Wi-Fi Transmission Research Articles

A Wireless Bi-Directional Brain–Computer Interface Supporting Both Bluetooth and Wi-Fi Transmission

Wireless neural signal transmission is essential for both neuroscience research and neural disorder therapies. However, conventional wireless systems are often constrained by low sampling rates, limited channel counts, and their support of only a single transmission mode. Here, we developed a wireless bi-directional brain–computer interface system featuring dual transmission modes. This system supports both low-power Bluetooth transmission and high-sampling-rate Wi-Fi transmission, providing flexibility for various application scenarios. The Bluetooth mode, with a maximum sampling rate of 14.4 kS/s, is well suited for detecting low-frequency signals, as demonstrated by both in vitro recordings of signals from 10 to 50 Hz and in vivo recordings of 16-channel local field potentials in mice. More importantly, the Wi-Fi mode, offering a maximum sampling rate of 56.8 kS/s, is optimized for recording high-frequency signals. This capability was validated through in vitro recordings of signals from 500 to 2000 Hz and in vivo recordings of single-neuron spike firings with amplitudes reaching hundreds of microvolts and high signal-to-noise ratios. Additionally, the system incorporates a wireless stimulation function capable of delivering current pulses up to 2.55 mA, with adjustable pulse width and polarity. Overall, this dual-mode system provides an efficient and flexible solution for both neural recording and stimulation applications.

Hardware-software implementation of a local Wi-Fi network for the transmission of biomedical signals

The object of this study is a wireless local Wi-Fi network for broadcasting biomedical signals, its structure, and principles of construction. The task of minimizing the power consumption of a Wi-Fi transmitter has been addressed, which provides the possibility of building a wireless system for long-term monitoring of biomedical signals. As a result, a functional diagram of a wireless Holter monitoring system based on an ESP32 microcontroller was constructed, which includes a subsystem for setting up and diagnosing system units using MATLAB software packages, an ECG signal generator, and a multifunctional PCIe board from National Instruments. Evaluation criteria and methods for minimizing power consumption by an autonomous Wi-Fi transmitter have been proposed. Methods for synchronizing the working cycles of the transmitter and receiver of the Holter monitoring system were determined. A procedure for determining the optimal biosignal measurement frequency is presented, at which the distortion of ECG signals would be minimal, which means that the signal could be transmitted without losses. The concept of constructing an algorithm for implementing a program for a Wi-Fi transmitter has been developed, ensuring parallel execution of ECG signal measurement operations and their transmission over a local network. The data from semi-naturalistic tests with an experimental Holter monitoring system with a pre-setup subsystem and using external measuring devices, a computer, and the MATLAB software environment are presented. A comparative analysis of the experimental data with primary ECG signals and ECG signals at the receiver output showed a fairly stable correspondence between the input and output ECG signals. The proposed algorithms make it possible to reduce the average current consumption of the ESP32 microcontroller to 50.5 mA. The results of the study demonstrate the possibility of constructing an energy-efficient wireless system for long-term monitoring of biomedical signals based on the Wi-Fi interface

A wall climbing robot based on machine vision for automatic welding seam inspection

With the ongoing progress of industrial technology such as shipbuilding, the importance of weld quality in industrial production is becoming increasingly prominent. Intelligent and automated welding seam inspection robots are more efficient than traditional manual inspection and can avoid dangerous accidents. This article describes the design of a welding seam inspection robot suitable for high-altitude ship operation. The robot uses machine vision and object segmentation models to automatically detect the position of welding seams, and uses a cubic polynomial to fit the welding seam path. The upper and lower computers of the robot communicate through WIFI transmission and TCP protocol, which can realize remote real-time detection of weld surface defects. In addition, this article designs a permanent magnet adsorption structure for robot high-altitude wall climbing, which has been verified through simulation and experimental verification. To verify the intelligence of the robot, this paper conducted performance analysis experiments on weld line recognition and tracking models and surface defect models. The experimental results showed that the average detection accuracy of the weld line recognition and tracking algorithm was 96.8%, and the average detection accuracy of surface defects in the three types of welds was 94.2%. The method proposed in this article combines two algorithms and a special robot structure, providing a new approach for the automated inspection of welds in large industrial products such as ships.

