WiFi Devices Research Articles

LiquImager

WiFi has gradually developed into one of the main candidate technologies for ubiquitous sensing. Based on commercial off-the-shelf (COTS) WiFi devices, this paper proposes LiquImager, which can simultaneously identify liquid and image container regardless of container shape and position. Since the container size is close to the wavelength, diffraction makes the effect of the liquid on the signal difficult to approximate with a simple geometric model (such as ray tracking). Based on Maxwell's equations, we construct an electric field scattering sensing model. Using few measurements provided by COTS WiFi devices, we solve the scattering model to obtain the medium distribution of the sensing domain, which is used for identifing and imaging liquids. To suppress the signal noise, we propose LiqU-Net for image enhancement. For the centimeter-scale container that is randomly placed in an area of 25 cm × 25 cm, LiquImager can identify the liquid more than 90% accuracy. In terms of container imaging, LiquImager can accurately find the edge of the container for 4 types of containers with a volume less than 500 ml.

Design and Evaluation of Wireless DYU Air Box for Environment-Monitoring IoT System on Da-Yeh University Campus

This paper presents an original wireless DYU Air Box of an environment-monitoring IoT (EMIoT) system on a campus to offer information on environmental conditions through the public ThingSpeak IoT platform for stakeholders including all the students and employees on the Da-Yeh University (DYU) campus in Taiwan. Firstly, the proposed wireless heterogeneous multi-sensor module aggregates BME680, SCD30, PMS7003, and BH1750 sensors with a TTGO ESP32 Wi-Fi device based on the I2C and UART interface standards of series communication. Through the DYU-802.1X Wi-Fi network with the WPA2 Enterprise security directly, the wireless multi-sensor monitoring module further forwards the observation data of environmental conditions on campus via the DYU-802.1X Wi-Fi network to the public ThingSpeak IoT platform, which is a cloud service platform to aggregate, visualize, and analyze live sensing data of air quality index (AQI), concentrations of PM1.0/2.5 and CO2, brightness, ambient temperature, and relative humidity (RH). The results illustrate the proposed DYU Air Box for monitoring the indoor environmental conditions on campus and validate them with sufficient accuracy and confidence with commercialized measurement instruments. In this work, the wireless smart environment-monitoring IoT system features monitoring and automatic alarm functions for monitoring AQI, CO2, and PM concentrations, as well as ambient illumination, temperature, and RH parameters and collaboration and interoperability through the Enterprise Intranet. All the organizational stakeholders interested in the environmental conditions of the DYU campus can openly access the information according to their interests. In the upcoming future, the information of the environmental conditions in the DYU campus will be developed to be simultaneously accessed by all the stakeholders through both the public ThingSpeak IoT platform and the private EMIoT system.

Evaluation of the Relationship Between Mobile Phone Usage and miRNA-574-5p and miRNA-30C-5p Levels in Thyroid Cancer Patients.

This study aimed to evaluate the relationship between mobile phone usage and miRNA-574-5p and miRNA-30C-5p levels in patients diagnosed with differentiated thyroid cancer (DTC). Fifty patients diagnosed with DTC and 50 healthy volunteers were included in the study. miRNA-574-5p and miRNA-30C-5p gene expression levels in the blood of all subjects were analyzed by real time-polymerase chain reaction, and a questionnaire including various questions was administered to both groups. Although there was a 7.60-fold increase in miRNA-30C-5p gene expression levels in the patient group compared with the control group, it was not found to be statistically significant. Considering the miRNA-574-5p gene expression levels, although there was a 2.96-fold increase in the patient group compared with the control group, no significant relationship was found. In our study, 85% of our patients were using mobile phones with internet access, whereas 98% of our healthy volunteers were using mobile phones (p<0.05). While 53.5% of the patients had their mobile phones with them while they were sleeping, this rate was 83.7% in healthy volunteers (p<0.05). However, 93.9% of the healthy volunteers did not have a Wi-Fi device in their bedrooms, and this rate was 75% in the patient group (p<0.05). Although miRNA-30C-5p and miRNA-574-5p gene expression levels were higher in patients than in healthy volunteers, the differences were not statistically significant. Although there was no significant difference in miRNA levels, we believe that due to the higher rate of Wi-Fi device presence in bedrooms in patients compared with healthy volunteers, the effects of electromagnetic radiation on the thyroid can be reduced by paying attention to this simple change.

