Decentralised Trust and Security Mechanisms for IoT Networks at the Edge: A Comprehensive Review
For researchers and practitioners in IoT security, this paper provides a comprehensive overview of current decentralised mechanisms, though it is a survey without novel contributions.
This review examines decentralised trust and security mechanisms for IoT edge networks, finding that approaches like federated learning and lightweight blockchain enhance privacy and reduce single points of failure, but face challenges in scalability and interoperability.
INTRODUCTION: The proliferation of the amalgamation of IoT and edge computing has increased the demand for decentralised trust and security mechanisms capable of operating across heterogeneous and resource-limited devices. Approaches such as federated learning, Zero Trust architectures, lightweight blockchain and distributed neural models offer alternatives to centralised control. OBJECTIVES: This review examines various state-of-the-art decentralised mechanisms and evaluates their effectiveness in terms of securing IoT networks at the edge. METHODS: Thirty recent studies were analysed to compare how decentralised architectures establish trust, support secure communication and enable intrusion and anomaly detection. Frameworks, such as DFGL-LZTA, SecFedDNN and COSIER were assessed. RESULTS: Decentralised designs enhance privacy, reduce single points of failure and improve adaptive threat response, though challenges remain in scalability, efficiency and interoperability. CONCLUSION: The study identifies key considerations and future research needs for building secure and resilient trust-aware IoT edge ecosystems.