WiFi-Based Human Identification with Machine Learning: A Comprehensive Survey.

In the modern world of human-computer interaction, notable advancements in human identification have been achieved across fields like healthcare, academia, security, etc. Despite these advancements, challenges remain, particularly in scenarios with poor lighting, occlusion, or non-line-of-sight. To overcome these limitations, the utilization of radio frequency (RF) wireless signals, particularly wireless fidelity (WiFi), has been considered an innovative solution in recent research studies. By analyzing WiFi signal fluctuations caused by human presence, researchers have developed machine learning (ML) models that significantly improve identification accuracy. This paper conducts a comprehensive survey of recent advances and practical implementations of WiFi-based human identification. Furthermore, it covers the ML models used for human identification, system overviews, and detailed WiFi-based human identification methods. It also includes system evaluation, discussion, and future trends related to human identification. Finally, we conclude by examining the limitations of the research and discussing how researchers can shift their attention toward shaping the future trajectory of human identification through wireless signals.

Improving Indoor WiFi Localization by Using Machine Learning Techniques.

Accurate and robust positioning has become increasingly essential for emerging applications and services. While GPS (global positioning system) is widely used for outdoor environments, indoor positioning remains a challenging task. This paper presents a novel architecture for indoor positioning, leveraging machine learning techniques and a divide-and-conquer strategy to achieve low error estimates. The proposed method achieves an MAE (mean absolute error) of approximately 1 m for latitude and longitude. Our approach provides a precise and practical solution for indoor positioning. Additionally, some insights on the best machine learning techniques for these tasks are also envisaged.

SWiLoc: Fusing Smartphone Sensors and WiFi CSI for Accurate Indoor Localization.

Dead reckoning is a promising yet often overlooked smartphone-based indoor localization technology that relies on phone-mounted sensors for counting steps and estimating walking directions, without the need for extensive sensor or landmark deployment. However, misalignment between the phone's direction and the user's actual movement direction can lead to unreliable direction estimates and inaccurate location tracking. To address this issue, this paper introduces SWiLoc (Smartphone and WiFi-based Localization), an enhanced direction correction system that integrates passive WiFi sensing with smartphone-based sensing to form Correction Zones. Our two-phase approach accurately measures the user's walking directions when passing through a Correction Zone and further refines successive direction estimates outside the zones, enabling continuous and reliable tracking. In addition to direction correction, SWiLoc extends its capabilities by incorporating a localization technique that leverages corrected directions to achieve precise user localization. This extension significantly enhances the system's applicability for high-accuracy localization tasks. Additionally, our innovative Fresnel zone-based approach, which utilizes unique hardware configurations and a fundamental geometric model, ensures accurate and robust direction estimation, even in scenarios with unreliable walking directions. We evaluate SWiLoc across two real-world environments, assessing its performance under varying conditions such as environmental changes, phone orientations, walking directions, and distances. Our comprehensive experiments demonstrate that SWiLoc achieves an average 75th percentile error of 8.89 degrees in walking direction estimation and an 80th percentile error of 1.12 m in location estimation. These figures represent reductions of 64% and 49%, respectively for direction and location estimation error, over existing state-of-the-art approaches.

Time synchronisation for millisecond-precision on bio-loggers.

