A Survey of Methods and Architectures for Trusted Cloud-Enabled IoT Networks Using Blockchain and Siamese Heterogeneous Convolutional Neural Networks

Abstract

The rapid growth of the Internet of Things (IoT) has enabled seamless connectivity among diverse devices and intelligent systems, but it has also introduced significant challenges related to security, trust, scalability, and privacy in cloud-enabled environments. Conventional centralized architectures often struggle to meet the demands of dynamic, distributed, and resource-constrained IoT networks. This survey comprehensively reviews the integration of blockchain technology with Siamese Heterogeneous Convolutional Neural Networks (SH-CNNs) as a promising solution for developing secure and intelligent IoT ecosystems. Blockchain provides decentralized, immutable, and transparent data management, while Siamese networks support similarity-based authentication, intrusion detection, and anomaly recognition. The heterogeneous CNN component further improves feature extraction from multimodal data, including sensor readings, images, and network traffic. The survey explores blockchain-enabled smart contracts, secure access control, and SH-CNN-based identity verification across applications such as smart healthcare, industrial IoT, smart cities, and autonomous transportation using benchmark datasets like NSL-KDD, CICIDS2017, and IoT-23. It also discusses optimization strategies, including lightweight CNNs, federated learning, hybrid blockchain architectures, and hardware-aware implementations. Despite challenges such as computational overhead, consensus latency, scalability, and system integration, the convergence of blockchain and SH-CNNs demonstrates strong potential for building secure, trustworthy, and scalable next-generation IoT systems. Future research should focus on quantum-resistant blockchain protocols, edge intelligence, and adaptive trust management.