DATA AGGREGATION TECHNIQUES AND SECURITY IN DISTRIBUTED SENSOR NETWORKS

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M. MDurgadevi
Dr. A . Banumathi,

Abstract

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.

 

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