Time-synchronised data streams from bio-loggers are becoming increasingly important for analysing and interpreting intricate animal behaviour including split-second decision making, group dynamics, and collective responses to environmental conditions. With the increased use of AI-based approaches for behaviour classification, time synchronisation between recording systems is becoming an essential challenge. Current solutions in bio-logging rely on manually removing time errors during post processing, which is complex and typically does not achieve sub-second timing accuracies.We first introduce an error model to quantify time errors, then optimise three wireless methods for automated onboard time (re)synchronisation on bio-loggers (GPS, WiFi, proximity messages). The methods can be combined as required and, when coupled with a state-of-the-art real time clock, facilitate accurate time annotations for all types of bio-logging data without need for post processing. We analyse time accuracy of our optimised methods in stationary tests and in a case study on 99 Egyptian fruit bats (Rousettus aegyptiacus). Based on the results, we offer recommendations for projects that require high time synchrony.During stationary tests, our low power synchronisation methods achieved median time accuracies of 2.72 / 0.43 ms (GPS / WiFi), compared to UTC time, and relative median time accuracies of 5 ms between tags (wireless proximity messages). In our case study with bats, we achieved a median relative time accuracy of 40 ms between tags throughout the entire 10-day duration of tag deployment. Using only one automated resynchronisation per day, permanent UTC time accuracies of ≤ 185 ms can be guaranteed in 95% of cases over a wide temperature range between 0 and 50 °C. Accurate timekeeping required a minimal battery capacity, operating in the nano- to microwatt range.Time measurements on bio-loggers, similar to other forms of sensor-derived data, are prone to errors and so far received little scientific attention. Our combinable methods offer a means to quantify time errors and autonomously correct them at the source (i.e., on bio-loggers). This approach facilitates sub-second comparisons of simultaneously recorded time series data across multiple individuals and off-animal devices such as cameras or weather stations. Through automated resynchronisations on bio-loggers, long-term sub-second accurate timestamps become feasible, even for life-time studies on animals. We contend that our methods have potential to greatly enhance the quality of ecological data, thereby improving scientific conclusions.

Multi-Fingerprints Indoor Localization for Variable Spatial Environments: A Naive Bayesian Approach.

Fingerprint-based indoor localization has been a hot research topic. However, the current fingerprint-based indoor localization approaches still rely on a single fingerprint database, where the average level of data at reference points is used as the fingerprint representation. In variable environmental conditions, the variations in signals caused by changes in the environmental states introduce significant deviations between the average level and the actual fingerprint characteristics. This deviation leads to a mismatch between the constructed fingerprint database and the real-world conditions, thereby affecting the effectiveness of fingerprint matching. Meanwhile, the sharp noise interference caused by uncertainties such as personnel movement has a significant interference on the creation of the fingerprint database and fingerprint matching in online stage. Examination of the sampling data after denoising with Robust Principal Component Analysis (RPCA) revealed distinct multi-fingerprint characteristics with clear boundaries at certain access points. Based on these observations, the concept of constructing a fingerprint database using multiple fingerprints is introduced and its feasibility is explored. Additionally, a multi-fingerprint solution based on naive Bayes classification is proposed to accurately represent fingerprint characteristics under different environmental conditions. This method is based on the online stage fingerprints. The corresponding state space is selected using the naive Bayes classifier, enabling the selection of an appropriate fingerprint database for matching. Through simulations and empirical evaluations, the proposed multi-fingerprints construction scheme consistently outperforms the traditional single-fingerprint database in terms of positioning accuracy across all tested localization algorithms.

Combining 5G New Radio, Wi-Fi, and LiFi for Industry 4.0: Performance Evaluation.

Fifth-generation mobile networks (5G) are designed to support enhanced Mobile Broadband, Ultra-Reliable Low-Latency Communications, and massive Machine-Type Communications. To meet these diverse needs, 5G uses technologies like network softwarization, network slicing, and artificial intelligence. Multi-connectivity is crucial for boosting mobile device performance by using different Wireless Access Technologies (WATs) simultaneously, enhancing throughput, reducing latency, and improving reliability. This paper presents a multi-connectivity testbed from the 5G-CLARITY project for performance evaluation. MultiPath TCP (MPTCP) was employed to enable mobile devices to send data through various WATs simultaneously. A new MPTCP scheduler was developed, allowing operators to better control traffic distribution across different technologies and maximize aggregated throughput. Our proposal mitigates the impact of limitations on one path affecting others, avoiding the Head-of-Line blocking problem. Performance was tested with real equipment using 5GNR, Wi-Fi, and LiFi -complementary WATs in the 5G-CLARITY project-in both static and dynamic scenarios. The results demonstrate that the proposed scheduler can manage the traffic distribution across different WATs and achieve the combined capacities of these technologies, approximately 1.4 Gbps in our tests, outperforming the other MPTCP schedulers. Recovery times after interruptions, such as coverage loss in one technology, were also measured, with values ranging from 400 to 500 ms.

Driving Wi-Fi IoT Sensors by a Hybrid Magneto-Mechano-Electric Energy Generator Extracting a Power of over 50 mW.

