WiFi Sensing Research Articles

WiCAM2.0: Imperceptible and Targeted Attack on Deep Learning based WiFi Sensing

With the widespread adoption of deep learning models in wireless sensing, substantial efforts have been made to develop sophisticated models that improve the accuracy and performance of sensing applications. However, the exploration of potential vulnerabilities in deep models has been limited, with existing studies primarily focusing on evaluating wireless adversarial performance in communication or sensing alone. Moreover, there is a lack of a comprehensive definition for attack imperceptibility. In this paper, we come up with a definition of the wireless attack imperceptibility for both communication and sensing. Our objective is to create an adversarial perturbation capable of degrading WiFi sensing performance while preserving WiFi communication integrity. To achieve this, we propose WiCAM2.0 to reveal the temporal and spatial attention of a DNN, capturing the crucial portions of its input. Then we design a mask to confine adversarial perturbations in the attended parts only, minimizing the impact on WiFi communication. WiCAM2.0 is a general adversarial framework that integrates adversarial methods like FGSM and PGD to generate perturbations, capable of initiating both non-targeted and targeted attacks. We carry out experiments on three popular WiFi sensing applications, including human activity recognition, gesture recognition, and user identification. Extensive experiments are conducted on both public datasets and self-collected datasets.

On the Design and Performance of QRD-Based Beamforming Feedback for Wi-Fi Sensing

On the Design and Performance of QRD-Based Beamforming Feedback for Wi-Fi Sensing

Contactless WIFI Sensing and Monitoring for Future Healthcare – Emerging Trends, Challenges and Opportunities Dr G.Kiranmaye,

WiFi sensing has received recent and significant interest from academia, industry, healthcare professionals, and other caregivers (including family members) as a potential mechanism to monitor our aging population at a distance without deploying devices on users’ bodies. In particular, these methods have the potential to detect critical events such as falls, sleep disturbances, wandering behavior, respiratory disorders, and abnormal cardiac activity experienced by vulnerable people. The interest in such WiFi-based sensing systems arises from practical advantages including its ease of operation indoors as well as ready compliance from monitored individuals. Unlike other sensing methods, such as wearables, camera-based imaging, and acoustic-based solutions, WiFi technology is easy to implement and unobtrusive. This paper reviews the current state-of-the-art research on collecting and analyzing channel state information extracted using ubiquitous WiFi signals, describing a range of healthcare applications and identifying a series of open research challenges, including untapped areas of research and related trends. This work aims to provide an overarching view in understanding the Cases from a perspective that considers hardware, advanced signal processing, and data acquisition.

Seeing Through Walls with Wi-Fi: Wi Vi Technology and its Security Implications

he technology known as WI- FI, or wireless networking, allows users to connect to the internet wirelessly “Wi-Fi" sis the acronym for “WIRELESS FIDELITY. “Data is transferred between transmitters and receivers using Wi-Fi waves. WI-VI (WIRELESS VISION) is a new technology introduced by WI-FI. WIVI is a device that uses WI-FI signals to see through any object on the wall. In a closed room, we can detect the number of moving humans. In the future, we will concentrate on detecting more than three moving objects using WI-FI signals. WI- VI can detect people's gestures and relative location via the WI-FI signal. WIVI technology can be utilized in the tax department to detect black money and weapons, as well as for security purpose. Wivi is used for defense at home. Wivi technology can help us with door-face detection. Within a limited radius of no more than 1000 square feet, Wi-Fi is linked to Wi-Fi. Wi-Vi technology can also be used to assist in paramedic circumstances, like a land slide. Wi-Fi, a popular technology, is nothing more than an information channel between transmitter and receiver. We demonstrate in this work that Wi-Fi can also extend our senses, allowing us to view moving objects across walls and behind closed doors. So, can Wi-Fi signals be used to identify persons in a closed room based on their Relative location? Yes, we Through the use of a technology known as Wi-Vi, things in Confined rooms can be identified. Simple movements can also be identified and combined behind the wall to express messages. This introduces two major changes. Initially, MIMO interference nulling is used to reduce static object reflections and focus on moving objects. It then demonstrates how to follow a human by treating. Keyword- Wi-Fi Sensing, Through-Wall Imaging, MIMO Interference Nulling, Object Detection, Moving Object, wivi, Detection, Human Gesture Recognition, Indoor Localization

Wi-CHAR: A WiFi Sensing Approach with Focus on Both Scenes and Restricted Data.

