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Research Article

, 09 Jun 2024 | 10.623460
Year : 2017 | Volume: 5 | Issue: 2 | Pages : 1-8

Uninterrupted Whole Result Involving Queries over WSN

Arokia Subancy Vinitha.B1 *, J.Prabula
  • 1Anna University Chennai, III MCA, Lord Jegannath College of Engineering & Technology, Tamilnadu, IN

Peer-to-peer networking offers a scalable solution for sharing multimedia data across the network. With a large amount of visual data spread among different nodes, it is an important but challenging issue to perform content-based retrieval in peer-to-peer networks. Compared with central environments, the key challenge is to efficiently obtain a global codebook, as images are distributed across the whole peer-to-peer network. In addition, a peer-to-peer network often evolves dynamically, which makes a static codebook less effective for retrieval tasks. Therefore, I propose a dynamic codebook updating method by optimizing the mutual information between the resultant codebook and relevance information, and the workload balance among nodes that manage different code words. The broad experimental results point to that the future approach is scalable in budding and distributed peer-to-peer networks, while achieving improved revival accuracy.

Conclusion

Our present a bag-of-visual-words (BoVW) model-based approach for content-based image retrieval (CBIR) in peer-to-peer (P2P) networks. I formulate the problem of updating an existing codebook as optimizing the retrieval accuracy and workload balance. Investigate DHT specific optimizations for cost reduction, more advanced matching refinement and multi-modal fusion techniques in P2P networks, and extensions of this approach to other distributed architectures. That is making the CAN address space corresponding to our feature space, and replace the CAN zones with codeword partitions. Such an embedding will eliminate the overhead of an additional DHT layer, as I can implement the SPLIT/MERGE operations as a CAN zone split/takeover, instead of adding and removing entries on DHT.