Energy harvesting technology is mainly used as a power source for driving Internet of Things (IoT) devices. However, the output power of conventional harvesting devices are limited, suitable only for low-power-consumption IoT sensors based on Bluetooth communication. In contrast to Bluetooth, wireless fidelity (Wi-Fi) communication offers superior real-time monitoring and transmission capabilities, but requires more power in the range of hundreds of milliwatts or higher. Therefore, the hybridization of three energy conversion devices, namely, piezoelectric magneto-mechano-electric (MME) generator, electromagnetic (EM) induction coil, and triboelectric nanogenerator (TENG) is proposed as a standalone power source for Wi-Fi communication sensors. By integrating these three mechanisms, the hybrid MME energy harvester can achieve an output power exceeding 50 mW at the second harmonic resonance condition under the alternating current (AC) magnetic field of 10 Oe. Furthermore, it can successfully drive the Wi-Fi sensor, enabling continuous real-time monitoring without the degradation of charged power in a supercapacitor. These results highlight that energy harvesting technology is not limited to low-power devices but can also be applied to Wi-Fi communication sensors and beyond.

Exploring edible bird nest's potential in mitigating Wi-Fi's impact on male reproductive health.

Purpose: This study aimed to evaluate the protective effects of edible bird nest (EBN) against the detrimental impact of Wi-Fi on male reproductive health. Specifically, it examines whether EBN can mitigate Wi-Fi-induced changes in male reproductive hormones, estrogen receptors (ER), spermatogenesis, and sperm parameters. Methods: Thirty-six adult male rats were divided into six groups (n = 6): Control, Control EBN, Control E2, Wi-Fi, Wi-Fi+EBN, and Wi-Fi+E2. Control EBN and Wi-Fi+EBN groups received 250 mg/kg/day EBN, while Control E2 and Wi-Fi+E2 groups received 12 µg/kg/day E2 for 10 days. Wi-Fi exposure and EBN supplementation lasted eight weeks. Assessments included organ weight, hormone levels (FSH, LH, testosterone, and E2), ERα/ERß mRNA and protein expression, spermatogenic markers (c-KIT and SCF), and sperm quality. Results: Wi-Fi exposure led to decreased FSH, testosterone, ERα mRNA, and sperm quality (concentration, motility, and viability). EBN supplementation restored serum FSH and testosterone levels, increased serum LH levels, and the testosterone/E2 ratio, and normalized mRNA ERα expression. Additionally, EBN increased sperm concentration in Wi-Fi-exposed rats without affecting motility or viability. Conclusions: EBN plays a crucial role in regulating male reproductive hormones and spermatogenesis, leading to improved sperm concentration. This could notably benefit men experiencing oligospermia due to excessive Wi-Fi exposure.

Design of a Compact Multiband Monopole Antenna with MIMO Mutual Coupling Reduction.

In this article, the authors present the design of a compact multiband monopole antenna measuring 30 × 10 × 1.6 mm3, which is aimed at optimizing performance across various communication bands, with a particular focus on Wi-Fi and sub-6G bands. These bands include the 2.4 GHz band, the 3.5 GHz band, and the 5-6 GHz band, ensuring versatility in practical applications. Another important point is that this paper demonstrates effective methods for reducing mutual coupling through two meander slits on the common ground, resembling a defected ground structure (DGS) between two antenna elements. This approach achieves mutual coupling suppression from -6.5 dB and -9 dB to -26 dB and -13 dB at 2.46 GHz and 3.47 GHz, respectively. Simulated and measured results are in good agreement, demonstrating significant improvements in isolation and overall multiple-input multiple-output (MIMO) antenna system performance. This research proposes a compact multiband monopole antenna and demonstrates a method to suppress coupling in multiband antennas, making them suitable for internet of things (IoT) sensor devices and Wi-Fi infrastructure systems.

Ada-LT IP: Functional Discriminant Analysis of Feature Extraction for Adaptive Long-Term Wi-Fi Indoor Localization in Evolving Environments.

