Cloud-Edge–Terminal Collaboration-Enabled Device-Free Sensing Under Class-Imbalance Conditions

With the rapid development of cloud-edge–terminal (CET) technology, ubiquitous sensing devices are able to collaborate with edge terminals, enabling real-time, intelligent environmental awareness. For device-free sensing systems, the number of each human gesture category may vary (class imbalance), which makes previously distributed device-free sensing algorithms ineffective. In this article, we propose a novel monitoring scheme for device-free human action sensing for CET collaboration under class-imbalance conditions. Specifically, the body-coordinated velocity profile (BVP) features of wireless fidelity (WiFi) signals are used to detect human actions. To recognize human gestures, we develop a convolutional neural network (CNN) using a monitor to detect gradient changes under class imbalance. To mitigate the effects of class imbalance, a corresponding correction is applied to the loss function. To validate the effectiveness of the proposed method, we conduct numerical experiments under class-imbalance conditions. Different parameter settings and proportions of participating nodes are explored for their effects on experimental results. Additionally, numerical experiment results demonstrate that the proposed method improves recognition accuracy by 3.85%–34.1% compared to baseline algorithms. Overall, the proposed method addresses the challenge of distributed device-free sensing under class-imbalance conditions and achieves superior recognition accuracy performance.