Human movement detection and classification capabilities using passive Wi-Fi based radar

Human detection and classification via Wi-Fi transmission have received a lot of attention in recent years as crucial facilitators in security and human-computer interaction (HCI). The passive Wi-Fi radar (PWR) system used by previous researchers applied cross-ambiguity function (CAF) and CLEAN algorithms to process the detected signals. This paper explores the feasibility and viability of a PWR system in detecting and classifying human movements without utilizing CAF and CLEAN algorithms. The movements are performed by four participants but with comparable body sizes and heights. Three daily human movements are investigated namely walking, bending, and sitting, with each participant performing each movement 24 times, providing a total of 96 samples per activity. The system is evaluated based on the consistency of the signal pattern in a frequency domain and the percentage accuracy is assessed using an artificial neural network (ANN) classifier and trained using a leave-one-out cross-validation (LOOCV) method. The frequency domain results reveal that the signals are consistent, with no noticeable variations or changes in the voltage intensity or shape of the main lobe. The classification of the movements shows that the classifier has an overall accuracy of 97.6%.

SmartWood: field-based analysis of large wood movement dynamics using inertial measurement units (IMUs)

Wood plays an important ecological role in rivers. Yet challenges arise when large wood (LW) is mobilised and transported during floods. Due to a lack of quantitative data, movement behaviour of LW during floods is still not well understood to date. A proof-of-concept study was conducted at three Swiss rivers to test state-of-the-art sensor-tagged logs, so-called “SmartWood” and collect quantitative field-scale data about LW movement behaviour. The experiments utilised innovative inertial measurement units (IMUs), which have been developed at the Laboratory of Hydraulics, Hydrology and Glaciology (VAW) at ETH Zurich and implanted into wood logs (SmartWood) at prototype scale. Each IMU comprised three individual sensors (gyroscope, accelerometer, and magnetometer) and was equipped with an on-board processor, an AA battery (4.35 V), a memory (8 MB), and a Wi-Fi transmitter (100 m) for data transfer. After successful initial verification tests of the sensors, the IMUs were installed into debranched wood logs, measuring 4.35 m in length and 0.33 m in diameter. At the time of the field experiments, each SmartWood-log weighted between 170 and 220 kg, yielding a density of roughly 500 kg∙m−3. At the Limmat, Thur, and Grosse Melchaa Rivers in Switzerland, innovative yet discontinuous data were obtained. Results revealed consistent movement dynamics across all field sites. Specifically, we observed positive yaw movement during transport of SmartWood along the left river bank and negative yaw movement along the right river bank. Furthermore, interactions of SmartWood with channel boundaries, riparian vegetation, and objects (e.g., ferry dock) were registered and quantified, even when the SmartWood-log was transported out of sight of traditional sensing methods. The conducted field experiments enabled the initial testing of SmartWood in the field and exposed critical limitations of the IMUs and software algorithms for the reconstruction and analysis of floating LW dynamics. The gained knowledge and introduced sensing method will benefit the quantitative assessment of LW dynamics in rivers to maintain safety and functionality for instream structures (e.g., considering LW movement dynamics for the robust design of LW retention and guiding structures), but also river restoration projects and numerical models that rely on quantitative field-scale data.

Algorithm-Driven Tele-otoscope for Remote Care for Patients With Otitis Media.

The COVID-19 pandemic has spurred a growing demand for telemedicine. Artificial intelligence and image processing systems with wireless transmission functionalities can facilitate remote care for otitis media (OM). Accordingly, this study developed and validated an algorithm-driven tele-otoscope system equipped with Wi-Fi transmission and a cloud-based automatic OM diagnostic algorithm. Prospective, cross-sectional, diagnostic study. Tertiary Academic Medical Center. We designed a tele-otoscope (Otiscan, SyncVision Technology Corp) equipped with digital imaging and processing modules, Wi-Fi transmission capabilities, and an automatic OM diagnostic algorithm. A total of 1137 otoscopic images, comprising 987 images of normal cases and 150 images of cases of acute OM and OM with effusion, were used as the dataset for image classification. Two convolutional neural network models, trained using our dataset, were used for raw image segmentation and OM classification. The tele-otoscope delivered images with a resolution of 1280 × 720 pixels. Our tele-otoscope effectively differentiated OM from normal images, achieving a classification accuracy rate of up to 94% (sensitivity, 80%; specificity, 96%). Our study demonstrated that the developed tele-otoscope has acceptable accuracy in diagnosing OM. This system can assist health care professionals in early detection and continuous remote monitoring, thus mitigating the consequences of OM.

