— Big Data analytics has come a long way since its inception. This field is growing day by day. With the advent of large handling capacity of computational analysis of modern computing systems as well as Internet of Things (IoT), this field has revolutionized the way we think about data. It has influenced the major domains such as healthcare, automobile, computing, climatology, and space communications. Of late, the health care sector has been largely influenced by this. This communication deals with the areas of healthcare where big data analytics has been largely influential. Encompassing the basics of Big Data Analytics (BDA) driven by IoT, the applications of it in healthcare sector are outlined, accompanied by future expectations. Additionally, it also presents a comprehensive analysis of recent application with special reference to Covid-19 in this sector.

The integration of Artificial Intelligence (AI) with Digital Twins (DTs) has emerged as a promising approach to revolutionize healthcare, particularly in the diagnosis and management of thoracic disorders. This study proposes a comprehensive framework, named Lung-DT, which leverages IoT sensors and AI algorithms to establish a digital representation of a patient’s respiratory health. Using the YOLOv8 neural network, the Lung-DT system accurately classifies chest X-Rays into five distinct categories of lung diseases, including "Normal," "Covid," "Lung Opacity," "Pneumonia," and "Tuberculosis". The system’s performance was evaluated on a chest X-Ray dataset, demonstrating an impressive average accuracy of 96.6% across all classes. Further tests (prediction) were conducted on the trained network using a third dataset available in the literature and completely unknown to the network, yielding an average accuracy of 98% across three classes. The proposed Lung-DT framework offers several advantages over conventional diagnostic methods. Firstly, it enables real-time monitoring of lung health through continuous data acquisition from IoT sensors, facilitating early diagnosis and intervention. Secondly, the AI-powered classification module provides automated and objective assessments of chest X-Rays, reducing dependence on subjective human interpretation. Thirdly, the twin digital representation of the patient’s respiratory health allows for comprehensive analysis and correlation of multiple data streams, providing valuable insights for personalized treatment plans. The integration of IoT sensors, AI algorithms, and DT technology within the Lung-DT system demonstrates a significant step towards improving thoracic healthcare. By enabling continuous monitoring, automated diagnosis, and comprehensive data analysis, the Lung-DT framework has enormous potential to enhance patient outcomes, reduce healthcare costs, and optimize resource allocation.

With Internet of Things (IoT) gaining presence throughout different industries a lot of new technologies have been introduced to support this undertaking. Implications on one such technology, wireless systems allowed for the use of different communication methods to achieve the goal of transferring data reliably, with more cost efficiency and over longer distances. Anywhere from a single house with only a few IoT devices such as a smart light bulb or a smart thermostat connected to the network, all the way to a complex system that can control power grids throughout countries, IoT has been becoming a necessity in everyday lives. This paper presents an overview of the devices, systems and wireless technologies used in different IoT architectures (Healthcare, Vehicular Networks, Mining, Learning, Energy, Smart Cities, Behaviors and Decision Making), their upbringings and challenges to this date and some foreseen in the future.

In this ‘New Normal' post-COVID-19 modern world, people's health is becoming increasingly important to track. An advanced technology that uses sensory instruments to track and record critical parameters and communicates with others is the need of the time. It's difficult to keep track of all of the medical parameters and post-operative data of people with non-communicable diseases like diabetes and heart disease on a continuous basis. The system discussed here to be designed for patients who are confined to their homes, particularly when going out and being exposed to the outer world is prohibited. This paper proposed a groundbreaking health management system based on the Internet of Things (IoT) for accessing patient medical parameters in both local and remote areas. When a person's wellbeing becomes urgent, this initiative seeks to send an emergency alert to family members or loved ones. A cloud server records data from the patient's temperature sensor and pulse sensor; the data is analyzed using support vector machine algorithms to identify irregular conditions, and an emergency message is sent to the rest of the family via a mobile application, as well as a warning message to the nearest hospital.

This paper is a review of the current state of IoT-based technologies in the healthcare field for the elderly population. The increasing population of elderly people has led to a greater demand for healthcare services to meet their unique needs. IoT-based technologies have emerged as a promising solution to address this demand. The paper analyses the current trends, challenges, and future opportunities for research in this area. It highlights how IoT-based technologies can be utilized to improve healthcare for older adults, from remote monitoring and telemedicine to fall detection and medication management. The paper also acknowledges that there are still challenges to be addressed, such as data privacy and security, but the potential benefits for older adults outweigh the challenges. The paper also examines the impact of the industrial revolution on the aging population and the corresponding increase in demand for geriatric healthcare. In summary, this paper delves into the current status of IoT-based technologies in geriatric healthcare and its future prospects, with a focus on addressing the needs of the aging population and improving their quality of life.

After conducting a detailed survey among the villagers of Pallissery and Karukutty, it was observed that in most of the villages the native people have to travel long distances for their basic health needs. Also most of the villagers lack knowledge regarding live health updates. At times, these problems have even resulted in death of many people including pregnant women and children. The objective of our research is to propose an integrated and easy to use Medical Kiosk that can be installed at various locations in rural areas. The Kiosk will provide an integrated environment for all medical related activities and would perform numerous functions like sending notifications regarding medical camps, mobile medical help, important dates for vaccinations, child care, insurance policies and provide other live medical updates to the villagers. It would also support the basic facilities for measurement of body parameters like height, weight, BMI, blood pressure, and heartbeat and also facilitate live consultation facilities with specialized doctors through video and voice chats.

The Internet of Things (IoT) has a growing presence in society's daily lives. These lightweight devices can be easily deployed and maintained, enabling extensive adoption in different environments. Furthermore, one of the most promising areas for using IoT devices is healthcare, comprising devices referred to as the Internet of Medical Things (IoMT). Several examples of healthcare services are supported by IoMT devices, e.g., continuous health monitoring. Conversely, there is an increasing concern with the cybersecurity aspects of these devices, and several attacks against IoT infrastructures have been launched in the past few years. These cybersecurity concerns also apply to healthcare applications, where the tradeoff between the benefits and security of IoMT devices must be observed. Given the complexity and amount of data IoMT network traffic generates, advanced methods become especially useful in these environments. Although Machine Learning (ML) brings various techniques and solutions to improve cyberattack detection, prevention, and mitigation, essential features are not addressed in the current state-of-the-art benchmark dataset contributions. Thereupon, the main goal of this research is to propose a realistic benchmark dataset to enable the development and evaluation of IoMT security solutions. In order to accomplish this, 18 attacks were executed against an IoMT testbed composed of 40 IoMT devices (25 real devices and 15 simulated devices), considering the plurality of protocols used in healthcare (e.g., Wi-Fi, MQTT, and Bluetooth). These attacks are categorized into five classes: DDoS, DoS, Recon, MQTT, and spoofing. This effort aims to establish a baseline complementary to the state-of-the-art contributions. The outcome supports researchers in investigating and developing new solutions to make healthcare systems more secure using different mechanisms (e.g., machine learning - ML). This research goes beyond merely conducting attacks on IoMT devices. We also attempt to capture the lifecycle of these devices in different vital phases, from the moment they join the network until they leave, which is called profiling. Profiling allows the different classifiers to identify anomalies of each device individually in the healthcare network. The CICIoMT2024 dataset has been published on CIC's dataset page, making it available for other researchers to use.

The world has changed. New technologies, new trends, new protocols, new efficient algorithms, and systems have been used widely by civilized countries to have better and more convenient living. In this research, there have been introduced many new terms, technologies, and mechanisms. Moreover, threats, attacks, and vulnerabilities have been studied and a novel scenario has been proposed in order to overcome all these violations that have been presented in this paper. The scenario is based on a security model that has been proposed for critical sectors, such as hospitals, industries, etc., which need efficient solutions to go one step further and improve the secure and efficient living and treatment of people. The aim of this research has been to understand through research the IoT security issues and propose the appropriate solutions based on the most suitable security algorithms. The results, which have been based on the complexity, the throughput, the power consumption, the strength, and the memory usage of the algorithms studied, have been hopeful and helpful for future research and experimentation.

Cloud computing is a strong and efficient technology facing some efficiency challenges attributable to its decentralized nature. The emergence of fog computing has subdivided the expansive cloud into numerous smaller units known as "cloudlets." Fog computing is one of the most recent advancements in today’s cloud computing landscape. Each cloudlet is a novel and diminutive architectural element extending the contemporary cloud computing infrastructure. The term "cloudlets" has been integrated with fog computing due to its augmenting impact on the attributes of cloud computing. F fog computing is a significant foundation and an indispensable cloud component. Consequently, this technology holds the potential to address efficiency concerns effectively. As a result, this paper focuses on enhancing the efficiency of the cloud computing environment by fine-tuning the efficiency parameters derived from fog computing. These parameters encompass response time, latency, and network utilization. The proposed solution involves developing a simulation system to minimize task redundancy and increase performance levels in the healthcare environment. The outcomes manifest a substantial improvement in the performance of the suggested system, with a clear increase depending on the three key parameters (response time, network utilization, and latency). Therefore, these findings are encouraging and carry significant implications, promoting the application of the proposed system in the real healthcare environment.

