An Open-World Time-Series Sensing Framework for Embedded Edge Devices
An Open-World Time-Series Sensing Framework for Embedded Edge Devices
复制标题
DOI：
10.1109/rtcsa55878.2022.00013
复制
发表时间：
2022-08
期刊：
影响因子：
--
通讯作者：
Abdulrahman Bukhari;Seyedmehdi Hosseinimotlagh;Hyoseung Kim
中科院分区：
文献类型：
--
作者：
Abdulrahman Bukhari;Seyedmehdi Hosseinimotlagh;Hyoseung Kim
物联网技术的快速发展引起了人们对在嵌入式边缘设备上开发基于学习的传感应用的浓厚兴趣。然而，这些努力正面临着需要适应开放世界环境中不可预见的条件的挑战。更新学习模型面临着缺乏训练数据以及超出边缘设备可用计算能力的高计算需求的困扰。在本文中，我们提出了一种开放世界的时间序列传感框架，用于从时间序列传感器数据中进行推断，并在资源有限的嵌入式边缘设备上实现增量学习。所提出的框架能够实现两个基本任务：推理和学习，而无需访问强大的云服务器。我们讨论了为确保令人满意的学习表现和有效的资源利用而做出的设计选择。实验结果证明了系统能够逐步适应不可预见的条件并在资源受限的设备上有效运行。
The rapid advancement of IoT technologies has generated much interest in the development of learning-based sensing applications on embedded edge devices. However, these efforts are being challenged by the need to adapt to unforeseen conditions in an open-world environment. Updating a learning model suffers from the lack of training data as well as the high computational demand beyond that available on edge devices. In this paper, we propose an open-world time-series sensing framework for making inferences from time-series sensor data and achieving incremental learning on an embedded edge device with limited resources. The proposed framework is able to achieve two essential tasks, inference and learning, without requiring access to a powerful cloud server. We discuss the design choices made to ensure satisfactory learning performance and efficient resource usage. Experimental results demonstrate the ability of the system to incrementally adapt to unforeseen conditions and to effectively run on a resource-constrained device.