Commodity WiFi Research Articles

High-Resolution WiFi Imaging With Reconfigurable Intelligent Surfaces

WiFi-based imaging enables pervasive sensing in a privacy-preserving and cost-effective way. However, most of existing methods either require specialized hardware modification or suffer from the poor imaging performance due to the fundamental limit of off-the-shelf commodity WiFi devices in spatial resolution. We observe that the recently developed reconfigurable intelligent surface (RIS) could be a promising solution to overcome these challenges. Thus, in this article, we propose an RIS-aided WiFi imaging framework to achieve high-resolution imaging with the off-the-shelf WiFi devices. Specifically, we first design a beamforming method to achieve the first-stage imaging by separating the signals from different spatial locations with the aid of the RIS. Then, we propose an optimization-based super-resolution imaging algorithm by leveraging the low-rank nature of the reconstructed object. During the optimization, we also explicitly take into account the effect of finite phase quantization in RIS to avoid the resolution degradation due to quantization errors. Simulation results demonstrate that our framework achieves median root-mean-square error (RMSE) of 0.03 and median structural similarity (SSIM) of 0.52. The visual results show that high-resolution imaging results are achieved with simulation signals at 5 GHz that are matched with commercial WiFi 802.11n/ac protocols.

Robust Saliency-Driven Quality Adaptation for Mobile 360-Degree Video Streaming

Mobile 360-degree video streaming has grown significantly in popularity but the quality of experience (QoE) suffers from insufficient and variable wireless network bandwidth. Recently, saliency-driven 360-degree streaming overcomes the buffer size limitation of head movement trajectory (HMT)-driven solutions and thus strikes a better balance between video quality and rebuffering. However, inaccurate network estimations and intrinsic saliency bias still challenge saliency-based streaming approaches, limiting further QoE improvement. To address these challenges, we design a robust saliency-driven quality adaptation algorithm for 360-degree video streaming, RoSal360. Specifically, we present a practical, tile-size-aware deep neural network (DNN) model with a decoupled self-attention architecture to accurately and efficiently predict the transmission time of video tiles. Moreover, we design a reinforcement learning (RL)-driven online correction algorithm to robustly compensate the improper quality allocations due to saliency bias. Through extensive prototype evaluations over real wireless network environments including commodity WiFi, 4G/LTE, and 5G links in the wild, RoSal360 significantly enhances the video quality and reduces the rebuffering ratio, thereby improving the viewer QoE, compared to the state-of-the-art algorithms.

Toward Low-Cost Passive Motion Tracking With One Pair of Commodity Wi-Fi Devices

Toward Low-Cost Passive Motion Tracking With One Pair of Commodity Wi-Fi Devices

WiCPD: Wireless Child Presence Detection System for Smart Cars

Child presence detection (CPD) is becoming a regulatory requirement for car manufacturers to save children’s lives when they are left alone in unattended vehicles. However, most of the existing solutions require dedicated devices and suffer from limited accuracy and coverage. In this article, we build WiCPD, the first-of-its-kind in-car CPD system using commodity Wi-Fi, which can cover the entire interior of a car with no blind spot. First, we introduce a statistical electromagnetic model which accounts for the impact of motion on all the multipaths inside a car, followed by a motion statistics metric indicating the ambient motion intensity and a signal-to-noise-ratio (SNR) boosting scheme to extract the minute chest movement. Then, we design a unified CPD framework consisting of three target detector modules, including a motion target detector to detect a child in motion/awake, a stationary target detector to detect a stationary/sleeping child, and a transition target detector to detect a sleeping child with sporadic motion who is missed by both the motion and stationary target detectors. We implement a real-time WiCPD system by using commercial Wi-Fi chipsets, deploy it over 20 different cars, and collect data for multiple children aging from 4 to 50 months. The results show that WiCPD can achieve 100% detection rate within 8 s when the child is awake/in-motion and 96.56% detection rate within 20 s for a static/sleeping child. Extensive experiments also demonstrate that WiCPD can be easily deployed in minutes without calibration and enjoys very low CPU and memory consumption, thus promising a practical candidate for CPD applications.

