Within the context of a smart home, detecting the operating status of appliances in the environment plays a pivotal role, estimating power consumption, issuing overuse reminders, and identifying faults. The traditional contact-based approaches require equipment updates such as incorporating smart sockets or high-precision electric meters. Non-constant approaches involve the use of technologies like laser and Ultra-Wideband (UWB) radar. The former can only monitor one appliance at a time, and the latter is unable to detect appliances with extremely tiny vibrations and tends to be susceptible to interference from human activities. To address these challenges, we introduce HomeOSD, an advanced appliance status-detection system that uses mmWave radar. This innovative solution simultaneously tracks multiple appliances without human activity interference by measuring their extremely tiny vibrations. To reduce interference from other moving objects, like people, we introduce a Vibration-Intensity Metric based on periodic signal characteristics. We present the Adaptive Weighted Minimum Distance Classifier (AWMDC) to counteract appliance vibration fluctuations. Finally, we develop a system using a common mmWave radar and carry out real-world experiments to evaluate HomeOSD’s performance. The detection accuracy is 95.58%, and the promising results demonstrate the feasibility and reliability of our proposed system. Download PDF

Radar spoofing attacks mislead victim radars by injecting false information. Successful attacks require prior knowledge of the victim radar’s mode and parameters, and existing works obtain this critical information with expensive equipment, e.g., software-defined radio or spectrum analyzer. In this paper, we propose WASTON, a low-cost system for radar mode detection and parameter estimation using commercial off-the-shelf (COTS) mmWave radars. To overcome the disadvantage of low sampling frequency of COTS mmWave radars, we design two special local signals to detect frequency points and spectral shapes for radar mode detection. We propose a novel parameter estimation algorithm to estimate frequency- and time-domain parameters for spoofing different radars. We have implemented a prototype on the TI AWR1843 platform and conducted extensive experiments to evaluate the performance of WASTON. Our experimental results demonstrate that WASTON achieves an accuracy of 100% for mode detection and 99% for parameter estimation. Furthermore, we demonstrate that the estimated parameters can be used to launch a successful spoofing attack against the victim radar. Download PDF

Wireless-based human activity recognition (WHAR) enables various promising applications. However, since WHAR is sensitive to changes in sensing conditions (e.g., different environments, users, and new activities), trained models often do not work well under new conditions. Recent research uses meta-learning to adapt models. However, they must fine-tune the model, which greatly hinders the widespread adoption of WHAR in practice because model fine-tuning is difficult to automate and requires deep-learning expertise. The fundamental reason for model fine-tuning in existing works is because their goal is to find the mapping relationship between data samples and corresponding activity labels. Since this mapping reflects the intrinsic properties of data in the perceptual scene, it is naturally related to the conditions under which the activity is sensed. To address this problem, we exploit the principle that under the same sensing condition, data of the same activity class are more similar (in a certain latent space) than data of other classes, and this property holds invariant across different conditions. Our main observation is that meta-learning can actually also transform WHAR design into a learning problem that is always under similar conditions, thus decoupling the dependence on sensing conditions. With this capability, general and accurate WHAR can be achieved, avoiding model fine-tuning. In this paper, we implement this idea through two innovative designs in a system called RoMF. Extensive experiments using FMCW, Wi-Fi and acoustic three sensing signals show that it can achieve up to 95.3% accuracy in unseen conditions, including new environments, users and activity classes. Download PDF