Wi-Fi fingerprint-based indoor localization methods are effective in static environments but encounter challenges in dynamic, real-world scenarios due to evolving fingerprint patterns and feature spaces. This study investigates the temporal variations in signal strength over a 25-month period to enhance adaptive long-term Wi-Fi localization. Key aspects explored include the significance of signal features, the effects of sampling fluctuations, and overall accuracy measured by mean absolute error. Techniques such as mean-based feature selection, principal component analysis (PCA), and functional discriminant analysis (FDA) were employed to analyze signal features. The proposed algorithm, Ada-LT IP, which incorporates data reduction and transfer learning, shows improved accuracy compared to state-of-the-art methods evaluated in the study. Additionally, the study addresses multicollinearity through PCA and covariance analysis, revealing a reduction in computational complexity and enhanced accuracy for the proposed method, thereby providing valuable insights for improving adaptive long-term Wi-Fi indoor localization systems.

Wi-Fi Fingerprint Indoor Localization by Semi-Supervised Generative Adversarial Network.

Wi-Fi fingerprint indoor localization uses Wi-Fi signal strength measurements obtained from a number of access points. This method needs manual data collection across a positioning area and an annotation process to label locations to the measurement sets. To reduce the cost and effort, this paper proposes a Wi-Fi Semi-Supervised Generative Adversarial Network (SSGAN), which produces artificial but realistic trainable fingerprint data. The Wi-Fi SSGAN is based on a deep learning, which is extended from GAN in a semi-supervised learning manner. It is designed to create location-labeled Wi-Fi fingerprint data, which is different to unlabeled data generation by a normal GAN. Also, the proposed Wi-Fi SSGAN network includes a positioning model, so it does not need a external positioning method. When the Wi-Fi SSGAN is applied to a multi-story landmark localization, the experimental results demonstrate a 35% more accurate performance in comparison to a standard supervised deep neural network.

Hypersensitivity to man-made electromagnetic fields (EHS) correlates with immune responsivity to oxidative stress: a case report.

There is increasing evidence that exposure to weak electromagnetic fields (EMFs) generated by modern telecommunications or household appliances has physiological consequences, including reports of electromagnetic field hypersensitivity (EHS) leading to adverse health effects. Although symptoms can be serious, no underlying mechanism for EHS is known and there is no general cure or effective therapy. Here, we present the case study of a self-reported EHS patient whose symptoms include severe headaches, generalized fatigue, cardiac arrhythmia, attention and memory deficit, and generalized systemic pain within minutes of exposure to telecommunications (Wifi, cellular phones), high tension lines and electronic devices. Tests for cerebral, cardiovascular, and other physiological anomalies proved negative, as did serological tests for inflammation, allergies, infections, auto-immune conditions, and hormonal imbalance. However, further investigation revealed deficits in cellular anti-oxidants and increased radical scavenging enzymes, indicative of systemic oxidative stress. Significantly, there was a large increase in circulating antibodies for oxidized Low-Density Lipoprotein (LDLox), byproducts of oxidative stress accumulating in membranes of vascular cells. Because a known primary effect of EMF exposure is to increase the concentration of cellular oxidants, we propose that pathology in this patient may be causally related to a resulting increase in LDLox synthesis. This in turn could trigger an exaggerated auto-immune response consistent with EHS symptoms. This case report thereby provides a testable mechanistic framework for EHS pathology with therapeutic implications for this debilitating and poorly understood condition.

A high-dimensional, multi-transceiver channel state information dataset for enhanced human activity recognition.

