Securing Real-Time IoT Systems with Blockchain-Enabled Edge Computing and Anomaly Detection
Basudeo Narayan Shrestha, Gloria S. Francis, Daniel Henrique Pohren, Alexandre dos Santos Roque, Edison Pignaton de Freitas · 2025
The rapid expansion of the Internet of Things (IoT) has introduced significant challenges in security, real-time data processing, and scalability. Traditional cloud-based IoT architectures suffer from high latency, single points of failure, and vulnerability to cyber threats. This study proposes a blockchain-enabled edge computing framework integrating decentralized smart contracts and AI-driven anomaly detection to address these issues. The framework leverages edge computing to perform localized data processing, reducing reliance on cloud services and improving response times. Blockchain technology ensures data integrity and secure transactions through an immutable ledger, while AI-based anomaly detection enhances threat detection in real time. Experimental results demonstrate that edge computing significantly reduces latency, blockchain enhances security at the cost of computational overhead, and AI-driven anomaly detection achieves high accuracy. The study underscores the effectiveness of integrating blockchain and edge computing to enhance IoT security while highlighting the need for further optimizations to improve scalability and efficiency.