With the availability of wearable health monitoring sensor modules like 3-Lead Electrocardiogram (ECG), Pulse Oximeter (SpO2), Galvanic Skin Response (GSR), Hall effect sensor (for measuring Respiratory Rate), Blood Pressure and Temperature measuring and sensing elements, it has now become possible to device a composite health status monitoring kit that can measure vital signs and other physiological parameters pertaining to human health in real time. Traditionally, the physiological parameters along with vital signs related examination was possible only in a hospitalized or ambulatory environment, however due to advances in sensing and embedded system technology and miniaturization of data acquisition and processing elements health monitoring has become possible even when individuals remain engaged in their day to day activities at the convenience of space and location. The patients or individuals subject to monitoring may suffer from a traumatic experience due to their medical condition and may need emergent incidence response and the critical care team may have to prepare for the treatment only after the patient arrives, which often is too late, as in case of cardiac arrests or severe injuries. The research focused on real-time health status monitoring and trauma scoring using standard physiological parameters along with standard telemetry protocols to make the critical care team aware of an emergent situation and prepare for a medical emergency. Vital signs and physiological parameters (heart rate, temperature, respiratory rate, and blood pressure, SpO2) were measured in real time from human subjects non-invasively. In order to enable monitoring of the patients engaged in day to day activities, errors due to the motion were removed using stationary wavelet transform correction (correlation coefficient of 0.9 after correction) and signals from various sensors were denoised, filtered and were encoded in a format suitable for further data analysis. A composite sensor kit capable of monitoring vital signs and physiological parameters can be very useful in incident response when an individual undergoes a traumatic experience related to stroke, cardiac arrest, fits or even injury, as along with monitoring information the kit can calculate scores related to trauma like the Injury Severity Score (ISS), National Early Warning Signs (NEWS), Revised Trauma Score (RTS). Trauma Injury Severity Score (TRISS), Probability of Survival (Ps) score. An open access database of vital signs and physiological parameters from Physionet, MIMIC 2 Numerics (mimicdb/numerics) database was used to calculate NEWS and RTS and to generate correlation and regression models using the vital signs/physiological parameters for a clinical class of patients with respiratory failure and admitted to Intensive Care Unit (ICU). NEWS and RTS scores showed no significant correlation (r = 0.25, p<0.001) amongst themselves, however together NEWS and RTS showed significant correlation with Ps (blunt) (r = 0.70, p<0.001). RTS and Ps (blunt) scores showed some correlation (r = 0.63, p<0.001) and NEWS score showed significant correlation (r = 0.79, p<0.001) with Ps (blunt) scores. Furthermore, since individuals have to be monitored regardless of location, these kits have to have a built-in capability to locate the individual so that the incident response team can locate the individual based on Global Positioning System coordinates (GPS). A Quantum GIS (Geographical Information System) application using real-time GPS coordinates (OpenStreetMap coordinates) was used to calculate the shortest path using QGIS Network Analysis tool to demonstrate the calculation of shortest path and direction to locate the nearest service provider in shortest time. Along with locating the nearest healthcare service provider, it would help if the critical care team could be made aware of the physiological parameters and trauma scores using standard protocols accepted across the globe. The physiological parameters from the sensors along with the calculated trauma scores were encoded according to a standard Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) coding system and International Code of Diseases (ICD) codes and the trauma information was logged to Electronic Health Records (EHR) using Fast Health Interoperability Resources (FHIR) servers. FHIR servers provided interoperable web services to log the event information in real time. It could be concluded that analytical models trained on existing datasets can help in analyzing a traumatic experience or an injury and the information can be logged using a standard telemetry protocol as a telemedicine initiative. These scores enable the healthcare service providers to estimate the extent of trauma and prepare for medical emergency procedures and find applications in general and military healthcare.

Recently, the Internet of Things (IoT) has attracted wide attention from many fields, especially healthcare, because of its large capacities of information perception and collection. In this paper, we present an IoT-based home telehealth system for providing smart healthcare management for individuals, especially older people. Each client node of the system is mainly composed of an electronic water meter that records the user's daily water usage and an unobtrusive sleep sensor that monitors the user's physiological parameters during sleep such as heart rate (HR), respiratory rate (RR), body movement (BM), and states on the bed or outside the bed. The sleep sensor senses the biosignals and states by a sensor matrix composed of 12 piezoelectric sensors. The collected data can be transmitted to a remote centralized cloud service by a wireless home gateway for analyzing the living pattern and rhythm of users. Furthermore, the periodic feedback of results can be provided to users themselves as well as their family and health advisers. In the present study, data collections from a total of 18 older subjects were made for one year to evaluate the effectiveness of the proposed system. By analyzing living pattern and rhythm, preliminary results indicate the effectiveness of the telehealth system and suggest the potential of the system regarding improvement of quality of life (QoL) of older people and promotion of their health.

Healthcare sector is a truly dynamic and multidisciplinary system. Progressive practice of computational technologies in healthcare has enhanced electronic therapeutic measures in the care units. The healthcare decision-makers are accenting on the operational usage of digital technology in the system, to improve the health service by reducing human error and excessive load also by increasing possibility to attend more patients simultaneously. Sophisticated medical instruments are using advanced robotics, artificial intelligence to enhance their efficacy. Virtual treatments, self-diagnostics is increasing over time with the advancement of technical support in health. Electronic Health Records (EHR) of patients has created huge medical data (clinical information), which is undoubtedly a good resource for health professionals to take critical decisions and implement them accordingly. The aim of this article is to elucidate how technological developments are associated with clinical improvements in terms of constructing an upgraded health ecosystem. It is imperative to analyze how recent digital paradigms in health are helping to tackle regular and rare clinical challenges.

Management of a healthcare unit demands high prerequisites. It is a challenging organizational job for the healthcare decision-makers or leaders to tackle biomedical threats and deliver care service to huge number of affected populations in different demographic area. Health administration take innovative strategies to provide fundamental and life-saving services to the patients, when keeping the safety of the frontline medical workers is undoubtedly a priority. COVID-19 pandemic has changed the overall functioning of the healthcare system globally with alteration in the health decision-making protocols. Through this article, I have elucidated the challenges of healthcare along with today’s managerial paradigms to tackle the problems and run the system in a regulated manner.

The ageing of the population is growing significantly and will challenge healthcare systems. Chronic diseases in the older population require a change in service delivery, and new technologies can be a key element in ensuring the viability and sustainability of these systems. However, the generation gap and the physical and cognitive decline commonly associated with the older generation are barriers to the transition to these models of care. Despite this, there has been a trend towards digital healthcare, which has many potential benefits for the older population. Numerous studies have assessed the acceptability of new technologies for older people in health care. These studies highlight the importance of perceived usefulness, compatibility, ease of use and personalisation of the technology. Personalisation is necessary to ensure that the system is useful for users, and different characteristics such as country of origin, gender, age or comfort with the technology should be taken into account. A person-centred approach in the development of new health technology systems is essential to ensure that applications can be better tailored to the needs of different ageing populations. Many organisations have dedicated time and resources to ensure a person-centred approach in the development of new health technology systems, and putting the individual first is the best way forward in digital health. This article presents the work carried out in this regard in the framework of the European TeNDER project together with an analysis of the results obtained in terms of satisfaction, usefulness and usability by end users.

This paper is focused on mapping the current evolution of Internet of Things (IoT) and its associated cyber risks for the Industry 4.0 (I4.0) sector. We report the results of a qualitative empirical study that correlates academic literature with 14 - I4.0 frameworks and initiatives. We apply the grounded theory approach to synthesise the findings from our literature review, to compare the cyber security frameworks and cyber security quantitative impact assessment models, with the world leading I4.0 technological trends. From the findings, we build a new impact assessment model of IoT cyber risk in Industry 4.0. We therefore advance the efforts of integrating standards and governance into Industry 4.0 and offer a better understanding of economics impact assessment models for I4.0.

Internet of Things (IoT) provides a huge business value for customers, organizations, and governments due to the developments of so many applications in different sectors like energy and healthcare. Nevertheless, as a new emerging technology, IoT faces several security concerns that are more challenging than conventional Internet because of its limited resources as well as its complex ecosystem. Toward this end, we first highlight IoT security challenges and briefly discuss its security goals like confidentiality and integrity. Second, we discuss the most common attacks against IoT, along with their violated security goals. We also review the existing frameworks of security and privacy guidelines for IoT and illustrate their shortcomings. Third, we propose a novel framework for securing IoT objects, the key objective of which is to assign different Security Level Certificates (SLCs) for IoT objects based on their hardware capabilities and protection measures. Objects with SLCs, therefore, will be able to communicate with each other or with the Internet in a secure manner. The proposed framework is composed of five main phases. In phase 1, we classify IoT assets into four components: (i) physical objects, (ii) protocols, (iii) data at rest, and (iv) software, which includes operating systems, middlewares, and applications. We also classify IoT objects into five categories based on their hardware capabilities. In phase 2, we propose security and privacy guidelines for previously mentioned IoT assets, along with their protection measures. In phase 3, we classify protection measures into five SLCs, and then we assign different SLCs for IoT objects. In phase 4: we develop a communication plan between objects based on their SLCs. In phase 5, we propose a four-step method to seamlessly integrate our objects with legacy objects ( objects are not developed according to our framework). Fourth, the feasibility and application of this framework are illustrated using smart homes as a case study. Finally, we investigate how our framework would lessen several attacks and threats against IoT like routing attacks and physical damage. We also provide qualitative arguments to show that this framework could be utilized to solve some of IoT security challenges such as tight resource constrains. Moreover, we discuss the shortcomings of our suggested framework.

The emergence of blockchain know-how currently presents the opportunity for the health sector to adopt such technologies in electronic health records. Blockchain assists in maintaining and sharing the relevant medical records of the patient with the relevant group of healthcare providers and the hospital. Numerous specific applications include traceability of drug and patient monitoring or Electronic Health Records (EHR). While Blockchain assists in maintaining and sharing the relevant medical records of the patient with the relevant group of healthcare providers and the hospital, it is important to note that the moral consciousness of the healthcare professionals is the main guide of the moral consciousness is ethics. This paper presents an overview of the application of blockchain in the healthcare and medical sector, highlighting the specific challenges and concerns. The study adopted a systematic review of secondary literature in answering the research question.

The IoT is recognized as one of the most important areas of future technology and is gaining vast attention from a wide range of industries. Although, after 20 years from the first published literature (2002) the technology (as a whole) is not yet mature. In this study we will review the basics of IoT with a general approach, by addressing the problems of a standard architecture, vulnerabilities and use cases of this promising technology. Moreover, we will review some of the communication protocols that have invented especially for IoT technology, security threats and general implementation challenges.

(1) Background: The aim of this study was to identify predictors of the unmet health care needs during the financial and recent health crisis in Greece. (2) Methods: Time series analysis was performed for the years 2008 through 2022 using the Eurostat database. The dependent variable was the percentage of people who reported unmet need for medical care. Demographic, socioeconomic and health data as well as health expenditures were used as independent variables. Correlation analysis and simple linear regression models were conducted to analyze the results. (3) Results: Unmet health needs in Greece increased from the start of the crisis until 2016, as a gradual de-escalation of the crisis is observed. However, in 2019 the country recorded the second highest level of unmet needs for medical care before the health crisis. People with limitations in usual activities, those who report bad/very bad health status, unemployed and with low-income increased the likelihood of unmet needs. Health expenditures (public or private) were also significant determinants of unmet healthcare needs. (4) Conclusions: The increased unmet health needs widen inequalities in health and healthcare access. Therefore, health policy should eliminate barriers which restrict the access to health and enhance healthcare services, developing conditions for citizens' well-being.

We performed an epi and molecular characterization of two healthcare workers MPXV occupational infection. Five days after the sampling collection, nurses developed typical MPXV infection symptoms. Infection was confirmed by qPCR and whole genome sequencing. The most likely transmission route was through contact with fomites in the patient belonging/house.

