Wireless Networks

, Volume 20, Issue 6, pp 1477–1494 | Cite as

TRack others if you can: localized proximity detection for mobile networks

  • Chi Zhang
  • Jun LuoEmail author


For a set of mobile users with designated friendship relations, it is a recurring issue to keep track of whether some friends appear in the vicinity of a given user. While both distributed and centralized solutions for proximity detection have been proposed, the cost metrics for evaluating these proposals are always based on counting the number of message (e.g., query or update) exchanges. However, as mobile users often rely on wireless networks to maintain their connectivity, the cost incurred by any message passing is strongly affected by the distance between the sender and receiver. In this paper, we propose TRack Others if You can (TROY) as a novel distributed solution for proximity detection. Extending the principle of spatial tessellations, TROY incurs only localized message exchanges and is thus superior to existing proposals in terms of more realistic cost metrics that take into account the actual energy consumption of message passing. Moreover, our spatial tessellations inspired analytical framework allows for a meaningful comparison with an existing work. Finally, we use extensive experiments to validate the efficiency of TROY.


Proximity detection Mobile networks Energy efficiency Spatial tessellation 


  1. 1.
    Amir, A., Efrat, A., Myllymaki, J., Palaniappan, L., & Wampler, K. (2004). Buddy tracking—efficient proximity detection among mobile users. In Proceedings of the 23rd IEEE INFOCOM.Google Scholar
  2. 2.
    Bash, B., & Desnoyers, P. (2007). Exact distributed Voronoi cell computation in sensor networks. In Proceedings of the 6th ACM/IEEE IPSN.Google Scholar
  3. 3.
    Brinkhoff, T., & Str, O. (2002). A framework for generating network-based moving objects. Geoinformatica, 6, 202.CrossRefGoogle Scholar
  4. 4.
    Cai, Y., Hua, K. A., & Cao, G. (2004). Processing range-monitoring queries on heterogeneous mobile objects. In Mobile data management, MDM, pp. 27–38.Google Scholar
  5. 5.
    Gedik, B., & Liu, L. (2006). Mobieyes: A distributed location monitoring service using moving location queries. IEEE Transactions on Mobile Computing, 5, 1384–1402.CrossRefGoogle Scholar
  6. 6.
    Hu, H., Xu, J., & Lee, D. L. (2005). A generic framework for monitoring continuous spatial queries over moving objects. In Proceedings of the ACM SIGMOD, pp. 479–490.Google Scholar
  7. 7.
    Hyytia, E., Lassila, P., & Virtamo, J. (2006). Spatial node distribution of the random waypoint mobility model with applications. IEEE Transactions on Mobile Computing, 5(6), 680–694.CrossRefGoogle Scholar
  8. 8.
    Ilarri, S., Mena, E., & Illarramendi, A. (2006). Location-dependent queries in mobile contexts: Distributed processing using mobile agents. IEEE Transactions on Mobile Computing, 5(8), 1029–1043.CrossRefGoogle Scholar
  9. 9.
    Iwerks, G. S., Samet, H., & Smith, K. P. (2006). Maintenance of k-nn and spatial join queries on continuously moving points. ACM Transactions on Database System, 31, 485–536.CrossRefGoogle Scholar
  10. 10.
    Kolahdouzan, M., & Shahabi, C. (2004). Voronoi-based K nearest neighbor search for spatial network databases. In Proceedings of the 30th VLDB.Google Scholar
  11. 11.
    Mokbel, M. F., Xiong, X., & Aref, W. G. (2004). Sina: Scalable incremental processing of continuous queries in spatio-temporal databases. In Proceedings of the ACM SIGMOD, pp. 623–634.Google Scholar
  12. 12.
    Mouratidis, K., Papadias, D., Bakiras, S., & Tao, Y. (2005). A threshold-based algorithm for continuous monitoring of k nearest neighbors. IEEE Transaction on Knowledge and Data Engineering, 17, 1451–1464.CrossRefGoogle Scholar
  13. 13.
    Mouratidis, K., Papadias, D., & Hadjieleftheriou, M. (2005). Conceptual partitioning: An efficient method for continuous nearest neighbor monitoring. In Proceedings of the ACM SIGMOD, pp. 634–645.Google Scholar
  14. 14.
    Newman, M. (2003). Structure and function of complex networks. SIAM Reviews, 45(2), 167–256.CrossRefzbMATHGoogle Scholar
  15. 15.
    Okabe, A., Boots, B., Sugihara, K., & Chui, S. (2000). Spatial tessellations: Concepts and applications of voronoi diagrams, 2 ed. Chichester: Wiley.CrossRefGoogle Scholar
  16. 16.
    Rahmati, A., & Zhong, L. (2007). Context-for-wireless: Context-sensitive energy-efficient wireless data transfer. In Proceedings of the 7th ACM/USENIX MobiSys.Google Scholar
  17. 17.
    Rappaport, T. (2002). Wireless communications: Principles and practice, 2 ed. Upper Saddle River: Prentice-Hall Inc.Google Scholar
  18. 18.
    Stoyan, D., Kendall, W., & Mecke, J. (1995). Stochasitc geormetry and its applications, 2nd ed. Chichester: Wiley.Google Scholar
  19. 19.
    Strogatz, S. (2001). Exploring complex networks. Nature, 420, 268–276.CrossRefGoogle Scholar
  20. 20.
    Treu, G., Wilder, T., & Küpper, A. (2006). Efficient proximity detection among mobile targets with dead reckoning. In Proceedings of the 4th ACM MobiWAC.Google Scholar
  21. 21.
    Wang, H., Zimmermann, R., & shinn Ku, W. (2006). Distributed continuous range query processing on moving objects. In DEXA, pp. 655–665.Google Scholar
  22. 22.
    Xiong, X., Mokbel, M. F., & Aref, W. G. (2005). Sea-cnn: Scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases. In Proceedings of the 21st international conference on data engineering, ICDE ’05, pp. 643–654.Google Scholar
  23. 23.
    Xu, Z., & Jacobsen, A. (2007). Adaptive location constraint processing. In Proceedings of the ACM SIGMOD, pp. 581–592.Google Scholar
  24. 24.
    Yiu, M.-L., Hou, H., Šaltenis, S., & Tzoumas, K. (2010). Efficient proximity detection among mobile users via self-tuning policies. In Proceedings of the 36th VLDB.Google Scholar
  25. 25.
    Yu, X., Pu, K. Q., & Koudas, N. (2005). Monitoring k-nearest neighbor queries over moving objects. In Proceedings of the 21st ICDE, pp. 631–642.Google Scholar
  26. 26.
    Zhang, J., Zhu, M., Papadias, D., Tao, Y., & Lee, D.-L. (2003). Location-based spatial queries. In Proceedings of the 30th ACM SIGMOD.Google Scholar
  27. 27.
    Zhang, R., Lin, D., Ramamohanarao, K., & Bertino, E. (2008). Continuous intersection joins over moving objects. In Proceedings of the 24th ICDE, pp. 863–872.Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.School of Computer EngineeringNanyang Technological UniversitySingaporeSingapore

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