Scheduling for Maximizing the Information Freshness in Vehicular Edge Computing- Assisted IoT Systems

Vehicular edge computing (VEC), as an emerging computing paradigm, enables the timely processing of computing tasks at the network edge through on-vehicle servers, thereby meeting users’ demands for information freshness. In this paper, we introduce the Age of Information (AoI) to measure information freshness and investigate the scheduling problem minimizing the long-term average AoI in VEC-assisted Internet of Things systems. The main challenge lies in the strong coupling between link scheduling and server selection under the location constraints of VEC. To address this issue, we design a scheduling strategy based on deep reinforcement learning and improve the neural network structure using a branch network approach, reducing complexity by decreasing the number of actions represented in the network’s output layer. Moreover, we introduce an action masking scheme that accelerates the algorithm’s convergence in this system. Numerical results show that the proposed scheduling algorithm can achieve up to a 25.4% performance gain compared to existing advanced algorithms.