Advertisement

Compact Data Format for Advertising and Discovery in Ubiquitous Networks

  • Pavel Poupyrev
  • Yoshihiro Kawahara
  • Peter Davis
  • Hiroyuki Morikawa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4836)

Abstract

In this paper, we describe a packet data size minimization method designed specifically for advertising and discovery in ubiquitous networks. The minimization is effective for achieving superior discovery performance characteristics such as discovery time and power consumption. The proposed method for data packet size minimization is based on indexing of advertisement text. In the method, dictionaries and indexed data are stored separately, i.e. dictionaries are stored on a server and indexed data is stored on ubiquitous wireless devices, and the same dictionaries are shared among all users. We evaluate an average packet data size and dictionary size for three indexing methods: regular indexing, category indexing and attribute indexing; and show that these methods achieve data packet sizes which are about two and three times smaller than raw data packets and zipped packet data sizes respectively. Also, we show that category indexing allows users to be less dependent on the infrastructure.

Keywords

discovery system service discovery compact data format 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Akyildiz, I.F., Weilian, S., Sankarasubramaniam, Y., Cayirci, E.: A survey on sensor networks. IEEE Commun. Mag. 40(8), 102–114 (2002)CrossRefGoogle Scholar
  2. 2.
    Borovoy, R., McDonald, M., Martin, F., Resnick, M.: Things that blink: computationally augemented name tags. IBM Systems Journal 35(3-4), 488–495 (1996)CrossRefGoogle Scholar
  3. 3.
    Borovoy, R., et al.: Meme tags and community mirrows: moving from cnferences to collaboration. In: Proc. of ACM Conf. Computer Supported Cooperative Work, pp. 159–169 (1998)Google Scholar
  4. 4.
    McCrone, J.: You buzzing me? New Scientist, 20–23 (2000)Google Scholar
  5. 5.
    Laibowwitz, M., Gips, J., Aylward, R., Pentland, A., Paradiso, J.A.: A sensor network for social dynamics. In: Proc. of Conf. on Information processing in sensor networks, pp. 483–491 (2006)Google Scholar
  6. 6.
    Poupyrev, P., Davis, P., Morikawa, H.: TinyObj: A Framework for Service discovery in Ubiquitous Environments. In: Dourish, P., Friday, A. (eds.) UbiComp 2006. LNCS, vol. 4206, Springer, Heidelberg (2006)Google Scholar
  7. 7.
    Poupyrev, P., Sasao, T., Surawatari, S., Davis, P., Morikawa, H., Aoyama, T.: Service Discovery in TinyObj: Strategies and Approaches. In: Gellersen, H.-W., Want, R., Schmidt, A. (eds.) PERVASIVE 2005. LNCS, vol. 3468, pp. 34–39. Springer, Heidelberg (2005)Google Scholar
  8. 8.
    Madden, S., Franklin, M.J., Hellerstein, J.: TAG: A tiny aggregation service for ad-hoc sensor networks. In: Proc. of 5th Simposium on Operating Systems Design and Implementation, pp. 131–146 (2002)Google Scholar
  9. 9.
    Klein, M., Konig-Ries, B., Obreiter, P.: Service Rings - a Semantic Overlay for Service Discovery in Ad Hoc Networks. In: Mařík, V., Štěpánková, O., Retschitzegger, W. (eds.) DEXA 2003. LNCS, vol. 2736, pp. 180–185. Springer, Heidelberg (2003)Google Scholar
  10. 10.
    Chakraborty, D., Joshi, A., Finin, T., Yesha, Y.: GSD: A novel groupbased service discovery protocol for MANETS. In: MWCN 2002. Proc. of 4th IEEE Conf. on Mobile and Wireless Communications Networks, pp. 3165–3182 (2002)Google Scholar
  11. 11.
    Ratsimor, O., Chakraborty, D., Tolia, S., Kushraj, D., Kunjithapatham, A., Gupta, G., Joshi, A., Finin, T.: Allia: Alliance-based service discovery for ad-hoc environments. In: Proc. of ACM Mobile Commerce Workshop, pp. 1–9 (2002)Google Scholar
  12. 12.
    Google-base (2007), http://base.google.com/
  13. 13.
    7-zip compression tool (2007), http://www.7-zip.org/
  14. 14.
    Finkenzeller, K.: RFID Handbook. John Wiley & Sons, New York (1999)Google Scholar
  15. 15.
    Sarma, S., Brock, D., Engels, D.: Radio Frequency Identification and the Electronic Product Code. IEEE Mag. Micro 21(6), 50–54 (2001)CrossRefGoogle Scholar
  16. 16.
    Bloom, B.: Space/time tradeoffs in hash coding with allowable errors. CACM 13(7), 422–426 (1970)zbMATHGoogle Scholar
  17. 17.
    Broder, A., Mitzenmacher, M.: Network applications of Bloom filters: A survey. In: Proc. of the 40th Annual Allerton Conference on Communications, Control, and Computing, pp. 636–646 (2002)Google Scholar
  18. 18.
    Sailhan, F., Issarny, V.: Scalable Service Discovery for MANET. In: PerCom 2005. Proc. of Third IEEE Int. Conf. on Pervasive Computing and Communications, pp. 235–244 (2005)Google Scholar
  19. 19.
    Bluetooth Consortium, Specification of the bluetooth system core version 1.0b, Service Discovery Protocol (1999)Google Scholar
  20. 20.
    Avancha, S., Joshi, A., Finin, T.: Enhanced service discovery in Bluetooth. IEEE Computer 35(6), 96–99 (2002)Google Scholar
  21. 21.
    Sun micosystems, Jini architecutre specification 2.0 (2003)Google Scholar
  22. 22.
    Guttman, E., Perkins, C.: Service Location Protocol (1999)Google Scholar
  23. 23.
    Liefke, H., Suciu, D.: XMill: An Efficient Compressor for XML Data. In: Proc. of the ACM SIGMOD Intl. Conf. on Management of Data, pp. 153–164. ACM Press, New York (2000)CrossRefGoogle Scholar
  24. 24.
    Min, J.-K., Park, M.-J., Chung, C.-W.: XPRESS: A Queriable Compression for XML data. In: Proc. Int. Conf. of Management of Data, ACM SIGMOD, pp. 122–133 (2003)Google Scholar
  25. 25.
    Tolani, P.M., Haritsa, J.R.: XGRIND: A query-friendly XML compressor. In: ICDE 2002. Proc. of 18th Int. IEEE Conf. on Data Engineering, pp. 225–234 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Pavel Poupyrev
    • 1
  • Yoshihiro Kawahara
    • 1
  • Peter Davis
    • 2
  • Hiroyuki Morikawa
    • 1
  1. 1.Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8904Japan
  2. 2.ATR Adaptive Communications Research Laboratories, 2-2-2 Hikaridai, Keihanna Science City, Kyoto 619-0288Japan

Personalised recommendations