Millimeter-wave (mmWave) has emerged as a promising solution for contact-free sensing due to its high resolution. Although promising, it faces several critical issues, including occlusion from the surrounding environment, unstable orientation-dependent sensing performance, and significant interference when multiple targets are in close proximity. These fundamental issues hinder the widespread adoption of mmWave sensing in the real world. In this paper, we propose SeRadar, the first systematic framework that leverages all useful secondary reflections to significantly enhance reliability and bring mmWave sensing one step closer to real-world adoption. Unlike primary reflections commonly used in wireless sensing, secondary reflections—typically much weaker due to being reflected multiple times—are generally ignored in existing literature. However, we observe that secondary reflections are common in various scenarios and carry valuable information about target movements, which could also contribute to sensing. To effectively utilize secondary reflections for sensing, SeRadar addresses several challenges associated with secondary reflections. Specifically, it boosts weak secondary reflections to improve their sensing capability, identifies useful ones from a large number of secondary reflections captured in the environment, and mitigates primary-secondary interference in multi-target scenarios. We evaluate the performance of SeRadar in various environments, including offices, apartments, and vehicle cabins. Extensive experiments demonstrate SeRadar can enhance accuracy and reliability in diverse sensing scenarios.

Deep learning (DL) based Wi-Fi sensing has witnessed great development in recent years. Although decent results have been achieved in certain scenarios, Wi-Fi based activity recognition is still difficult to deploy in real smart homes due to the limited cross-environment adaptability, i.e. a well-trained Wi-Fi sensing neural network in one environment is hard to adapt to other environments. To address this challenge, we proposeAdaWiFi, a DL-based Wi-Fi sensing framework that allows multiple Internet-of-Things (IoT) devices to collaborate and adapt to various environments effectively. The key innovation ofAdaWiFiincludes a collective sensing model architecture that utilizes complementary information between distinct devices and avoids the biased perception of individual sensors and an accompanying model adaptation technique that can transfer the sensing model to new environments with limited data. We evaluate our system on a public dataset and a custom dataset collected from three complex sensing environments. The results demonstrate thatAdaWiFiis able to achieve significantly better sensing adaptation effectiveness (e.g. 30% higher accuracy with one-shot adaptation) as compared with state-of-the-art baselines.

WiFi-based human activity recognition is a promising sensing application in smart homes due to the low cost, wide availability, and privacy preservation of WiFi devices. However, pushing WiFi sensing technology to industry-scale deployment is difficult due to its poor robustness against environment differences. How to systematically test such sensing system is crucial to improve its practicality, and is also challenging because the sensing performance is significantly influenced by the underlying physical environments. In this paper, we introduce the problem of testing environment-dependent sensing systems, including how to measure test coverage and how to effectively generate data to improve the coverage. We describe our initial attempts on examining test sufficiency with environment-neuron joint coverage and improving the coverage through targeted environment variations and signal transformations. Our experiments have demonstrated the higher effectiveness of using environment-neuron coverage to represent test sufficiency, as compared with using the conventional neuron coverage. Meanwhile, the coverage-guided sensing data generation can lead to higher accuracy of the sensing system under changing environments.