Access Points Research Articles

High Speed Dual‐Band Photodetector for Dual‐Channel Optical Communications in Wavelength Division Multiplexing and Security Enhancement

AbstractThe forthcoming wireless networks must integrate to a high density of end‐user devices while ensuring a superior quality of service. It is imperative to devise solutions that reduce the density of wireless access points and associated costs, while simultaneously ensuring security and privacy throughout propagation. Herein, a novel dual‐band photodetector with high selective responsivities in distinct visible and near‐infrared (NIR) band is developed as an optical signal receiver. The receiver has an unprecedented fast switch speed > 500 kHz between different operation modes. Leveraging this fast switch speed, a high‐speed Multiple‐Input‐Single‐Output (MISO) system is developed, fusing spectrum resources from visible to infrared bands. The system achieves a data rate of 600 Kbps in the visible channel and 500 Kbps in the NIR channel, with a sum rate of 1.1 Mbps. This is the highest single‐channel data rate and sum rate among reported dual‐band photodetectors. Additionally, two previously overlooked mechanisms, the Subtraction and Addition modes, are studied in detail. The four operation modes are developed to establish a novel physical encryption method in terms of arithmetic relation. The dual‐channel coupled link can support a data rate of 200 Kbps, significantly enhancing communication security.

Unradical Feminisms: Whitney and the World of Women’s Work

ABSTRACT Isabella Whitney is often celebrated as the first professional woman English writer. Such “firsts”, however, are sticky—a matter of shifting data and definitions. More importantly, a focus on a “first” woman can often obfuscate the reality of women’s historical involvements in a field. When Whitney’s first work was published, the printing press had not yet been in England a full century, and professional writers of any description were still thin on the ground. But women who wrote, who were involved in the book trade, or who otherwise participated in the publishing world were no anomaly. While gender is important to both Whitney’s work and her historical place, this article will prioritize placing those considerations in conversation with the larger contextual community of early modern print. How might we continue to reconceptualize Whitney’s place in the history of women’s writing, circumventing tokenization and promoting a fuller appreciation of all women’s places in the world of early modern writing, print, and books? Whitney’s work then emerges as an exemplar of early professional authorship qua professional authorship, full stop, and as one access point for appreciating the rich and extensive involvement of women in the early English publishing and writing networks.

Distributed photovoltaic siting optimisation considering load centre similarity

The load fluctuation law remains challenging for traditional distribution line users. Furthermore, as the number of distributed photovoltaic installations connected to the distribution network rises, the issue of voltage overload on the distribution lines becomes more prevalent. This, in turn, adds complexity to power system operation. To configure the PV access location, whilst ensuring the line voltage is qualified, this paper applies the DBSCAN density clustering and K-means optimization algorithm to classify the distribution network line load. Different characteristics of the load collection are then constructed, and the concept of the load centre distance is proposed. PV access points are selected based on the load center characteristic curve and PV curve similarity. Model simulation is used to verify the conclusions, which demonstrate the rationality and effectiveness of the proposed method. The model simulation is conducted to confirm the soundness and credibility of the conclusions drawn.

Outage performance and energy efficiency optimization of wireless-powered millimeter-wave sensor networks

AbstractWith the widespread use of wireless sensor networks, one of the most pressing concerns is extending the lifetime of the sensors. By deploying directional antenna arrays, millimeter wave (mmWave) is a possible candidate for wireless energy transfer (WPT). This paper investigates a beneficial combination of WPT and data transmission in a typical mmWave sensor network with Rayleigh channels, where a transmission interval can be divided into two sub-intervals. During the first sub-interval, one hybrid access point (HAP) employs beamforming techniques to transfer energy for serving multiple sensors within the service sector. The sensors then transmit their individual signal in turn to the HAP based on time division multiple address (TDMA) strategy by using the whole harvested energy. According to stochastic geometry, the exact and approximate expressions of beam outage probability for the considered system are determined, respectively. The optimal time allocation of energy harvesting and data transmission for sensors is examined in order to maximize the energy efficiency of the system. The optimization problem can be translated into corresponding parametric form, and the resulting optimization problem can be solved using the Lagrange dual method with Karush–Kuhn–Tucker (KKT) conditions. The numerical results show the variation trend of the beam outage probability under various parameters and verify the accuracy of the theoretical analyses. Furthermore, the simulation results illustrate that the proposed optimal time allocation strategy can significantly enhance the overall energy efficiency of the system compared with a similar scheme.