Moving beyond the challenges of learning through technologies: The current status of ICT integration in South African schools

The purpose of this study was to understand the current status of ICT integration in South African schools. Yet, this study focused on the Tshwane West District schools in Gauteng Province, South Africa. This study employed a descriptive research design to understand the aforesaid phenomenon. Data was collected through interviews and open-ended questionnaires with 10 teachers and one curriculum specialist. This study employed thematic analysis as a qualitative research method. Furthermore, Roger's Diffusion of Innovations is used as a framework. Despite the existence of policies, this study revealed that the integration of ICT remained a significant challenge for several instructors in South Africa. The most interesting part was that schools had improved on most ICT tools to enable access to learning with technologies, but to a greater extent, insufficient internet coverage in classrooms made it impossible for most teachers to appreciate the benefits that come with using tools such as laptops, whiteboards, and online videos. This research proposes the installation of pocket Wi-Fi devices in every classroom in schools to provide comprehensive internet access. This will facilitate interaction between instructors and students through up-to-date learning management systems subscribed to by schools, as well as films. This will promote a digitally viable learning environment that promotes creative opportunities for learners.

MDPose: Human Skeletal Motion Reconstruction Using WiFi Micro-Doppler Signatures

Motion tracking systems based on optical sensors typically suffer from poor lighting conditions, occlusion, limited coverage, and may raise privacy concerns. More recently, radiofrequency (RF) based approaches using commercial WiFi devices have emerged which offer low-cost ubiquitous sensing whilst preservin privacy. However, RF sensing systems typically output range-Doppler maps, time-frequency spectrograms, cross-range plots etc which cannot represent human motion intuitively and usually requires further processing. In this study, we propose MDPose, a novel framework for human skeletal motion reconstruction base on WiFi micro-Doppler signatures. MDPose provides an effective solution to represent human activity by reconstructing a skeleton model with 17 key points, which can assist with the interpretation of conventional RF sensing outputs in a more understandable way. Specifically, MDPose is implemented over three sequential stage to address a series of challenges: First, a denoising algorithm is employed to remove any unwanted noise that may affect feature extraction and enhance weak Doppler measurements. Secondly, a convolutional neural network (CNN)-recurrent neural network (RNN) architecture is applied to learn temporal spatial dependenc from clean micro-Doppler signatures and restore velocity information to key points under the supervision of the motion capture (Mocap) system. Finally, a pose optimisation mechanism based on learning optimisation vectors is employed to estimate the initial state of the skeleton and to limit additional errors. We hav conducted a comprehensive set of tests in a variety of environments using numerous subjects with a single receiver radar system to demonstrate the performance of MDPose, and report 29.4mm mean absolute error over all key points positions on several common daily activities, which has performance comparable to that of state-ofthe- art RF-based pose estimation systems.

Towards Energy Efficient Wireless Sensing by Leveraging Ambient Wi-Fi Traffic

Wireless-based sensing of physical environments has garnered tremendous attention recently, and its applications range from intruder detection to environmental occupancy monitoring. Wi-Fi is positioned as a particularly advantageous sensing medium, due to the ubiquity of Wi-Fi-enabled devices in a more connected world. Although Wi-Fi-based sensing using Channel State Information (CSI) has shown promise, existing sensing systems commonly configure dedicated transmitters to generate packets for sensing. These dedicated transmitters substantially increase the energy requirements of Wi-Fi sensing systems, and hence there is a need for understanding how ambient transmissions from nearby Wi-Fi devices can be leveraged instead. This paper explores the potential of Wi-Fi-based sensing using CSI derived from ambient transmissions of Wi-Fi devices. We demonstrate that CSI sensing accuracy is dependent on the underlying traffic type and the Wi-Fi transceiver architecture, and that control packets yield more robust CSI than payload packets. We also show that traffic containing upload data is more suitable for human occupancy counting, using the Probability Mass Function (PMF) of CSI. We further demonstrate that multiple spatially diverse streams of Wi-Fi CSI can be combined for sensing to an accuracy of 99%. The experimental study highlights the importance of training Wi-Fi sensing systems for multiple transmission sources to improve accuracy. This research has significant implications for the development of energy-efficient Wi-Fi sensing solutions for a range of applications.

