Open Access

ARTICLE

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

Maram Fahaad Almufareh^1,*, Mamoona Humayun², Sadia Din^3,*, Khalid Haseeb⁴, Amr Munshi⁵

1 Department of Information System, College of Computer and Information Sciences, Sakaka, Al-Jouf, Saudi Arabia
2 School of Computing, Engineering and the Built Environment, University of Roehampton, London, UK
3 School of Computer Engineering, Gachon University, Seongnam-si, Republic of Korea
4 Department of Computer Science, Islamia College Peshawar, Pakistan
5 Department of Computer and Network Engineering, College of Computing, Umm Al-Qura University, Makkah, Saudi Arabia

* Corresponding Authors: Maram Fahaad Almufareh. Email: ; Sadia Din. Email:

(This article belongs to the Special Issue: Emerging Technologies in Information Security: Modeling, Algorithms, and Applications)

Computer Modeling in Engineering & Sciences 2026, 147(1), 49 https://doi.org/10.32604/cmes.2026.080287

Received 06 February 2026; Accepted 25 March 2026; Issue published 27 April 2026

Abstract

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.

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Keywords

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