Integrated indoor positioning methods to optimize computations and prediction accuracy enhancement

AbstractIndoor GPS location estimation encounters accuracy challenges from intricate building structures and diverse signal interferences. Trilateration methods utilising APs are typically employed to estimate indoor locations. Nevertheless, estimation errors from multipath effects and high power consumption of sensors employed in location estimation curtail battery life. To address this issue, research into location estimation methods utilising machine learning has been conducted. However, challenges involving the selection of the optimal access point locations and obtaining dense RSSI data have been noted. In this article presents a solution based on sparse radio maps for decreasing the expenses of collecting RSSI data while simultaneously enhancing indoor location accuracy through the integration of image data. The proposed approach integrates matrix‐based RSSI indoor positioning (M‐RIP) for initial location estimation and feature‐based image indoor positioning (F‐IIP) for position determination via image feature matching. Furthermore, extended area‐based post‐processing (EA‐PP) is employed to augment M‐RIP's precision and minimize image matching computation in F‐IIP, improving overall performance. This article utilizes actual building data to validate the precision of the position estimation and efficiency of computation reduction using the proposed method.

Asynchronous Bidirectional Communication in Cell-Free Networks

We consider a bidirectional communication between two single-antenna transceivers using multiple multi-antenna access points (APs) in a cell-free network architecture. In such a network, because of different propagation delays associated with different APs, the end-to-end link is a multi-path channel that results in inter-symbol-interference (ISI) in the signals received at the transceivers. To tackle ISI, we resort to cyclic prefix (CP) assisted block transmission of the information symbols and employ joint pre- and post-channel equalizers at both the transceivers to mitigate the impact of intra-block interference. Considering the amplify-and-forward technique at the APs, we cast the joint design of equalizers, beamforming matrices, and transceivers’ transmit powers as a power minimization problem while guaranteeing predefined data rates at the transceivers. Assuming symmetric beamforming matrices at the APs, we devise a semi-closed-form solution for this problem. We prove rigorously that at the optimum only a synchronous subset of the APs should participate in the information exchange between the two transceivers. This is achieved by proving that at the optimum, the pre-equalizer matrices should be unitary and the post-equalizer matrices should be invertible.

A novel energy-saving method for campus wired and dense WiFi network applying machine learning and idle cycling techniques

University campus networks need wired (ethernet) and dense wireless fidelity networks that have devices like access points, switches, and routers that are always turned on. Consequently, they generate two important problems: the energy bill and the influence of carbon dioxide into the atmosphere. Energy savings are the solution to those problems. There are several proposals to augment the energy savings separately in ethernet and wireless fidelity, but there is no integrated method to simultaneously reduce them in both parts of the networks. Our novel method combines idle cycling and machine learning techniques to efficiently obtain energy savings in both parts of the network simultaneously. We categorize network devices into two groups: (a) those that are always turned on and (b) those that can be dynamically turned on or off based on network performance. We formulated two algorithms that decide when to turn on and off access points. We use Ward's machine learning hierarchical clustering technique to optimize the energy savings of our model in the network of the Unidades Tecnológicas de Santander (Bucaramanga, Colombia). We showed that energy savings of 21.5 kWh per day are possible. The success of the model in this context highlights its potential to achieve substantial energy savings.

