A Human-Centered Trust and Optimization Framework for Adaptive Access Management in Industrial IoT Water Treatment Systems

Industrial Internet of Things (IIoT) technologies have significantly improved the automation and monitoring of critical infrastructures such as water treatment facilities. However, the increasing interaction between human operators and cyber-physical systems has intensified insider-related cybersecurity risks. Existing trust management approaches primarily focus on connected devices or static role-based access control mechanisms, providing limited support for continuously assessing employee trustworthiness. This paper proposes a Human-Centered Trust Framework for adaptive access management in Industrial IoT water treatment systems. The framework integrates organizational, behavioral, and operational information into a unified employee decision matrix. Criterion importance is objectively determined using the CRITIC method, while employee trustworthiness is evaluated through the TOPSIS multi-criteria decision-making approach. The resulting trust coefficients are then incorporated into a Simulated Annealing optimization model to assign employees to safety-critical industrial tasks under operational and security constraints. The framework was validated using the IBM HR Analytics Employee Attrition and Performance dataset, semantically adapted to represent employee profiles in Industrial IoT environments. Experimental results across four task-allocation scenarios involving 30, 60, 120, and 240 tasks demonstrate that the proposed trust-aware optimization strategy consistently outperforms the evaluated baseline approaches. The Simulated Annealing solution achieved average improvements of 12.53% over Random Feasible Assignment and 11.36% over the RBAC-like strategy, while remaining slightly superior to the stronger Risk-Aware Access-Control baseline, with an average improvement of 0.03%. Furthermore, the proposed optimization procedure maintained an average optimality gap of only 1.21% relative to the exact optimization solution. The proposed framework provides an explainable, data-driven, and optimization-based approach for integrating human trust assessment into Industrial IoT access management, thereby strengthening resilience against insider threats while improving the security, transparency, and adaptability of critical industrial infrastructures.