Recent years have witnessed considerable efforts in repurposing ubiquitous wireless communication signals for non-contact sensing.$\bf{NearLink}$is a new-generation short-range wireless communication protocol, which is designed to address the high-quality network connectivity requirements of low power, low latency and high reliability. Given these notable advantages, NearLink has great potential for widespread application in various Internet of Things (IoT) areas. However, NearLink-based wireless sensing has not yet been explored. To bridge this gap, this work explores for the first time the sensing potential and opportunities of NearLink. Specifically, we systematically investigate the sensing capability of NearLink through answering two key questions: (1) How can NearLink's communication-oriented signals be adapted for sensing tasks? (2) How can sensing performance be enhanced under multipath interference and low-power constraints? We prototype the NearLink sensing system-NearSense, and take the respiration detection as a case study to demonstrate its effectiveness. Extensive experiments demonstrate that NearSense can achieve an average detection rate of 98% and a false alarm rate below 1.5% in case of various real-life challenging interference. We believe this work opens up new directions for the new-generation wireless sensing towards high-quality network connections.

This paper aims to provide a sub-meter localization and tracking capability for vehicle key fob detection in challenging outdoor environments. We take an experimentation-oriented approach and design a UWB-based real-time localization and tracking system called ULTRA. We introduce two novel algorithmic components to improve the accuracy and reliability: (i) We first generate a set of potential locations based on their geometric features, then propose an algorithm based on A-DBSCAN (adaptive density-based spatial clustering of applications with noise) to dynamically form clusters, estimate sensor trustworthiness and the respective probabilities. (ii) We design a Bayesian algorithm without requiring extra motion sensors, which seamlessly balances the real-time measurements with history-based motion tracking. Thanks to these novel algorithms, ULTRA is able to better handle signal blockage, uncertain ranging noises caused by non-line-of-sight (NLOS) caused by nearby vehicles and parking structures. Using a combination of vehicle testbed systems and high-fidelity emulation platforms, we experimentally demonstrate that ULTRA has a localization accuracy of ≤30cm, and achieves a directional angle error of ≤ 5° under challenging outdoor environments, outperforming the state-of-the-art linear/nonlinear least squares algorithms, as well as other unsupervised-learning-based approaches (i.e., K-means clustering, SVM unsupervised outlier detection).

With the development of integrated sensing, communication and computation (ISCC), wireless sensing technology is turning a WiFi device into a special sensor. Specifically, the current research focuses on extending the inherent communication attributes of ubiquitous WiFi signals to achieve indoor position tracking. However, previous works based on geometric positioning like triangulation are limited by the number of antennas and bandwidth, and can only achieve decimeter-level positioning accuracy. To this end, we present WiFineTrack, enabling fine-grained position tracking based on Channel State Information (CSI). Instead of representing the continuous absolute positions directly, WiFineTrack first characterizes the relative motion traces by mapping the target’s movements in relation to its prior position. Then the model correlates the angle-based triangulation with the derived relative motion traces to reconstruct the absolute motion traces. Thus, the model shifts the tracking task from traditional absolute geometry to relative length variation. WiFineTrack designs an elaborate denoising framework for tracking refinement, including phase correcting based on the bidirectional transmission mechanism and accumulated error suppression based on the Extended Kalman Filter (EKF). We implement WiFineTrack using off-the-shelf WiFi devices, and empirical results show that WiFineTrack can achieve an accuracy of 10.45 cm under diverse environment conditions.

Previous work have verified the feasibility of Wi-Fi - based indoor position tracking. However, these research rely heavily on anchors' position as prior knowledge, which lowers the deployability in practical scenarios. To this end, we propose a novel system named EasyWiTrack to realize a plug-and-play, rel-ative position tracking by establishing a time-domain correlation model to associate the target path, angle information with target motion, and deduce the target's relative position compared to the previous moment for trajectory shape without any anchor position information. During the system implementation, to address the issue of error introduced by asynchronous Wi-Fi transceivers, we adopt bi-directional data acquisition for denoising and achieve millimeter-level target path length estimation. Additionally, to eliminate ambiguity in linear array angle estimation, we employ circle array and apply Multiple Signal Classification algorithm to estimate signal azimuth. Finally through extensive experimentation, we have validated that EasyWiTrack achieves a fine-grained indoor position tracking performance with 1.48cm and 1.73cm average error respectively in LoS and NLoS scenarios with commodity Wi-Fi.