Enhancing Indoor Positioning Systems Accuracy with Optimal Placement of Wi-Fi Access Points

The indoor positioning system (IPS) has generated a considerable amount of interest in recent times, and the prosperity of the system's development is heavily reliant on its ability to accurately locate objects. The performance measure is significantly impacted by the location of access points (APs). However, the bulk of previous studies have tended to overlook the matter of optimal AP placement and efficient design for IPS due to the dependence on pre-existing installed APs, which were chiefly formulated for coverage objectives. In this investigation, an optimal placement function - which is reliant on mean and variance - has been developed using received signal strength (RSS) measurements data. The performance evaluation in this research is based on experimentation and compared with currently employed placement methods. The results indicate that the most optimal function value for the suggested method is 1.5714, which is substantially smaller than the values for rectangular, triangular, and triangular II, which are 12.468, 5.5364, and 8.5147, respectively. When the recommended placement strategy is employed instead of the existing ones, the weighted K-nearest algorithm (WKNN) for location error, using average RSS as the fingerprint radio map database, yielded a heightened degree of precision.

The symptom to assessment pathway for suspected chronic limb-threatening ischaemia (CLTI) affects quality of care: a process mapping exercise

BackgroundDelays in the pathway from first symptom to treatment of chronic limb-threatening ischaemia (CLTI) are associated with worse mortality and limb loss outcomes. This study examined the processes used by...

An analytical approach to network with IoT security using NodeMCU access point-prevention technique

This paper focuses on a very pertinent issue in the field of cybersecurity, particularly concerning the increased use of public Wi-Fi and the proliferation of IoT devices. The scenario, where an attacker captures user details through a malicious access point set up through a NodeMCU, is a practical illustration of a Man-in-the-Middle (MitM) attack. These attacks are especially concerning in the context of smart homes and smart cities, where security breaches can lead to severe privacy invasions and disruptions of essential services. The NodeMCU, being a low-cost, open-source IoT platform, is accessible for creating such malicious access points. It can be programmed to mimic legitimate Wi-Fi hotspots to intercept the data of unsuspecting users. This type of attack can enable unauthorized access to sensitive information such as login credentials, personal data, and even control over connected IoT devices. This approach is user-friendly and can significantly reduce the risk of users unknowingly connecting to malicious websites. The contribution of this paper includes a demonstration and analysis of a common network and IoT attack and a novel method for detecting fraudulent URLs, particularly those used in phishing attacks. By addressing these aspects, the paper makes a significant contribution to the field of cybersecurity and helps foster a more secure and resilient network and IoT ecosystem.

Four‐port MIMO Vivaldi antenna for access points

AbstractIn this article, a 4‐port multiple input, multiple output (MIMO) antenna array is presented, based on a Vivaldi design for nonplanar applications like access points above 6 GHz. The antenna is configured with four Vivaldi antennas arranged orthogonally, one to each other, to increase the electromagnetic isolation among elements. The Vivaldi element is fed with a microstrip with an integrated balun in only one arm, and each arm is set in an antipodal position to the other, giving an easy‐to‐design and build prototype, with no complicated feeding techniques. Each antenna plane has dimensions of 90 × 40 mm2. The array presents a bandwidth from 5.3 GHz to beyond 20 GHz, with electromagnetic isolation above 20 dB, an envelope correlation coefficient below 0.001 for the entire bandwidth, giving a diversity gain around 20 dB, and a total active reflection coefficient below −10 dB from 7 to 20 dB, fulfilling the metrics for MIMO applications.

Selection of Signal Sources Influence at Indoor Positioning System

The indoor positioning task is an exciting problem because of the practical applications and the scientific challenges. This work emphasises the impact of signal source selection on the effectiveness of positioning models relying on Wi-Fi received signal strength data by eliminating unstable signal sources such as mobile access points. Two localisation algorithms, based on k-Nearest Neighbour and Random Forest, were applied to data from three academic buildings to test the effectiveness of various access points (APs) selection filters. Additionally, new hierarchical selection models were proposed. The methods divide building into zones to improve the characteristics of the positioning error by a local selection of APs. Hierarchical methods reduced the median number of AP to 67 in comparison to over 100 selected by existing methods, obtaining a statistically similar median error of 6.01 m.

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.

