Enhancing Secure Data Transmission in Wireless Sensor Networks through Blockchain and Optimized Fuzzy-Embedded Recurrent Neural Networks

Wireless sensor networks (WSN) face critical challenges like security vulnerabilities, high energy consumption, and reduced network lifetime during data transmission. Also, lags in creating a significant need for a secure, efficient, and reliable data transmission framework. In this manuscript, enhancing Secure Data Transmission in Wireless Sensor Networks through Blockchain and Optimized Fuzzy-Embedded Recurrent NeuralNetworks (SDT-WSN-FERNN) is proposed. To secure data transmission, the Martino Homomorphic Encryption Algorithm (MHEA) is used. The protected data are transferred to the Unsupervised Multi-View K-Means Clustering Algorithm (UMKCA), which selects the optimal Cluster Head (CH). Binary Waterwheel Plant Optimization Algorithm (BWPOA) increases the network's lifetime. For secure multipath routing, Fuzzy-Embedded Recurrent Neural Network (FERNN) is used. The Green-Proof of Work Consensus algorithm (GPoWCA) is used to store transactions and ensure enhanced security. The proposed SDT-WSN-FERNN method is evaluated using performance metrics like energy consumption, throughput, and packet delivery ratio, which are evaluated and compared with existing methods, respectively.