Wi-Fi-based continuous activity sensing is of great importance to personal healthcare, security monitoring, and healthy lifestyle assessment. However, it remains challenging to understand multiperson continuous activities as the Wi-Fi signals reflected by each person are mixed up in the Wi-Fi channel state information (CSI). To this end, we present MuSense, the first Wi-Fi-based system that enables multiperson continuous activity segmentation and recognition using commodity devices. In MuSense, we design a Wi-Fi network interface card (NIC) combination and calibration (NICCC) algorithm to construct a high-resolution receiving array and calibrate the CSI measurement noises of this array. On this basis, we propose a multiperson reflection signal separation (MPRSS) algorithm to completely and practically separate each person’s Wi-Fi reflection signals, obtaining the amplitude attenuation and phase shift corresponding to each person’s activities on the subcarrier dimension. Finally, we design an unsupervised adversarial continuous activity sensing network (UACAS-Net), with a two-stage adversarial training method to achieve generalized multiperson continuous activity sensing. Through two-stage adversarial training, UACAS-Net can capture distinguishing features of continuous activities from separated reflection signal streams in the source and target domains while minimizing the feature domain discrepancies to segment and recognize each person’s continuous activities in different domains. Intensive experiments are conducted under three different scenarios and the results demonstrate the effectiveness and practicality of MuSense for multiperson continuous activity sensing.

Sensing in Integrated Sensing and Communications (ISAC) systems with clock asynchronism between the transmitter and receiver poses significant challenges. Understanding the fundamental limits of sensing performance in such setups, which remain largely unknown, is crucial. This paper investigates the sensing performance bounds in the presence of clock asynchronism. In both single-carrier and multi-carrier models, we derive the Cramér-Rao bounds (CRB) for estimating dynamic channel path parameters including angle of arrival, delay, and complex gain sequence (CGS). Through mathematical analyses and numerical simulations, we conduct a comprehensive study on how these bounds depend on various system parameters and the impact of clock asynchronism. Our findings highlight the degradation of parameter estimation performance due to clock asynchronism and reveal low-accuracy zones for CGS estimation in strong-line-of-sight scenarios. Additionally, we observe asymptotic mitigation in performance degradation with larger bandwidth, providing valuable insights for system design and optimization.

Gesture awareness plays a crucial role in promoting human–computer interface. Previous works either depend on customized hardware or need a priori learning of wireless signal patterns, facing downsides in terms of the privacy concern, availability, and reliability. In this article, we propose CentiTrack, the first centimeter-level passive gesture-tracking system that works with only three commodity WiFi devices, without any extra hardware modifications or wearable sensors. To this end, we first identify the channel state information (CSI) measurement error sources in the physical-layer process, and then denoise CSI by the complex ratio between adjacent antennas. Principal component analysis (PCA) is further adopted to separate the reflected signals from noises. Benchmark experiments are conducted to verify that the phase changes of denoised CSI are proportional to the length changes of the dynamic path reflected off the hand. In addition, we adopt the multiple signal classification (MUSIC) algorithm to estimate the Angle-of-Arrivals (AoAs) of dynamic paths, and then locate the initial position of hands with triangulation. We also propose a novel static componnets elimination algorithm for tracking correction by eliminating the components unrelated to motion. A prototype of CentiTrack is fully realized and evaluated in various real scenarios. Extensive experiments show that CentiTrack is superior in terms of tracking accuracy, sensing range, and device cost, compared with the state-of-the-arts.

Daily activity monitoring is essential to healthy lifestyle assessment and personal healthcare, among which Wi-Fi-based solutions have attracted increasing attention due to their no-intrusive and privacy-protected characters. However, related researches are based on the assumption that there is an interval between two activities, during which the target is thought to be static. This assumption falls short of reality as human activities are performed continuously in daily life. Therefore, this article aims to design a nonintrusive and privacy-protected system, namely, Wi-Monitor, to monitor human activities in daily life. In Wi-Monitor, we first fragmentize Wi-Fi channel state information (CSI) streams into CSI bins and design a feature extraction network to extract activity fragmentation features (AFFs) from these CSI bins. From the extracted AFFs, a temporal convolutional network (TCN) is further used to capture activity continuity features (ACFs), which are used as distinguishing characteristics of continuous activities. Finally, Wi-Monitor utilizes these distinguishing characteristics to segment and recognize human activities in daily life simultaneously to achieve daily activity monitoring. In addition, we design an over-segmentation suppression mechanism with two training stages in Wi-Monitor to overcome the over-segmentation issue and enhance the activity monitoring accuracy. Intensive experiments are conducted in three different scenarios and the results demonstrate the effectiveness and practicality of Wi-Monitor for daily activity monitoring.

