In this work, we consider a real-time IoT monitoring system in which an energy harvesting sensor with a finite-size battery measures a physical process and transmits the status updates to an aggregator. The aggregator, equipped with caching capabilities, can serve the external requests of a destination network with either a stored update or a fresh update from the sensor. We assume the destination network acts as a gossiping network in which the update packets are forwarded among the nodes in a randomized setting. We utilize the Markov Decision Process framework to model and optimize the network's average Version Age of Information (AoI) and obtain the optimal policy at the aggregator. The structure of the optimal policy is analytically demonstrated and numerically verified. Numerical results highlight the effect of the system parameters on the average Version AoI. The simulations reveal the superior performance of the optimal policy compared to a set of baseline policies.

翻译：本工作考虑一个实时物联网监控系统，其中配备有限容量电池的能量采集传感器测量物理过程并将状态更新传输至聚合器。聚合器具有缓存能力，可通过存储的更新或传感器的新鲜更新来响应目标网络的外部请求。假设目标网络作为闲话网络运行，其中更新数据包在节点间以随机化方式转发。我们利用马尔可夫决策过程框架对网络的平均版本信息年龄（AoI）进行建模和优化，并获取聚合器的最优策略。通过理论分析证明最优策略的结构特性，并通过数值验证加以证实。数值结果揭示了系统参数对平均版本AoI的影响。仿真表明，与一组基线策略相比，最优策略具有更优越的性能。