Dynamic Trust Management in Decentralized Edge Computing Using Q-Learning for Adaptive System

The rapid expansion of edge computing has introduced significant challenges in trust management due to its decentralized and heterogeneous nature. Traditional trust models, such as rule-based or reputation-based systems, often fail to address the dynamic behavior of edge nodes and the constraints of computational resources. This study aims to develop a dynamic trust management model utilizing Q-learning, a reinforcement learning algorithm, to assess and adjust trust levels among edge nodes in real time. The model treats each node as an autonomous agent that learns from past interactions, refining trust decisions via a reward-based mechanism. Simulations were conducted in a dynamic mesh network environment using Python, with varying node behaviors such as honest, opportunistic, and malicious actions. The results show that the Q-learning-based model significantly outperforms traditional models, achieving trust classification accuracy of 91.2%, communication efficiency of 89.4%, and enhanced adaptability to behavioral changes. These findings suggest that reinforcement learning techniques, particularly Q-learning, can offer a more adaptive, lightweight, and resilient approach to trust management in decentralized edge computing systems, making them more robust to the inherent challenges of such environments. The proposed model has practical implications for improving security and efficiency in edge computing applications, supporting the development of more intelligent and reliable decentralized networks.