论文标题
状态在部分电池知识下使用能量收集传感器进行更新
Status Updating with an Energy Harvesting Sensor under Partial Battery Knowledge
论文作者
论文摘要
我们考虑在不精确的了解能量收集（EH）传感器的电池水平下更新状态，该传感器通过启用缓存的边缘节点向用户发送有关随机过程的状态更新。更准确地说，控制决策是通过仅依靠从最后一个接收的状态更新包中捕获的电池级知识来执行的。在接收到用户的新信息请求后，Edge节点使用可用信息来决定是否命令传感器发送状态更新或从缓存中检索最近收到的测量值。我们寻求边缘节点的最佳动作，以最大程度地减少服务测量的平均AOI，即平均按点数AOI。考虑到部分电池知识，我们将问题建模为可观察到的马尔可夫决策过程（POMDP），并通过表征其关键结构，开发动态编程算法以获得最佳策略。仿真结果说明了基于阈值的最佳策略的结构，并表明了与请求感知贪婪（Myopic）策略相比，提出的基于POMDP的最佳策略所获得的收益。
We consider status updating under inexact knowledge of the battery level of an energy harvesting (EH) sensor that sends status updates about a random process to users via a cache-enabled edge node. More precisely, the control decisions are performed by relying only on the battery level knowledge captured from the last received status update packet. Upon receiving on-demand requests for fresh information from the users, the edge node uses the available information to decide whether to command the sensor to send a status update or to retrieve the most recently received measurement from the cache. We seek for the best actions of the edge node to minimize the average AoI of the served measurements, i.e., average on-demand AoI. Accounting for the partial battery knowledge, we model the problem as a partially observable Markov decision process (POMDP), and, through characterizing its key structures, develop a dynamic programming algorithm to obtain an optimal policy. Simulation results illustrate the threshold-based structure of an optimal policy and show the gains obtained by the proposed optimal POMDP-based policy compared to a request-aware greedy (myopic) policy.