A low-cost Wi-Fi Enabled Vehicle Speed Management System

This proposed work presents the WiFi technology-based approach for dual WiFi module-based vehicle speed governance in the fledgling zones. The second WiFi module should be inside the car and the centralized transmitter in the middle of the limited area. The transmitter device sends speed limits and other information to the receiver unit, which dynamically controls speed within the zone. The WiFi transmitter acts as a command and control station and continuously reports zone-based enforcement and maximum value criteria. It meets the same standards with innovative algorithms that adapt to road conditions. The receiver module immobilizes the vehicle, so take action and spend to maintain the signaled speed. The scheme includes real-time data transmission and road condition adaptation. The WiFi-based system allows for scalable and low-cost speed restrictions in limited zones. Simulations and field tests show the system can reduce traffic, improve road safety, and boost transportation efficiency. Our road map may include adding sensors and communication protocols to position our system for any use case and increase its capability. Finally, using dual WiFi modules as vehicle speed control systems in restricted areas could solve traffic issues and defragment the transportation network. This paper is a positive step toward using technology to improve road safety and urban mobility.

Using the wi-fi capabilities of routers for determining a mobile terminal and its network activity during сyber crimes investigation

The possibilities of Wi-Fi routers for establishing a mobile terminal and its network activity during the investigation of cybercrimes were investigated. In particular, the following problematic issues are revealed: what evidentiary information can be found in a Wi-Fi router, how to record and remove it, as well as circumstances that can hinder the search for an intruder and proving his guilt. The role and importance of electronic communications expertise or forensic comprehensive electronic communications expertise and computer-technical expertise to analyze log files for signs of a cyber attack are highlighted. The peculiarities of searching, fixing and extracting information about the MAC address of the network interface of the Wi-Fi transmitter are detailed.

Energy efficient resource allocation algorithms combining PSO with FLC and Taguchi method in hybrid opportunistic networks

In order to reduce power consumption and delay time when mobile devices use wireless networks, this paper proposes dividing groups of mobile devices into clusters, and proposes two cluster architectures, which are dual-radio opportunistic networking for energy efficiency combining particle swarm optimization with fuzzy logic control and Taguchi by 2-hop of priority weighted round robin (DRONEE-PFT2-PWRR) and dual-radio opportunistic networking for energy efficiency combining particle swarm optimization with fuzzy logic control and Taguchi by multi-hop of priority weighted rate control (DRONEE-PFTM-PWRC). Furthermore, our proposed DRONEE-PFT2-PWRR algorithm increases cluster coverage using 2-hop and our proposed DRONEE-PFTM-PWRC algorithm improves power consumption of the system by multi-hop cluster architecture. Internal cluster communication uses Wi-Fi transmission packets, which can reduce energy consumption when a mobile device communicates with a long term evolution advanced (LTE-A) base station, and reduce interference on signals between mobile devices. In the selection of cluster heads, this paper uses particle swarm optimization (PSO) to find the best cluster head position, and the PSO fitness parameter value is adjusted using fuzzy logic control (FLC) combined with the Taguchi method. Priority weighted round robin (PWRR) architecture is proposed for LTE-A base station and cluster network resource allocation, and quality of service is introduced to give priority weight to packets according to data type. Furthermore, priority weighted rate control (PWRC) is proposed. This method determines whether the network resources of the base station meet the demands of the cluster, and then assigns weights according to the requirements of the cluster and its priorities, and finally allocates network resources according to those weights. This increases the uplink throughput of network resource allocation. Simulation results show that our proposed methods significantly outperform the DRONEE-weighted (DRONEE-W) algorithm method in terms of power consumption, network throughput, and transmission delay time.

Estimation of Electromagnetic Radiation Distribution in the Human Heart Rate at Different Frequencies‏‏

