Meta-Learning Based Runtime Adaptation for Industrial Wireless Sensor-Actuator Networks
Meta-Learning Based Runtime Adaptation for Industrial Wireless Sensor-Actuator Networks
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DOI：
10.1109/iwqos57198.2023.10188720
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Xia Cheng;M. Sha
中科院分区：
文献类型：
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作者：
Xia Cheng;M. Sha
基于IEEE 802.15.4的工业无线传感器-致动器网络（WSAN）已被广泛部署以连接工业设施中的传感器、致动器和控制器。配置工业WSAN以满足应用指定的服务质量（QoS）要求是一个复杂的过程，其中涉及理论计算、仿真和现场测试等任务。由于工业无线网络变得越来越分层、异构和复杂，因此已经进行了许多研究工作来将无线仿真和高级机器学习技术应用于网络配置。不幸的是，我们的研究表明，由最先进的方法生成的网络配置模型会随着时间的推移迅速衰减。为了解决这个问题，我们开发了一种基于元学习的自适应（MERA）方法，该方法可以在运行时有效地适应工业WSAN的网络配置模型。在MERA下，网络配置模型的参数被显式地训练，使得在网络条件改变后，仅具有少量新测量的少量优化步骤将产生良好的泛化性能。实验结果表明，MERA实现更高的预测精度与更少的物理测量，更少的计算时间，更长的适应间隔相比，一个国家的最先进的基线。
IEEE 802.15.4-based industrial wireless sensor-actuator networks (WSANs) have been widely deployed to connect sensors, actuators, and controllers in industrial facilities. Configuring an industrial WSAN to meet the application-specified quality of service (QoS) requirements is a complex process, which involves theoretical computation, simulation, and field testing, among other tasks. Since industrial wireless networks become increasingly hierarchical, heterogeneous, and complex, many research efforts have been made to apply wireless simulations and advanced machine learning techniques for network configuration. Unfortunately, our study shows that the network configuration model generated by the state-of-the-art method decays quickly over time. To address this issue, we develop a MEta-learning based Runtime Adaptation (MERA) method that efficiently adapts network configuration models for industrial WSANs at runtime. Under MERA, the parameters of the network configuration model are explicitly trained such that a small number of optimization steps with only a few new measurements will produce good generalization performance after the network condition changes. Experimental results show that MERA achieves higher prediction accuracy with less physical measurements, less computation time, and longer adaptation intervals compared to a state-of-the-art baseline.