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.

Wireless sensing technologies have shown significant promise in various applications, but their spatial coverage is confined to the vicinity of the transmitters, limiting their applicability in broader environments. In this paper, we introduce an innovative wireless sensing approach based on the globally covered Global Navigation Satellite System (GNSS) signals. While GNSS signals have been widely used in remote sensing to monitor slow changes in the Earth’s surface, like sea level and snow depth, their ability to accurately detect highly dynamic target motions, such as human respiration, gestures, and intrusions, remains unclear. The main challenge arises from the interference brought by the large-scale satellite movement and severe GNSS signal errors. In this study, we present a novel GNSS signal enhancement system named FineSat to address these interference. Specifically, we first utilize polynomial representations to cancel satellite movement interference. Then, based on the analysis of GNSS signal errors, we propose a signal differential processing module to mitigate the errors. We implement our system on commercial devices and validate its performance in three sensing applications: respiration monitoring, gesture recognition, and intrusion detection. Results show that we achieve 0.42 bpm mean absolute error in respiration monitoring, 96.5% average accuracy in gesture recognition, and 98.6% accuracy in intrusion detection.

In the last decade, the employment of ubiquitous WiFi/4G/5G signals for wireless sensing has seen remarkable advancements, opening new vistas in the realm of wireless sensing. Despite these technological strides, the exploration into the fundamental theoretical aspects of wireless sensing, particularly concerning the sensing coverage and the mechanisms for its control, remains relatively uncharted. Addressing this critical gap, this paper introduces an innovative conceptual framework centered around the sensing signal-to-noise ratio and the application of diffraction theory to ubiquitous wireless sensing. This framework not only provides a quantitative characterization of the sensing coverage but also offers theoretical insights into controlling the sensing coverage. Understanding and adjusting sensing coverage paves the way for future innovations in sophisticated wireless signal-based sensing applications.

Despite intensive research efforts in radio frequency noncontact sensing, capturing fine-grained geometric properties of objects, such as shape and size, remains an open problem using commodity WiFi devices. Prior attempts are incapable of characterizing object shape or size because they predominantly rely on weak signals reflected off objects in a very small number of directions. In this paper, motivated by the observation that the diffracted signals around an object between two WiFi devices carry the contour information of the object, we formulate the problem of reconstructing the 2D target profile and develop WiProfile, the first WiFi-based system that unlocks the diffraction effects for target profiling. We introduce a CSI-Profile model to characterize the relationship between the CSI measured at different target positions and the target profile in the diffraction zone. With suitable approximations, the inverse problem of deriving the target profile from CSI can be solved by the inverse Fresnel transform. To mitigate CSI measurement errors on commodity WiFi devices, we propose a novel antenna placement strategy. Comprehensive experiments demonstrate that WiProfile can accurately reconstruct profiles with median absolute errors of less than 1 cm under various conditions, and effectively estimate the profiles of everyday objects of diverse shapes, sizes, and materials. We believe this work opens up new directions for fine-grained target imaging using commodity WiFi devices.

While a large range of sensing applications such as activity sensing and vital sign monitoring have been realized with WiFi sensing, using commercial WiFi devices to obtain fine-grained size information of objects remains challenging due to the narrow bandwidth of WiFi. Very recent studies attempted to measure object sizes using WiFi signals. However, these systems are still far from practical with a lot of limitations including requiring multiple transceiver pairs and can only measure one-dimensional size, hindering their real-life adoption. Also, these systems rely on Channel State Information (CSI) to work, which is only available on few commercial WiFi cards. In this work, we propose to employ a new channel data, i.e., Beamforming Feedback Information (BFI), widely available on almost all new generation WiFi cards for fine-grained size measurement. Through thoroughly analyzing the mathematical relationship between BFI and CSI, we show how to use BFI to achieve fine-grained size measurement. We propose a novel method to accurately measure the two-dimensional size of an object using a single transceiver pair by identifying the positions of singularities when the object passes through the diffraction zone of the transceiver pair. Experiment results show that Wi2DMeasure can accurately measure the two-dimensional size of objects under various conditions, achieving a small median error of only 3.7 mm.