Toward Robust and Accurate Device-Free Localization in Cluttered Environments With Commodity WiFi Devices

In the past decade, great progresses have been made in WiFi-based device-free localization (DFL). However, some challenging issues still hinder the large-scale implementation of DFL techniques, mainly including fingerprint vanishing and environmental dynamics. In order to enhance the localization performance in cluttered environments, in this article, a modified hierarchical framework for DFL is designed, which consists of several functional modules. Specifically, the collected data are first divided into several subsets in the spatiotemporal separation module. Next, the raw data are mapped to another feature space to mitigate the effects of fingerprint vanishing with the help of a deep neural network. In the distributed modeling module, local DFL models are built to separately represent the subsets. Additionally, probability distributions of local DFL models are calculated to estimate and control the effects of the noise. Finally, a global DFL model is built by integrating all the local DFL models with the embedding of the probability distribution information of those local DFL models. In this manner, the localization performance in cluttered environments could be significantly enhanced by the proposed hierarchical framework. Comprehensive experiments in several indoor environments demonstrate the robustness and generalization performance of the proposed hierarchical framework.

Deep-Learning-Based Wireless Human Motion Tracking for Mobile Ship Environments

Being able to track passengers’ movement without invasion of their privacy plays an important role in cruise ships; it enables crucial location-based services, such as maritime search and rescue, tourist services, and epidemic prevention. The past few years have witnessed commodity WiFi holding great potential that provides such services available thanks to its ubiquitous in indoor scenarios. However, existing WiFi-based tracking methods suffer from huge performance degradation in sailing ships due to their complex metal structures and dynamic hull deformation caused by engines and waves/payloads pressure. In this article, we present CRLoc, a deep learning-based passive human tracking system that can overcome the practical limitations of traditional WiFi-based localization approaches applied in a multipath-rich and mobile ship environment, and provide decimeter-level tracking accuracy in cruise ships. Specifically, we make two contributions, i.e., we propose a super-resolution parameter estimation algorithm that better characterizes ship indoor environments, and a deep neural network-based end-to-end solution to remove the impact of noise, interference, and mobility in ships. The real-world implementation and extensive experiments in several passenger ships demonstrate that CRLoc tracks human motions with a median error of 92 cm, better than state-of-the-art localization methods. To our knowledge, this is one of the first WiFi-based passive human motion tracking system in a cruise ship environment.

Wi-Gym: Gymnastics Activity Assessment Using Commodity Wi-Fi

Practicing gymnastics activities at home with online resources has become an increasingly popular choice due to its convenience and accessibility. However, without face-to-face guidance by a trainer, a major challenge is how to assess the quality of performed gymnastics activities, effectively and fairly. Existing intrusive assessing approaches usually require live cameras or wearable sensors, which usually generate privacy and feasibility concerns. There is a lacking of accurate approaches to assess the quality of the activities. To address these challenges, a gymnastics activity assessment approach is proposed in this article, and Wi-Gym, an effective first-of-its-kind gymnastics activity assessment system is developed utilizing commodity Wi-Fi. Wi-Gym is designed to compare the activity-induced channel state information (CSI) dynamics by an exerciser and that of a trainer utilizing dynamic time warping (DTW). The comparison results are provided by a fuzzy inference system (FIS). To make Wi-Gym robust to the changes in the environment, domain adaptation is leveraged to mitigate the data distribution imbalance caused by the environment changes. Extensive experimental studies have been conducted using Wi-Gym, acoustic, and video-based sensing systems. The experimental results validate the effectiveness and robustness of the proposed approach.

Three-Dimensional Indoor Localization and Tracking for Mobile Target Based on WiFi Sensing