Online Learning-Based Adaptive Device-Free Localization in Time-Varying Indoor Environment

With the widespread use of WiFi devices and the availability of channel state information (CSI), CSI-based device-free localization (DFL) has attracted lots of attention. Fingerprint-based localization methods are the primary solutions for DFL, but they are faced with the fingerprint similarity problem due to the complex environment and low bandwidth of the commercial WiFi. Meanwhile, fingerprints may change unpredictably due to multipath WiFi signal propagation in time-varying environments. To tackle these problems, this paper proposes an adaptive online learning DFL method, which adaptively updates the localization model to ensure long-term accuracy and adaptability. Specifically, the CSI signals of the target located at different reference points are first collected and transformed to discriminable fingerprints using the weights of Multilayer Online Sequence Extreme Learning Machine (ML-OSELM). After that, an online learning DFL model is built to adapt to the changes of the environment. Experimental results in a time-varying indoor environment validate the adaptability of the proposed method against environmental changes and show that our method can achieve 10% improvement over other methods.

CSI-based WIFI Device Identification Solution

In recent years, wireless devices such as WIFI routers have become increasingly common in various fields. Rogue access point (RAP) is one of the threats that has persisted in wireless LAN (WLAN) for many years and can cause varying degrees of property damage and privacy leaks. In response to these threats, we propose a new security mechanism, called Rdi, which uses environment-independent features extracted from channel state information (CSI) as fingerprints for device identification. We found that the phase errors between multiple antennas on a single MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) transmitter are not the same. This phase offset is due to the I/Q imbalance and imperfect oscillator of each WiFi network card, and it will not change with factors such as environment and time. Therefore, we inferred and verified that there must be some relationship between the phase errors of multiple groups of antennas, that is, the relative phase error (RPE). In addition, RPE will also vary with different WiFi devices. Compared with some similar fingerprint detection methods in the past, the use of specific connections between group phase errors between antennas can better reveal the different attributes between devices, thereby enhancing the uniqueness of features. Therefore, we believe that RPE can be used as an effective fingerprint to detect RAP attacks. We conduct a large number of experimental demonstrations on this, and innovatively built a multi-modal convolutional neural network (CNN) model to perform efficient classification work for our solution. Experiments on 22 WiFi devices and various scenarios show that the detection rate of Rdi can reach more than 98% in both dynamic and static device states.

RFTrack: Stealthy Location Inference and Tracking Attack on Wi-Fi Devices

RFTrack: Stealthy Location Inference and Tracking Attack on Wi-Fi Devices

COMPACT INSET-FED RECTANGULAR PATCH ANTENNA FOR C-BAND APPLICATIONS

This study presents the theoretical design and analysis of a rectangular microstrip patch antenna with inset feeding, employing HFSS software. The antenna is fabricated using an FR-4 epoxy substrate, characterized by a dielectric constant of 4.4 and a thickness of 1.6 mm. The resonance frequency is targeted at 4 GHz. The influence of the inset feed on various antenna parameters, including return loss, bandwidth, radiation pattern, gain, and directivity, is investigated. Also, the impact of varying the inset feed length on the system is analyzed. The antenna exhibits a remarkable reflection coefficient of -31.8 dB at 4 GHz. The simple yet efficient design and low-profile nature of this microstrip patch antenna render it well-suited for a wide array of wireless applications, including satellite communication, optical transmissions, Wi-Fi devices, and more.

Adversarial AI applied to cross-user inter-domain and intra-domain adaptation in human activity recognition using wireless signals.

In recent years, researchers have successfully recognised human activities using commercially available WiFi (Wireless Fidelity) devices. The channel state information (CSI) can be gathered at the access point with the help of a network interface controller (NIC card). These CSI streams are sensitive to human body motions and produce abrupt changes (fluctuations) in their magnitude and phase values when a moving object interacts with a transmitter and receiver pair. This sensing methodology is gaining popularity compared to traditional approaches involving wearable technology, as it is a contactless sensing strategy with no cumbersome sensing equipments fitted on the target with preserved privacy since no personal information of the subject is collected. In previous investigations, internal validation statistics have been promising. However, external validation results have been poor, due to model application to varying subjects with remarkably different environments. To address this problem, we propose an adversarial Artificial Intelligence AI model that learns and utilises domain-invariant features. We analyse model results in terms of suitability for inter-domain and intra-domain alignment techniques, to identify which is better at robustly matching the source to target domain, and hence improve recognition accuracy in cross-user conditions for HAR using wireless signals. We evaluate our model performance on different target training data percentages to assess model reliability on data scarcity. After extensive evaluation, our architecture shows improved predictive performance across target training data proportions when compared to a non-adversarial model for nine cross-user conditions with comparatively less simulation time. We conclude that inter-domain alignment is preferable for HAR applications using wireless signals, and confirm that the dataset used is suitable for investigations of this type. Our architecture can form the basis of future studies using other datasets and/or investigating combined cross-environmental and cross-user features.

