https://scholars.lib.ntu.edu.tw/handle/123456789/607162
標題: | Age-Optimal Power Allocation in Industrial IoT: A Risk-Sensitive Federated Learning Approach | 作者: | Hsu Y.-L Liu C.-F Samarakoon S Bennis M. HUNG-YU WEI |
關鍵字: | 5G and beyond;age of information (AoI);extreme value theory (EVT);federated learning (FL);industrial IoT;smart factory;Energy utilization;Higher order statistics;Internet of things;5g and beyond;Age of information;Extreme value theory;Federated learning;Learning approach;Optimal power allocation;Real-time environment;Smart factory;Work study;5G mobile communication systems | 公開日期: | 2021 | 卷: | 2021-September | 起(迄)頁: | 1323-1328 | 來源出版物: | IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC | 摘要: | This 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. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113889564&doi=10.1109%2fPIMRC50174.2021.9569536&partnerID=40&md5=89d5f5aed0c58e60bc9ece016036747b https://scholars.lib.ntu.edu.tw/handle/123456789/607162 |
DOI: | 10.1109/PIMRC50174.2021.9569536 |
顯示於: | 電機工程學系 |
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