Energy-Efficient Routing in Wireless Sensor Networks Using Blockchain-Driven Deep Learning Architectures

Abstract

Wireless Sensor Networks (WSNs) have emerged as a pivotal technology for diverse applications, yet they face significant challenges related to energy consumption and data security. This study proposes an innovative approach leveraging blockchain-driven deep learning architectures to address these challenges in WSNs. The integration of blockchain technology ensures secure data transmission and validates transactions while deep learning models optimize energy-efficient routing protocols. The primary focus of this research lies in developing a novel framework that harnesses the advantages of blockchain's decentralized ledger for secure and tamper-resistant data handling in WSNs. Additionally, deep learning techniques, such as neural networks and reinforcement learning, are employed to optimize routing strategies and minimize energy consumption across sensor nodes. The proposed architecture aims to enhance network performance by mitigating the overhead associated with traditional routing protocols while ensuring data integrity and confidentiality. The integration of blockchain technology enables a transparent and immutable record of data transactions, thereby fortifying the network against various security threats. The study conducts comprehensive simulations and experiments to evaluate the performance of the proposed framework. Metrics including energy efficiency, network lifetime, latency, and security are assessed to validate the effectiveness of the blockchain-driven deep learning architecture in WSNs. The findings of this research demonstrate the potential of this hybrid approach in significantly improving the energy efficiency and security of wireless sensor networks. The results showcase promising advancements in mitigating the energy constraints of sensor nodes while maintaining robust security measures, thereby contributing to the evolution of sustainable and secure WSNs.