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 Failing to keep up with these updates results in outdated information.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.

翻译：在物联网（IoT）的实时状态更新服务中，需要及时更新的信息能否及时传播对于维持其相关性至关重要。若未能跟上这些更新，将导致信息过时。信息年龄（AoI）是量化信息新鲜度的度量指标。现有优化AoI的研究主要关注从信息源到监控器的传输时间，而忽略了从监控器到目的地的传输时间。这一疏忽显著影响了信息的新鲜度，进而影响了决策的准确性。为弥补这一不足，我们设计了一种边缘赋能的车载雾系统，以减轻物联网设备的计算负担。我们研究了信息传输和请求响应时间如何影响端到端AoI。作为解决方案，我们提出了Dueling-Deep Queue Network（dueling-DQN），一种基于深度强化学习（DRL）的算法，并将其性能与DQN策略及解析结果进行了比较。我们的仿真结果表明，所提出的dueling-DQN算法在性能上优于DQN和解析方法，突显了其在提升实时系统信息新鲜度方面的有效性。考虑到完整的端到端传输过程，我们的优化方法能够提升决策性能和整体系统效率。