Human Activity Recognition (HAR) has emerged as a critical research area due to its extensive applications in various real-world domains. Numerous CSI-based datasets have been established to support the development and evaluation of advanced HAR algorithms. However, existing CSI-based HAR datasets are frequently limited by a dearth of complexity and diversity in the activities represented, hindering the design of robust HAR models. These limitations typically manifest as a narrow focus on a limited range of activities or the exclusion of factors influencing real-world CSI measurements. Consequently, the scarcity of diverse training data can impede the development of efficient HAR systems. To address the limitations of existing datasets, this paper introduces a novel dataset that captures spatial diversity through multiple transceiver orientations over a high dimensional space encompassing a large number of subcarriers. The dataset incorporates a wider range of real-world factors including extensive activity range, a spectrum of human movements (encompassing both micro-and macro-movements), variations in body composition, and diverse environmental conditions (noise and interference). The experiment is performed in a controlled laboratory environment with dimensions of 5 m (width) × 8 m (length) × 3 m (height) to capture CSI measurements for various human activities. Four ESP32-S3-DevKitC-1 devices, configured as transceiver pairs with unique Media Access Control (MAC) addresses, collect CSI data according to the Wi-Fi IEEE 802.11n standard. Mounted on tripods at a height of 1.5 m, the transmitter devices (powered by external power banks) positioned at north and east send multiple Wi-Fi beacons to their respective receivers (connected to laptops via USB for data collection) located at south and west. To capture multi-perspective CSI data, all six participants sequentially performed designated activities while standing in the centre of the tripod arrangement for 5 s per sample. The system collected approximately 300-450 packets per sample for approximately 1200 samples per activity, capturing CSI information across the 166 subcarriers employed in the Wi-Fi IEEE 802.11n standard. By leveraging the richness of this dataset, HAR researchers can develop more robust and generalizable CSI-based HAR models. Compared to traditional HAR approaches, these CSI-based models hold the promise of significantly enhanced accuracy and robustness when deployed in real-world scenarios. This stems from their ability to capture the nuanced dynamics of human movement through the analysis of wireless channel characteristic from different spatial variations (utilizing two-diagonal ESP32 transceivers configuration) with higher degree of dimensionality (166 subcarriers).

Assessing the influence of rural residence and economic distress on lower extremity risk stratification among diabetic foot ulcer patients utilizing the Wound, Ischemia, and Foot Infection (WIfI) classification system.

OBJECTIVE: Diabetic foot ulcers (DFU) are a major sequela of uncontrolled diabetes with a high risk of adverse outcomes. Poor DFU outcomes disproportionately impact patients living in rural and economically distressed communities with lack of access to consistent, quality care. This study aimed to analyze the risk of geographic and economic disparities, including rural status and county economic distress, on the disease burden of DFU at presentation utilizing the SVS WIfI classification system. METHODS: We conducted a retrospective review of 454 patients diagnosed with a DFU from 2011 to 2020 at a single institution's inpatient and outpatient wound care service. Patients >18 years old, with type II diabetes mellitus, and diabetic foot ulcer were included. RESULTS: ANCOVA analyses showed rural patients had significantly higher WIfI composite scores (F(1,451) = 9.61, p = .002), grades of wound (F(1,439) = 11.03, p = .001), and ischemia (F(1,380) = 12.574, p = .001) compared to the urban patients. Patients that resided in at-risk economic counties had significantly higher overall WIfI composite scores (F(2,448) = 3.31, p = .037) than patients who lived in transitional economic counties, and higher foot infection grading (F(2,440) = 3.02, p = .05) compared to patients who lived in distressed economic counties. DFU patients who resided in distressed economic counties presented with higher individual grades of ischemia (F(2, 377) = 3.14, p = .04) than patients in transitional economic counties. Chi-Square analyses demonstrated patients who resided in urban counties were significantly more likely to present with grade 1 wounds (χ2(3) = 9.86, p = .02) and grade 0 ischemia (χ2(3) = 16.18, p = .001) compared to patients in rural areas. Economically distressed patients presented with significantly less grade 0 ischemia compared to patients in transitional economic counties (χ2(6) = 17.48, p = .008). CONCLUSIONS: Our findings are the first to demonstrate the impact of geographic and economic disparities on the disease burden of DFU at presentation utilizing the SVS WIfI classification system. This may indicate need for improved multidisciplinary primary care prevention strategies with vascular specialists in these communities to mitigate worsening DFU and promote early intervention.

DATIV-Remote Enhancement of Smart Aerosol Measurement System Using Raspberry Pi-Based Distributed Sensors.

Enclosed public spaces are hotspots for airborne disease transmission. To measure and maintain indoor air quality in terms of airborne transmission, an open source, low cost and distributed array of particulate matter sensors was developed and named Dynamic Aerosol Transport for Indoor Ventilation, or DATIV, system. This system can use multiple particulate matter sensors (PMSs) simultaneously and can be remotely controlled using a Raspberry Pi-based operating system. The data acquisition system can be easily operated using the GUI within any common browser installed on a remote device such as a PC or smartphone with a corresponding IP address. The software architecture and validation measurements are presented together with possible future developments.

Effect of Electromagnetic Radiation of Wi-Fi Router on Thyroid Gland and the Possible Protective Role of Combined Vitamin C and Zinc Administration in Adult Male Albino Rats.

