Bald Eagle Search Optimized Deep CNN-Based Routing Scheme for WSN Using IoT-Based Blockchain Technology

Wireless Sensor Networks (WSN) with changing environments are potentially vulnerable to various forms of threatening cyber-attacks, and they rely heavily on encryption and authentication techniques to tackle this problem. Because of the present changes in routing data, most prevalent routing algorithms face failure in characterizing harmful network nodes. Regarding the cybersecurity challenges of Internet of Things (IoT) devices, it’s a critical desire to identify novel approaches that improve the overall safety of WSNs. A problem that requires consideration is IoT device data storage and secure management. More existing determinations rely on a centralized framework, ecosystems are easy to interfere with, and data obtained from sensors are not authentic. Thus, this work presents a trustworthy and secure inter-correlated routing strategy that depends on the IoT-based blockchain, optimization algorithm, and deep learning techniques. The distributed routing data in the WSN is managed by the blockchain method, which achieves the best possible routing with the assistance of the Bald Eagle Search Optimization (BES) algorithm. The Deep Convolutional Neural Network (DCNN) algorithm makes effective routing decisions based on routing information variations among the nodes. The suggested routing strategy is carried out, and its efficacy is measured using latency, energy usage and throughput parameters. The suggested approach’s performance is enhanced to high efficiency, and it is evaluated for malicious attacks and delays.