The health implications of electromagnetic radiation have been extensively debated due to the sharp increase in the usage of cell phones, Wi-Fi transmitters and other microwave equipment. Electromagnetic radiation affects the autonomic nervous system, heart rate, blood pressure and other cardiovascular functions. This study aims to present a Numerical Simulation of Electromagnetic Radiation (EMR) on the human heart tissue and to explore the effect of different frequencies in the spectral range (900, 1,800 and 2,400 MHz) on Specific Absorption Rate (SAR), power density, the Distribution of Electromagnetic Fields by Matlab program, and Finite-Difference Time-Domain (FDTD) method in One Dimension (1D). A 1-dimensional finite difference method was used to solve Maxwell’s equations in heart tissue. The heart model was subjected to electromagnetic radiation that has dielectric properties according to frequency. Frequency was chosen and the operation of the software program as Matlab and the dielectric attribute has been calculated. Concurring with the Frequency in a private program, are the conductivity, relative permittivity, wavelength and infiltration profundity. The amplitudes of the reflected and transmitted sinusoid waves, relative to the occurrence wave, were portrayed by the reflection coefficient and the transmission coefficient, which relate to the amplitudes of the electric field wave. The electric field, magnetic field and power density were simulated along the line X = Y = 0. The study showed that the impact of electromagnetic radiation depends on the frequency of the wave which hit the heart. It was found that the heart tissue reacts more at 900 MHz compared to 1,800 and 2,400 MHz, and the tissue absorption is higher at the lower frequency. There was a relation between the magnetic field in the y-dimension and the time step the in x-dimension. HIGHLIGHTS The effect of electromagnetic has been theoretically studied where the electromagnetic fields produced from electromagnetic radiation at 900 MHz,1800 MHz, and 2400 MHz on layered biological tissues (heart model) by the finite difference time domain (FDTD) method A one-dimensional finite difference method was used to solve Maxwell's equations in heart tissue The amplitudes of the reflected and transmitted sinusoid waves, relative to the occurrence wave, were portrayed by the reflection coefficient and the transmission coefficient, which relate to the amplitudes of the electric field wave The impact of electromagnetic radiation depends on the frequency of the wave which hit the heart. There was a relation between the magnetic field in the y-dimension and the time step the in x-dimension GRAPHICAL ABSTRACT

Defining The Pattern of the Thai Osteoarthritis Movement Data Model

This study proposed an algorithm to identify the patterns of Thai osteoarthritis, a condition that has seen an increase in prevalence in recent years, significantly affecting patients' quality of life and mobility. We aimed to define the data patterns for analysis. For data collection, we employed a Razor-IMU sensor and a WIFI transmitter, allowing testers to independently perform full-range knee motions. Data collection was categorized into two groups: healthy and unhealthy. Our goal was to categorize the data and establish criteria for pattern classification. Once the patterns and criteria were established, our objective was to validate movement models for both normal individuals and osteoarthritis patients. To achieve this, we conducted statistical hypothesis testing to verify the accuracy of the data. This testing comprised three main steps: first, evaluating the accuracy of data collection and data cleaning. Second, assessing the precision of converting data from linear to angular format, including degree coordinates selection. The last, Evaluating the accuracy of data sorting and grouping using Louvain clustering. The researcher thoroughly scrutinized each step to confirm the results. Each step demonstrated an accuracy test As Thailand transitions into an aging society, the prevalence of osteoarthritis is increasing due to the natural deterioration of the body. This deterioration can be decelerated by avoiding risky behaviors. Osteoarthritis significantly impacts patients' physical and mental well-being, making it a critical health concern. The objective of this research is to develop movement prototypes for both normal individuals and osteoarthritis patients by leveraging computer knowledge. This includes data collection with motion sensor devices, enhancing data quality using data mining techniques such as data cleaning and data transformation into suitable formats, and grouping of data. Additionally, the study seeks to validate the accuracy of the algorithm using statistical hypothesis testing methods and graph pattern detection. Based on the experimental results, an accuracy rate of 97% was achieved, demonstrating a high level of reliability. This prototype can be applied in treatment analysis, monitoring treatment outcomes, and even injury prevention. Furthermore, the dataset can serve as a model for the Thai population and can be expanded to accommodate larger datasets.

Design and verification of a wearable wireless 64-channel high-resolution EEG acquisition system with wi-fi transmission.

