IoT网络中基于学习的多无人机AoI最小化轨迹规划
A Learning-Based Trajectory Planning of Multiple UAVs for AoI Minimization in IoT Networks
摘要：
许多新兴的物联网（IoT）应用依赖于传感器节点收集的信息，其中信息的新鲜度是一个重要标准。\textit{Age of Information}（AoI）是量化信息及时性的度量，即，接收到的信息或状态更新的新鲜度。这项工作考虑了物联网网络中部署的传感器的设置，其中多个无人机（UAV）作为传感器和基站之间的移动中继节点。我们制定了一个优化问题，共同规划无人机的轨迹，同时最小化收到的消息的AoI。这确保了在基站处接收的信息尽可能新鲜。使用深度强化学习（DRL）算法有效地解决了复杂的优化问题。特别是，我们提出了一个深度Q网络，它作为一个函数近似估计的状态-动作价值函数。所提出的方案是快速收敛，并在一个较低的AoI比随机游走计划的结果。我们提出的算法减少了约$25\%$的平均年龄，并需要下降到$50\%$更少的能量相比，基线计划。
英文摘要：
Many emerging Internet of Things (IoT) applications rely on information collected by sensor nodes where the freshness of information is an important criterion. \textit{Age of Information} (AoI) is a metric that quantifies information timeliness, i.e., the freshness of the received information or status update. This work considers a setup of deployed sensors in an IoT network, where multiple unmanned aerial vehicles (UAVs) serve as mobile relay nodes between the sensors and the base station. We formulate an optimization problem to jointly plan the UAVs' trajectory, while minimizing the AoI of the received messages. This ensures that the received information at the base station is as fresh as possible. The complex optimization problem is efficiently solved using a deep reinforcement learning (DRL) algorithm. In particular, we propose a deep Q-network, which works as a function approximation to estimate the state-action value function. The proposed scheme is quick to converge and results in a lower AoI than the random walk scheme. Our proposed algorithm reduces the average age by approximately $25\%$ and requires down to $50\%$ less energy when compared to the baseline scheme.
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