Introduction: Electromagnetic radiation (EMR) is widely used nowadays in various fields due to rapid expansion of technology and affects different organs such as endocrine glands. Antioxidants protect the cells and act as a free radical scavenger. Aim of Work: The aim of the study was to clarify the effect of EMR emitted from Wi-Fi router on the thyroid gland of adult male albino rats and the possible protective role of combined Vitamin C and zinc. Materials and Methods: Thirty adult male albino rats were divided into three groups: Group I (control group), Group II (received combined Vitamin C and Zinc in one tablet called IMMUNO-MASH), and Group III (experimental groups). Group III was divided into two subgroups (A and B) according to the duration of exposure: 6 h and 24 h/day. Each of these groups was divided into two equal subgroups. One was exposed only to EMR while the other was exposed to EMR and received combined Vitamin C and zinc. All rats were weighed at the beginning and at the end of the experiment. The thyroid gland was prepared for general histological, anti-calcitonin immunostaining, and ultrastructural study. Furthermore, measurement of total serum T3, T4, and thyroid-stimulating hormone (TSH) hormone levels and quantitative analysis of immunoreactive C-cells were done. Then, statistical analysis was done on the number of immunoreactive C-cells, data of the body weight, and the hormonal levels. Results: A highly significant increase in the body weight in subgroups exposed to EMR for 24 h/day was observed. Furthermore, they showed a highly significant decline in T3 and T4 levels together with a highly significant increase in TSH level. With increasing period of exposure, there was a variable degree of deterioration in the form of congestion and dilatation of blood vessels, cellular infiltration, follicular disintegration, vacuolar degeneration, and desquamated follicular cells in the colloid. The C-cells showed a significant increase in the mean number compared with the control group. Ultrastructural analysis of follicular cells revealed colloid droplets, deteriorations in rough endoplasmic reticulum, degenerating nuclei, and swollen mitochondria according to the dose of exposure. There was apparent improvement with the use of combined Vitamin C and zinc. Conclusion: Wi-Fi radiation has a very serious effect on thyroid gland morphology and activity. Moreover, experimentally induced hypothyroidism by radiation resulted in increased C-cell number. Combined Vitamin C and zinc could have a protective role against this tissue damage.

Design of Miniaturized and Wideband Four-Port MIMO Antenna Pair for WiFi.

A miniaturized and wideband four-port multiple-input multiple-output (MIMO) antenna pair for Wi-Fi mobile terminals application is proposed. The proposed antenna pair consists of four multi-branch antenna elements arranged orthogonally, with an overall size of 40 × 40 × 3.5 mm3 and each antenna element size of 15.2 × 3.5 mm × 0.8 mm3. The performance of the proposed antenna shows the advantages of a wide frequency band, low mutual coupling, high efficiency, and a compact structure. The wideband characteristics of the antenna elements are achieved through multi-mode resonance. The suppression of coupling is accomplished by strategically positioning the four compact antenna elements to ensure their maximum radiation directions are orthogonal, thus eliminating the need for an additional decoupling structure. In this paper, the proposed antenna is optimized in terms of the parameters then simulated and measured. The simulated results illustrate that an impedance bandwidth of the antenna is about 15% (5.06~5.88 GHz) with S11 < -10 dB, excellent port isolation exceeds 20 dB between all ports, a high radiation efficiency ranges from 51.2% to 89.9%, the maximum gain is 4.5 dBi, and the ECCs are less than 0.04. The measured results show that the -10 dB impedance bandwidth of the antenna is about 13% (5.13~5.80 GHz), the isolation between the antenna elements is better than 21 dB, the radiation efficiency ranges from 51.8% to 92.3%, the maximum gain is 5.3 dBi, and the ECCs are less than 0.05. The proposed four-port MIMO antenna works on the 5G LTE band 46 and Wi-Fi 6E operating bands. As a mobile terminal antenna, the proposed design scheme demonstrates excellent performance and applicability, fulfilling the requirements for 5G mobile terminal applications.

The impact of revascularization strategy on clinical failure, hemodynamic failure, and chronic limb-threatening ischemia symptoms in the BEST-CLI Trial.