Thanks to the wide bandwidth, large antenna array, and short wavelength, millimeter wave (mmWave) has superior performance in both communication and sensing. Thus, the integration of sensing and communication is a developing trend for the mmWave band. However, the directional transmission characteristics of the mmWave limits the sensing scope to a narrow sector. Existing works coordinate sensing and communication in a time-division manner, which takes advantage of the sector level sweep during the beam training interval for sensing and the data transmission interval for communication. Beam training is a low frequency (e.g., 10Hz) and low duty-cycle event, which makes it hard to track fast movement or perform continuous sensing. Such time-division designs imply that we need to strike a balance between sensing and communication, and it is hard to get the best of both worlds. In this paper, we try to solve this dilemma by exploiting side lobes for sensing. We design Sidense, where the main lobe of the transmitter is directed towards the receiver, while in the meantime, the side lobes can sense the ongoing activities in the surrounding. In this way, sensing and downlink communication work simultaneously and will not compete for hardware and radio resources. In order to compensate for the low antenna gain of side lobes, Sidense performs integration to boost the quality of sensing signals. Due to the uneven side-lobe energy, Sidense also designs a target separation scheme to tackle the mutual interference in multi-target scenarios. We implement Sidense with Sivers mmWave module. Results show that Sidense can achieve millimeter motion tracking accuracy at 6m.We also demonstrate a multi-person respiration monitoring application. As Sidense does not modify the communication procedure or the beamforming strategy, the downlink communication performance will not be sacrificed due to concurrent sensing. We believe that more fascinating applications can be implemented on this concurrent sensing and communication platform. Download PDF

Millimeter-wave (mmWave) radars have found applications in a wide range of domains, including human tracking, health monitoring, and autonomous driving, for their unobtrusive nature and high range accuracy. These capabilities, however, if used for malicious purposes, could also result in serious security and privacy issues. For example, a user’s daily life could be secretly monitored by a spy radar. Hence, there is a strong urge to develop systems that can detect and locate such spy radars. In this paper, we propose Radar2 , a practical system for passive spy radar detection and localization using a single commercial off-the-shelf (COTS) mmWave radar.Specifically, we propose a novel Frequency Component Detection method to detect the existence of mmWave signals, distinguish between mmWave radar and WiGig signals using a waveform classifier based on a convolutional neural network (CNN), and localize spy radars using triangulation based on the detector’s observations at multiple anchor points. Not only does Radar2 work for different types of mmWave radar, but it can also detect and localize multiple radars simultaneously. Finally, we performed extensive experiments to evaluate the effectiveness and robustness of Radar2 in various settings. Our evaluation results show that the radar detection rate is above 96% and the localization error is within 0.3m. The results also reveal that Radar2 is robust against various environmental factors (e.g., room layout and human activities). Download PDF

We design a double auction mechanism for WiFi-based multilateration location verification, which is used to motivate users to participate in performing location verification. The proposed double auction mechanism achieves desirable economical properties including truthfulness, individual rationality, computational efficiency, budget balance, and non-negative social welfare. Download PDF

Millimeter-wave (mmWave)-based human motion sensing, such as activity recognition and skeleton tracking, enables many useful applications. However, it suffers from a scarcity issue of training datasets, which fundamentally limits a widespread adoption of this technology in practice, as collecting and labeling such datasets are difficult and expensive. This paper presents SynMotion, a new mmWave-based human motion sensing system. Its novelty lies in harvesting available vision-based human motion datasets, for knowing the coordinates of body skeletal points under different motions, to synthesize mmWave sensing signals that bounce off the human body, so that the synthesized signals could inherit labels (skeletal coordinates and the name of each motion) from vision-based datasets directly. SynMotion demonstrates the ability to generate such labeled synthesized data at high quality to address the training data scarcity issue and enable two sensing services that can work with commercial radars, including 1) zero-shot activity recognition, where the classifier reads real mmWaves for recognition, but it is only trained on synthesized data; and 2) body skeleton tracking with few/zero-shot learning on real mmWaves. To design SynMotion, we address the challenges of both the inherent complication of mmWave synthesis and the micro-level differences compared to real mmWaves. Extensive experiments show that SynMotion outperforms the latest zero-shot mmWave-based activity recognition method. For skeleton tracking, SynMotion achieves comparable performance to the state-of-the-art mmWave-based method trainedon the labeled mmWaves, and SynMotion can further outperform it for the unseen users. Download PDF