This paper presents an in-depth contextualized tutorial on Agricultural IoT (Agri-IoT), covering the fundamental concepts, assessment of routing architectures and protocols, and performance optimization techniques via systematic survey and synthesis of related literature. The negative impacts of climate change and the increasing global population on food security and unemployment threats have motivated the adoption of the wireless sensor network (WSN)-based Agri-IoT as an indispensable underlying technology in precision agriculture and greenhouses to improve food production capacities and quality. However, most related Agri-IoT testbed solutions have failed to achieve their performance expectations due to the lack of an in-depth and contextualized reference tutorial that provides a holistic overview of communication technologies, routing architectures, and performance optimization modalities based on users’ expectations. Thus, although IoT applications are founded on a common idea, each use case (e.g., Agri-IoT) varies based on specific performance and users expectations as well as its technological, architectural, and deployment requirements. Likewise, the agricultural setting is a unique and hostile area where conventional IoT technologies do not apply, hence the need for this tutorial. Consequently, this tutorial addresses these via the following contributions: (1) a systematic overview of the fundamental concepts, technologies, and architectural standards of WSN-based Agri-IoT, (2) an evaluation of the technical design requirements of a robust, location-independent, and affordable Agri-IoT, (3) a comprehensive survey of the benchmarking fault tolerance techniques, communication standards, routing and medium access control (MAC) protocols, and WSN-based Agri-IoT testbed solutions, and (4) an in-depth case study on how to design a self-healing, energy-efficient, affordable, adaptive, stable, autonomous, and cluster-based WSN-specific Agri-IoT from a proposed taxonomy of multi-objective optimization (MOO) metrics that can guarantee an optimized network performance. Furthermore, this tutorial established new taxonomies of faults, architectural layers, and MOO metrics for cluster-based Agri-IoT (CA-IoT) networks and a 3-tier objective framework with remedial measures for designing an efficient associated supervisory protocol for cluster-based Agri-IoT networks.

Information Technology (IT) has become an essential part of our lives and due to the emergence of Internet-of-Things (IoT), technology has encompassed a majority of things that humans rely on in their daily lives. Further, as IT becomes more relevant in daily lives, the need for IT to serve public emergency services has become more important. However, due to the infancy status of IoT, there is a need for a data consortium that would prove to be best used in servicing policing in a technological driven society. This paper will discuss the plausibility of creating a universal format for use in carrying out public services, such as emergency response by the police and regular law maintenance. In this research we will discuss what the police requires in their line-of-duty and how smart devices can be used to satisfy those needs. A data formatting framework is developed and demonstrated, with the goal of showing what can be done to unifying data from smart city sensors.

Intelligent Transportation Systems (ITS) are emerging field characterized by complex data model, dynamics and strict time requirements. Ensuring cybersecurity in ITS is a complex task on which the safety and efficiency of transportation depends. The imposition of standards for a comprehensive architecture, as well as specific security standards, is one of the key steps in the evolution of ITS. The article examines the general outlines of the ITS architecture and security issues. The main focus of security approaches is: configuration and initialization of the devices during manufacturing at perception layer; anonymous authentication of nodes in VANET at network layer; defense of fog-based structures at support layer and description and standardization of the complex model of data and metadata and defense of systems, based on AI at application layer. The article oversees some conventional methods as network segmentation and cryptography that should be adapted in order to be applied in ITS cybersecurity. The focus is on innovative approaches that have been trying to find their place in ITS security strategies recently. The list of innovative approaches includes blockchain, bloom filter, fog computing, artificial intelligence, game theory, and ontologies. In conclusion, a correspondence is made between the commented methods, the problems they solve and the architectural layers in which they are applied.

Due to breakthroughs in embedded system development, sensing technologies, and ubiquitous connectivity in recent years, technologies such as Wireless Sensor Networks (WSN) and Internet of Things (IoT) have captured the imagination of researchers, businesses, and general public. That resulted in the emergence of an enormous, difficult-to-navigate body of work related to WSN and IoT. To highlight trends and developments in these technologies and to see whether they are actually deployed rather than subjects of theoretical research with presumed potential use cases, we gathered and codified a dataset of scientific publications from a 5-year period from 2013 to 2017 involving actual sensor network deployments. In the first iteration, 15010 potentially relevant articles were identified in SCOPUS and Web of Science databases; after two iterations, 3059 actual sensor network deployments were extracted from those articles and classified in a consistent way according to different categories such as type of nodes, field of application, communication types, etc. We publish the resulting dataset with the intent that its further analysis may identify prospective research fields and future trends in WSN and IoT.

The rapid growth of the smart Internet of Things (IoT) and massive propagation of wireless technologies revealed the recent opportunities for development in various domains of real life such as smart cities and E-Health applications. A slight defense against different forms of attacks is offered for the current secure and lightweight Routing Protocol for Low Power and Lossy Networks (RPL) of IoT resource-constrained devices. Data packets are highly likely to be exposed while transmitting them during data packets routing. The RPL rank and version number attacks, which are two forms of RPL attacks, can have critical consequences for RPL networks. The studies conducted on these attacks have several security defects and performance shortcomings. The research proposes a Secure RPL Routing Protocol (SRPL-RP) for rank and version number attacks. It mainly detects, mitigates and isolates attacks in the RPL networks. The detection is based on a comparison of ranks strategy. The mitigation uses threshold and attacks status tables, and the isolation adds them to a blacklist table and alerts relevant nodes to skip them. SRPL-RP supports diverse types of network topologies and is comprehensively analyzed with multiple studies such as Standard RPL with Attacks, SBIDS and RPL+ Shield. The analysis results showed that the SRPL-RP achieves great improvements with Packet Delivery Ratio (PDR) of 98.48%, control message value of 991 packets/second, and average energy consumption of 1231.75 joules. It provides a better accuracy rate with 98.17% under the attacks.

Internet of things (IoT) is a network of smart things. This indicates the ability of these physical things to transfer information with other physical things. IoT has introduced various services and daily human life depends on its reliable and accessible operation. The characteristics of these networks, such as topology dynamicity and energy constraint, challenges the routing problem in these networks. Previous routing methods could not achieve the required performance in this type of network. Therefore, developers of this network designed and developed specific methods in order to satisfy the requirements of these networks. One of the routing methods is utilization of multi-path protocols which send data to its destination using routs with separate links. One of such protocols is AOMDV routing protocol. AOMDV protocol is a multi-path protocol which uses multiple different paths for sending information in order to maintain the network traffic balance, manage and control node energy, decrease latency, etc. In this paper, this method is improved using gray system theory which chooses the best paths used for separate routes to send packets. To do this, AOMDV packet format is altered and some fields are added to it so that energy criteria, link expiration time, and signal to noise ratio can also be considered while selecting the best route. The proposed method named GSTMPR-IoT is introduced which chooses the routs with highest rank for concurrent transmission of data, using a specific routine based on the gray system theory. In order to evaluate and report the results, the proposed GSTMPR-IoT method is compared to the EECRP and AOMDV approaches with regard to throughput, packet delivery rate, end to end delay, average residual energy, and network lifetime. The results demonstrate the superior performance of the proposed GSTMPR-IoT compared to the EECRP and AOMDV approaches.

Switzerland, a wealthy country, has a cutting-edge healthcare system, yet per capita, it emits over one ton of CO2, ranking among the world's most polluting healthcare systems. To estimate the carbon footprint of the healthcare system of Geneva’s canton, we collected raw data on the activities of its stakeholders. Our analysis shows that when excluding medicines and medical devices hospitals are the main greenhouse gas emitter by far, accounting for 48% of the healthcare system’s emission, followed by nursing homes (20%), private practice (18%), analysis laboratories (7%), dispensing pharmacies (4%), the homecare institution (3%) and the ambulance services (

Artificial Intelligence (AI) in healthcare has ushered in a revolutionary era by transforming the way we provide care and reduce adverse consequences. This paper explores the field of AI applications in healthcare, including side effect management, streamlined administrative procedures, and customized treatment programs.

The medical sector is of the utmost importance in every country for well-known reasons such as their economic development, national security, quality of life, and so on. Many researchers were dedicated to this topic, however, in Romania there are limited studies on this subject. One reason is that most of the businesses in this sector are individual enterprises or small family businesses. In our research paper we deploy several analysis methods to see the financial sustainability of companies operating in the medical industry. By analyzing a sample of 23931 companies and we have concluded that their performance has increase in the peak of the pandemic and that the financial stability is high. Our results are in line with other similar studies that were conducted in other regions.

In the context of the COVID-19 pandemic, thousands of healthcare workers (HCWs) infected with COVID-19 have lost their lives worldwide. At the early stage of the epidemic, when COVID-19 was still not considered as a pandemic, a large number of Chinese HCWs were infected. Officials reported that more than 3,000 HCWs in Hubei contracted the virus at the early stage of the outbreak due to limited knowledge of the virus. Following reports of overloaded local hospitals, more than 42,000 medical staff, including those from the military, were dispatched to Hubei from across the country. At the peak of the fight, one in 10 intensive care medics in China were working in Wuhan. During fighting against COVID-19 in China, although a large number of HCWs were infected by SARS-CoV-2 in the early stages of the epidemic, the timely adoption of measures indicated that, a faster rate of diagnosis could be achieved, patients were isolated in-time, HCWs’ safety was prioritized, training on basic protective knowledge and unified management of HCWs was strengthened, and effective protective measures were implemented. This resulted in the accomplishment of zero SARS-CoV-2 infection among the 42,632 members of the national medical teams sent to Hubei, and the number of COVID-19 cases among HCWs in local hospitals also significantly decreased, thereby indicating that hospital-acquired infections of SARS-CoV-2 among HCWs are fully preventable.

In the field of low power wireless networks, one of the techniques on which many researchers are putting their efforts is related to positioning methodologies such as fingerprinting in dense urban areas. This paper presents an experimental study aimed at quantifying the mean location estimation error in densely urbanized areas.Using a dataset made available by the University of Antwerp, a neural network was implemented with the aim of providing the position of the end-devices. In this way it was possible to measure the mean location estimation error in an area with high urban density. The results obtained show an accuracy in the localization of the end-device of less than 150 meters.This result would make it possible to use the fingerprint instead of alternative, energy consuming, methodologies such as GPS in IoT (Internet of Things) applications where battery life is the primary requirement to be met.

In this paper, we address the problem of compatibility between digital signature schemes and in-network aggregation approaches. In the IoT world, the gateways alter the signed network flows when performing in-network aggregation. Therefore, existing conventional approaches are not suitable for verifying the authenticity of the original flows. This raises the need for energy-effective and secure schemes that enable the destination to validate aggregated network flows. In this regard, a lightweight homomorphic signature scheme is proposed which supports the implementation of aggregation procedures without affecting the verification process. We demonstrate the unforgeability and the privacy of our scheme. We also perform an analytical study of its energy-efficiency. The results suggest that the proposed scheme considerably decreases the processing overhead of the existing set-homomorphic signature schemes. Moreover, it does not add any communication overhead to traditional (non-homomorphic) signature schemes. This, in turn, improves the energy consumption by 30% compared to existing homomorphic signature techniques.

