M. MDurgadevi, Dr. A . Banumathi,


Data aggregation is attracting much attention from researchers as efficient way to reduce the huge volume of data generated in wireless sensor networks by eliminating the redundancy among sensing data. Existing system integrated an efficient data aggregation technique for clustering-based periodic wireless sensor networks Further to a local aggregation at sensor node level, our technique allows cluster-head to eliminate redundant data sets generated by neighboring nodes by applying three data aggregation methods such as Vector similarity function, Jaccard function, Euclidean and Cosine distance (K-Mean) functions using to analyze the sensor data performances according to the energy consumption, data latency and accuracy. It’s not providing the security for the data we introduce the security to data. To rectify these problems, this work focuses on efficient CDAMA protocol to obtain the additive encryption model and a novel key management technique to support large plaintext space. The paper also extends the aggregation protocol to obtain the secure aggregate of time-series data. It shows that the proposed protocols are faster than existing solutions, and it has much security communication overhead. In addition, proposes system a new Concealed Data Aggregation Scheme (CDAMA) which is homomorphism public encryption system based multi-application environment, extracts application-specific data from Aggregated Encrypted Ciphertexts  and degrades the damage from unauthorized aggregations process.



WSN, Data Aggregation, Similarity Function, KNN, PFF, CDAMA, AEC

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S. Cheng, Z. Cai, J. Li, and X. Fang, “Drawing dominant dataset from big sensory data in wireless sensor networks,” 2015 IEEE Conference Computer Communications (INFOCOM), pp. 531–539, 2015

K. R. Bhakare, R. Krishna, and S. Bhakare, “An energy-efficient grid based clustering topology for a wireless sensor network,” International Journal of Computer Applications, vol. 39, no. 14, 2012

M. Shanmukhi and O. Ramanaiah, “Cluster-based comb-needle model for energy-efficient data aggregation in wireless sensor networks,” Applications and Innovations in Mobile Computing (AIMoC), pp. 42–47,2015

Y. Lu, I. Comsa, P. Kuonen, and B. Hirsbrunner, “Dynamic data aggregation protocol based on multiple objective tree in wireless sensornetworks,” Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), IEEE, pp. 1–7, 2015.

J. Li, S. Cheng, Y. Li, and Z. Cai, “Approximate holistic aggregation in wireless sensor networks,” Proceeding 35th IEEE International Conference on Distributed Computing Systems (ICDCS), pp. 740–741, 2015.

J. Hicks, N. Ramanathan, D. Kim,M.Monibi, J. Selsky, M. Hansen, and D. Estrin, “AndWellness: An Open Mobile System for Activity and Experience Sampling,” Proc. Wireless Health, pp. 34-43, 2010.

N.D. Lane, M. Mohammod, M. Lin, X. Yang, H. Lu, S. Ali, A. Doryab, E. Berke, T. Choudhury, and A. Campbell, “Bewell: A Smartphone Application to Monitor, Model and Promote Well-being,” Proc. Fifth Int’l ICST Conf. Pervasive Computing Technologies for Healthcare, 2011.

V. Rastogi and S. Nath, “Differentially Private Aggregation of Distributed Time-Series with Transformation and Encryption,” Proc. ACM SIGMOD Int’l Conf. Management of Data, 2010.

P.-A. Fouque, G. Poupard, and J. Stern, “Sharing Decryption in the Context of Voting or Lotteries,” Proc. Fourth Int’l Conf. Financial Cryptography (FC ’00), pp. 90-104, 2000.

E.G. Rieffel, J. Biehl, W. van Melle, and A.J. Lee, “Secured Histories: Computing Group Statistics on Encrypted Data While Preserving Individual Privacy,”, 2010.

E. Shi, T.-H.H. Chan, E. Rieffel, R. Chow, and D. Song, “Privacy-Preserving Aggregation of Time-Series Data,” Proc. Network and Distributed System Security Symp. (NDSS ’11), 2011.

S.B. Eisenman, E. Miluzzo, N.D. Lane, R.A. Peterson, G.-S. Ahn, and A.T. Campbell, “The Bikenet Mobile Sensing System for Cyclist Experience Mapping,” Proc. ACM Fifth Int’l Conf. Embedded Networked Sensor Systems (SenSys ’07), pp. 87-101, 2007.

R. Norris, D. Carroll, and R. Cochrane, “The Effects of Physical Activity and Exercise Training on Psychological Stress and Well-being in an Adolescent Population”, Journal of Psychosomatic Research, vol. 36, no. 1, pp. 55–65, 1992.

K.R. Fox, “The Influence of Physical Activity on Mental Well-being”, Public Health Nutrition, vol. 2, no. 3a, pp. 411–418, 1999.

L.K. George, D.G. Blazer, D.C. Hughes, and N. Fowler, “Social Support and the Outcome of Major Depression”, The British Journal of Psychiatry, vol. 154, no. 4, pp. 478, 1989.

Nicholas D. Lane, Emiliano Miluzzo, Hong Lu, Daniel Peebles Tanzeem Choudhury, and Andrew T. Campbell, “A Survey of Mobile Phone Sensing”, Comm. Mag., vol. 48, pp. 140–150, September 2010.

Pedro Ferreira, Pedro Sanches, Kristina H¨ o¨ ok, and Tove Jaensson, “License to Chill!: How to Empower Users to Cope with Stress”, in Proc. of the 5th Nordic Conference on Human-computer Interaction, pp. 123–132, Lund, Sweden, Oct 20-22, 2008.

K. Patrick, F. Raab, M.A. Adams, L. Dillon, M. Zabinski, C.L. Rock, W.G. Griswold, and G.J. Norman, “A Text Message–based Intervention for Weight Loss: Randomized Controlled Trial”, Journal of Medical Internet Research, vol. 11, no. 1, 2009.



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