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.

翻译：引言：物联网与边缘计算融合的蓬勃发展，增加了对能够在异构、资源受限设备上运行的去中心化信任与安全机制的需求。联邦学习、零信任架构、轻量级区块链以及分布式神经网络等方法，为集中式控制提供了替代方案。目标：本综述审视了多种前沿的去中心化机制，并评估其在保障物联网边缘网络安全方面的有效性。方法：分析了三十项近期研究，比较了去中心化架构如何建立信任、支持安全通信以及实现入侵和异常检测。评估了DFGL-LZTA、SecFedDNN和COSIER等框架。结果：去中心化设计增强了隐私性，减少了单点故障，并改善了自适应威胁响应能力，但在可扩展性、效率和互操作性方面仍存在挑战。结论：本研究为构建安全、弹性的信任感知物联网边缘生态系统，指出了关键考量因素和未来研究方向。