LIFI was first introduce by Professor Harald Haas on July 2011. Light fidelity (LI-FI) may be a technology which uses light as a medium to travel from one place to a different. It uses Light like Light Emitting Diode (LED) visible radiation Communication (VLC) for data Transferring and Internet connection. LIFI may be a technique which uses light as a medium because it uses light to travel it's 20 times faster than any WIFI within the world. during this paper we speak about things like what's LIFI and the way it's better then WIFI why we want LIFI and What changes did bring LIFI in our Future LIFI. How it'll make IOT devices far better than before The Technique to implement LIFI and also the problem we face of implementation of LIFI

Objectives: This study aimed to analyze the awareness of local residents regarding healthcare projects and to suggest some ideas for the revision of local ones. Methods: To delve into the opinions of local residents, the author of this study created a questionnaire composed of eight questions on the general characteristics of the respondents, eight questions on satisfaction with health centers, 16 questions on the awareness of healthcare projects, and 22 questions on local healthcare. The survey was conducted for 409 residents who visited public centers in Gimcheon from 15 March to 14 April 2021. Results: Data analysis revealed the following: The proportion of local residents who use health centers was 39.1%, and those users visit health centers, on average, 3.92 times a year. Among healthcare projects, the project known by the highest proportion of people was vaccinations (84.5%), which was also the project that was most used (38.1%). Among healthcare projects needed in the with-COVID-19 age, respondents awarded the highest score to vaccinations (4.15 points on a five-point Likert scale) and the second highest score to infectious disease management (4.12). Conclusions: For healthcare projects, central and local governments should focus on activating vaccinations, solving the problem of medical accessibility through untact remote treatment and establishing national infectious disease-specializing hospitals and local infectious disease management based on such national hospitals.

Photoplethysmography (PPG) is widely utilized in wearable healthcare devices due to its convenient measurement capabilities. However, the unrestricted behavior of users often introduces artifacts into the PPG signal. As a result, signal processing and quality assessment play a crucial role in ensuring that the information contained in the signal can be effectively acquired and analyzed. Traditionally, researchers have discussed signal quality and processing algorithms separately, with individual algorithms developed to address specific artifacts. In this paper, we propose a quality-aware signal processing mechanism that evaluates incoming PPG signals using signal quality index (SQI) and selects the appropriate processing method based on the SQI. Unlike conventional processing approaches, our proposed mechanism recommends processing algorithms based on the quality of each signal, offering an alternative option for designing signal processing flows. Furthermore, our mechanism achieves a favorable trade-off between accuracy and energy consumption, which are the key considerations in long-term heart rate monitoring.

The management of large medical files is a critical challenge in the health sector. Conventional systems have security, scalability, and efficiency deficiencies. This project proposes a hybrid architecture between blockchain and cloud computing to address these challenges. Blockchain ensures the immutability and traceability of medical records, while the cloud allows scalable and efficient storage. Together, these technologies can transform data management in electronic health record applications. The essential objective of this research is to thoroughly look at the existing strategies, devices, and conventions that encourage a secure interaction between blockchain applications and the cloud. Special emphasis is set on maintaining the integrity and security of the blockchain while tackling the potential and efficiency of cloud infrastructures. Through a meticulous investigation of these approaches, along with their successful usage and encountered challenges, this paper offers a comprehensive overview of the present state of this integration. Altogether, this paper does not propose new concepts or developments. Its central focus is to provide a comprehensive and insightful examination of the modern landscape concerning the integration of blockchain and cloud advances. By shedding light on the existing approaches and assessing their viability, this work aims to highlight the current challenges and build up a solid establishment for future development. This, in turn, will cultivate a consistent integration of blockchain security with the dynamic potential of cloud computing while guaranteeing information integrity and security remain uncompromised. In conclusion, this paper serves as a important resource for analysts, specialists, and partners looking for to dig into and development the integration of blockchain and cloud innovations, subsequently contributing to a more secure and productive cloud environment.

Pituitary adenomas are benign brain tumors that pose a heavy burden on patients worldwide. However, the local burden of disease is yet to be established due to scarcity of data. In line with this, this study aims to present the challenges and gaps in the treatment of pituitary adenomas in the Philippines. A scoping review of available relevant literature on epidemiology, clinical experience with treatment, health financing, and healthcare delivery system based on the Preferred Reporting Items for Systematic reviews and Meta-analysis guidelines extension for Scoping Reviews was conducted. The scarcity of updated local clinical data, inequity of distribution of resources, inadequate government support, and lack of affordable diagnostic testing, medications and neurosurgical procedures are the factors that hinder provision of adequate care of pituitary adenomas in the Philippines. There are notable treatment gaps in the management of pituitary adenomas in the Philippines, which may be addressed by strengthening universal healthcare. Strategies to address these gaps were proposed, including improving public-private insurance coverage, increasing manpower, enhancing accessibility to resources, and spreading more awareness.

Premortem clinical diagnoses and postmortem autopsy findings do not match throughout history. These clinicopathological discrepancies are expected to be raised during the coronavirus disease 2019 (COVID-19) pandemic due to increased pressure on healthcare. At the same time, autopsies and clinicopathological discrepancies accurately display the quality of the healthcare system. To assess that, we examined the relevant works of literature according to the PRISMA-based protocol. As discrepancy rate changes in the correlation to the length of stay in the hospital, we also checked whether studies with patients from the intensive care unit (ICU) differed in discrepancies significantly. We found overall risk differences pretty much alike in "pre-COVID" and "during the pandemic" groups of studies. Based on that, we concluded that healthcare quality did not drop significantly during the pandemic. Be it as it may, the pandemic exposed some shortcomings in mortem healthcare regarding consensus to the autopsy, organ retention, burial, and the postponed burial or the digitalization of post mortem healthcare. All of these issues should be addressed in the future.

This article focuses on the economic dimensions of maternal health, highlighting the benefits of investing in maternal healthcare and the policy implications for sustainable development. Through a comprehensive review of the literature, the article explores the economic impact of maternal health, including its effects on human capital, productivity, and healthcare costs. It emphasizes the importance of addressing social determinants such as access to healthcare services, education, employment, income, and geographical disparities. By prioritizing maternal health, societies can unlock the economic potential of women, promote inclusive growth, and reduce gender disparities. The article discusses policy interventions, including the importance of healthcare infrastructure, financial support for maternal healthcare, and promoting gender equality. It also emphasizes the need for future research to quantify the economic benefits and evaluate the cost-effectiveness of different interventions. Overall, the article underscores the economic imperative of investing in maternal health and provides valuable insights for policymakers and researchers seeking to enhance maternal health outcomes and promote sustainable economic development.

Human Activity Recognition (HAR) is a process to automatically detect human activities based on stream data generated from various sensors, including inertial sensors, physiological sensors, location sensors, cameras, time, and many others. Unsupervised contrastive learning has been excellent, while the contrastive loss mechanism is less studied. In this paper, we provide a temperature (τ) variance study affecting the loss of SimCLR model and ultimately full HAR evaluation results. We focus on understanding the implications of unsupervised contrastive loss in context of HAR data. In this work, also regulation of the temperature(τ) coefficient is incorporated for improving the HAR feature qualities and overall performance for downstream tasks in healthcare setting. Performance boost of 1.3% is observed in experimentation.

The Ministry of Health evaluates hospital management in accordance with the standard of quality of service. The concept of Shariah hospitals offers management services that exceed the standard of quality of hospital care. The study aims to illustrate the concept of Shariah hospitals in Indonesia. We collect related literature from various media via online search with the keywords “Shariah hospitals,” “implementation of Shariah hospitals,” and “application of Shariah hospitals.” Main findings: The study finds that the Shariah hospitals built by the philosophy of Islam are willing to provide the best health-care services to patients. A code of conduct must be fulfilled by the hospital management in Shariah hospitals: (1) general liability, (2) obligations to society and the environment, (3) obligations to patients, (4) obligations to the leaders, staff, and employees, and (4) relationships with related institutions. The foremost challenges include the improvement of health personnel performance and the quality of services in addition to perceptions that are not inclusive of the system of Shariah hospitals. This implementation should run consistently and with the commitment of all parties. Such insight, in turn, can be counted as an input to an approach to health services, particularly in increasing the performance rates, such as hospital. This study is the first to provide new insight into discussion about shariah hospital by presenting its focuses on Islamic approaches in meeting the quality standards of health services in hospitals so as to obtain more value. However, exclusive principles—Islamization, heterogeneity, and the performance of health workers—challenge the implementation of this hospital system.

Reflection in healthcare education is an emergent topic with many studies and reviews being published. The purpose of the present review is to map the literature in this field by performing a systematic review of reviews (umbrella review) and to explore which definitions and models are currently in use, how reflection impacts on design, evaluation and assessment and future challenges. Nineteen reviews were identified that satisfied inclusion criteria. Emerging themes were: reflection is currently portrayed as self-reflection and critical reflection with the epistemology-of-practice notion not being as much as expected in tandem with the evidence-based-medicine paradigm modern science advocates. Reflective techniques were recognised in multiple settings (e.g. summative, formative, group vs individual etc.) and have been associated with learning but assessment remains a research topic with issues of validity, reliability and replicability. Future challenges involve the epistemology of reflection in healthcare education and how to practice and assess reflection without losing its theoretical background.

Background: In a Beveridgean decentralized healthcare system, like the Italian one, where regions are responsible for their own health planning and financing, the analysis of patients’ migration appears very interesting as it has economic and social implications. The study aims to analyze both patients’ migration for hospital rehabilitation and if the beds endowment is a driver for these flows; Methods: from 2011 to 2019, admissions data were collected from the Hospital Dis-charge Cards database of the Italian Ministry of Health, population data from Italian National Institute of Statistics and data of beds endowment from the Italian Ministry of Health website. To evaluate patients’ migration, we used Gandy’s Nomogram, while to assess if beds endowments are mobility drivers, we have created two matrices, one with attraction indexes (AI) and one with escape indexes (EI). The beds endowment, for each Italian region, were correlated with AI and EI. Spearman’s test was carried out through STATA software; Results: Gandy’s Nomogram showed that only some northern regions had good hospital planning for rehabilitation. A statistically significant correlation between beds endowment and AI was found for four regions, while with EI for eight regions. Conclusions: Only some northern regions appear able to satisfy the care needs of their residents, with an attraction minus escapes positive epidemiological balance. The beds endowment seems to be a driver of Patients’ migration, mainly for escapes. Certainly, the search for mobility drivers needs further investigation given the events in Molise and Basilicata.

