Trust-Centric Security Architecture and Anomaly Analytics for Distributed Fog-IoT Systems

The real-time systems perform key functionalities in various fields to automate the communication and response in critical events. The Internet of Things (IoT), integrated with numerous physical objects, gathers environmental data, processes it at the edge, and provides intelligent decisions while routing health records to processing units. However, the dynamic and resource-constrained nature of IoT-based healthcare environments introduces significant challenges related to latency, transmission costs, and the reliable interaction of devices amid uncertain activities. In this work, we propose a framework for a consistent and trustworthy system that uses a weighted trust aggregation model to consider multiple parameters and support timely routing decisions in a Fog-driven healthcare environment. Furthermore, authorized access to critical and sensitive health data is achieved through mutual authentication among devices, ensuring data integrity. The analysis of trust scores dynamically enhances resilience and the timely detection of malicious actions, thereby improving the healthcare system’s performance across unpredictable channels. The performance of the proposed framework is tested and validated against CLCSR and FSRF, and performance results revealed the significance for energy consumption, response time, network throughput, trust level, and accuracy across varying fog node capacity and interference scenarios.