  In real-time status update services for the Internet of Things (IoT), the
timely dissemination of information requiring timely updates is crucial to
maintaining its relevance. Failing to keep up with these updates results in
outdated information. The age of information (AoI) serves as a metric to
quantify the freshness of information. The Existing works to optimize AoI
primarily focus on the transmission time from the information source to the
monitor, neglecting the transmission time from the monitor to the destination.
This oversight significantly impacts information freshness and subsequently
affects decision-making accuracy. To address this gap, we designed an
edge-enabled vehicular fog system to lighten the computational burden on IoT
devices. We examined how information transmission and request-response times
influence end-to-end AoI. As a solution, we proposed Dueling-Deep Queue Network
(dueling-DQN), a deep reinforcement learning (DRL)-based algorithm and compared
its performance with DQN policy and analytical results. Our simulation results
demonstrate that the proposed dueling-DQN algorithm outperforms both DQN and
analytical methods, highlighting its effectiveness in improving real-time
system information freshness. Considering the complete end-to-end transmission
process, our optimization approach can improve decision-making performance and
overall system efficiency.
