Effective Data Aggregation in WSN for Enhanced Security and Data Privacy

Abstract

The two biggest problems with wireless sensor networks are security and energy usage. In sensing devices, malicious nodes could be found in large numbers. The researchers have proposed several methods to find these rogue nodes. To prevent assaults on these networks and data transmission, the data must be secured. Data aggregation aids in reducing the number of messages transmitted within the network, which in turn lowers total network energy consumption. Additionally, when decrypting the aggregated data, the base station can distinguish between encrypted and consolidated analysis based on top of the cryptographic keys. By examining the effectiveness of the data aggregation in this research. To solve the above problem, the system provides a method in which an efficient cluster agent is preferred pedestal on its location at the access point and energy availability. The sensor network's energy consumption is reduced by selecting an effective cluster agent, extending the network's lifespan. The cluster's agent is in indict of compiling data for each member node. The clustering agent validates the data and tosses any errors before aggregation. The clustering agent only aggregates confirmed data. To provide end-to-end anonymity, ElGamal elliptic curve (ECE) encryption is used to secure the client data and reassign the encrypted information en route for the cluster agent. Only the base station (BS) can decrypt the data. Furthermore, an ID-based signature system is utilized to enable authenticity. This research presents a technique for recuperating lost data. The access point employs a cache-based backup system to search for lost data. In the end, the results are contrasted based on many factors, such as encryption and decryption time, aggregation time, and the sum of processing time by adjusting the sensor nodes and altering the cluster agents. The suggested cluster analysis, routing, and security protocol, predicated on the ECE algorithm, significantly outperforms the current practice.