Although WiFi-based contactless sensing has made significant progress in the past decade, most prior work still focus on the reflection zone far from WiFi transceivers, while few studies explore the diffraction zone near transceivers. Additionally, previous diffraction models only consider the CSI amplitude signal and ignore the impact of multipath. In this work, we develop an accurate diffraction model to characterize the relationship between both CSI amplitude and phase and target's movement in the diffraction zone. We further put forward the deformation forms of the model under static multipath conditions and find that the CSI patterns vary significantly with multipath. Consequently, the common assumption of a one-to-one mapping between CSI patterns and activities in existing work fails due to multipaths, degrading sensing performance when multipath changes. To address this challenge, we propose to extract a relative change pattern from CSI signals to recover the one-to-one mapping relations and eliminate the impact of static multipath. Extensive experiments under various multipath conditions demonstrate an accuracy higher than 96% for the coarse-grained intrusion detection and an average error rate of 0.6 bpm for the fine-grained respiration monitoring.

The fast app launch is crucial to users' experience and it is one of the eternal pursuits of manufacturers. Page fault is a critical factor leading to long app launch latency. Prefetching is the current method of reducing page faults during app launch. Before app launch, prefetching all demanded pages of the target app can speed up the app launch effectively, but it always uses the memory of several hundred MB, leading to low memory and slowing other apps' launch. Prefetching during application launch uses memory effectively, however, current methods are not aware of the order of pages accessed, causing noticeable accessing-prefetching order inversions, which results in limited acceleration of app launch. In order to accelerate the application launch effectively with little memory usage, we propose a Two-Phase Prefetching schema (TPP), which performs prefetching via two phases: 1) Before the app launch, to increase the efficiency of memory usage in prefetching, TPP prefetches few critical pages with app prediction, which is based on Long Short-Term Memory (LSTM) with high accuracy. 2) During app launch, TPP prefetches the rest of the critical pages via an order-aware sliding window method, resolving the accessing-prefetching order inversions and significantly reducing the app launch latency. We evaluate TPP on Google Pixel 3, compared to the state-of-the-art method, TPP reduces the application launch time by up to 52.5%, and 37% on average, and the data prefetched before the target application started is only 1.31 MB on average.

Enabling pervasive WiFi devices with non-contact sensing capability is an important topic in the field of integrated sensing and communication. Doppler effect has been widely exploited to estimate targets’ velocity from wireless signals. However, the separation of signal sources and receivers complicates the relationship between Doppler frequency shift (DFS) and target velocity in WiFi-based non-contact sensing systems. In contrast to existing works that rely on either approximated relations or coarse-grained information such as whether a target is moving toward or away from WiFi transceivers, this paper investigates rigorously the dependency of velocity estimation accuracy on target locations and headings in WiFi sensing systems. The theoretical insights allow us to derive a closed-form solution and understand the fundamental limitation of velocity estimation. To optimize velocity estimation performance, we devise a receiving device selection scheme that dynamically chooses the optimal set of receivers among multiple available WiFi devices. A prototype real-time target tracking system has been implemented using commodity WiFi devices. Extensive experimental results show that the proposed system outperforms state-of-the-art approaches in velocity estimation and tracking, and is able to achieve$9.38cm/s$, 13.42°,$31.08cm$median errors in speed, heading and location estimation amongst experiments conducted in three indoor environments with three device placements and eight human subjects over 15 trajectories.

Recent years have witnessed rapid development in the research area of WiFi sensing, which senses human activities in a contactless and non-intrusive manner. One major issue that hinders real-world deployment of these systems is position dependence, i.e., once the human target changes location and orientation, the sensing performance degrades significantly. Existing machine learning based methods aim to solve this problem by either generating high-dimensional features or transfer learning the environment knowledge. However, these methods require significant training effort and yet acquire limited improvement. In this paper, we start by understanding and analyzing the Doppler frequency shift in WiFi sensing. We then develop a WiFi frequency model to quantify the relationship between signal frequency and target position, motion direction and speed for human activities. Based on this theoretical model, we prove that the commonly-used movement speed and motion direction features are position dependent, and further identify movement fragments and relative motion direction changes as two position-independent features. Building upon the frequency model and the position-independent features, we design a suite of position-independent gestures and develop the gesture recognition system accordingly. Evaluation results show that under various conditions (i.e., different locations, orientations, environments, and persons), our system achieves more than 96 percent recognition accuracy without any training, significantly outperforming state-of-the-art machine learning based solutions.