文献类型：期刊文献

英文题名：A Machine-learning-enabled Context-driven Control Mechanism for Software-defined Smart Home Networks

作者：Huang, Ru[1];Chu, Xiaoli[1,2];Zhang, Jie[1,2];Hu, Yu Hen[3];Yan, Huaicheng[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Meilong Rd 130, Shanghai 200237, Peoples R China;[2]Univ Sheffield, Dept Elect & Elect Engn, Sheffield S1 3JD, S Yorkshire, England;[3]Univ Wisconsin, Dept Elect & Comp Engn, 1415 Engn Dr, Madison, WI 53706 USA

年份：2019

卷号：31

期号：6

起止页码：2103

外文期刊名：SENSORS AND MATERIALS

收录：;EI(收录号：20192807170909);WOS:【SCI-EXPANDED(收录号:WOS:000473421300007)】；

基金：This work was supported by the Science and Technology Commission of Shanghai Municipality (17511108604), the National Natural Science Foundation of China (61501187 and 61673178), the Shanghai Natural Science Foundation (17ZR1444700), and the Shanghai Shuguang Project (16SG28).

语种：英文

外文关键词：smart home control mechanism (SHCM); machine learning; software-defined networks; context

摘要：To address the challenges of autonomous capability enhancement in a smart home scenario, in this paper, we present a context-driven smart home control mechanism (SHCM) following software-defined network (SDN) design principles and a context cognition process. SHCM has three SDN-based layers: a control plane, a fog computing plane, and a data plane. We integrate a machine learning (ML) algorithm and an ontology model into the context cognition process, which will be leveraged to enhance the context-awareness-enabled automation level of smart home control systems. In the control plane, a controller adopts a ML-based tool to make connotative clustering and association rules via mining multiattribute context features inherent in diverse sensing applications, and utilizes an ontology model to automate integrated context management. Additionally, the fog computing plane applies edge-computing-supported context middleware to perform compressive sensing (CS)-based cross-layer context fusion. Furthermore, smart home devices implement context feedback in the data plane instructed by context-driven control strategies, which are mapped into the parameter matrix and matching rules in the lightweight flow-table mode. The effectiveness of this proposed control mechanism is validated by experiments using a context-oriented smart home prototype platform, which implements a closed-loop context-oriented feedback control from cognition-deduced knowledge generation to knowledge-driven cooperation in a cyber-physical smart home scenario. It is observed that the control mechanism can improve smart home automation and outperform baseline schemes.