OBJECTIVE: Sustained clinical and hemodynamic benefit after revascularization for chronic limb-threatening ischemia (CLTI) is needed to resolve symptoms and prevent limb loss. We sought to compare rates of clinical and hemodynamic failure as well as resolution of initial and prevention of recurrent CLTI after endovascular (ENDO) vs bypass (OPEN) revascularization in the Best-Endovascular-versus-best-Surgical-Therapy-in-patients-with-CLTI (BEST-CLI) trial. METHODS: As planned secondary analyses of the BEST-CLI trial, we examined the rates of (1) clinical failure (a composite of all-cause death, above-ankle amputation, major reintervention, and degradation of WIfI stage); (2) hemodynamic failure (a composite of above-ankle amputation, major and minor reintervention to maintain index limb patency, failure to an initial increase or a subsequent decrease in ankle brachial index of 0.15 or toe brachial index of 0.10, and radiographic evidence of treatment stenosis or occlusion); (3) time to resolution of presenting CLTI symptoms; and (4) incidence of recurrent CLTI. Time-to-event analyses were performed by intention-to-treat assignment in both trial cohorts (cohort 1: suitable single segment great saphenous vein [SSGSV], N = 1434; cohort 2: lacking suitable SSGSV, N = 396), and multivariate stratified Cox regression models were created. RESULTS: In cohort 1, there was a significant difference in time to clinical failure (log-rank P < .001), hemodynamic failure (log-rank P < .001), and resolution of presenting symptoms (log-rank P = .009) in favor of OPEN. In cohort 2, there was a significantly lower rate of hemodynamic failure (log-rank P = .006) favoring OPEN, and no significant difference in time to clinical failure or resolution of presenting symptoms. Multivariate analysis revealed that assignment to OPEN was associated with a significantly lower risk of clinical and hemodynamic failure in both cohorts and a significantly higher likelihood of resolving initial and preventing recurrent CLTI symptoms in cohort 1, including after adjustment for key baseline patient covariates (end-stage renal disease [ESRD], prior revascularization, smoking, diabetes, age >80 years, WIfI stage, tissue loss, and infrapopliteal disease). Factors independently associated with clinical failure included age >80 years in cohort 1 and ESRD across both cohorts. ESRD was associated with hemodynamic failure in cohort 1. Factors associated with slower resolution of presenting symptoms included diabetes in cohort 1 and WIfI stage in cohort 2. CONCLUSIONS: Durable clinical and hemodynamic benefit after revascularization for CLTI is important to avoid persistent and recurrent CLTI, reinterventions, and limb loss. When compared with ENDO, initial treatment with OPEN surgical bypass, particularly with available saphenous vein, is associated with improved clinical and hemodynamic outcomes and enhanced resolution of CLTI symptoms.

Contention-Less Multi-Link Synchronous Transmission for Throughput Enhancement and Heterogeneous Fairness in Wi-Fi 7.

Multi-link operation (MLO) is a new and essential mechanism of IEEE 802.11be Extremely High Throughput (Wi-Fi 7) that can increase throughput and decrease latency in Wireless Local Area Networks (WLANs). The MLO enables a Multi-Link Device (MLD) to perform Simultaneous Transmission and Reception (STR) in different frequency bands. However, not all MLDs can support STR due to cross-link or in-device coexistence interference, while an STR-unable MLD (NSTR-MLD) can transmit multiple frames simultaneously in more than two links. This study focuses on the problems when NSTR-MLDs share a link with Single-Link Devices (SLDs). We propose a Contention-Less Synchronous Transmission (CLST) mechanism to improve fairness between NSTR-MLDs and SLDs while increasing the total network throughput. The proposed mechanism classifies links as MLD Dominant Links (MDLs) and Heterogeneous Coexistence Links (HCLs). In the proposed mechanism, an NSTR-MLD obtains a Synchronous Transmission Token (STT) through a virtual channel contention in the HCL but does not actually transmit a frame in the HCL, which is compensated for by a synchronous transmission triggered in the MDL. Moreover, the CLST mechanism allows additional subsequent transmissions up to the accumulated STT without further contention. Extensive simulation results confirm the outstanding performance of the CLST mechanism in terms of total throughput and fairness compared to existing synchronous transmission mechanisms.