Background: Health Care Workers (HCWs) are at increased risk of acquiring and transmitting COVID-19 infection. Also, they present role models for communities with regards to attitudes towards COVID-19 vaccination. Hence, hesitancy of HCWs towards vaccination can crucially affect the efforts aiming to contain the pandemic. Previously published studies paid little attention to HCWs in Arab countries, which has a population of over 440 million. Objectives: to assess the rates of COVID-19 vaccine hesitancy in Arabic-speaking HCWs residing in and outside the Arab countries, and their perceived barriers towards vaccination. Methods: a cross-sectional study based on an online survey was conducted from 14-Jan 2021 to 29-Jan 2021, targeting Arabic-speaking HCWs from all around the world. Results: the survey recruited 5,708 eligible participants (55.6% males, 44.4% females, age 30.6±10 years) from 21 Arab countries (87.5%) and 54 other countries (12.5%). Our analysis shows a significant rate of vaccine hesitancy among Arabic-speaking HCWs residing in and outside Arab countries (25.8% and 32.8%, respectively). The highest rates of hesitancy were among participants from the west region of the Arab world (Egypt, Morocco, Tunisia, and Algeria). The most cited reasons for hesitancy were concerns about side effects and distrust in vaccine expedited production and healthcare policies. Factors associated with higher hesitancy included age of 30-59, previous or current suspected or confirmed COVID-19, female gender, not knowing the vaccine type authorized in the participant’s country, and not regularly receiving the influenza vaccine. Conclusion: this is the first large-scale, multinational, post-vaccine-availability study on COVID-19 vaccine hesitancy among HCWs. It reveals high rates of hesitancy among Arab-speaking HCWs. Unless addressed properly, this hesitancy can impede the efforts for achieving widespread vaccination and collective immunity.

Scales are widely used in many agricultural applications, ranging from weighing crops at harvest to determine crop yields to regularly weighing animals to determine growth rate. In agricultural research applications, there is a long history of measuring crop water use (evapotranspiration) using a particular type of scale called weighing lysimeters. Typically, weighing lysimeters require very accurate data logging systems that tend to be expensive. Recent developments in open-source technologies, such as micro-controllers and Internet of Things (IoT) platforms, created opportunities for developing effective and affordable ways to monitor crop water use and transmit the data to the Internet in near real-time. Therefore, this study aimed to create an affordable Internet of Things (IoT) scale system to measure crop evapotranspiration. A scale system to monitor crop evapotranspiration was developed consisting of an Arduino-compatible microcontroller with cell phone communication, electronic load cells, an Inter-Integrated Circuit (I2C) multiplexer, and analog-to-digital converters (ADC). The system was powered by a LiPo battery, charged by a small (6 W) solar panel. The IoT scale system was programmed to collect data from the load cells at regular time intervals and send the data to the ThingSpeak.com IoT platform. The system performed successfully during indoor and outdoor experiments conducted in 2023 at the Clemson University Edisto Research and Education Center, Blackville, SC. The results from the experiments showed that the IoT scale system could accurately measure crop evapotranspiration using the weighing lysimeter technique.

Fruits and vegetable diagnosis and inspection is a crucial part of quality control and health safety. From harvesting to retailing, stakeholders encounter tons of food losses due to the lack of monitoring of the fruit condition in retail. The problem gets worse because it is being handled by many people, including retailers and customers. We propose a transparent fiberglass-made vertical fruit showcase, which provides 80 percent more visibility, takes lesser shop space, and prevents human contact. It is convenient for the retailer and customer to use. Moreover, the controlled fruit environment is easy to integrate with IoT, which will help to generate consensus data for fruit management decisions in machine learning.

IoT is a computational perception that defines a situation, where smart objects are interconnected to the internet/ network. In the last few years, IoT objects have increased in all sectors/ fields and use in all domains of our lives. As the number of devices increases, the privacy of data becomes an important issue. To deal with this issue different techniques have been used by researchers in this field. Unfortunately, these methods have less traceability, privacy of data, and accountability. This paper aims to design a blockchain-based network architecture for Traceability, Data privacy, and Accountability (TDA). Technologies of blockchain are known as a distributed ledger of transactions record, which record the life span information with a timestamp. The major features of blockchain are persistency, decentralization, and audibility. These features help to decrease the budget and increase efficiency. Moreover, to strengthen the TDA architecture, this paper also discusses the performance of the proposed architecture.

The commonly accepted definition of sustainability considers the availability of relevant resources to make an activity feasible and durable while also recognizing users' support as essential part of the social side of sustainability. IoT represents a disruption in the general scenario of computing for both users and professionals, The real expansion and integration of applications based on IoT depend on our capacity of exploring the necessary skills and professional profiles that are essential for implementation of IoT projects, but also on the perception of relevant aspects for users, e.g., on privacy, legal, IPR and security issues. Our participation in several EU-funded projects with focus in this area has enabled the collection of information on both sides of IoT sustainability through surveys but also collecting data from a variety of sources. Thanks to these varied and complementary sources of information, this article will explore the user and professional aspects for sustainability of Internet of Things in practice.

We introduce a device for automatic detection and reporting of crawling insects in urban environments. It is a monitoring device for urban pests that complies with the context of smart homes, smart cities and is compatible with the emerging discipline of the Internet of Things (IoT). We believe it can find its place to every room of a hotel, hospital, military camp and residence. This box-shaped device attracts targeted insect pests, senses the entering insect and takes automatically a picture of the internal space of the box. The picture is communicated through the Wi-Fi commonly found in such establishments to an authorized person/stakeholder receiving the picture to take proper action. The e-trap includes strong attractants (pheromone and/or food) to increase capture efficiency. The insect is trapped on the sticky floor of the device. The device carries the necessary optoelectronic sensors to guard all entrances of the trap. As the insect enters it interrupts the infrared light source. This triggers a detection event; a picture is taken, and a time-stamp is set before reporting the event through the Wi-Fi. The device can be integrated seamlessly in urban environments and operates unobtrusively to human activities. We report results on various insect pests and depending on the insect species, can reach a detection accuracy ranging from 96-99%.

Because of the increasing relevance of the Internet of Things and location-based services, researchers are evaluating wireless positioning techniques, such as fingerprinting, on Low Power Wide Area Network (LPWAN) communication. In order to evaluate fingerprinting in large outdoor environments, extensive, time-consuming measurement campaigns need to be conducted to create useful datasets. This paper presents three LPWAN datasets which are collected in large-scale urban and rural areas. The goal is to provide the research community with a tool to evaluate fingerprinting algorithms in large outdoor environments. During a period of three months, numerous mobile devices periodically obtained location data via a GPS receiver which was transmitted via a Sigfox or LoRaWAN message. Together with network information, this location data is stored in the appropriate LPWAN dataset. The first results of our basic fingerprinting implementation, which is also clarified in this paper, indicate a mean location estimation error of 214.58 m for the rural Sigfox dataset, 688.97 m for the urban Sigfox dataset and 398.40 m for the urban LoRaWAN dataset. In the future, we will enlarge our current datasets and use them to evaluate and optimize our fingerprinting methods. Also, we intend to collect additional datasets for Sigfox, LoRaWAN and NB-IoT.

This paper explores the dynamic intersection of Artificial Intelligence (AI) and the Internet of Things (IoT), focusing on emerging trends in intelligent data analysis and privacy protection. Employing a systematic literature review, we analyze recent advancements and challenges in AI-IoT integration. The paper highlights key developments in sensor data anomaly detection, AI-driven big-data analytics, and IoT-based communication techniques. It delves into the complexities of privacy protection in IoT, examining innovative solutions like federated learning and blockchain technologies. The study also addresses the challenges in AI for IoT, prioritizing the most pressing issues such as data security, ethical implementation, and compliance with regulations like GDPR. The research underscores the need for adaptable solutions that balance technological advancements with privacy and security considerations, setting a path for future exploration in AI-empowered IoT applications.

The Internet of Things (IoT) continues to grow at a rapid pace, becoming integrated into the daily operations of individuals and organisations. IoT systems automate crucial services within daily life that users may rely on, which makes the assurance of security towards entities such as devices and information even more significant. In this paper, we present a comprehensive survey of papers that model cyber risk management processes within the context of IoT, and provide recommendations for further work. Using 39 collected papers, we studied IoT cyber risk management frameworks against four research questions that delve into cyber risk management concepts and human-orientated vulnerabilities. The importance of this work being human-driven is to better understand how individuals can affect risk and the ways that humans can be impacted by attacks within different IoT domains. Through the analysis, we identified open areas for future research and ideas that researchers should consider.

Ideas about Artificial Intelligence appeared about half a century ago, but only now is it becoming an essential element of everyday life. The data provided is getting bigger and bigger and, in this case, we need artificial intelligence that helps us with superhuman powers. The interaction with medicine is getting closer and closer, medicine being a field that continues to be perfected. Machine learning, deep learning, neural networks, computer vision are some of the mechanisms that are already applied in medicine, becoming a trend in healthcare worldwide. Artificial intelligence will be the thing that will change contemporary medicine. Developed countries such as Japan, France, Germany have already implemented artificial intelligence in their medical systems. The help is in medical diagnosis, patient monitoring, personalized therapy, and workflow optimization. Artificial intelligence will help surgeons to perfect their skills, to standardize techniques, to choose the best surgical technique. The goal is to predict complications, reduce diagnostic time, diagnose complex pathologies, guide the surgeon intraoperatively and reduce medical errors. We are at the beginning but the potential is enormous.

