In this study, we present ML-Track, an innovative uncooperative passive tracking system leveraging WiFi communication signals between multiple devices. Our approach is realized with three pivotal techniques. First, we introduce a novel protocol termed multi-link round-trip CSI, which enables multi-link bistatic Doppler detection within a WiFi network. Second, a phase error cancellation method is developed, and we demonstrate a 0.92 rad reduction in error (0.96 to 0.04 rad) experimentally. Lastly, we propose a particle-filter-based back-end to track a moving human in the room passively without the need for the participant to carry any type of cooperative or active device. A prototype system is constructed using four Raspberry Pi CM4 units and subjected to real-world evaluations. Experimental results indicate a median error of approximately 0.23 m for tracking, which corresponds to a relative error of 5.8% based on the 4 m side length of the experimental field. Compared to existing studies, a distinct advantage of our system is it can run with non-MIMO (single-antenna) WiFi devices, making it particularly suitable for budget or low-profile WiFi hardware. This compatibility makes it an ideal fit for real-world Internet-of-Things (IoT) devices. Moreover, in terms of computational demands, our solution excels, delivering real-time performance on the Raspberry Pi CM4 while utilizing just 20% of its CPU capability and drawing a modest 2.5 watts of power.

Reconfigurable intelligent surfaces (RISs), as ar-tificial electromagnetic structures with programmable electro-magnetic characteristics, have the potential to enhance the signals collected by service base stations in multi-cell Internet of things (IoT) networks. This capability improves the information extraction and utilization in communication systems, presenting numerous promising applications in the sixth-generation (6G) communication systems-IoT network environment. This paper proposes an optimization framework for the transmission sum-rate in a multicell, multi-user wireless communication system assisted by RIS. Firstly, establish transmission power allocation constraints for training and data symbols based on the distances of users from the transmitter, along with corresponding sum-rate objective functions. Utilize the multidimensional feature parameters of the RIS and long short-term memory (LSTM) to establish the relationship between user mobility distance and transmission power. Then, based on the obtained power prediction data, calculate the corresponding sum-rate using the previously defined objective functions. The obtained predictions contribute to more accurately assessing future communication environments, thereby aiding in optimizing the performance of communication systems. Simulation results validate the significant potential of LSTM in RIS-assisted multi-user wireless network systems.

Indoor localization using WiFi signals has been studied since the emergence of WiFi communication. This paper presents a novel training-free approach to indoor localization using a customized WiFi protocol for data collection and a factor graph-based back-end for localization. The protocol measures the round-trip phase, which is very sensitive to small changes in displacement. This is because the sub-wavelength displacements introduce significant phase changes in WiFi signal. However, the phase cannot provide absolute range information due to angle wrap. Consequently, it can only be used for relative distance (displacement) measurement. By tracking the round-trip phase over time and unwrapping it, a relative distance measurement can be realized and achieve a mean absolute error (MAE) of 0.06m. For 2-D localization, factor graph optimization is applied to the round-trip phase measurements between the STA (station) and four APs (access points). Experiments show the proposed concept can offer a decimeter-level (0.26m MAE and 0.24m 50%CDF) performance for real-world indoor localization.

Evaluation of crack severity in flexible pavements predominantly centers around the analysis of cracks’ surface characteristics. However, this study highlights the critical importance of 3D crack parameters, including volume and depth, for comprehensive assessment. The objective here is to develop an autonomous crack severity assessment, by predicting the vertical parameters of cracks exclusively from their surface properties. To achieve this, a dataset of 3D parameters comprising 200 cracks from eight flexible pavements was acquired, and both linear and nonlinear correlations were conducted among these 3D parameters. Subsequently, five single-output and one multi-output machine learning models were developed to explore the potential of utilizing surface parameters to predict the vertical parameters of cracks. The outcomes validated the effectiveness of two specific methods, namely, Artificial Neural Network and Extreme Gradient Boosting models, in predicting crack volume based on surface parameters, with R2 scores of 0.832 and 0.748, respectively. Additionally, the multi-output machine learning model we developed achieved classification prediction of the crack damage penetration depth using surface parameters, yielding optimal precision, recall, and F1 scores of 0.790, 0.779, and 0.761, respectively. This study has introduced a crack damage evaluation index, based on a 3D assessment, that relates crack depth classification to severity. We provide suggestions that could pave the way for informed decision-making on maintenance strategies that could be adopted to extend asset life cycle.

This paper presents a wireless Doppler sensing system using WiFi round-trip channel state information (RTCSI), which is implemented using the channel state information (CSI) from the Raspberry Pi CM4 onboard WiFi chip and a customized WiFi protocol. Utilizing the CSI phase in WiFi sensing is challenging as hardware asynchronization introduces significant phase errors. Similar to WiFi round-trip time (RTT) ranging, RTCSI was proposed to cancel the adverse effect of asynchronization through two-way communication. However, previous work mainly focuses on measuring RTCSI over frequency (different WiFi channels) to simulate a wide-band ranging. In this work, RTCSI is measured over time and a Doppler sensing prototype is built to detect a moving target in the wireless channel. Our findings show that this RTCSI-based Doppler sensing system is sensitive and effective in the real world. Moreover, it may be integrated with other techniques further to improve the performance in joint communications and sensing.

