论文标题
年龄感知状态的状态更新控制能源收集IoT传感器通过增强学习
Age-Aware Status Update Control for Energy Harvesting IoT Sensors via Reinforcement Learning
论文作者
论文摘要
我们考虑了一个具有多个用户，多个能量收集传感器的物联网传感网络，以及用户和传感器之间的网关的无线边缘节点。用户请求更新有关物理过程的价值，每个过程均由一个传感器测量。边缘节点具有一个缓存存储，该存储存储存储了每个传感器最近接收的测量值。收到请求后，边缘节点可以命令相应的传感器发送状态更新，也可以使用缓存中的数据。我们旨在找到边缘节点的最佳动作，以最大程度地减少信息和能源消耗时代之间的平均长期成本。我们提出了一种实用的强化学习方法，该方法在不知道传感器的确切电池水平的情况下找到了最佳政策。
We consider an IoT sensing network with multiple users, multiple energy harvesting sensors, and a wireless edge node acting as a gateway between the users and sensors. The users request for updates about the value of physical processes, each of which is measured by one sensor. The edge node has a cache storage that stores the most recently received measurements from each sensor. Upon receiving a request, the edge node can either command the corresponding sensor to send a status update, or use the data in the cache. We aim to find the best action of the edge node to minimize the average long-term cost which trade-offs between the age of information and energy consumption. We propose a practical reinforcement learning approach that finds an optimal policy without knowing the exact battery levels of the sensors.