Background and Aims: The present study analyzed retrospectively the association between socioeconomic deprivation and graft and patient survival in a cohort of 2,568 adult recipients of a liver transplant between 1996 and 2022. Materials and methods: The primary exposure was a nationally validated socioeconomic deprivation index (DI) at census block level and ranking from 1-5, with higher ranks indicating more significant socioeconomic deprivation. Results: At a median (IQR) follow-up of 144.8 (204) months, the overall patient and graft survival rates were 92% and 90.4% at 1 year, 78.4% and 72.2% at 5 years, and 58% and 56.7% at 10 years. Recipients with a DI rank above the median (i.e., more deprived) had 1, 5, and 10-year patient and graft survival rates of 91% and 89.5%, 70.4% and 68.4%, 48% and 46.7%, respectively, versus 92% and 90.4%, 78.4% and 72.2%, and 58% and 56.7% for less deprived patients (log-rank p<0.001). More deprived patients had a higher risk of death (p<0.0001), hypertension (p<0.0001), obesity (p=0.02), diabetes mellitus (p=0.02), graft rejection (p<0.0001), chronic kidney dysfunction (p<0.0001), major cardiovascular events (p<0.0001) and de novo malignancies (p<0.0001) than less deprived recipients. The factors associated with the survival probability were younger donor and recipient ages (p=0.03 and 0.02, respectively), female recipient sex (p=0.04), absence of HCV (p<0.01), absence of HCC (p=0.02), absence of DM at transplantation (p=0.03) and 1 year (p=0.01), lower DI (p=0.02), lower MELD (0.02), shorter CIT (p=0.03), TAC (p=0.01), and EVR in the immunosuppressive regimen (p=0.02). Conclusions: Patients from more deprived areas have a higher risk of death after liver transplantation. Pre- and post-transplant socioeconomic risk profiling is warranted to better tailor care to patient’s needs and expectations.

The healthcare system is intricate and dynamic. For the purpose of organising and planning a system, where allocating funds appropriately is essential and therefore managerial skills play a critical role. A certain portion of each nation's GDP is allotted to the health sectors. Managers and decision-makers in the healthcare systems are using resources effectively, yet the quantity of funding they receive from the government or other stakeholders is insufficient sometimes. A sound understanding of both basic and advanced economics is necessary for the care management, to make cost-effective judgements in public health. Healthcare officials at all levels (regional, state, federal, or international), need to be knowledgeable about public health issues and the related modern economic climates. Business executives and healthcare managers need effective practical knowledge to handle partner-financial allocation (demand-supply chain) and handle economic volatility.

Achieving Universal Health Coverage (UHC) is a strategic objective of the Jordanian Government and has been prioritized in its strategies and plans. However, there are several challenges affecting primary health care in Jordan and the health system in general that prevent Jordan from achieving UHC. This paper highlights the importance of team-based care in the form of Family Health Teams (FHTs) to realize Jordan’s goal of achieving UHC. FHTs are a team-based approach that brings together diverse professionals to provide a comprehensive, efficient, patient-centered primary care system that meets the changing needs of Jordan's population and refugees. However, the implementation of FHT may encounter obstacles, including individual, organizational and institutional, and external barriers. To overcome such obstacles, several actions and processes need to be taken, including political commitment and leadership, implementing good governance and policy frameworks, allocating resources and funding, multisectoral collaboration, and engagement of communities and stakeholders. The successful implementation of FHTs requires participation from government officials, parliamentarians, civil society, and influential community, religious, and business leaders. A strategic policy framework, effective oversight, coalition building, regulation, attention to system design, and accountability are also essential. In conclusion, adopting the FHT approach in Jordan's Primary Health Care system offers a promising path towards achieving UHC, improving healthcare access, quality, and efficiency while addressing the unique challenges faced by the country's healthcare system.

This retrospective observational study describes the results of an ad-hoc designated prevention protocol aimed at containing the spread of the scabies infestation among healthcare workers (HCWs) of a large University Hospital in Italy. The outbreak started on October 2022 and a preventive protocol was set up thanks to a multidisciplinary approach. HCWs at high scabies risk were defined as subjects working in UOs with more than five confirmed cases of scabies, close contacts of a confirmed case of scabies, or HCWs with signs and symptoms of the disease. All cases of at high scabies risk underwent a dermatological examination and the infested HCWs were suspended from work until definitive healing. Mass prophylactic treatment was established for all HCWs working in UOs with more than five confirmed cases of scabies. Until March 2023, out of 183 screening dermatological examinations, 21 (11.5%) were diagnostic for scabies. We obtain a prevalence of scabies of 2.6% (21/800) and a duration of the outbreak of 14.7 weeks. Statistical analysis shows a significant association between scabies and being a nurse and have an allergy to dust mites. We obtained a low prevalence of scabies infection, limiting the duration of the outbreak and the related economic burden.

Early experience with Covid-19 shows that vaccines can be the most effective way of preventing the spread of infection. However, vaccine hesitancy is among the most significant hurdles in preventing the spread of novel infections. Monkeypox (MPX) has already been declared a global health emergency by WHO. Thus, there is an urgent need to understand the MPX vaccine willingness in various population groups. In this cross-sectional study, an online survey was conducted among Saudi healthcare workers (HCWs) to understand the monkeypox vaccine willingness in healthcare workers in Saudi Arabia. Saudi has already confirmed multiple MPX cases, and thus it is essential to initiate timely protective measures, including vaccination. The study had 743 respondents. The study found that among Saudi HCWs, 52.7% were willing to receive the MPX vaccine. The study found that sociodemographic factors had a small impact on vaccine willingness. However, early experience with vaccination had a significant impact. Thus, more than 70% who had influenza or COVID-19 vaccine were willing to receive the MPX vaccine. Some of the most significant concerns influencing vaccine acceptability were the safety and efficacy of the vaccine. Hence, it is strongly recommended to focus on disseminating information regarding the safety and efficacy of the MPX vaccine.

(1) Background: Emergencies confront civilian and military healthcare providers with medical and hygienic challenges due to the lack of potable water. This pilot study aimed to describe the application of two different methods for microbiological monitoring of water in a harsh environment in terms of performance, ease of use, availability, and the possibility of using the results to evaluate water quality. (2) Methods: Samples from raw water, Potable water, and water for consumers were taken from two different camps with the same raw water source. The samples were analyzed by using IDEXX industry-standard methods (Colilert and Enterolert enzymatic test kits) and a combination of membrane filtration and 3M-Petrifilm. (3) Results: The IDEXX method used at the Norwegian Camp are easier to utilize and has a broader range of analyzing kits for drinking water analysis. In addition, IDEXX is better adapted to the requirements of the national legislation. However, the combination of membrane filtration followed by incubation on 3M-Petrifilm ™, as used at the Swedish camp, is a better field alternative compared to traditional bacteriology, as it eliminates the need to produce and store agar plates. (4) Conclusions: This pilot study highlights the need for adapted technical equipment and tools for internal microbiological control of water production in a harsh field environment and may facilitate the use of a relatively simple method for water control and ensure the safety of deployed staff in both civilian and military settings.

Hand hygiene is among the most important factors of infection control in healthcare settings. Healthcare workers are considered the primary source of hospital acquired infection. We assessed the current state of hand hygiene knowledge, perception and practice among the healthcare workers in Qassim, Saudi Arabia. In this cross-sectional study we used the hand hygiene knowledge and perception questionnaire developed by the World Health Organization. Knowledge and perceptions were classified into good (80 – 100%), moderate (60 – 79%) and poor (<60% score). Majority of the healthcare workers had moderate knowledge (57.8%) and perception (73.4%) of hand hygiene. Males were less likely to have moderate/good knowledge compared to females (OR: 0.52, p<.05). Private healthcare workers were less likely (OR: 0.33, p<0.01) to have moderate/good perception compared to the government healthcare workers. Healthcare workers who received training on hand hygiene were 3.2 times likely (p<.05) to have good/moderate perception and 3.8 times likely (p<0.05) to routinely use alcohol-based hand-rub than the ones without such training. Physicians were 4.9 times likely (p<0.05) to routinely use alcohol-based hand-rub than the technicians. Our research highlighted gaps on hand hygiene knowledge and perception and practice among healthcare workers in Qassim and importance of training in this regard.

Background: The practices of various health-care professionals have been improvised to accommodate the on-going covid-19 pandemic situation. Different guidelines have been set in place to ease the process of re-opening of non-elective healthcare services like out-patient physiotherapy clinics. Although the measures taken should be guided by evidence based information, major consensus amongst practicing therapists needs to guide the India physiotherapy clinics. Objective: To identify and present the opinions of different physiotherapists about the various strategies for re-opening the out-patient physiotherapy clinics. Methods: An online cross-sectional survey was conducted. Over 169 participants were selected to participate in the survey according to the pre-decided inclusion and exclusion criteria. The data was collected and saved via google forms. Result and conclusion: A majority of respondents had a consensus over different strategies for re-opening the physiotherapy OPDs. These were regarding different measures to be adapted including modifications in the clinic infrastructure and the practice pattern. This would help in smoothly re-instating the physiotherapy services post the covid-19 lockdown.

The rise in the volume, variety and complexity of data in healthcare has made it as a fertile-bed for Artificial intelligence (AI) and Machine Learning (ML). Several types of AI are already being employed by healthcare providers and life sciences companies. The review summarises a classical machine learning cycle, different machine learning algorithms; different data analytical approaches and successful implementation in haematology. Although there are many instances where AI has been found to be great tool that can augment the clinician’s ability to provide better health outcomes, implementation factors need to be put in place to ascertain large-scale acceptance and popularity.

With the rapid development of urbanization and industrialization in China, environmental issues have become a knotty problem, especially issues related to air, water, and solid-waste pollution. These pollutants pose threats to the health of the population and to that of communities and have a vicious influence on the healthcare system. Additionally, pollution also exhibits spill-over effects, which means that pollution in the local region could affect the healthcare services in a neighbouring region. Therefore, it is necessary to explore the relationship between pollution and healthcare. A spatial autocorrelation analysis was conducted and spatial panel econometric models were constructed to explore the characteristics of pollution and healthcare services in China and the relationship between them using data on all 31 provinces over twelve consecutive years (2006-2017). The results showed that the utilization of healthcare services and environmental pollution were not randomly distributed; unsurprisingly, air pollution and solid-waste pollution were mainly found in parts of northern China, while water pollution was highest in southern and coastal China. In addition, environmental pollution exhibited spill-over effects on healthcare services. For example, a 1% increase in solid waste in one specific geographical unit was estimated to increase the inpatient visits per capita in adjacent counties by 0.559%. Specifically, pollution showed different degrees of influence on healthcare services, which means that the impact of environmental pollution on the number of outpatient visits is greater than on the number of inpatient visits. Our results provide the government with evidence for effectively formulating and promulgating policies, especially policies aimed at tackling spill-over effects among different regions.