Reconfigurable intelligent surfaces (RISs)-assisted Internet of Things (IoT) networks have attracted a great deal of interest due to the potential contributions to the next generation wireless networks. In this article, an adaptive RIS clustering system with weak channels or obstacles is investigated. Ideally, there is a suitable RIS that adopts the self-organized RIS-assisted (SORA) scheme between a base station (BS) and a user equipment (UE) for reflection transmission. However, due to the nonideal channel scenarios, a single RIS may not be capable of connecting the BS with the UE. To tackle these nonideal cases, we propose a complementary RIS-cluster-assisted (C-RCA) strategy by reflecting signals among clusters to improve the performance of the overall system. The performances of the two strategies in terms of outage probability (OP) and ergodic capacity (EC), derived using approximate closed-form expressions, are analyzed. By increasing the numbers of complementary RISs and the reflecting elements on each RIS simultaneously, these performance metrics as well as the effective throughput and energy efficiency (EE) are evaluated under different signal-to-noise ratios (SNRs). The obtained results demonstrated that, in contrast to the ideal case, the network EE of the proposed C-RCA strategy is higher than the SORA scheme under the larger target spectral efficiency (SE).

Micro-Doppler signatures contain considerable information about target dynamics. However, the radar sensing systems are easily affected by noisy surroundings, resulting in uninterpretable motion patterns on the micro-Doppler spectrogram. Meanwhile, radar returns often suffer from multipath, clutter and interference. These issues lead to difficulty in, for example motion feature extraction, activity classification using micro Doppler signatures ($\mu$-DS), etc. In this paper, we propose a latent feature-wise mapping strategy, called Feature Mapping Network (FMNet), to transform measured spectrograms so that they more closely resemble the output from a simulation under the same conditions. Based on measured spectrogram and the matched simulated data, our framework contains three parts: an Encoder which is used to extract latent representations/features, a Decoder outputs reconstructed spectrogram according to the latent features, and a Discriminator minimizes the distance of latent features of measured and simulated data. We demonstrate the FMNet with six activities data and two experimental scenarios, and final results show strong enhanced patterns and can keep actual motion information to the greatest extent. On the other hand, we also propose a novel idea which trains a classifier with only simulated data and predicts new measured samples after cleaning them up with the FMNet. From final classification results, we can see significant improvements.

The use of Wi-Fi signals for human sensing has gained significant interest over the past decade. Such techniques provide affordable and reliable solutions for healthcare-focused events such as vital sign detection, prevention of falls and long-term monitoring of chronic diseases, among others. Currently, there are two major approaches for Wi-Fi sensing: (1) passive Wi-Fi radar (PWR) which uses well established techniques from bistatic radar, and channel state information (CSI) based wireless sensing (SENS) which exploits human-induced variations in the communication channel between a pair of transmitter and receiver. However, there has not been a comprehensive study to understand and compare the differences in terms of effectiveness and limitations in real-world deployment. In this paper, we present the fundamentals of the two systems with associated methodologies and signal processing. A thorough measurement campaign was carried out to evaluate the human activity detection performance of both systems. Experimental results show that SENS system provides better detection performance in a line-of-sight (LoS) condition, whereas PWR system performs better in a non-LoS (NLoS) setting. Furthermore, based on our findings, we recommend that future Wi-Fi sensing applications should leverage the advantages from both PWR and SENS systems.

Human sensing using WiFi signal transmissions is attracting significant attention for future applications in e-healthcare, security, and the Internet of Things (IoT). The majority of WiFi sensing systems are based around processing of channel state information (CSI) data which originates from commodity WiFi access points (APs) that have been primed to transmit high data-rate signals with high repetition frequencies. However, in reality, WiFi APs do not transmit in such a continuous uninterrupted fashion, especially when there are no users on the communication network. To this end, we have developed a passive WiFi radar system for human sensing which exploits WiFi signals irrespective of whether the WiFi AP is transmitting continuous high data-rate Orthogonal Frequency-Division Multiplexing (OFDM) signals, or periodic WiFi beacon signals while in an idle status (no users on the WiFi network). In a data transmission phase, we employ the standard cross ambiguity function (CAF) processing to extract Doppler information relating to the target, while a modified version is used for lower data-rate signals. In addition, we investigate the utility of an external device that has been developed to stimulate idle WiFi APs to transmit usable signals without requiring any type of user authentication on the WiFi network. In this article, we present experimental data which verifies our proposed methods for using any type of signal transmission from a standalone WiFi device, and demonstrate the capability for human activity sensing.

