proc. of the 4th International Workshop on Algorithm Engineering and Experiments (ALENEX), pp. 178–193 (2002)

    Google Scholar 

  26. Tao, Y., Papadias, D., Sun, J.: The tpr*-tree: an optimized spatio-temporal access method for predictive queries. In: Proc. of the 29th VLDB, pp. 790–801 (2003)

    Google Scholar 

  27. Chen, S., Jensen, C.S., Lin, D.: A benchmark for evaluating moving object indexes. Proc. VLDB Endow. 1(2), 1574–1585 (2008)

    Google Scholar 

  28. Brinkhoff, T.: A framework for generating network-based moving objects. GeoInformatica 6(2), 153–180 (2002)

    Article  MATH  Google Scholar 

  29. Lou, Y., Zhang, C., Zheng, Y.: Map-matching for low-sampling-rate gps trajectories. In: Proc. of the 17th SIGSPATIAL International Conference on Advances in Geographic Information Systems (2009)

    Google Scholar 

Download references

Acknowledgements

We sincerely thank Professor Alexandra Poulovassilis from London Knowledge Lab (LKL) for her valuable suggestions to our work, Yu Lu for her assistance in preparing data for the experiments, and the anonymous reviewers for their valuable comments.

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing, 210003, P.R. China

    Jingyu Han & Kejia Chen

  2. Institute of Software, Chinese Academy of Science, Beijing, 100080, P.R. China

    Zhiming Ding

  3. Department of Computer Science, New Mexico State University, Las Cruces, USA

    Huiping Cao

Authors
  1. Jingyu Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kejia Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhiming Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Huiping Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jingyu Han.

Additional information

Communicated by Divyakant Agrawal.

This research is partially supported by National Natural Science Foundation of China under the grant numbers 61003040, 91124001, 61100135, and China 973 program 2011CB302903.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Han, J., Chen, K., Ding, Z. et al. An efficient location reporting and indexing framework for urban road moving objects. Distrib Parallel Databases 32, 271–311 (2014). https://doi.org/10.1007/s10619-013-7135-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10619-013-7135-5

Keywords

Search

Navigation