Past few years have witnessed the great potential of exploiting WiFi signals for positioning. Prior work focus on discovering the absolute locations of a radio source, and have achieved promising accuracies of tens of centimeters. However, many applications such as aerial gesture or handwriting tracking are more concerned with the detailed motion shape of the target rather than its exact locations, which require a several fold higher accuracy. To this end, we present MagicInput, a virtual handwriting interface by tracking the motion traces of a WiFi source. Based on channel state information (CSI), MagicInput elaborately devises an incremental motion-based tracking model by correlating the motion traces with the angle and length variations of propagation paths. The model shifts the tracking task from the transceiver view to the antenna array-oriented view, and eliminates the need for prior knowledge of anchor locations. MagicInput proposes an end-to-end pipeline for tracking refinement, by interference suppression, motion segmentation, and an integrated grasp pressure sensor-based motion instance detection. We prototype MagicInput using off-the-shelf WiFi radios, and extensive experiments attest that MagicInput can achieve the accuracy of 8.5 mm confronting diverse users and environment conditions. With ubiquitous WiFi signals, MagicInput can transform any region into an interactive handwriting interface with millimeter accuracy.

WiFi-based passive gesture tracking plays a crucial role in promoting human-computer interface, due to its pervasive availability and cost-effectiveness. Prior works focus on tracking gestures on 2D sensing plane by aggregating multiple WiFi links (typically 2), with Uniform Linear Arrays (ULAs). However, gestures actually contain 3D spatial information instead of just 2D, thus interpreting the 3D traces as 2D may lead to enormous tracking errors. This paper aims at exploring the possibility of passively tracking 3D hand traces likewise leveraging two WiFi links with standard 3-element ULAs, and presents a generic 3D centimeter-level passive gesture tracking system, called CentiTrack-3D. To this end, we make two key observations: (1) The ULA-resolved angle contains the integrated information of the azimuth and elevation in 3D space, despite its inability to estimate azimuth and elevation separately. (2) The radial motion deviating from the sensing plane also leads to length variations of paths. Motivated by the observations, a 3D tracking model namedChaosis elaborately designed to deduce the hand 3D coordinates, so as to track the traces. Extensive experiments yield that CentiTrack-3D achieves an overall median tracking granularity of 2.5 cm in 3D space in case of diverse users and environment conditions.

Device-free gesture recognition plays a crucial role in smart home applications, setting human free from wearable devices and causing no privacy concerns. Prior WiFi-based recognition systems have achieved high accuracy in a static environment, but with limitations in adapting changes in environments and locations. In this paper, we propose a fine-grained deep adaptation networks based gesture recognition scheme (DANGR) using the Channel State Information (CSI). DANGR applies wavelet transformation for amplitude denoising, and conjugate calibration to remove CSI time-variant random phase offsets. A Generative Adversarial Networks (GAN) based data augmentation approach is proposed to reduce the large consumptions of data collection and the over-fitting risks caused by incomplete dataset. The distribution of CSI in various environments may be biased. In order to shrink these domains discrepancies in environments, we adopt domain adaptation based on multikernel Maximum Mean Discrepancy scheme, which matches the mean-embeddings of abstract representations across domains in a reproducing kernel Hilbert space. Extensive empirical evidence shows that DANGR yields mean 94.5% accuracy of gesture recognition confronting environmental variations, providing a promising scheme for practical and long-run implementation.

In the era of Internet of Things, human sensing, which detects and interprets human motions including gestures or postures, has emerged as a challenging problem in areas such as assisted living and remote monitoring. Besides conventional sensing methodologies that rely on wearable devices and camera systems, WiFi-based technologies are evolving as a promising solution for indoor monitoring and activity recognition recently. In this paper, we propose WiRoI, a device-free human sensing scheme which is able to precisely detect and interpret human motions within certain spatial region of interest (RoI) using only commodity WiFi devices. To this end, the channel state information (CSI) data in PHY layer is obtained directly by upgrading the firmware. To make the system robust enough, we propose a method to eliminate the noises caused by other humans between TX and RX. And particularly, we are the first to explore spatial region of interest towards accurate and robust human sensing. Experiments were conducted in a common office and the results demonstrated that WiRoI is able to accurately detect and interpret gestures (In order to test the performance of the proposed method, we build a gesture recognition system.) with the accuracy lowered by less than 5% when there are other humans walking or standing between the TX and RX.