Accelerating Trust Convergence in IIoT: A ML Approach for Dynamic Network Conditions

2026-06-18

AI Summary

This study addresses the limitations of traditional industrial IoT trust models, which often neglect network quality fluctuations, resulting in slow convergence and inaccurate trust assessments. To overcome these issues, the authors propose a Trust Convergence Acceleration (TCA) method that, for the first time, integrates machine learning with trust modeling under dynamic network conditions. By predicting convergence time and dynamically adjusting transition probabilities, TCA adaptively optimizes the trust convergence process. Evaluated through IEEE 802.11 Wi-Fi channel simulations under adverse network conditions and in the presence of malicious nodes, the proposed approach reduces convergence time by up to 28.6% while significantly improving the accuracy of trust evaluations, thereby enhancing system robustness.

Abstract

In Industrial Internet of Things (IIoT) environments, trust management plays a vital role in securing systems, especially when dealing with resource-constrained devices. Traditional trust models often overlook the impact of fluctuating network quality, leading to slower trust convergence and inaccurate assessments. In this paper, we propose a dynamic trust management solution, known as the Trust Convergence Acceleration (TCA) approach, which integrates Machine Learning (ML) to accelerate trust convergence under poor network conditions. Our model predicts the number of time units needed for trust convergence based on key network metrics and dynamically adapts transition probabilities in the trust model to enhance convergence speed. Using a simulation framework that incorporates realistic Wi-Fi channel conditions based on the IEEE 802.11 standard, we demonstrate the effectiveness of the TCA-based approach, achieving up to a 28.6% reduction in trust convergence time under challenging conditions. Furthermore, the proposed solution exhibits resilience in scenarios involving malicious nodes, improving trust evaluation accuracy. This work provides a scalable and adaptive trust framework for IIoT systems in dynamic industrial environments, ensuring robust performance under varying network conditions.

Problem

• Trust Convergence
• Industrial Internet of Things
• Dynamic Network Conditions
• Trust Management
• Network Quality

Innovation

• Trust Convergence Acceleration
• Machine Learning
• Dynamic Trust Management
• Industrial Internet of Things
• Network Resilience