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In recent days, the usage of data transmission has increased in Wireless Sensor Network (WSN) environments due to its dynamic nature. However, WSNs face many issues during data transmission, such as less energy efficiency, less security, and less network lifetime. Here, in this research it presents secure and energy-efficient clustering and routing techniques for an edge-assisted WSN environment to address these problems. The proposed work includes four major processes: Quad tree-based network construction, energy-efficient clustering, RL-based duty cycling, and secure multipath routing. This work constructs the network based on a quad-tree structure to increase network management performance and reduce complexity. After network construction, authentication of sensors is performed by considering ID and location using the Lightweight Encryption Algorithm (LEA), which provides high security by eliminating illegitimate sensor nodes. Then, this research model performs clustering using Tasmanian Devil Optimization (TDO), which selects optimal CH and performs clustering. In contrast, the CH selection and clustering are performed dynamically by considering time and event metrics, which increases communication efficiency and reduces energy depletion. To reduce energy consumption, This research model performs duty cycling using the Improved Twin Delayed Deep Deterministic Policy Gradient (ITD3) algorithm, increasing the network lifetime. Finally, A secure multipath routing is performed using a game theory-based Generative Adversarial Network (GTGAN). During routing, GTGAN ranks the selected multipath based on their hop counts. The highest-ranked paths are selected for transmitting an emergency message, and medium-ranked paths are selected for non-emergency message transmission, which reduces data loss due to energy depletion. Here, all the transactions are stored on the blockchain for increased security. The NS-3.26 network simulator conducts the simulation of this research, and the performances are evaluated based on various performance metrics, proving that the proposed work achieves superior performance compared to existing works.

Secure-Energy Efficient Bio-Inspired Clustering and Deep Learning based Routing using Blockchain for Edge Assisted WSN Environment