Decentralised Trust and Security Mechanisms for IoT Networks at the Edge: A Comprehensive Review

TL;DR

This review analyzes state-of-the-art decentralised trust and security mechanisms for IoT edge networks, highlighting their benefits and challenges in enhancing privacy, resilience, and adaptive threat response.

Contribution

It provides a comprehensive comparison of recent decentralised architectures and frameworks, identifying key considerations and future research directions.

Findings

Decentralised designs improve privacy and reduce single points of failure.

Frameworks like DFGL-LZTA, SecFedDNN, and COSIER show promising results.

Challenges remain in scalability, efficiency, and interoperability.

Abstract

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.