An In-Depth Control of Blockchain Enabled and AI based Security Mechanisms in Distributed WSN-IoT Environments

Abstract

This WSNs coupled with the Internet of Things (IoT) is very much needed in facilitating smart environment like smart cities, industrial automation, healthcare monitoring and environmental monitoring. Nevertheless, WSN-IoT systems are extremely susceptible to security risks such as denial-of-service attacks, malicious node behaviors, manipulation of routing, data manipulation, and privacy breaches due to their distributed, heterogeneous, and resource-constrained nature. Conventional centralized security models are usually insufficient to deal with these dynamic and scale cyber threats. The paper describes a detailed overview of blockchain-based and artificial intelligence (AI)-based security solutions to the distributed WSN-IoT scenarios. The paper examines the security dangers at varying layers of a IoT architecture and surveys the recent frameworks that incorporate machine learning, deep learning, and federated learning along with blockchain technology in managing decentralized trust and identify intrusions. Moreover, performance trade-offs on the effectiveness of security, energy use, latency, and scalability are discussed. Lastly, the research indicates the presence of an open challenge and future research topics of creating secure, scalable, and intelligent WSN-IoT infrastructure. The paper also suggests a solution of integrative AI-blockchain security framework that would have a combination of AI-based intrusion detection and decentralized blockchain trust management.