A Comprehensive Review of Trusted Cloud-Enabled IoT Networks Using Blockchain and Siamese Heterogeneous Convolutional Neural Networks

Abstract

The rapid expansion of Internet of Things (IoT) networks has enabled intelligent data-driven applications across domains such as smart cities, healthcare, and industrial automation. However, cloud-enabled IoT systems face significant challenges related to security, trust, data privacy, and efficient anomaly detection. This paper presents a comprehensive review of trusted cloud-enabled IoT networks leveraging blockchain technology and Siamese Heterogeneous Convolutional Neural Networks (SHCNN). Blockchain provides decentralized, tamper-proof data storage and trust management, addressing vulnerabilities associated with centralized cloud architectures. Siamese neural networks, combined with heterogeneous CNN architectures, enable efficient similarity learning and anomaly detection in IoT data streams. These models are particularly effective in identifying malicious activities and ensuring secure data transmission. The integration of blockchain with deep learning further enhances system reliability by enabling secure model sharing and verification. This review analyzes recent advancements, compares different architectures, and evaluates their performance in terms of accuracy, scalability, and security. The study concludes that hybrid architectures combining blockchain and SHCNN offer a promising solution for building secure, scalable, and intelligent IoT ecosystems.