Clustering Performance Analysis of the K-Medoids Algorithm for Improved Fingerprint-Based Localization

Fingerprint-based localization, which uses received signal strength (RSS) measurements from spatially deployed wireless access points (APs), is a popular technique for indoor positioning. The size of the fingerprint database has a significant impact on the accuracy of localization. The higher the density of the fingerprint database, the more accurate the localization, but the longer the localization time. Clustering is one of the techniques used such systems to improve localization accuracy and reduce localization time. To cluster fingerprints, the majority of clustering techniques employ a distance-based fingerprint similarity metric. However, the choice of distance metric has a significant impact on the performance of the clustering algorithm. Using four publicly available RSS-based fingerprint databases, this paper investigates the clustering performance of the k-medoids algorithm using six distance metrics, namely Euclidean, Manhattan, cosine, Mahalanobis, Chebyshev, and Canberra distance. Using the silhouette score as a performance metric, the cosine and Euclidean distance metrics outperform the others, with the highest silhouette score values of about 0.38, 0.43, 0.34, and 0.31 on the SEUG_IndoorLoc, IIRC_IndoorLoc, MSI_IndoorLoc, and IPIN_2019_PIEP_UM databases, respectively. It demonstrates that on these four databases, using Euclidean distance as well as the angle between fingerprint measurement vectors is the best option for generating efficient clusters that will result in high localization accuracy and low localization time.

Accessibility of Patients With Special Healthcare Needs to Dental Care in the Eastern Province of Saudi Arabia: A Multicentre Study From Perspective of Caregiver and Dental Care Providers.

Dental care accessibility is subject to a dentist's qualification, practice and intention to treat patients, regardless of whether those patients have special healthcare needs (SCN) and should receive treatment in a dental setting. This multicentre study aimed to evaluate the characteristics of patients with SCN, their access to dental care and behaviour towards dental care from their caregiver's perspective. In addition, the perspective of dental care providers who care for patients with SCN and the factors affecting the provision of treatment was also appraised. The Eastern Province of Saudi Arabia served as the site of this cross-sectional study from 1 February 2020 to 31 January 2022. Caregivers of 272 patients with SCN, regardless of age and gender, were recruited in the study. The caregiver's proforma sought information on the demographic characteristics, type of disability, cooperation, medical history, occupation of the parent and patient's behaviour towards oral hygiene and dental healthcare. The second proforma had the dental care provider's perspective about the common disabilities, factors that affect the decision to provide treatment, difficulties patients face in getting their dental treatment and, from the dentist's experience, recommendations to improve the access to dental care for patients with SCN. Statistical analysis was carried out by using SPSS version 22.0. The demographic features, caregiver's perception about oral healthcare accessibility and dental professional's point of view were presented as frequencies and percentages. Chi-square test was applied to compare the proportions. The majority of the caregivers were satisfied with the dental service providers (91.9%) for their patients with SCN. The dental care provider's survey results indicated a shortage of dentists (54.7%) in the region and other factors that pose challenges to special care, like the severity of the disability of patients with SCN (50%), family structure (46.7%), treatment cost (35.6%) and transportation (32.8%). Patients with SCN in Saudi Arabia had a high appraisal of access to dental care and were very satisfied with dental treatment results. However, the presence of a dental care provider in the same rehabilitation centre was a major concern. The severity of the disability and the patient's cooperation were the major factors that may have affected the decision of the dental care provider.

Qualitative Description as an Introductory Method to Qualitative Research for Master’s-Level Students and Research Trainees

Qualitative description (QD) offers an accessible entry point for master’s-level students and research trainees embarking on a qualitative research learning journey, emphasizing direct, rich descriptions of experiences and events without extensive theorization or abstraction. This method, rooted in naturalistic inquiry, allows for flexibility in theoretical approaches, sampling techniques, and data collection strategies, making it well-suited for a wide range of disciplines, particularly in health research. QD’s strengths lie in its straightforward approach, focusing on participants’ perspectives and staying close to the data. Despite its advantages, QD faces challenges regarding perceived academic credibility and its limited contribution to theoretical development due to its simplicity and focus on low inference interpretation. Nonetheless, QD’s role in fostering critical thinking, analytical skills, and an understanding of qualitative methodologies in novice researchers highlights its significance as a foundational method in qualitative research education.

