Given the fact that WiFi-based sensing can be realized by reusing WiFi communication facilities and communication frequency bands, integrated sensing and communication (ISAC) is considered a crucial development direction for future WiFi standards, such as IEEE 802.11bf. Traditional WiFi sensing systems extract channel state information (CSI) from customized WiFi packets to quantify the characteristics of the sensing target. This poses challenges for existing WiFi systems originally designed for communication purposes, as it requires high-quality and sufficient CSI measurements. In this paper, we propose SenCom, which extracts CSI from general WiFi packets. SenCom enables CSI calibration across different WiFi communication modes and provides unified CSI measurements for upper-layer sensing applications. We also devise a fitting-resampling scheme to derive evenly sampled CSI with consistent dimensionality, and an incentive strategy to ensure sufficient CSI measurements over time. We build a prototype of SenCom and perform extensive experiments with 15 participants. The results show that SenCom is competent for a variety of sensing tasks, while incurring little compromise to the WiFi communication performance.

Radio frequency (RF) devices such as Wi-Fi transceivers, radio frequency identification tags, and millimeter-wave radars have appeared in large numbers in daily lives. The presence and movement of humans can affect the propagation of RF signals, further, this phenomenon is exploited for human action recognition. Compared to camera solutions, RF approaches exhibit greater resilience to occlusions and lighting conditions, while also raising fewer privacy concerns in indoor scenarios. However, current works have many limitations, including the unavailability of datasets, insufficient training samples, and simple or limited action categories for specific applications, which seriously hinder the growth of RF solutions, presenting a significant obstacle in transitioning RF sensing research from the laboratory to a wide range of everyday life applications. To facilitate the transitioning, in this paper, we introduce and release a large-scale multiple radio frequency dataset, named XRF55, for indoor human action analysis. XRF55 encompasses 42.9K RF samples and 55 action classes of human-object interactions, human-human interactions, fitness, body motions, and human-computer interactions, collected from 39 subjects within 100 days. These actions were meticulously selected from 19 RF sensing papers and 16 video action recognition datasets. Each action is chosen to support various applications with high practical value, such as elderly fall detection, fatigue monitoring, domestic violence detection, etc. Moreover, XRF55 contains 23 RFID tags at 922.38MHz, 9 Wi-Fi links at 5.64GHz, one mmWave radar at 60-64GHz, and one Azure Kinect with RGB+D+IR sensors, covering frequency across decimeter wave, centimeter wave, and millimeter wave. In addition, we apply a mutual learning strategy over XRF55 for the task of action recognition. Unlike simple modality fusion, under mutual learning, three RF modalities are trained collaboratively and then work solely. We find these three RF modalities will promote each other. It is worth mentioning that, with synchronized Kinect, XRF55 also supports the exploration of action detection, action segmentation, pose estimation, human parsing, mesh reconstruction, etc., with RF-only or RF-Vision approaches.

Given the fact that WiFi-based sensing can be realized through the reuse of WiFi communication facilities and frequency bands, integrated sensing and communication (ISAC) emerges as a pivotal direction for future WiFi standards, such as IEEE 802.11bf. Traditional WiFi sensing systems extract channel state information (CSI) from exclusive WiFi packets to quantify the characteristics of the sensing target. This poses challenges for existing WiFi systems originally designed for communication purposes, as it demands high-quality and sufficient CSI measurements. In this paper, we propose SenCom as a step towards forward-compatible ISAC solution. SenCom extracts CSI from general WiFi packets, enabling CSI calibration across different WiFi communication modes and delivering quality CSI measurements for upper-layer sensing applications. A fitting-resampling scheme and an incentive strategy are also developed. The former one is to obtain evenly sampled CSI with consistent dimensionality and the latter one is to guarantee sufficient CSI measurements over time. We build a prototype of SenCom and conduct extensive experiments involving 15 participants. The results show that SenCom’s competence for a variety of sensing tasks while making minimal compromises to WiFi communication performance.

Recent years have witnessed the booming development of RF sensing, which supports both identity authentication and behavior recognition by analysing the signal distortion caused by human body. In particular, RF-based identity authentication is more attractive to researchers, because it can capture the unique biological characteristics of users. However, the openness of wireless transmission raises privacy concerns since human behaviors could expose massive private information of users, which impedes the real-world implementation of RF-based user authentication applications. It is difficult to filter out the behavior information from the collected RF signals. In this paper, we propose a privacy-preserving deep neural network named BPCloak to erase the behavior information in RF signals while retaining the ability of user authentication. We conduct extensive experiments over mainstream RF signals collected from three real wireless systems, including the WiFi, radio frequency identification (RFID), and millimeter-wave (mmWave) systems. The experimental results show that BPCloak significantly reduces the behavior recognition accuracy, i.e., 85%+, 75%+, and 65%+ reduction for WiFi, RFID, and mmWave systems respectively, merely with a slight penalty of accuracy decrease when using these three systems for user authentication, i.e., 1%-, 3%-, and 5%-, respectively.