With the prevalence of commodity WiFi devices and development of the Internet of Things (IoT), the usage of WiFi has been extended from communication to context aware. Based on this, indoor localization has attracted increasing attention in the academic community, without the need for additional sensors and any active engagement from the users. However, the localization performance is vulnerable to the background noises due to relying on signals changes. To address this issue, in this article, we propose a passive 3-D indoor localization with a radio map for the mobile target by exploiting channel state information (CSI) of WiFi signals, realizing human–computer interaction (HCI). To this end, 3-D space is first divided into multiple independent regions and we construct a spatial radio map by traversing all the subspaces using CSI measurements. Next, to fully characterize the profiles of the locations of the mobile target, we reconstruct CSI time series to form a CSI tensor through integrating WiFi transmission links and a CANDECAMP/PARAFAC (CP) decomposition method is applied to this tensor for obtaining representative features. Then, the features-location data set is optimized by combining <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> -distributed stochastic neighbor embedding (t-SNE) and information theory method to reconstruct a fine-grained fingerprint map for improving system performance. Finally, a recurrent neural network (RNN) model is introduced to learn the features data set optimized and then build a nonlinear correlation between input and output for realizing the purpose of accurate indoor localization. The proposed scheme is implemented on a set of commodity WiFi devices and evaluated in indoor scenarios. Based on real-world CSI data, our experimental results confirm the effectiveness of the proposed scheme in terms of localization accuracy and robustness against the noises.

HandGest: Hierarchical Sensing for Robust-in-the-Air Handwriting Recognition With Commodity WiFi Devices

Recent advances in wireless sensing techniques have made it possible to recognize hand gestures using channel state information (CSI) in commodity WiFi devices. Existing WiFi-based gesture recognition systems mainly use learning-based pattern recognition methods to recognize different gestures, however, these methods fail to work well when the locations of transceivers, the relative location and orientation of the hand with respect to transceivers, and/or the hand gesturing size change, leading to inconsistent signal patterns caused by those factors. Although some recent efforts have been made to address the so-called “domain-dependent” gesture recognition problem, they either require prior knowledge on initial locations of the hand and WiFi devices or need to train several classifiers for the specific domains. Different from the state-of-the-art methods, we construct two distinct features from a hand-oriented view (rather than from a transceiver’s view), namely, the dynamic phase vector (DPV) and motion rotation variable (MRV), which are quite consistent in characterizing a big set of handwriting gestures, despite significant change in locations of transceivers, the relative location and orientation of the hand with respect to transceivers, and the drawing sizes. We further incorporate a hierarchical sensing framework and develop HandGest—a real-time handwriting gesture recognition system using commodity WiFi devices, to precisely recognize a great number of “in-the-air” handwritings based on the aforementioned two domain-independent features and a pipeline of specific features. Extensive experiments have been done in practical settings with 20 volunteers, evaluation results demonstrate that HandGest outperforms state-of-the-art methods on a large number of handwritings with different transceivers’ location, different initial hand locations and orientations, as well as different drawing sizes. Given its superior performance, we believe that HandGest paves a new way to enhance the real-world practicality of WiFi-based gesture recognition.

Adversarial Deep Learning for Indoor Localization With Channel State Information Tensors

Fingerprinting-based indoor localization has been a research focus for GPS denied areas. The development of neural networks has greatly promoted its application in indoor localization systems. However, recent studies showed that the machine learning models, including state-of-the-art neural networks, are vulnerable to adversarial examples, and thus, neural network-based indoor localization systems are also under the threat of adversarial attacks. To investigate the effect of adversarial attacks on indoor localization systems and to make such systems resilient to adversarial attacks, we propose AdvLoc, an adversarial deep learning for indoor localization system. With the proposed AdvLoc system, the effect of adversarial attacks on indoor localization is studied under six types of adversarial attack methods in both black-box attack and white-box attack scenarios. Furthermore, adversarial training is utilized in offline training of the proposed AdvLoc system, which is effective against first-order adversarial attacks. The proposed AdvLoc system is implemented with commodity WiFi devices and evaluated with extensive experiments in two representative indoor environments. The experimental results verify the robustness of the proposed system against first-order adversarial attacks in representative indoor environments.

Commodity WiFi Sensing in Ten Years: Status, Challenges, and Opportunities

The prevalence of WiFi devices and ubiquitous coverage of WiFi networks provide us the opportunity to extend WiFi capabilities beyond communication, particularly in sensing the physical environment. In this article, we survey the evolution of WiFi sensing systems utilizing commodity devices over the past decade. It groups WiFi sensing systems into three main categories: 1) activity recognition (large scale and small scale); 2) object sensing; and 3) localization. We highlight the milestone work in each category and the underline techniques they adopted. Next, this work presents the challenges faced by existing WiFi sensing systems. Finally, we comprehensively discuss the future trending of commodity WiFi sensing.

