Adaptive Q-Learning-Based Event-Prioritized QoS and incentive optimization for enhancing safety in vehicular fog networks

In the rapidly changing world of Intelligent Transportation Systems (ITS), achieving fast, reliable, and energy-efficient communication in vehicle fog computing (VFC) networks is crucial for safety–critical applications. Current VFC approaches are not apt for safety–critical applications as they are based on static heuristics, QoS focus design which neglects trust, energy and reliability; slow convergence and does not support fairness and responsiveness. Moreover, they do not adaptively prioritize concurrent emergencies, which motivates the development of mobility and criticality-aware adaptive approaches. This study proposes a novel reinforcement learning framework named Q-APERF based on tabular Q-learning agent improved by the Augmented Priority-Entropy Reward Function (APERF). Our approach dynamically adjusts multiple QoS metrics, including latency, reliability, trustworthiness, and energy consumption, while prioritizing overlapping emergencies such as ambulances, crash alerts, and road hazards exponentially. The agent achieves adaptive QoS weighting and discrete vehicular state, and therefore, the message forwarding performance can be enhanced in a highly dynamic environment (i.e., the IoV). Extensive simulations show that it outperforms some of the existing state-of-the-art approaches. The Q-APERF achieves 95.5% of message prioritization accuracy, 75.4% of transmission efficacy in packet loss situation, and 83% of energy efficiency and 80% faster response to emergency events, which illustrates its dynamic resilience adaptability balance QoS and energy consumption perspective. Moreover, we introduce a novel metric, Survival-Weighted Data Integrity (SWDI), to evaluate incentive mechanisms that promote the sustained participation of vehicles and encourage them to share their resources. This holistic view will enable safer and more fault-tolerant smart transportation systems through offering a secure, scalable, and context-aware vehicular communication solution.