Brain-computer interfaces (BCIs) allow communication between the brain and the external world. This type of technology has been extensively studied. However, BCI instruments with high signal quality are typically heavy and large. Thus, recording electroencephalography (EEG) signals is an inconvenient task. In recent years, system-on-chip (SoC) approaches have been integrated into BCI research, and sensors for wireless portable devices have been developed; however, there is still considerable work to be done. As neuroscience research has advanced, EEG signal analyses have come to require more accurate data. Due to the limited bandwidth of Bluetooth wireless transmission technology, EEG measurement systems with more than 16 channels must be used to reduce the sampling rate and prevent data loss. Therefore, the goal of this study was to develop a multichannel, high-resolution (24-bit), high-sampling-rate EEG BCI device that transmits signals via Wi-Fi. We believe that this system can be used in neuroscience research. The EEG acquisition system proposed in this work is based on a Cortex-M4 microcontroller with a Wi-Fi subsystem, providing a multichannel design and improved signal quality. This system is compatible with wet sensors, Ag/AgCl electrodes, and dry sensors. A LabVIEW-based user interface receives EEG data via Wi-Fi transmission and saves the raw data for offline analysis. In previous cognitive experiments, event tags have been communicated using Recommended Standard 232 (RS-232). The developed system was validated through event-related potential (ERP) and steady-state visually evoked potential (SSVEP) experiments. Our experimental results demonstrate that this system is suitable for recording EEG measurements and has potential in practical applications. The advantages of the developed system include its high sampling rate and high amplification. Additionally, in the future, Internet of Things (IoT) technology can be integrated into the system for remote real-time analysis or edge computing.

Deep reinforcement learning based spectrum prediction for bursty bands

Spectrum prediction plays an important role for the secondary user (SU) to utilize the shared spectrum resources. However, currently utilized prediction methods are not well applied to spectrum with high burstiness, as parameters of prediction models cannot be adjusted properly. This paper studies the prediction problem of bursty bands. Specifically, we first collect real WiFi transmission data in 2.4GHz Industrial, Scientific, Medical (ISM) band which is considered to have bursty characteristics. Feature analysis of the data indicates that the spectrum occupancy law of the data is time-variant, which suggests that the performance of commonly used single prediction model could be restricted. Considering that the match between diverse spectrum states and multiple prediction models may essentially improve the prediction performance, we then propose a deep-reinforcement learning based multilayer perceptron (DRL-MLP) method to address this matching problem. The state space of the method is composed of feature vectors, and each of the vectors contains multi-dimensional feature values. Meanwhile, the action space consists of several multilayer perceptrons (MLPs) that are trained on the basis of multiple classified data sets. We finally conduct experiments with the collected real data and simulations with generated data to verify the performance of the proposed method. The results demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of the prediction accuracy.

Over-the-Air Adversarial Attacks on Deep Learning Wi-Fi Fingerprinting

Empowered by deep neural networks (DNNs), Wi-Fi fingerprinting has recently achieved astonishing localization performance to facilitate many security-critical applications in wireless networks, but it is inevitably exposed to adversarial attacks, where subtle perturbations can mislead DNNs to wrong predictions. Such vulnerability provides new security breaches to malicious devices for hampering wireless network security, such as malfunctioning geofencing or asset management. The prior adversarial attack on localization DNNs uses additive perturbations on channel state information (CSI) measurements, which is impractical in Wi-Fi transmissions. To transcend this limitation, this paper presents FooLoc, which fools Wi-Fi CSI fingerprinting DNNs over the realistic wireless channel between the attacker and the victim access point (AP). We observe that though uplink CSIs are unknown to the attacker, the accessible downlink CSIs could be their reasonable substitutes at the same spot. We thoroughly investigate the multiplicative and repetitive properties of over-the-air perturbations and devise an efficient optimization problem to generate imperceptible yet robust adversarial perturbations. We implement FooLoc using commercial Wi-Fi APs and Wireless Open-Access Research Platform (WARP) v3 boards in offline and online experiments, respectively. The experimental results show that FooLoc achieves overall attack success rates of about 70% in targeted attacks and of above 90% in untargeted attacks with small perturbation-to-signal ratios of about -18 dB.

Intrusion Detection Framework for Invasive FPV Drones Using Video Streaming Characteristics

Cheap commercial off-the-shelf (COTS) First-Person View (FPV) drones have become widely available for consumers in recent years. Unfortunately, they also provide low-cost attack opportunities to malicious users. Thus, effective methods to detect the presence of unknown and non-cooperating drones within a restricted area are highly demanded. Approaches based on detection of drones based on emitted video stream have been proposed, but were not yet shown to work against other similar benign traffic, such as that generated by wireless security cameras. Most importantly, these approaches were not studied in the context of detecting new unprofiled drone types. In this work, we propose a novel drone detection framework, which leverages specific patterns in video traffic transmitted by drones. The patterns consist of repetitive synchronization packets (we call pivots), which we use as features for a machine learning classifier. We show that our framework can achieve up to 99% in detection accuracy over an encrypted WiFi channel using only 170 packets originated from the drone within 820ms time period. Our framework is able to identify drone transmissions even among very similar WiFi transmissions (such as video streams originated from security cameras) as well as in noisy scenarios with background traffic. Furthermore, the design of our pivot features enables the classifier to detect unprofiled drones in which the classifier has never trained on and is refined using a novel feature selection strategy that selects the features that have the discriminative power of detecting new unprofiled drones.