Multiview Features Fusion and AdaBoost Based Indoor Localization on WiFi Platform

With the increasing demand for location-based service (LBS), WiFi-based localization has attracted considerable attentions due to the wide deployment and low cost of WiFi devices. However, most of existing approaches need to work within line-of-sight (LoS) range or require users to take dedicated devices. Thus, a device-free and passive indoor localization scheme is still desired. In this paper, a multi-view features fusion and AdaBoost-based indoor localization scheme by exploiting channel state information (CSI) of WiFi signals is proposed. To this end, we firstly formulate a shared consistent representation by using CSI measurements from all WiFi receivers, modeling the common properties among all views. Based on this, a features extraction is applied to the consistent representation for charactering channel properties. Meanwhile, these features extracted are optimized via a t-distributed stochastic neighbor embedding (t-SNE) algorithm for selecting the effective features, contributing to system performance. AdaBoost then builds a non-linear mapping between the CSI features and locations, realizing the purpose of localizing a user relying solely on WiFi signals with a high level of granularity, without any active engagement from the users. We implement a prototype on commodity WiFi devices and conduct comprehensive experiments in indoor scenarios. Based on real-world CSI measurements, the results confirm that the proposed scheme can achieve average localization errors of 0.8 m and 1.1 m in two indoor scenarios.

ECC: Passenger Counting in the Elevator Using Commodity WiFi

Elevators have become a kind of indispensable facility for everyday life, which bring people both convenience and safety hazards. Specifically in the household environment, an elevator’s lifespan is expected to be more than 20 years. An appropriate and regularly maintained counterweight is conducive to extending elevator life. This paper proposes a passenger counting approach in the elevator for regular counterweight adjustment based on commodity WiFi called ECC. Since the running time of the elevator between two adjacent floors is short, the major challenge of ECC is how to count passengers from the limited captured data. This paper first theoretically analyzes the relationship between the number of passengers and the variation of channel state information (CSI). Then ECC constructs a multi-dimensional feature by extracting the average of amplitude (AOA), time-varying spectrum (TVS), and percentage of non-zero elements (PEM) features from the limited data. Finally, the random forest (RF) classifier is used for passenger counting and the local optimization problem is solved by expanding the feature dataset through data segmentation. ECC is implemented by using off-the-shelf IEEE 802.11n devices, and its performance is evaluated via extensive experiments in typical real-world scenes. The estimated precision of ECC can reach more than 95%, and more than 97% of estimation errors are less than 2 persons, which demonstrates the superior effectiveness and generalizability of ECC.

DiverSense

The ubiquity of Wi-Fi infrastructure has facilitated the development of a range of Wi-Fi based sensing applications. Wi-Fi sensing relies on weak signal reflections from the human target and thus only supports a limited sensing range, which significantly hinders the real-world deployment of the proposed sensing systems. To extend the sensing range, traditional algorithms focus on suppressing the noise introduced by the imperfect Wi-Fi hardware. This paper picks a different direction and proposes to enhance the quality of the sensing signal by fully exploiting the signal diversity provided by the Wi-Fi hardware. We propose DiverSense, a system that combines sensing signal received from all subcarriers and all antennas in the array, to fully utilize the spatial and frequency diversity. To guarantee the diversity gain after signal combining, we also propose a time-diversity based signal alignment algorithm to align the phase of the multiple received sensing signals. We implement the proposed methods in a respiration monitoring system using commodity Wi-Fi devices and evaluate the performance in diverse environments. Extensive experimental results demonstrate that DiverSense is able to accurately monitor the human respiration even when the sensing signal is under noise floor, and therefore boosts sensing range to 40 meters, which is a 3x improvement over the current state-of-the-art. DiverSense also works robustly under NLoS scenarios, e.g., DiverSense is able to accurately monitor respiration even when the human and the Wi-Fi transceivers are separated by two concrete walls with wooden doors.

Rethinking Doppler Effect for Accurate Velocity Estimation With Commodity WiFi Devices

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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$9.38cm/s$ </tex-math></inline-formula> , 13.42°, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$31.08cm$ </tex-math></inline-formula> 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.

