To Trust or Not to Trust: Optimal Trust-Aware Task Offloading in Vehicular Fog Computing

Given a Vehicular Fog Computing (VFC) scenario, computing tasks with varying sensitivity levels must be offloaded while jointly balancing latency, energy, and trust requirements. Existing trust-based schemes often neglect some of these trade-offs or fail to adapt to task sensitivity and node reliability in dynamic vehicular settings. This paper proposes a trust-based task offloading (TTO) framework that employs a multi-dimensional trust model, combining Reputation, Experience, and Knowledge (REK) to compute a trust-aware overhead function that adapts to task requirements.We also incorporate computable encryption options, enabling task owners (TOs) to protect highly sensitive tasks. The interaction among TOs is modeled as a potential game, which implies the finite-improvement property and the existence of a Nash equilibrium, enabling distributed, self-adaptive decision-making that aligns individual incentives with overall system efficiency. This design allows vehicles to achieve an optimal balance between trust assurance and performance without centralized coordination. The efficiency of the framework against centralized approaches is validated by analyzing the price of anarchy. The simulation results confirm that TTO converges in 60 iterations for 100 nodes, achieving a 30-35% lower average delay and up to 25% lower total overhead compared to the considered baselines. These results indicate that TTO effectively balances trust, latency, and energy requirements.