Head-Mounted Miniature Motorized Camera and Laser Pointer Driven by Eye Movements

Recording a video scene as seen by an observer, materializing where is focused his visual attention and allowing an external person to point at a given object in this scene, could be beneficial for various applications such as medical education or remote training. Such a versatile device, although tested at the experimental laboratory demonstrator stage, has never been integrated in a compact and portable way in a real environment. In this context, we built a low-cost, light-weight, head-mounted device integrating a miniature camera and a laser pointer that can be remotely controlled or servo-controlled by an eye tracker. Two motorizations were implemented and tested (pan/tilt and Rilsey-prisms-based). The video was both recorded locally and transmitted wirelessly. Risley prisms allowed finer remote control of camera or laser pointer orientation (0.1° vs. 0.35°), but data processing and Wi-Fi transmission incur significant latency (~0.5 s) limiting the servo-controlling by eye movements. The laser beam was spatially shaped by a Diffractive Optical Element to facilitate object illumination or recognition. With this first proof-of-concept prototype, the data stream needs to be optimized to make full use of the eye tracker, but this versatile device can find various applications in education, healthcare or research.

Design of fretting signal acquisition system based on AD7177

In Fretting is an important research direction in the detection of urban underground space structure. Traditional geophones have three issues with achieving fretting detection in the field, high cost, large size and low accuracy. A fretting signal acquisition system based on AD7177 is proposed to solve the above problems, which takes STM32 as the design core. A decision-making scheme that combines the signal acquisition system with the vibration sensor is introduced, which is used to settle the large size and high cost of the instrument. To improve the system accuracy, the AD7177 high-precision A/D conversion module and GPS are used for signal processing and positioning timing respectively. Finally, tests and field experiments were conducted in a state equipped with large storage space and WIFI transmission, which shows the system is convenient and successful. The experimental results show that the scheme is effective, the AD7177-based fretting signal acquisition system has high accuracy and small volume, which has a good ability to complete fretting detection.

Cross-Technology Communication for Heterogeneous Wireless Devices Through Symbol-Level Energy Modulation

The coexistence of heterogeneous devices in wireless networks brings a new topic on cross-technology communication (CTC) to improve the coexistence efficiency and boost collaboration among these devices. Current advances on CTC mainly fall into two categories, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">physical-layer CTC</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">packet-level energy modulation</i> (PLEM). The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">physical-layer CTC</i> achieves a high CTC data rate, but with channel incompatible to commercial devices, making it hard to be deployed in current wireless networks. PLEM is channel and physical layer compatible, but with two main drawbacks of the low CTC data rate and MAC incompatibility, which will induce severe interference to the other devices’ normal data transmissions. In this paper, we propose symbol-level energy modulation (SLEM), the first CTC method that is fully compatible with current devices in both channel and the physical/MAC layer processes, having the ability to be deployed in commercial wireless networks smoothly. SLEM inserts extra bits to WiFi data bits to generate the transmitting bits, so as to adjust the energy levels of WiFi symbols to deliver CTC information. We make theoretical analysis to figure out the performance of both CTC and WiFi transmissions. We also conduct experiments to demonstrate the feasibility of SLEM and its performance under different network situations.

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.

Sequential Transient Detection for RF Fingerprinting

In this paper, a sequential transient detection method for radio frequency (RF) fingerprinting used in the identification of wireless devices is proposed. To the best knowledge of the authors, sequential detection of transient signals for RF fingerprinting has not been considered in the literature. The proposed method is based on an approximate implementation of the generalized likelihood ratio algorithm. The method can be implemented online in a recursive manner with low computational and memory requirements. The transients of wireless transmitters are detected by using the likelihood ratio of the observations without the requirement of any a priori knowledge about the transmitted signals. The performance of the method was evaluated using experimental data collected from 16 Wi-Fi transmitters and compared to those of two existing methods. The experimental test results showed that the proposed method can be used to detect the transient signals with a low detection delay. Our proposed method estimates transient starting points 20-times faster compared to an existing robust method, as well as providing a classification performance of a mean accuracy close to 95%.