Device-Free Human Activity Recognition Based on Dual-Channel Transformer Using WiFi Signals

Human activity recognition plays a significant role in smart building applications, healthcare services, and security monitoring. In particular, WiFi-based indoor wireless sensing system becomes increasingly popular due to its noninvasiveness. This work presents the design and implementation of DARMS, a Device-free human Activity Recognition and Monitoring System that can be deployed with low-cost commodity WiFi devices. DARMS is a passive wireless sensing system, and it can accurately distinguish various daily activities without the user wearing any sensor. DARMS makes two key technical contributions. First, an effective signal processing methodology is designed to extract the CSI features both in the time domain and frequency domain. Second, a dual-channel neural network that combines temporal and frequency information is proposed to achieve fine-grained activity recognition. In our experiments, DARMS shows outstanding performance in different indoor environments, with an average accuracy of 96.9% for fall detection and 93.3% for human activity recognition.

Breathing-based continuous non-intrusive user verification leveraging commodity WiFi

This paper opens up a new pathway of the utility of breathing pattern for user verification. We demonstrate that it is possible to capture people's breathing pattern leveraging commodity WiFi devices. While prior solutions for biometrics-based user recognition usually require dedicated devices (e.g., video cameras or IR sensors), this paper introduces the first general, low-cost breathing-based user verification system using commodity WiFi devices. The proposed system is based on the fact that the breathing pattern always keeps consistent for the same user but distinct among different people. Our innovative method successfully extracts the breathing pattern of different people based on channel state information of WiFi signal to facilitate user verification. The prototype study using two commodity WiFi devices can differentiate people with an average verification accuracy over 90%, suggesting that our breathing-based user verification system using commerical off-the-shelf (COTS) WiFi is promising to be one of the most critical methods in biometrics.

Wi-Wheat+: Contact-free wheat moisture sensing with commodity WiFi based on entropy

In this paper, we propose a contact-free wheat moisture monitoring system, termed Wi-Wheat+, to address the several limitations of the existing grain moisture detection technologies, such as time-consuming process, expensive equipment, low accuracy, and difficulty in real-time monitoring. The proposed system is based on Commodity WiFi and is easy to deploy. Leveraging WiFi CSI data, this paper proposes a feature extraction method based on multi-scale and multi-channel entropy. The feasibility and stability of the system are validated through experiments in both Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) scenarios, where ten types of wheat moisture content are tested using multi-class Support Vector Machine (SVM). Compared with the Wi-Wheat system proposed in our prior work, Wi-Wheat+ ​has higher efficiency, requiring only a simple training process, and can sense more wheat moisture content levels.

Decimeter Level Indoor Localization Using WiFi Channel State Information

Indoor localization using WiFi signal parameters is challenging, with encouraging decimeter localization results available with enough line-of-sight coverage and hardware infrastructure. This paper proposes a new 2-dimensional multiple packets based matrix pencil (2D M-MP) method to estimate the Angle of Arrival (AoA) and Time of Flight (ToF) based on WiFi channel state information (CSI). Compared with the conventional parameter estimation algorithms, this method has two advantages. First, 2D M-MP method uses the discrete Fourier transform (DFT) to convert the complex computation into real computation to reduce the computational complexity significantly without losing accuracy. Second, it accumulates multiple CSI packets to improve the parameter estimation accuracy effectively, especially at low values of signal-to-noise-ratio (SNR) environment. To verify the practicability of our proposed 2D M-MP method, we set up a localization system in an actual scenario using commodity WiFi cards which demonstrates that the performance of 2D M-MP method is better than conventional parameter estimation algorithms and can achieve a localization accuracy of 42 cm in indoor hall deployment.

Wi-Fi Direction Finding With Frequency-Scanned Antenna and Channel-Hopping Scheme

A novel and simple technique for Direction of Arrival (DoA) estimation in 802.11 Wi-Fi networks is proposed. It is based on the use of a dual-port frequency-beam scanning antenna, which is connected to single MIMO access point. By simply performing a channel hopping scheme and acquiring Received Signal Strength Information (RSSI) from each channel, the DoA can be estimated in a wide Field of View of 180° with a mean angular error of 5°. Due to its simplicity, a single commodity Wi-Fi AP can be used without the need of Channel State Information (CSI), or complex electronically reconfigurable or mechanically revolving antennas.