Both sensor networks and data fusion are essential foundations for developing the smart home Internet of Things (IoT) and related fields. We proposed a multi-channel sensor network construction method involving hardware, acquisition, and synchronization in the smart home environment and a smart home data fusion method (SHDFM) for multi-modal data (position, gait, voice, pose, facial expression, temperature, and humidity) generated in the smart home environment to address the configuration of a multi-channel sensor network, improve the quality and efficiency of various human activities and environmental data collection, and reduce the difficulty of multi-modal data fusion in the smart home. SHDFM contains 5 levels, with inputs and outputs as criteria to provide recommendations for multi-modal data fusion strategies in the smart home. We built a real experimental environment using the proposed method in this paper. To validate our method, we created a real experimental environment - a physical setup in a home-like scenario where the multi-channel sensor network and data fusion techniques were deployed and evaluated. The acceptance and testing results show that the proposed construction and data fusion methods can be applied to the examples with high robustness, replicability, and scalability. Besides, we discuss how smart homes with multi-channel sensor networks can support digital twins.

翻译：传感器网络与数据融合是发展智能家居物联网及相关领域的重要基础。针对智能家居环境中多通道传感器网络的配置问题、多种人体活动与环境数据采集的质量与效率提升需求，以及多模态数据融合的难度降低问题，我们提出了一种涵盖硬件、采集与同步的智能家居多通道传感器网络构建方法，并设计了面向智能家居环境生成的多模态数据（包括位置、步态、语音、姿态、面部表情、温湿度）的智能家居数据融合方法（SHDFM）。SHDFM包含5个层级，以输入与输出为准则，为智能家居中的多模态数据融合策略提供建议。我们利用所提方法搭建了真实的实验环境，并在类家庭场景的物理装置中部署并评估了多通道传感器网络与数据融合技术。验收与测试结果表明，所提出的构建与数据融合方法具有高鲁棒性、可复制性与可扩展性。此外，我们探讨了多通道传感器网络支持的智能家居如何赋能数字孪生技术。