Significant strides have been made in the field of WiFi-based human activity recognition, yet recent wireless sensing methodologies still grapple with the reliance on copious amounts of data. When assessed in unfamiliar domains, the majority of models experience a decline in accuracy. To address this challenge, this study introduces Wi-CHAR, a novel few-shot learning-based cross-domain activity recognition system. Wi-CHAR is meticulously designed to tackle both the intricacies of specific sensing environments and pertinent data-related issues. Initially, Wi-CHAR employs a dynamic selection methodology for sensing devices, tailored to mitigate the diminished sensing capabilities observed in specific regions within a multi-WiFi sensor device ecosystem, thereby augmenting the fidelity of sensing data. Subsequent refinement involves the utilization of the MF-DBSCAN clustering algorithm iteratively, enabling the rectification of anomalies and enhancing the quality of subsequent behavior recognition processes. Furthermore, the Re-PN module is consistently engaged, dynamically adjusting feature prototype weights to facilitate cross-domain activity sensing in scenarios with limited sample data, effectively distinguishing between accurate and noisy data samples, thus streamlining the identification of new users and environments. The experimental results show that the average accuracy is more than 93% (five-shot) in various scenarios. Even in cases where the target domain has fewer data samples, better cross-domain results can be achieved. Notably, evaluation on publicly available datasets, WiAR and Widar 3.0, corroborates Wi-CHAR's robust performance, boasting accuracy rates of 89.7% and 92.5%, respectively. In summary, Wi-CHAR delivers recognition outcomes on par with state-of-the-art methodologies, meticulously tailored to accommodate specific sensing environments and data constraints.

RoboFiSense: Attention-Based Robotic Arm Activity Recognition With WiFi Sensing

RoboFiSense: Attention-Based Robotic Arm Activity Recognition With WiFi Sensing

IEEE 802.11bf WLAN Sensing Procedure: Enabling the Widespread Adoption of WiFi Sensing

IEEE 802.11bf WLAN Sensing Procedure: Enabling the Widespread Adoption of WiFi Sensing

HandFi: WiFi Sensing based Hand Gesture Recognition using Channel State Information

Gesture recognition using WiFi sensing is an emerging and innovative approach that leverages the variations in WiFi signals caused by human hand movements to enable non-contact and intuitive human-computer interaction. The identification of the hand used to perform a gesture significantly impacts the channel state information (CSI) characteristics. It plays a crucial role in data analysis and interpretation. This paper presents a model for classifying the gesture and the hand with which it is performed. We use an Intel 5300 network interface card as a receiver and a commodity WiFi router to collect the CSI values. The CSI values are collected for six left-hand and six right-hand gestures (push left, push right, increase, decrease, stop, and next). The collected CSI values are pre-processed to find the CSI ratio among the sub-carriers. Then four different methods including proposed method are used for classifying the hand and the gesture. The four methods are (1) use of the Hampel filter, (2) use of CSI ratio only, (3) use of correlation matrix along with CSI ratio, and (4) proposed CSI ratio and interquartile distance based method. Then various machine learning techniques such as KNN, Decision trees, and SVM are used to classify the hand (right or left) used for signaling the gestures and the type of gestures. It is found that using the top 30 inter-quartile distance of the CSI-ratios corresponding to the CSI values of the sub-carriers while preserving their positions as the feature vector, the KNN machine learning model can classify the hand and gesture with approximately 99% accuracy for test data. Similar performance is also observed for other performance evaluation parameters like precision and recall.