An innovative technological infrastructure for managing SARS-CoV-2 data across different cohorts in compliance with General Data Protection Regulation.

The ORCHESTRA project, funded by the European Commission, aims to create a pan-European cohort built on existing and new large-scale population cohorts to help rapidly advance the knowledge related to the prevention of the SARS-CoV-2 infection and the management of COVID-19 and its long-term sequelae. The integration and analysis of the very heterogeneous health data pose the challenge of building an innovative technological infrastructure as the foundation of a dedicated framework for data management that should address the regulatory requirements such as the General Data Protection Regulation (GDPR). The three participating Supercomputing European Centres (CINECA - Italy, CINES - France and HLRS - Germany) designed and deployed a dedicated infrastructure to fulfil the functional requirements for data management to ensure sensitive biomedical data confidentiality/privacy, integrity, and security. Besides the technological issues, many methodological aspects have been considered: Berlin Institute of Health (BIH), Charité provided its expertise both for data protection, information security, and data harmonisation/standardisation. The resulting infrastructure is based on a multi-layer approach that integrates several security measures to ensure data protection. A centralised Data Collection Platform has been established in the Italian National Hub while, for the use cases in which data sharing is not possible due to privacy restrictions, a distributed approach for Federated Analysis has been considered. A Data Portal is available as a centralised point of access for non-sensitive data and results, according to findability, accessibility, interoperability, and reusability (FAIR) data principles. This technological infrastructure has been used to support significative data exchange between population cohorts and to publish important scientific results related to SARS-CoV-2. Considering the increasing demand for data usage in accordance with the requirements of the GDPR regulations, the experience gained in the project and the infrastructure released for the ORCHESTRA project can act as a model to manage future public health threats. Other projects could benefit from the results achieved by ORCHESTRA by building upon the available standardisation of variables, design of the architecture, and process used for GDPR compliance.

Enacting Relational Public Health: Federally Qualified Health Centers During the COVID-19 Pandemic.

Federally Qualified Health Centers (FQHCs) proved to be critical points of access for people of color and other underserved populations during the COVID-19 pandemic, administering 61% of their COVID-19 vaccinations to people of color, compared to the 40% rate for the overall United States' vaccination effort. To better understand the approaches and outcomes of FQHCs in pandemic response, we conducted semi-structured interviews with FQHC health care providers and outreach workers and analyzed them using an inductive qualitative methodology.

Distributed Medical Data Storage Mechanism Based on Proof of Retrievability and Vector Commitment for Metaverse Services.

The metaverse is a unified, persistent, and shared multi-user virtual environment with a fully immersive, hyper-temporal, and diverse interconnected network. When combined with healthcare, it can effectively improve medical services and has great potential for development in realizing medical training, enhanced teaching, and remote surgical treatment. The metaverse provides immersive services for users through massive and multimodal data, and its data scale and data growth rate are bound to show exponential growth. Blockchain-based distributed storage is a fundamental way to keep the metaverse running continuously; however, many blockchains, such as Ethereum and Filecoin, suffer from low transaction throughput and high latency, which seriously affect the efficiency of distributed storage services and make it difficult to apply them to the metaverse environment. To this end, this paper first proposes a network architecture for distributed storage systems based on proof of retrievability to address the problem of centralized decision making and single point of access in centralized storage. The secure data storage of the metaverse health system is ensured. Secondly, we designed two data transmission protocols through vector commitment and encoding functions to achieve the transfer of time cost from the critical path to storage nodes and improve the efficiency of data verification between nodes as well as the scalability of the metaverse health system. Finally, this paper also conducts security analysis and performance analysis of the proposed scheme, and the results show that our scheme is secure and efficient.

Passive Reflection Codebook Design for IRS-Integrated Access Point

Passive Reflection Codebook Design for IRS-Integrated Access Point