Infection control among patients is critical for diseases like COVID-19. The concentration of patients in a few facilities burdens healthcare providers and the healthcare system. This study examined the operations of an extended anteroom in a dedicated COVID-19 hospital. It presents issues to consider in the deployment and operation of an extended anteroom through discussions by expert working groups. The subjects covered included efficient space, staffing, equipment management, and education. The process involved wearing personal protective equipment (PPE; in this case, Level D), and if necessary, wearing additional powered air purification respirators (PAPR), after moving from the preparation room to the dressing room, and when entering the hospital through the entrance passage. When leaving the hospital, personnel used a mandatory exit-only passage; in the dressing room, they undressed, and then went outside, in this order. The efficient spatial composition of the anteroom facilitated entry and exit and the separation of contaminated areas and non-contaminated areas using colors and lines. It is necessary to develop operational guidelines for hospitals that treat infectious diseases and conduct research to improve care. The study indicated the need to develop educational programs and use educational simulations to address regionally spread infectious diseases

Hospital indebtedness is a complex and very diverse phenomenon. Thus, the goal of this study is the comparison of the financial performance of public hospitals in accordance with their ownership and size. The results of the research lead to the conclusion that the vast majority of public hospitals are indebted, and their ownership structure does not affect their financial condition. What is more, the statistical analysis depicted that large Marshall hospitals are less indebted than poviat-commune ones. In the group of medium-size hospitals, the situation was the opposite. Moreover, the study did not confirm the significant relationship between the size or ownership and the financial status of the hospital. The analysis conducted in the article is aimed at filling in the gap in studies comparing the indebtedness between different types of public hospitals.

Currently, some non-conventional therapies (NCT), such as acupuncture, homeopathy, osteopathy, naturopathy, herbology, and Traditional Chinese Medicine are structured and well-regulated for a market of teaching and clinical practice in Portugal, among which acupuncture has become one of the most appealing and functional branches. Through investigation of acupuncture laws, field surveys, teaching work and interviews with people from NCT field in Portugal, we showed the current acupuncture education in Portugal and found that according to the academic norms and rules of education in Portugal, there is a gradual difficulty in the progression and maintenance of the degree training dynamics, due to the lack of more tolerant transitional measures and also, a timid commitment on the part of the institutions that embark on these complementary programs. Therefore, it will be necessary to promote additional programs and measures that avoid a total emptiness of the teaching of acupuncture and at the same time losses of clinicians, competencies and quality of information that are difficult to recover. It could be very meaningful and thought-provoking to the future development and improvement of acupuncture in Portugal and in other countries that welcome acupuncture and intend to have a better legislation and application.

The emerging concept of planetary health needs to be discussed in a more organized and sustainable way within the global public health and healthcare disciplines. Therefore, planetary health should be considered a cardinal component of the global academic framework for healthcare professionals. The availability of related curricula and courses is crucial to equip health professionals in this relatively new discipline of planetary health. In this review article, we aimed to explore published articles and online databases of courses to summarize the available planetary health education opportunities and discussions for health professionals, to identify the gaps in resource allocation and to suggest future recommendations. We observed a visible re-source inequity in global south with the lack of a universal planetary health module for healthcare professionals. Additionally, there is minimal inclusion of allied health disciplines in this learning process. We, therefore, recommend a dedicated network of motivated healthcare professionals and regional hubs with an agenda to ensure a comprehensive, uniform, and inclusive planetary health education curriculum and practice.

IP Multimedia Subsystem (IMS) supports high-speed transmission of multi-media like data, audio, and video between mobiles and other wireless devices. IMS is the most suitable for multimedia communication across multiple networks using the Internet of Things (IoT) and cloud computing. IoT-IMS can efficiently manage the huge amount of data generated by smart devices for audio-video during live streaming applications. During these services, the main problem is that an intruder can launch a Session Initialization Protocol (SIP) messages flooding attack on IMS that does not have the functionality to prevent itself from these types of attacks. In this paper, an intrusion detecting and preventing system (IDPS) is proposed that effectively detects the register flooding and spoofing attacks using SIP messages. We proposed a watermark embedding algorithm on the user side and an extraction algorithm on receiving side. A test-bed is set up for IMS where we have considered four different scenarios to evaluate the IDPS approach as compared to counterparts. Results illustrate the dominance of our scheme in terms of response time, register flooding attack detection ratios, CPU load utilization, fault detection ratio, and memory utilization. The fault detection ratio averagely 98.18

IoTs and WSNs utilize their connectivity to enable solutions supporting a spectrum of industries in different and volatile environments. To effectively enhance the security and quality of service of networks, empirical research should take into account a variety of factors and be structured in a replicable manner. This will not only ensure scalability but also enable verification of conclusions, leading to more constructive outcomes. Cooja offers limited performance analysis capabilities of simulations, which are often extracted and calculated manually. In this paper, we introduce a framework and a toolkit that enable researchers to conduct structured and conclusive experiments considering different factors and metrics, experiment design to results analysis. Besides, this tool summarizes multiple network metrics and allows multiple scenarios. As proof of concept, this paper studies the flooding attacks on IoT and illustrates their impact on the network, leveraging the proposed framework and tools.

Transfer learning has proven to be a valuable technique for deploying machine learning models on edge devices and embedded systems. By leveraging pre-trained models and fine-tuning them on specific tasks, practitioners can effectively adapt existing models to the constraints and requirements of their application. In the process of adapting an existing model, a practitioner may make adjustments to the model architecture, including the input layers, output layers, and intermediate layers. In this study, we examine the effects of these adjustments on the runtime and energy performance of an edge processor performing inferences. Based on our observations, we make recommendations for how to adjust convolutional neural networks during transfer learning to maintain runtime performance. We observe that the Edge TPU is generally more efficient than a CPU at performing inferences on convolutional neural networks, and continues to outperform a CPU as the depth and width of the convolutional network increases. We explore multiple strategies for adjusting the input and output layers of an existing model and demonstrate important performance cliffs for practitioners to consider when modifying a convolutional neural network model.

This research focuses on the development of a foot-worn device to assist individuals with limited mobility in walking. The device aims to reduce the risk of accidents by closely moni-toring the wearer's movements and detecting potential hazards. It incorporates sensors, in-cluding the MPU6050 gyro sensor and KY-031 vibration sensor, to track the wearer's walking behavior. In case of abnormalities, such as falls, the device triggers an auditory alert and noti-fies the caregiver via LINE Notify. The system is controlled by the Node MCU ESP8266, enabling seamless integration with the Internet of Things (IoT). The objective of this study is to design an IoT system for medical assistance. The researchers created a prototype of a smart ankle device capable of detecting falls and sending notifications to the user's smartphone. The effectiveness of the notification system was evaluated using the Line Notify application. Re-sults demonstrated a successful detection of falls in 48 out of 50 trials, with two instances of false alarms during normal walking. The overall efficiency of the smart ankle device was 98%. In summary, the proposed smart ankle device demonstrates promising potential for var-ious applications in fall detection and safety alert systems. Further improvements and collab-orations with healthcare experts are recommended to enhance its performance and make it commercially viable. The device's connectivity and responsiveness to IoT systems are vital aspects to be considered. Future research should aim to develop additional features in collab-oration with physiotherapists and healthcare professionals to meet user requirements. The de-vice holds significant benefits in terms of fall detection and safety alerts, and efforts should be made to enhance its durability, robustness, and commercialization prospects.

Botnet Attacks are one of the many types of ways to attack users in their day to day browsing, often of different trends and techniques to take your information without your control. Most firewalls can amply protect you against these trends and techniques, but scarily enough, recent articles and research have shown that they are starting to bypass said firewalls through layers of attacks, confusing your best line of defense. In this Report, we will be looking into exactly what are Botnets and the different subsets, what we can do to combat Botnet Attacks, where do they come from, their origin, the analysis of their kit and what we can put together from information collected. Through this we will be able to reach a conclusion of what we can use to possibly mitigate the effect they have on the internet and stop them from enacting their purpose.

The goal of this study is to categorize stakeholders in Internet of Things (IoT) security based on their roles in order to address the challenges associated with managing the large amount of data generated by IoT devices. With the increasing number of connected devices, the security risks also escalate, necessitating skilled stakeholders to mitigate these risks and prevent potential attacks. This study proposes a two-part approach that involves clustering stakeholders according to their responsibilities and determining relevant features to enhance decision-making. By grouping stakeholders into shared categories, the study aims to provide a more efficient and effective method for managing IoT security. The insights gained from this research will not only benefit stakeholders involved in IoT security but also assist policymakers and regulators in developing effective strategies.

The use of electronic means to support tasks such as pastoralism is a way of intelligently optimizing that activity. As any autonomous system, it requires human intervention in case of failure, and therefore it needs an autonomous mechanism that draws the attention of the human operator whenever the system or the animals evolve to undesired conditions. The present work progresses an existing alarm system, used in the SheepIT gateway, which can monitor the behavior of animals and equipment, warning human supervisors of the occurrence of unwanted events and the need for intervention. Concretely, given the lack of coverage of Internet access in rural areas, the system was integrated with a satellite interface to guarantee communication and the timely delivery of alarm messages. The paper compares the overall networking performance of the satellite link, against a Wi-Fi laboratorial baseline.

This work focused on the evaluation of some machine learning (ML) models and their application in e-health, using intermediate nodes within an Internet of Things (IoT) platform used for heartbeat anomaly detection. For the evaluation of ML models, a set of statistical validation metrics was selected. These metrics were applied in the training, testing and validation phases of the models. The results obtained can determine relevant factors for the selection of ML models, either based on the statistical and intrinsic efficiency of the ML models, or on their suitability to be implemented in intermediate nodes within an IoT platform. the more Lightweight models such as Simple Linear Regression, Logistic Regression, and K Nearest Neighbors, could easily operate in intermediate nodes, and they are models that require low processing and storage to work. In conclusion, the approach for intermediate nodes of Internet of Things platforms using cognitive networks decreases the processing cost in cloud computing and transfers it to the fog layer.

When we talk about the Internet of Things, we are referring to the connecting of things to a1 physical network that is embedded with software, sensors, and other devices that allow information2 to be exchanged between devices. It is possible that the interconnection of devices will present3 issues in terms of security, trustworthiness, reliability, and confidentiality, among other things.4 The proposed approach is effective at detecting intrusions into the Internet of Things network.5 Initially, the privacy-preserving technology was deployed utilising a Blockchain-based methodology6 to ensure that personal information was protected. Patients’ health records (PHR) security is the7 most crucial component of encryption over the Internet because of the value and importance of these8 records, particularly in the context of the Internet of Medical Things (IoMT). The search terms access9 mechanism is one of the most common approaches used to access personal health records from a10 database, but it is vulnerable to a number of security flaws. However, while blockchain-enabled11 healthcare systems provide increased security, they may also introduce weaknesses into the current12 state of the art. Blockchain-enabled frameworks have been proposed in the literature as a means13 of resolving those challenges. These solutions, on the other hand, are primarily concerned with14 data storage, with Blockchain serving as a database. To enable secure search and keyword-based15 access to a distributed database, this study proposes the use of blockchain technology as a distributed16 database, together with a homomorphic encryption mechanism. Aside from that, the suggested17 system includes a secure key revocation mechanism that can be used to automatically update various18 policies.As a result, our proposed approach provides greater security, efficiency, and transparency19 while also being more cost-effective. We have compared the findings of our proposed models with20 those of the benchmark models, if appropriate. Our comparison research demonstrates that our21 suggested framework provides a more secure and searchable mechanism for the healthcare system22 than the current state of the art.