Wi-Fitness: Improving Wi-Fi Sensing With Video Perception for Smart Fitness

Wi-Fitness: Improving Wi-Fi Sensing With Video Perception for Smart Fitness

Privacy-Enhanced Federated WiFi Sensing for Health Monitoring in the Internet of Things

Privacy-Enhanced Federated WiFi Sensing for Health Monitoring in the Internet of Things

Understanding the Diffraction Model in Static Multipath-Rich Environments for WiFi Sensing System Design

Understanding the Diffraction Model in Static Multipath-Rich Environments for WiFi Sensing System Design

Pushing the Limits of WiFi Sensing with Low Transmission Rates

Pushing the Limits of WiFi Sensing with Low Transmission Rates

Non-Contact Wi-Fi Sensing of Respiration Rate for Older Adults in Care: A Validity and Repeatability Study

Non-Contact Wi-Fi Sensing of Respiration Rate for Older Adults in Care: A Validity and Repeatability Study

Efficient Beamforming Feedback Information-Based Wi-Fi Sensing by Feature Selection

Efficient Beamforming Feedback Information-Based Wi-Fi Sensing by Feature Selection

AdaWiFi, Collaborative WiFi Sensing for Cross-Environment Adaptation

AdaWiFi, Collaborative WiFi Sensing for Cross-Environment Adaptation

Enabling WiFi Sensing on New-generation WiFi Cards

The last few years have witnessed the rapid development of WiFi sensing with a large spectrum of applications enabled. However, existing works mainly leverage the obsolete 802.11n WiFi cards (i.e., Intel 5300 and Atheros AR9k series cards) for sensing. On the other hand, the mainstream WiFi protocols currently in use are 802.11ac/ax and commodity WiFi products on the market are equipped with new-generation WiFi chips such as Broadcom BCM43794 and Qualcomm QCN5054. After conducting some benchmark experiments, we find that WiFi sensing has problems working on these new cards. The new communication features (e.g., MU-MIMO) designed to facilitate data transmissions negatively impact WiFi sensing. Conventional CSI base signals such as CSI amplitude and/or CSI phase difference between antennas which worked well on Intel 5300 802.11n WiFi card may fail on new cards. In this paper, we propose delicate signal processing schemes to make wireless sensing work well on these new WiFi cards. We employ two typical sensing applications, i.e., human respiration monitoring and human trajectory tracking to demonstrate the effectiveness of the proposed schemes. We believe it is critical to ensure WiFi sensing compatible with the latest WiFi protocols and this work moves one important step towards real-life adoption of WiFi sensing.

Few-Shot Cross-Domain-Based WiFi Sensing System for Online Learning in IoT

Few-Shot Cross-Domain-Based WiFi Sensing System for Online Learning in IoT

Toward Accurate Crowd Counting in Large Surveillance Areas Based on Passive WiFi Sensing

Toward Accurate Crowd Counting in Large Surveillance Areas Based on Passive WiFi Sensing

Wi-Fi Sensing for Indoor Localization via Channel State Information: A Survey

Wireless Fidelity (Wi-Fi) sensing utilization has been widespread, especially for human behavior/activity recognition. It provides high flexibility since it does not require the person/object to carry any device known as device-free. This "passive" concept is also helpful for another application of Wi-Fi sensing, i.e., indoor localization. The "sensing" is conducted using particular parameters extracted from communication links of Wi-Fi devices, i.e., channel state information (CSI). This paper explores the recent trends in CSI-based indoor localization with Wi-Fi technology as its core, including their advantages, challenges, and future directions. We found tremendous benefits can be gained by employing Wi-Fi sensing in localization supported by its performance and integrability for other intelligent systems for activity recognition.

Toward Practical Lightweight Passive Human Tracking Using WiFi Sensing

With the wide adoption of versatile IoT devices, device providers may desire to locate users around those devices to plan context-aware intelligence, which may improve the quality of daily life. As most IoT devices are Wi-Fi enabled, the Wi-Fi-based indoor positioning system is supposed to achieve this future scene. However, the state-of-art Wi-Fi indoor positioning systems face challenges when being practically deployed as they may have to trade-off between, say accuracy and computational overhead. In light of this, this article mainly introduces PLP-Track, a Practical Lightweight Passive indoor tracking system based on CSI (channel state information) fingerprints. To settle the low granularity of fingerprints in distinguishing different positions, we propose a fingerprint preprocessing algorithm based on unsupervised learning and incorporate this algorithm with a state-space model to enable lightweight real-time tracking. Our implementation and evaluation of commodity WiFi devices demonstrate that PLP-Track can achieve indoor localization with high accuracy and low computation cost.