Privacy and security enhancement in WSNs with blockchain and deep learning

Wireless sensor networks (WSNs) offer cost-effective solutions for real-world challenges, but rapid advancements make security a
critical concern. Blockchain technology offers a promising solution, particularly for securing wireless sensor networks. While past
research has largely focused on homogeneous systems, our study emphasizes blockchain-based security in this domain. The Internet
of Things (IoT) has had substantial advancements over more than a decade. It consists in combining smart gadgets in the systems of
smart cities, learning, agriculture, and health. Many real-time monitoring applications rely on IoT. However, due to limited storage
and evaluating power, smart devices were subjected to attacks, as traditional cryptographic methods are inadequate for ensuring robust
security in these environments. The proposed method enhances WSN security using blockchain technology. By integrating blockchain
(BC) with deep learning, particularly CNN-based feature extraction, the approach ensures secure data transfer. Built on an IoT-based
wireless network, this model strengthens data transmission reliability through blockchain. Performance evaluation considers latency,
throughput, packet delivery ratio, energy consumption, and communication overhead. Experimental results indicate that the fusion of
deep learning and blockchain significantly bolsters data integrity, reducing vulnerabilities and ensuring robust WSN protection.