Hsu Y.-LLiu C.-FSamarakoon SBennis M.HUNG-YU WEI2022-04-252022-04-252021https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113889564&doi=10.1109%2fPIMRC50174.2021.9569536&partnerID=40&md5=89d5f5aed0c58e60bc9ece016036747bhttps://scholars.lib.ntu.edu.tw/handle/123456789/607162This work studies a real-time environment monitoring scenario in the industrial Internet of things, 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 age of information (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. ? 2021 IEEE.5G and beyondage of information (AoI)extreme value theory (EVT)federated learning (FL)industrial IoTsmart factoryEnergy utilizationHigher order statisticsInternet of things5g and beyondAge of informationExtreme value theoryFederated learningLearning approachOptimal power allocationReal-time environmentSmart factoryWork study5G mobile communication systems[SDGs]SDG7conference paper10.1109/PIMRC50174.2021.95695362-s2.0-85113889564