AN EFFICIENT ITERATIVE FILTERING ALGORITHM USING WIRELESS SENSOR NETWORKS
Authors/Creators
- 1. Assistant Professor, Department of Computer Science and Engineering, Dhanalakshmi Srinivasan Institute of Technology, Samayapuram, Trichy, Tamilnadu
Description
The need for lustiness of watching, wireless detector networks (WSN) square measure sometimes redundant. Knowledge from multiple sensors is mass at a mortal node that then forwards to the bottom station solely the combination values. At present, because of limitations of the computing power and energy resource of detector nodes, knowledge is mass by very simple algorithms like averaging. However, such aggregation is understood to be terribly liable to faults, and a lot of significantly, malicious attacks. This can't be remedied by cryptanalytic ways, as a result of the attackers typically gains complete access to data hold on within the compromised nodes. For that reason, knowledge aggregation at the mortal node has to be in the course of an assessment of trustiness of information from individual detector nodes. Thus, better, a lot of subtle algorithms square measure required for knowledge aggregation within the future WSN. Such a formula ought to have 2 vital options. We have a tendency to gift a formula to alter the bottom station to firmly cypher predicate Count or add even within the presence of such an attack. Our attack-resilient computation formula computes truth combination by filtering out the contributions of compromised nodes within the aggregation hierarchy.
Files
150.pdf
Additional details
References
- 1. S. Ozdemir and Y. Xiao, "Secure data aggregation in wireless sen-sor networks: A comprehensive overview," Comput. Netw., vol. 53, no. 12, pp. 2022–2037, Aug. 2009. 2. L. Wasserman, All of Statistics : A Concise Course in Statistical Infer-ence. New York, NY, USA: Springer,. 3. A. Jøsang and J. Golbeck, "Challenges for robust trust and reputa-tion systems," in Proc. 5th Int. Workshop Security Trust Manage., Saint Malo, France, 2009, pp. 253–262. 4. K. Hoffman, D. Zage, and C. Nita-Rotaru, "A survey of attack and defense techniques for reputation systems," ACM Comput. Sur-veys, vol. 42, no. 1, pp. 1:1–1:31, Dec. 2009. 5. R. Roman, C. Fernandez-Gago, J. Lopez, and H. H. Chen, "Trust and reputation systems for wireless sensor networks," in Security and Privacy in Mobile and Wireless Networking, S. Gritzalis, T. Karygiannis, and C. Skianis, eds.,Leicester, U.K.: Troubador Publishing Ltd, 2009 pp. 105–128,. 6. H.-S. Lim, Y.-S. Moon, and E. Bertino, "Provenance-based trust-worthiness assessment in sensor networks," in Proc. 7th Int. Work-shop Data Manage. Sensor Netw., 2010, pp. 2–7. 7. H.-L. Shi, K. M. Hou, H. ying Zhou, and X. Liu, "Energy efficient and fault tolerant multicore wireless sensor network: E2MWSN," in Proc. 7th Int. Conf. Wireless Commun., Netw. Mobile Comput., 2011, pp. 1–4. 8. C. de Kerchove and P. Van Dooren, "Iterative filtering in reputa-tion systems," SIAM J. Matrix Anal. Appl., vol. 31, no. 4, pp. 1812– 1834, Mar. 2010. 9. Y. Zhou, T. Lei, and T. Zhou, "A robust ranking algorithm to spamming," Europhys. Lett., vol. 94, p. 48002, 2011. 10. P. Laureti, L. Moret, Y.-C. Zhang, and Y.-K. Yu, "Information filtering via iterative refinement," Europhys. Lett., vol. 75, pp. 1006– 1012, Sep. 2006.