References

  1. M. Steiner, T. En-Najjary, and E. W. Biersack, “Long term study of peer behavior in the KAD DHT,” IEEE/ACM Transactions on Networking, vol. 17, no. 5, pp. 1371–1384, Oct. 2009.
  2. H. Schulze and K. Mochalski, “Internet study 2008/2009,” Internet Studies, ipoque, 2009.
  3. I. Stoica, R. Morris, D. Karger, M. F. Kaashoek, and H. Balakrishnan, “Chord: A scalable peer-to-peer lookup service for internet applications,” in ACM Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, 2001, pp. 149–160.
  4. S. Ratnasamy, P. Francis, M. Handley, R. Karp, and S. Shenker, “A scalable content-addressable network,” in ACM Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, 2001, pp. 161–172.
  5. M. Mordacchini, L. Ricci, L. Ferrucci, M. Albano, and R. Baraglia, “Hivory: Range queries on hierarchical voronoi overlays,” in IEEE International Conference on Peer-to-Peer Computing, Aug. 2010, pp. 1–10.
  6. Y. Tang, S. Zhou, and J. Xu, “LIGHT: A query-efficient yet low maintenance indexing scheme over DHTs,” IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 1, pp. 59–75, Jan. 2010.
  7. L. Zhang, Z. Wang, and D. Feng, “Efficient high- dimensional retrieval in structured P2P networks,” in IEEE International Conference on Multimedia and Expo Workshops, Jul. 2010, pp. 1439–1444.
  8. H. J´egou, M. Douze, and C. Schmid, “Improving bag-of-features for large scale image search,” International Journal of Computer Vision, vol. 87, pp. 316–336, 2010.
  9. J. Sivic and A. Zisserman, “Video Google: A text retrieval approach to object matching in videos,” in IEEE International Conference on Computer Vision, vol. 2, 2003, pp. 1470–1477.
  10. J. Yang, Y.-G. Jiang, A. G. Hauptmann, and C.-W. Ngo, “Evaluating bag-of-visual-words representations in scene classification,” in ACM International Workshop on Multimedia Information Retrieval, 2007, pp. 197–206.
  11. K. Mikolajczyk and C. Schmid, “An affine invariant interest point detector,” in European Conference on Computer Vision, 2002, pp. 128– 142.
  12. D. G. Lowe, “Object recognition from local scale-invariant features,” in IEEE International Conference on Computer Vision, vol. 2, 1999, pp. 1150–1157.
  13. S. B¨orzs¨onyi, D. Kossmann, and K. Stocker. The skyline operator. In ICDE, pages 421–430, 2001.
  14. J. Chomicki, P. Godfrey, J. Gryz, and D. Liang. Skyline with presorting. In ICDE, pages 717–816, 2003.
  15. K. L. Tan, P. K. Eng, and B. C. Ooi. Efficient progressive skyline computation. In VLDB, pages 301–310, 2001.
  16. D. Kossmann, F. Ramsak, and S. Rost. Shooting stars in the sky: An online algorithm for skyline queries. In VLDB, pages 275–286, 2002.
  17. D. Papadias, Y. Tao, G. Fu, and B. Seeger. An optimal and progressive algorithm for skyline queries. In SIGMOD Conference, pages 467–478, 2003.
  18. J. Pei, W. Jin, M. Ester, and Y. Tao. Catching the best views of skyline: A semantic approach based on decisive subspaces. In VLDB, pages 253–264, 2005.
  19. Y. Yuan, X. Lin, Q. Liu, W. Wang, J. X. Yu, and Q. Zhang. Efficient computation of the skyline cube. In VLDB, pages 241–252, 2005.
  20. P. Godfrey, R. Shipley, and J. Gryz. Maximal vector computation in large data sets. In VLDB, pages 229–240, 2005.
  21. X. Lin, Y. Yuan, W. Wang, and H. Lu. Stabbing the sky: Efficient skyline computation over sliding windows. In ICDE, pages 502–513, 2005.
  22. C. Y. Chan, H. V. Jagadish, K.-L. Tan, A. K. H. Tung, and Z. Zhang. On high dimensional skylines. In EDBT, pages 478–495, 2006.
  23. W. T. Balke, U. G¨untzer, and J. X. Zheng. Efficient distributed skylining for web information systems. In EDBT, pages 256–273, 2004.
  24. Z. Huang, C. S. Jensen, H. Lu, and B. C. Ooi. Skyline queries against mobile lightweight devices in manets. In ICDE, 2006.
  25. .Wu, C. Zhang, Y. Feng, B. Y. Zhao, D. Agrawal, and A. E. Abbadi. Parallelizing skyline queries for scalable distribution. In EDBT, pages 112–130, 2006.
  26. M. Cai, M. R. Frank, J. Chen, and P. A. Szekely. Maan: A multi-attribute addressable network for grid information services. In GRID, pages 184–191, 2003.
  27. A. R. Bharambe, M. Agrawal, and S. Seshan. Mercury: supporting scalable multi-attribute range queries. In SIGCOMM, pages 353–366, 2004.
  28. C. Zhang, A. Krishnamurthy, and R. Y. Wang. Skipindex: Towards a scalable peer-to-peer index service for high dimensional data. Technical report, Princeton University, 2004.
  29. Y. Shu, K. L. Tan, and A. Zhou. Adapting the content native space for load balanced indexing. In DBISP2P,  Pages 122– 135, 2004.
  30. M. Batko, D. Novak, F. Falchi, and P. Zezula, “Scalability comparison of peer-to-peer similarity search structures,” Future Generation Computer Systems, vol. 24, no. 8, pp. 834 – 848, 2008.

Keywords: peer-to-peer network, WSN, Computer Network

Citation: Arokia Subancy Vinitha.B*, Arokia Subancy Vinitha.B ( 2017), Uninterrupted Whole Result Involving Queries over WSN. , 5(2): 1-8

Received: 09/06/2024; Accepted: 09/06/2024;
Published: 09/06/2024

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*Correspondence: Arokia Subancy Vinitha.B, arolesuban14@gmail.com

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