This work studies a real-time environment monitoring scenario in the Industrial Internet of Things (IIoT), where wireless sensors proactively collect environmental data and transmit it to the controller. We adopt the notion of risk-sensitivity in financial mathematics as the objective to jointly minimize the mean, variance, and other higher-order statistics of the network energy consumption subject to the constraints on the AoI threshold violation probability and the AoI exceedances over a pre-defined threshold. We characterize the extreme AoI staleness using results in extreme value theory and propose a distributed power allocation approach by weaving in together principles of Lyapunov optimization and federated learning (FL). Simulation results demonstrate that the proposed FL-based distributed solution is on par with the centralized baseline while consuming 28.50% less system energy and outperforms the other baselines.

翻译：这项工作在物业工业互联网(IIoT)中研究实时环境监测情景,无线传感器积极主动地收集环境数据并将其传送给控制器。我们采用金融数学风险敏感性概念,目的是共同尽量减少网络能源消耗的平均值、差异和其他较高层次的统计数据,但受AoI临界值违反概率和AoI超出预定阈值的限制。我们利用极端价值理论的结果来描述极端AoI的陈旧性,并通过将Lyapunov优化和联合学习(FL)原则结合起来,提出分配权力的办法。模拟结果显示,拟议的FL分布式解决方案与集中基线相同,同时消耗28.50%的系统能量,超出其他基线。