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

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Abstract

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.

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Notes

  1. Existing techniques [10, 26] on the dynamic maintenance of Voronoi cells are centralized, and hence do not apply to our case.

  2. As each user only maintains its own cell, the database has a very small size and can hence be contained in one network packet. Therefore, no extra cost is incurred.

References

  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.

  2. Bash, B., & Desnoyers, P. (2007). Exact distributed Voronoi cell computation in sensor networks. In Proceedings of the 6th ACM/IEEE IPSN.

  3. Brinkhoff, T., & Str, O. (2002). A framework for generating network-based moving objects. Geoinformatica, 6, 202.

    Article  Google Scholar 

  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.

  5. Gedik, B., & Liu, L. (2006). Mobieyes: A distributed location monitoring service using moving location queries. IEEE Transactions on Mobile Computing, 5, 1384–1402.

    Article  Google Scholar 

  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.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  10. Kolahdouzan, M., & Shahabi, C. (2004). Voronoi-based K nearest neighbor search for spatial network databases. In Proceedings of the 30th VLDB.

  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.

  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.

    Article  Google Scholar 

  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.

  14. Newman, M. (2003). Structure and function of complex networks. SIAM Reviews, 45(2), 167–256.

    Article  MATH  Google Scholar 

  15. Okabe, A., Boots, B., Sugihara, K., & Chui, S. (2000). Spatial tessellations: Concepts and applications of voronoi diagrams, 2 ed. Chichester: Wiley.

    Book  Google Scholar 

  16. Rahmati, A., & Zhong, L. (2007). Context-for-wireless: Context-sensitive energy-efficient wireless data transfer. In Proceedings of the 7th ACM/USENIX MobiSys.

  17. Rappaport, T. (2002). Wireless communications: Principles and practice, 2 ed. Upper Saddle River: Prentice-Hall Inc.

    Google Scholar 

  18. Stoyan, D., Kendall, W., & Mecke, J. (1995). Stochasitc geormetry and its applications, 2nd ed. Chichester: Wiley.

    Google Scholar 

  19. Strogatz, S. (2001). Exploring complex networks. Nature, 420, 268–276.

    Article  Google Scholar 

  20. Treu, G., Wilder, T., & Küpper, A. (2006). Efficient proximity detection among mobile targets with dead reckoning. In Proceedings of the 4th ACM MobiWAC.

  21. Wang, H., Zimmermann, R., & shinn Ku, W. (2006). Distributed continuous range query processing on moving objects. In DEXA, pp. 655–665.

  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.

  23. Xu, Z., & Jacobsen, A. (2007). Adaptive location constraint processing. In Proceedings of the ACM SIGMOD, pp. 581–592.

  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.

  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.

  26. Zhang, J., Zhu, M., Papadias, D., Tao, Y., & Lee, D.-L. (2003). Location-based spatial queries. In Proceedings of the 30th ACM SIGMOD.

  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.

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  1. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Chi Zhang & Jun Luo

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  1. Chi Zhang
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Correspondence to Jun Luo.

Appendix: Simulation stationarity under mobility

Appendix: Simulation stationarity under mobility

As our simulations all rely on some form of randomized mobility model, we need to be careful in choose a simulation period to guarantee the validity of our simulations. Roughly speaking, as there is no guarantee that the initial states of the randomly moving nodes are stationary, the results obtained from a very short simulation period may not be representative or can even be misleading. Here we use the Slashdot09 social graph over the Oldenburg model to illustrate the changes of the two metrics in time. Obviously, it takes about 100 s to make a mobile network enter its steady state. Therefore, the simulation results reported in [24] may well be measured during the transient state of the networks given their 100 s simulation time, and their validity is highly questionable. In our simulation, we set the simulation period to 500 s and only start to measure the cost after the first 100 s (Fig. 23).

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Zhang, C., Luo, J. TRack others if you can: localized proximity detection for mobile networks. Wireless Netw 20, 1477–1494 (2014). https://doi.org/10.1007/s11276-014-0690-5

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