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273 results on '"Hsu, Y.-L."'

13. Optimized data sampling and energy consumption in IIoT:a federated learning approach

Author

Hsu, Y.-L. (Yung-Lin), Liu, C.-F. (Chen-Feng), Wei, H.-Y. (Hung-Yu), Bennis, M. (Mehdi), Hsu, Y.-L. (Yung-Lin), Liu, C.-F. (Chen-Feng), Wei, H.-Y. (Hung-Yu), and Bennis, M. (Mehdi)

Abstract

Real-time environment monitoring is a key application in Industrial Internet of Things, where sensors proactively collect and transmit environmental data to the controller. However, due to limited wireless resources, keeping sensors’ sampled data fresh at the controller is critical. This work aims to investigate the trade-off between the sensor’s data-sampling frequency and long-term data transmission energy consumption while maintaining information freshness. Leveraging the entropic risk measure (ERM), we jointly minimize the global transmission energy’s mean and variance subject to probabilistic constraints on information freshness. Furthermore, while jointly saving the model training energy, we adopt the federated learning (FL) paradigm and propose an FL-based two-stage iterative optimization framework to optimize the aforementioned objective. Specifically, we iteratively learn the sampling frequency via Bayesian optimization and minimize the long-term ERM of the global energy consumption via Lyapunov optimization. Numerical results show that the proposed FL-based scheme saves substantial executing energy with less performance loss. Quantitatively, compared with the centralized learning baseline, the proposed FL-based framework saves up to 69% model training energy at the expense of a mere increased objective outcome, i.e., 6.3% in the global data transmission energy consumption ( 9.936×10⁻⁵ in ERM) under 0.4% bias from the global optimal data-sampling frequency.

Published
2022

16. Age-optimal power allocation in industrial IoT:a risk-sensitive federated learning approach

Author

Hsu, Y.-L. (Yung-Lin), Liu, C.-F. (Chen-Feng), Samarakoon, S. (Sumudu), Wei, H.-Y. (Hung-Yu), and Bennis, M. (Mehdi)

Subjects
age of information (AoI), extreme value theory (EVT), smart factory, 5G and beyond, federated learning (FL), industrial IoT
Abstract

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.

Published
2021

17. An Approach to Embedding Traditional Non-Volatile Memories into a Deep Sub-Micron CMOS

Author

Lin, C. S., primary, Huang, W. T., additional, Huang, Allan, additional, Yang, Y. H., additional, Hsu, Y. L., additional, Yang, S. K., additional, Liu, Jack, additional, Tseng, H. W., additional, Huang, Jarcle, additional, Chou, B. Y., additional, Huang, Kevin, additional, Chang, W. K., additional, Chang, Daniel, additional, Chien, C. H., additional, Yeh, Harry, additional, Liu, P. W., additional, Hsieh, C. D., additional, Chuang, Harry, additional, and Kalnitsky, Alexander, additional

Published
2020
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25. Green fog offloading strategy for heterogeneous wireless edge networks

Author

Hsu, Y.-L. (Yung-Lin), Wei, H.-Y. (Hung-Yu), and Bennis, M. (Mehdi)

Subjects
Green communication, MEC, Fog node
Abstract

Multi-access/Mobile Edge Computing (MEC) and fog computing are promising techniques to satisfy low latency requirements for emerging next generation applications. Moving computation entities closer to a user could reduce the overall serving latency. In terms of green communications, given the latency constraint, how to minimize the power consumption at the user equipment (UE) and the edge node (EN) sides is important. Considering several edge nodes, partially offloading a user’s task to one or more edge nodes is key. In this paper, a multi-node partial task offloading MEC scenario is discussed, in which UEs locally compute the task and share the remainder with other edge nodes. In addition, a green task distribution algorithm which minimizes the system power consumption is proposed, considering queueing, transmitting and computing delay. The simulation results show that the proposed algorithm minimizes the power consumption while meeting the latency requirements, and the power saving efficiency outperforms a binary offloading strategy. Moreover, the coupling effects between the latency requirements, offloading signal strength within the edge nodes, computation capability of edge nodes and the number of subcarriers used to transmit the offloading task are discussed.

Published
2018

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