The rapidly increasing traffic volumes using local road networks allied to the implications of climate change drive the demand for cost-effective, reliable and accurate road condition assessment. A particular concern for local road asset managers is the loss of material from the road surface known as fretting which unchecked can lead to potholes. In order to assess the road condition quantitatively and affordably, a system should be designed with low complexity, be capable of operating in a variety of weather conditions and operate at normal traffic-speeds. Many different techniques have been developed for road condition assessment such as ground penetrating radar, visual sensors and mobile scanning lasers. In this work, the use of the point laser technique for scanning the road surface is investigated. It has the advantages of being sufficiently accurate, is relatively unaffected by levels of illumination and it produces relatively low volumes of data. In this work, road fretting/surface disintegration was determined using a novel signal processing approach which considers a number of features of reflected laser signals. The proposed methodology was demonstrated using data collected from the UK's local road network. The experimental results indicate that the proposed system can assess road fretting to an accuracy which is comparable to a visual inspection, and at Information Quality Level (IQL) 3 which is sufficient for tactical road asset management whereby road sections requiring treatment are selected and appropriate treatments identified.

Passive wireless sensing using WiFi signals has become a very active area of research over the past few years. Such techniques provide a cost-effective and non-intrusive solution for human activity sensing especially in healthcare applications. One of the main approaches used in wireless sensing is based on fine-grained WiFi Channel State Information (CSI) which can be extracted from commercial Network Interface Cards (NICs). In this paper, we present a new signal processing pipeline required for effective wireless sensing. An experiment involving five participants performing six different activities was carried out in an office space to evaluate the performance of activity recognition using WiFi CSI in different physical layouts. Experimental results show that the CSI system has the best detection performance when activities are performed half-way in between the transmitter and receiver in a line-of-sight (LoS) setting. In this case, an accuracy as high as 91% is achieved while the accuracy for the case where the transmitter and receiver are co-located is around 62%. As for the case when data from all layouts is combined, which better reflects the real-world scenario, the accuracy is around 67%. The results showed that the activity detection performance is dependent not only on the locations of the transmitter and receiver but also on the positioning of the person performing the activity.

WiFi signals for physical activity sensing show great practical potentials for pervasive healthcare applications due to the widespread WiFi deployments and high levels of public acceptance of such systems. Traditionally, WiFi-based sensing uses the Channel State Information (CSI) from an off-the-shelf WiFi Access Point (AP) which transmits signals that have high pulse repetition frequencies. However, when there are no users on the local network only beacon signals are transmitted from the WiFi AP which significantly deteriorates the sensitivity and specificity of such systems. Surprisingly, WiFi based sensing under these conditions have received little attention given that WiFi APs are frequently in idle state. This paper presents a practical system based on passive radar techniques which does not require any special setup or firmware changes to be able to work with any commercial WiFi device. To cope with the low duty cycles associated with beacon signal transmissions, a modified Cross Ambiguity Function (CAF) has been proposed to reduce redundant samples. In addition, an external device has been developed to send WiFi probe request signals which stimulates an idle AP to transmit WiFi probe responses, thus generate usable transmission signals for sensing applications without the need to authenticate and join the network. Detection performance shows that the proposed concept can significantly improve activity detection and is a viable candidate in future healthcare applications.

Physical activity classification is an important tool for various applications such as activity of daily living (ADL) recognition and fall detection. Additionally, the non-contact nature of radar systems provides minimally invasive sensing platform. Doppler-based radar has been used for activity classification in the past. However, most of these studies considered supervised classification which requires labeled training data sets. In this paper, we propose a novel procedure of using micro Doppler radar for unsupervised classification with Hidden Markov Models (HMM). A low-complexity time alignment method for capturing activity is developed and an Elbow test has been adopted for model selection. Test results confirm the efficacy of the selected feature set and the proposed methodology. The results prove the proposed system can deliver a very good performance in ADL recognition tasks.

Physical activity envelope provides invaluable information in numerous pervasive health applications. Physical activity is traditionally gleaned using a range of wearable inertial sensors and/or video technology. This paper introduces a novel opportunistic and non-intrusive monitoring system which can quantify activity levels based on analysis of ambient WiFi signal scatter. A real-time signal processing framework is developed, and the proposed system is implemented in software defined radio platform. Experimental results corroborate the efficacy of the proposed system in long term ADL monitoring in residential healthcare applications.

Non-contact breathing monitoring systems are very attractive for a range of e-Healthcare applications. This paper proposes a passive radar based system for measuring human breathing rate. A novel signal processing method is introduced to extract breathing rate based on micro Doppler derived from cross ambiguity function (CAF). The passive radar system is built within a software defined radio (SDR) platform. The proposed system uses opportunistically energy harvesting transmitter as an illumination signal. Passive detection is compared and verified using the ground truth from clinical chest belt respiration detector. Two experiments have been conducted to show the feasibility of passive detection system in the line of sight and also through-wall conditions. We conclude that a low frequency narrow band signal with non-contact passive detection can offer a realistic alternative to UWB based radars for future e-Healthcare passive sensing applications.