ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2020, Vol. 57 ›› Issue (9): 1800-1809.doi: 10.7544/issn1000-1239.2020.20200253

所属专题: 2020边缘计算专题

• 网络技术 • 上一篇    下一篇



  1. 1(南方科技大学未来网络研究院 广东深圳 518055);2(鹏城实验室 广东深圳 518055);3(香港科技大学计算机科学及工程系 香港特别行政区 999077) (
  • 出版日期: 2020-09-01
  • 基金资助: 
    香港优配研究金资助项目(CERG 16204418,16203719,FP909,R8015);国家自然科学基金项目(61872420);广东省自然科学基金项目(2017A030312008);广东省重点领域研发计划资助项目(2019B121204009);鹏城实验室大湾区未来网络试验与应用环境项目(LZC0019)

Edge Computing in Smart Homes

Huang Qianyi1,2, Li Zhiyang3, Xie Wentao3, Zhang Qian3   

  1. 1(Institute of Future Networks, Southern University of Science and Technology, Shenzhen, Guangdong 518055);2(Peng Cheng Laboratory, Shenzhen, Guangdong 518055);3(Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong 999077)
  • Online: 2020-09-01
  • Supported by: 
    This work was supported by RGC General Research Fund (CERG 16204418, 16203719, FP909, R8015), the National Natural Science Foundation of China (61872420), the Natural Science Foundation of Guangdong Province (2017A030312008), the Key-Area Research and Development Program of Guangdong Province (2019B121204009), and the Project of “FANet: PCL Future Greater-Bay Area Network Facilities for Large-scale Experiments and Applications” (LZC0019).

摘要: 近年来,智能音箱、扫地机器人已经成为很多用户生活中不可或缺的一部分.随着物联网技术的发展,越来越多的智能设备走进家庭场景,让用户的生活变得更加便捷和舒适.当种类繁多、功能细分的智能设备通过网络进行连接和控制时,为了解决网络延时、数据安全等诸多问题,基于边缘计算的智能家居成为未来趋势.探讨智能家居场景中的边缘计算,介绍围绕感知、通信和计算3个方向所展开的研究.在感知方面,关注边缘节点的泛在感知能力,介绍在非接触式呼吸监测上取得的进展;在通信方面,研究无线感知和无线通信的融合设计,在有限的频谱资源上兼顾感知和通信;在计算方面,关注基于边缘节点的个性化机器学习,在不泄露用户数据的前提下建立个性化机器学习模型.

关键词: 边缘计算, 智能家居, 物联网, 泛在感知, 联邦学习

Abstract: In recent years, smart speakers and robotic vacuum cleaners have played important roles in many peoples daily life. With the development in technology, more and more intelligent devices will become parts of home infrastructure, making life more convenient and comfortable for residents. When different types of specialized intelligent devices are connected and operated over the Internet, how to minimize network latency and guarantee data privacy are open issues. In order to solve these problems, edge computing in smart homes becomes the future trend. In this article, we present our research work along this direction, covering the topics on edge sensing, communication and computation. As for sensing, we focus on the pervasive sensing capability of the edge node and present our work on contactless breath monitoring; as for communication, we work on the joint design of sensing and communication, so that sensing and communication systems can work harmoniously on limited spectrum resources; as for computation, we devote our efforts to personalized machine learning at the edge, building personalized model for each individual while guaranteeing their data privacy.

Key words: edge computing, smart home, Internet-of-Things, ubiquitous sensing, federated learning