WiAi-ID: Wi-Fi-Based Domain Adaptation for Appearance-Independent Passive Person Identification

Wi-Fi signal-based person identification has become a hot research topic due to the widespread deployment of Wi-Fi devices and the fact that these approaches are non-contact, passive, and privacy-preserving. While the existing related methods and systems have achieved good performance for person identification, they also encounter many significant challenges in practical applications. Due to the propagation properties of Wi-Fi signals, the signal at the receiver will change significantly when the user’s appearance changes. This makes single-appearance trained models unusable for cross-appearance recognition tasks. To address this challenge, we propose a deep learning-based framework for appearance-independent identification using Wi-Fi signals (WiAi-ID), the core of which lies in the fact that the domain discriminator and feature extractor are trained together in an adversarial manner, thus forcing the model to extract identity-inherent features independent of human appearance, and introduces a multi-scale CNN adaptation module to capture time-span-based features. We collected Wi-Fi signal data of pedestrians with different appearances. The experimental results show that WiAi-ID can effectively eliminate the impact on identification due to pedestrian appearance variations and accordingly outperforms the current state-of-the-art video and wireless signal-based recognition methods.

Radio Frequency Fingerprinting for WiFi Devices Using Oscillator Drifts

Radio Frequency Fingerprinting for WiFi Devices Using Oscillator Drifts

Distinguishable IQ Feature Representation for Domain-Adaptation Learning of WiFi Device Fingerprints

Distinguishable IQ Feature Representation for Domain-Adaptation Learning of WiFi Device Fingerprints

Air Quality Monitoring System: A Comprehensive Overview of an advanced Air Quality monitoring system using Arduino

This study focuses on enhancing sensor technology and shrinking the size of sensors utilized in monitoring the quality of atmospheric air. With the escalating concerns over air pollution due to factors such as increased vehicular traffic, industrialization, and urbanization, there is a pressing need for effective monitoring solutions. The innovation at the core of this study involves implementing IoT technology via a Wi-Fi module interfaced with a gas sensor, facilitated by an Arduino UNO microcontroller. Specifically, the study proposes the utilization of the MQ 135 Air Quality Monitoring Gas Sensor for measuring air pollutant concentrations. The setup primarily comprises an Arduino UNO board integrated with an ESP8266 module for IoT connectivity. This IoT which depends on Air Quality Monitoring System is designed to monitor the air quality via a web server using the ESP8266 Wi-Fi device. Additionally, it is equipped to trigger an alarm when air quality deteriorates to a certain level, indicating the presence of harmful gases like CO2. The system displays air quality depends in Parts Per Million categorizing it into Fresh Air, Poor Air, or Danger Air, allowing users to monitor the air condition status effectively.

Direction-agnostic gesture recognition system using commercial WiFi devices

In recent years, channel state information (CSI) has been used to recognize hand gestures for contactless human–computer interaction. However, most existing solutions require precision hardware or prior learning at the same angle both during training and for inference/training in order to achieve high recognition accuracy. This requirement is unrealistic for practical instrumentation, where the orientation of a subject relative to the RF receiver may be arbitrary. We present direction-agnostic hand gesture recognition utilizing commercial WiFi devices to overcome low accuracy in non-trained observation angles. To achieve equal conditions in all recognition angles, first of all, through the circular antenna arrangement to mitigate the impact of user direction changes. Then, the orientation of users is estimated by the Fresnel zone model. Finally, the feature mapping model of users in different orientations is established, and the gesture features in the estimated direction are mapped to the benchmark direction to eliminate the influence caused by the change of user orientation. Experimental results in a typical indoor environment show that WiNDR has superior performance, and the average recognition accuracy of five common gestures is 92.38%.

RLoc

In recent years, decimeter-level accuracy in WiFi indoor localization has become attainable within controlled environments. However, existing methods encounter challenges in maintaining robustness in more complex indoor environments: angle-based methods are compromised by the significant localization errors due to unreliable Angle of Arrival (AoA) estimations, and fingerprint-based methods suffer from performance degradation due to environmental changes. In this paper, we propose RLoc, a learning-based system designed for reliable localization and tracking. The key design principle of RLoc lies in quantifying the uncertainty level arises in the AoA estimation task and then exploiting the uncertainty to enhance the reliability of localization and tracking. To this end, RLoc first manually extracts the underutilized beamwidth feature via signal processing techniques. Then, it integrates the uncertainty quantification into neural network design through Kullback-Leibler (KL) divergence loss and ensemble techniques. Finally, these quantified uncertainties guide RLoc to optimally leverage the diversity of Access Points (APs) and the temporal continuous information of AoAs. Our experiments, evaluating on two datasets gathered from commercial off-the-shelf WiFi devices, demonstrate that RLoc surpasses state-of-the-art approaches by an average of 36.27% in in-domain scenarios and 20.40% in cross-domain scenarios.

