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When Mobile Objects’ Energy Is Not So Tight: A New Perspective on Scalability Issues of Continuous Spatial Query Systems

  • Tai T. Do
  • Fuyu Liu
  • Kien A. Hua
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4653)

Abstract

The two dominant costs in continuous spatial query systems are the wireless communication cost for location update, and the evaluation cost for query processing. Existing works address both of these scalability factors by employing the distributed computation strategy, in which some part of query processing is carried out by mobile objects. In this paper, we make one important assumption about mobile objects’ energy; that is for many applications, mobile objects’ energy is not limited, as opposed to the battery-powered objects assumed in existing works. Under this new assumption, we re-examine the scalability issues for continuous spatial query systems. Our examination points out that the major bottleneck of these systems is now the wide-area wireless uplink bandwidth, which has not been addressed adequately in the past. We attack the problem by leveraging the local-area wireless communication between mobile objects, leading to our proposal of a hybrid communication architecture to be used in these continuous spatial query systems. The hybrid communication architecture unifies the two communication paradigms, wide-area and local-area wireless networks. We then propose a proof of concept system, called P2MRQ (Peer-to-peer technique for Moving Range Queries), to answer continuous moving range queries over moving objects. While MobiEyes [1], an existing continuous range query system, only utilizes distributed computation, our P2MRQ is able to leverage both distributed computation and local-area wireless communication. Our performance study shows that the required wide-area wireless uplink bandwidth from P2MRQ is consistently less than that of MobiEyes; for all considered cases, P2MRQ requires at most 50% of the wide-area wireless uplink bandwidth as MobiEyes does.

Keywords

Mobile Device Query Processing Range Query Mobile Client Scalability Issue 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Gedik, B., Liu, L.: Mobieyes: Distributed processing of continuously moving queries on moving objects in a mobile system. In: Bertino, E., Christodoulakis, S., Plexousakis, D., Christophides, V., Koubarakis, M., Böhm, K., Ferrari, E. (eds.) EDBT 2004. LNCS, vol. 2992, pp. 67–87. Springer, Heidelberg (2004)Google Scholar
  2. 2.
    Prabhakar, S., Xia, Y., Kalashnikov, D.V., Aref, W.G., Hambrusch, S.E.: Query indexing and velocity constrained indexing: Scalable techniques for continuous queries on moving objects. IEEE Trans. Computers 51, 1124–1140 (2002)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Cai, Y., Hua, K.A.: An adaptive query management technique for efficient real-time monitoring of spatial regions in mobile database systems. In: IPCCC, pp. 259–266 (2002)Google Scholar
  4. 4.
    Mokbel, M.F., Xiong, X., Aref, W.G.: Sina: Scalable incremental processing of continuous queries in spatio-temporal databases. In: SIGMOD Conference, pp. 623–634 (2004)Google Scholar
  5. 5.
    Liu, F., Do, T.T., Hua, K.A.: Dynamic range query in spatial network environments. In: Bressan, S., Küng, J., Wagner, R. (eds.) DEXA 2006. LNCS, vol. 4080, pp. 254–265. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Wang, H., Zimmermann, R., Ku, W.S.: Distributed continuous range query processing on moving objects. In: Bressan, S., Küng, J., Wagner, R. (eds.) DEXA 2006. LNCS, vol. 4080, pp. 655–665. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Stemm, M., Katz, R.H.: Measuring and reducing energy consumption of network interfaces in hand-held devices. IEICE Trans. on Communications E80-B, 1125–1131 (1997)Google Scholar
  8. 8.
    Luo, H., Ramjee, R., Sinha, P., Li, L.E., Lu, S.: Cellular and hybrid networks: Ucan: a unified cellular and ad-hoc network architecture. In: ACM MobiCom., ACM Press, New York (2003)Google Scholar
  9. 9.
    Inc, Q.: 1xev: 1x evolution is-856 tia/eia standard. White Paper (2001)Google Scholar
  10. 10.
    Wei, H.: Integrating mobile ad hoc networks with cellular networks. PhD thesis, Department of Electrical Engineering, Columbia University (2004)Google Scholar
  11. 11.
    Giordano, S., Stojmenovic, I., Blazevic, L.: Position-Based Routing Algorithms for AdHoc Networks: A Taxonomy. In: Ad hoc Wireless Networks, Kluwer Academic Publishers, Dordrecht (2003)Google Scholar
  12. 12.
    Ko, Y.B., Vaidya, N.H.: Location-aided routing (lar) in mobile ad hoc networks. In: MOBICOM, pp. 66–75 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Tai T. Do
    • 1
  • Fuyu Liu
    • 1
  • Kien A. Hua
    • 1
  1. 1.School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816-2362 

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