An enhanced soft computing-based formulation for secure data aggregation and efficient data processing in large-scale wireless sensor network

Rapid growth in wireless technologies and communication, wireless sensor network (WSN) skills, data gathering and management models has paved the sensor technology a great impact on all factors of human life. In WSN, maximum consumption of constrained resources is considered to be the major challenge. Additionally, secure data aggregation has made the research domain more interesting. For consuming the limited sensor node resources optimally, data aggregation model plays a vital role. It reduces the redundant and unwanted data transmission and enhances the accuracy of data, thereby reducing the energy consumption rate and consumption overhead. Hence, for balancing the energy efficient data processing with secure data aggregation in large-scale WSN, optimized security model using enhanced fully homomorphic encryption (OSM-EFHE) has been developed in this work. First, the network is divided into clusters and cluster head which acts as an aggregator is selected based on the fuzzy if-then rule which helps in consumption of energy. Second, it provides data confidentiality and maintains subjective aggregation functions through fully homomorphic encryption (FHE). In this work, Van Dijk, Gentry, Halevi and Vaikunathan key generation plan with public key compression is used which condenses the public key dimension which is one of the major computations overhead for FHE. Finally, data integrity operation has also been induced with message authentication code. When comparing with the existing approaches, simulation results make a clear note of average delay of the network as 1.2 ms and a higher throughput of 4500 bps approximately. Thus, the overall transmission of data has been increased by means of employing OSM-EFHE model.