HyperTracking

Wireless sensing technology allows for non-intrusive sensing without the need for physical sensors worn by the target, enabling a wide range of applications, such as indoor tracking, and activity identification. To theoretically reveal the fundamental principles of wireless sensing, the Fresnel zone model has been introduced in the field of Wi-Fi sensing. While the Fresnel zone model is effective in explaining the sensing mechanism in line-of-sight (LoS) scenarios, achieving accurate sensing in non-line-of-sight (NLoS) situations continues to pose a significant challenge. In this paper, we propose a novel theoretical model called the Hyperbolic zone to reveal the fundamental sensing mechanism in NLoS scenarios. The main principle is to eliminate the complex NLoS path shared among different transmitter-receiver pairs, which allows us to obtain a series of simple "virtual" reflection paths among receivers. Since these "virtual" reflection paths satisfy the properties of the hyperbola, we propose the hyperbolic tracking model. Based on the proposed model, we implement the HyperTracking system using commercial Wi-Fi devices. The experimental results show that the proposed hyperbolic model is suitable for accurate tracking in both LoS and NLoS scenarios. We can reduce 0.36 m tracking error in contrast to the Fresnel zone model in NLoS scenarios. When we utilize the proposed hyperbolic model to train a typical LSTM neural network, we are able to further reduce the tracking error by 0.13 m and save the execution time by 281% with the same data. As a whole, our method can reduce the tracking error by 54% in NLoS scenarios compared with the Fresnel zone model.

Wi-Painter

WiFi has gradually developed into one of the main candidate technologies for indoor environment sensing. In this paper, we are interested in using COTS WiFi devices to identify material details, including location, material type, and shape, of stationary objects in the surrounding environment, which may open up new opportunities for many applications. Specifically, we present Wi-Painter, a model-driven system that can accurately detects smooth-surfaced material types and their edges using COTS WiFi devices without modification. Different from previous arts for material identification, Wi-Painter subdivides the target into individual 2D pixels, and simultaneously forms a 2D image based on identifying the material type of each pixel. The key idea of Wi-Painter is to exploit the complex permittivity of the object surface which can be estimated by the different reflectivity of signals with different polarization directions. In particular, we construct the multi-incident angle model to characterize the material, using only the power ratios of the vertically and horizontally polarized signals measured at several different incident angles, which avoids the use of inaccurate WiFi signal phases. We implement and evaluate Wi-Painter in the real world, showing an average classification accuracy of 93.4% for different material types including metal, wood, rubber and plastic of different sizes and thicknesses, and across different environments. In addition, Wi-Painter can accurately detect the material type and edge of the word "LOVE" spliced with different materials, with an average size of 60cm × 80cm, and material edges with different orientations.

Design and Simulation of Elliptical Shaped Microstrip Patch Antenna Using HFSS

Abstract: The elliptical patch antenna presents a comprehensive study on the design, fabrication, and performance analysis of an operating at 2.4 GHz, employing FR4 substrate material. The elliptical shape is chosen for its unique radiation characteristics and compact form factor, making it suitable for integration into modern wireless communication systems. The design process involves careful consideration of key parameters, including patch dimensions, feedline structure, and ground plane geometry. Advanced simulation tools, such as electromagnetic simulators, are employed to optimize the antenna's performance, achieving desirable characteristics such as low return loss and enhanced bandwidth. Utilizing FR4 as the substrate material offers several advantages, including costeffectiveness, ease of procurement, and compatibility with standard PCB manufacturing processes. The dielectric properties of FR4 are thoroughly analyzed and incorporated into the antenna design process to ensure accurate simulation results. The proposed elliptical patch antenna has promising applications in various wireless communication systems, including Wi-Fi, Bluetooth, and IoT devices, operating in the 2.4 GHz frequency band. Its compact size and robust performance make it a compelling choice for space-constrained and high-performance wireless communication applications. This contributes valuable insights into the design and optimization of patch antennas, particularly those using FR4 substrates, and serves as a foundation for the development of efficient and cost-effective wireless communication solutions in the 2.4 GHz spectrum.