Whereas the use of distributed ledger technologies has previously been limited to cryptocurrencies, other sectors—such as healthcare, supply chain, and finance—can now benefit from them because of bitcoin scripts and smart contracts. However, these applications rely on oracles to fetch data from the real world, which cannot reproduce the trustless environment provided by blockchain networks. Despite their crucial role, academic research on blockchain oracles is still in its infancy, with few contributions and a heterogeneous approach. This study undertakes a bibliometric analysis and aims to shed light on the institutions and authors that are actively contributing to the oracle literature, intending to promote progress and cooperation. On the one hand, although worldwide collaboration is still lacking, various authors and institutions are working in similar directions. On the other hand, most areas of research are scarcely explored, and others are even untouched.

This paper presents an OSA patient interactive monitoring system based on the Internet of Things (IoT) framework. This system allows OSA patients to get timely rescue when they are sleepy outside. Because the Beidou position marker has an interactive function, it can reduce the anxiety of the patient while waiting for the rescue. At the same time, if a friend helps the OSA patients to call the doctor, the friend can also report the patient's condition in time. This system uses the popular IoT framework. At the bottom is the data acquisition layer, which uses wearable sensors to collect vital signs from patients, with a focus on ECG and SpO2 signals. The middle layer is the network layer that transmits the collected physiological signals to the Beidou indicator using the Bluetooth Low Energy (BLE) protocol. The top layer is the application layer, and the application layer uses the mature rescue interactive platform of Beidou. Since the GPS indicator has not included the communication satellite, So it has no SMS function. OSA patients can only passively wait for a rescue. Moreover, due to the lack of satellites in Asia and the insufficient density of the ground-enhanced system, the positioning error of OSA patients is large. The Beidou system developed by China itself, the main coverage of the satellite is in Asia, and is equipped with a high-density ground-based augmentation system. Therefore, the Beidou model improves the positioning accuracy and is equipped with a special communication satellite, which increases the short message interaction function. Therefore, patients can report disease progression in time while waiting for a rescue. After our simulation test, the effectiveness of the OSA patient rescue monitoring system based on the Internet of Things framework and the positioning accuracy of OSA patients have been greatly improved. Especially when OSA patients work outdoors, the cell phone base station signal coverage is relatively weak. The satellite signal is well covered, plus the SMS function of the Beidou indicator. Therefore, the system can be used to provide timely patient progress and provide data support for the medical rescue team to provide a more accurate rescue plan. After a comparative trial, the rescue rate of OSA patients using the detection device of this system was increased by 15 percentage points compared with the rescue rate using only GPS satellite phones.

Network slicing is a promising technology for 5G networks in which operators can sell customized services to different tenants at various prices and Quality of Services (QoS) demands. Thus, the latest 4th Generation (4G) and upcoming 5th Generation (5G) mobile technologies are expected to offer massive connectivity and management of high volume of data traffic in the presence of immense interferences from mobile networks of IoT devices. Further, it will face challenges of congestion and overload of data traffic due to the humongous number of IoT devices. Nevertheless, these devices are likely to demand high throughput, low latency, and high level of reliability especially for critical real-time applications such as in Vehicular Communication System (VCS). To address these issues in 5G mobile networks, this paper proposes a Slice Allocation Management (SAM) Model based on the critical services of smart systems such as VCS to satisfy QoS demands. The proposed model aims at providing dedicated slices on the basis of service requirements such as expected throughput and latency for VCS. To ensure such performance and provide data traffic priorities of IoT devices in the uplink of Relay Nodes (RNs) cells in the 5G mobile networks, we have sliced the Radio Access Networks (RAN), along with the assignment of the nearest Mobile Edge Computing (MEC) with isolated slices based on the priorities for each IoT nodes to reduce latency level. The proposed model was simulated and validated using the OPNET simulator. The results obtained demonstrate that SAM Model is able to achieve improvement of end to end delays and uplink throughputs of the networks in high-density networks of IoT devices.

In this research paper we describe the development phase of a low-cost LoRa IoT solution applied to a kindergarten school with three years results. A set of sensors solution was developed in a LoRa communication board, battery powered, providing a simplified setup process. These sensors were used in order to measure temperature, humidity, luminosity, air quality and presence. Also, energy monitor solutions were integrated. The acquired data is transmitted and analysed for knowledge extraction, identifying savings and other related KPIs. From data, automatic saving actions were performed towards heating and cooling systems, lighting and a set of if-then actions were developed for automatic cost-saving actions, based on infrared signals to heating/cooling systems using some procedure of external command devices. This approach avoids the usage of proprietary vendor solutions in a flexible approach that can easily be deployed to any building facility. This is an important achievement since most of the building consumption is based on heating and cooling systems. In a three years test of the solution, the total energy consumption savings surpassed 20%

This work introduces a device for long term systematic monitoring of trees against borers. A widely applied way to detect wood-boring insects is to insert a piezoelectric probe with uncoated waveguide in the tree trunk and listen for locomotion or feeding sounds through headphones. This approach has several shortcomings: a) frequent manual inspection of trees is costly and impractical to be scaled to hundreds or thousands of trees, b) the larvae could be present but inactive during the inspection time and, c) when the trees are in urban environments the background noise can be significant and can mask the feeble sounds of wood-boring insects even with the use of specialized headphones. We introduce a remotely controlled device that records and wirelessly transmits on a scheduled basis short recordings of the internal vibrations of a tree to a server. The user can listen remotely or process the recording automatically to infer the infestation state of the tree with wood-boring insects that feed or move inside the tree. When integrated within the IoT framework this device can scale up to automatically monitoring the trees of the entire city. The proposed approach led to detection results in field trials of the pests Xylotrechus chinensis and Rhynchophorus ferrugineus.

The new IoT/IoE (internet of things/everythings) paradigm and architecture permits to rethink about the way the Smart City infrastructures are designed and managed, on the other hand a number of problems have to be solved. In terms of mobility the cities that embrace the sensoring era can take advantage of this disruptive technology to improve the quality of life of their citizen, also thanks the rationalization in the use of their resources. In Sii-Mobility, a national smart city project on mobility and transportation, a flexible platform has been designed and here, in this paper, is presented. It permits to setup heterogeneous and complex scenarios that integrate sensors/actuators as IoT/IoE in an overall scenario of Big Data, Machine Learning and Data Analytics. A detailed and complex case-study has been presented to validate the solution in the context of a system that dynamically reverse the traveling direction of a road segment, with all the safety conditions in place. This case study composes several building blocks of the IoT platform, which demonstrate that a flexible and dynamic set-up is possible, supporting off-grid, security, safety, cloud and mixed solutions.

This paper presents technical solutions to increase Industry 4.0 maturity. Within the “5G-Enabled Manufacturing” project, a 5G network has been deployed at the shop floor to enable fast and scalable connectivity. This network was used to connect a grinding machine to a private cloud to enable visibility and transparency of the production data, which is the basis for Industry 4.0 and smart manufacturing. Results indicate a present lack of commercially available product independent solutions for interconnection and data transfer despite the availability of open standards and well-documented demonstrator projects. The solution is described and discussed regarding technical interoperability, focusing on the system layout, communication standards, and open systems. From the discussion, it is derived that manufacturing end-users are in need to expand and further internalize knowledge on future information and communication technologies to reduce their dependency on equipment and technology providers.

Smart home and building systems are popular solution that support maintaining comfort, safety and improving energy efficiency in buildings. However, dynamically developing distributed network technologies, in particular the Internet of Things, are increasingly entering the above-mentioned application areas of building automation, offering new functional possibilities. The result of these processes is the emergence of many different solutions that mix technologies of field-level and ICT networks in various configurations and architectures. New paradigms are also emerging, such as edge and fog computing, providing support for local monitoring and control networks in the implementation of advanced functions and algorithms, including machine learning and artificial intelligence mechanisms. This paper collects state-of-the-art information in these areas, along with a systematic review of the literature, case-studies and an analysis of selected development trends. The author systematized this information in the context of the potential development of building automation systems. Based on the conclusions of this analysis and discussion, a framework for the development of the Generic IoT paradigm in smart home and building applications has been proposed, along with a SWOT analysis of its usability. Future works are proposed as well.

The proliferation of the Internet of Things (IoT) has revolutionized traditional services, giving rise to emerging smart infrastructures by connecting the physical and digital worlds. Sensory data is essential in IoT-based systems for providing context-aware and location-based services. Hence, the accurate localization of IoT devices is paramount. Anchor misplacement can significantly affect location information and coverage services in IoT. We study the effect of anchor misplacement in typical IoT settings where sensors are randomly deployed, can be mobile and may belong to multiple providers. We identify sensing coverage holes formed by anchor misplacement and analyze their presence and impact. To mitigate the impact of anchor misplacement on network reliability, we propose a framework to identify the affected sensor nodes and then identify and remove misplaced anchor nodes. The validity of our approach is verified, and its effectiveness is demonstrated by several experiments with different network topologies and parameters. Our results are promising and can be utilized in multiple coverage applications, such as smart agriculture systems and habitat monitoring, regardless of the sensors or deployment types. It also sheds light on best practices and methods for a reliable design of IoT-based systems.

This paper is an attempt to forecast the potential application of Digital Twin in an industrial environment by adopting scenario analysis as part of technology monitoring. In recent years Digital Twin has become one of the most emerging topics in both the tech industry and academia. This field is the latest addition to industry 4.0. It has paved a great growth opportunity in the fields of Healthcare, manufacturing, and smart city areas. The Digital twin can be defined as mirroring the physical entities into a virtual environment where the status, health, and control of these entities can be monitored and controlled. It is a field where Artificial intelligence and the Internet of Things play a major role in the handshake between the two environments. In this paper we have done a thorough analysis to find out the possible future outcome of this technology in the manufacturing industry from the perspective of our fictious company. These scenarios expose various challenges and opportunities our organization may face in our future endeavour.

This study delves into hospital mobility, understood as an indicator of perceived service quality, across the Italian regions of Apulia and Emilia Romagna, utilizing logistic regression among machine learning techniques. The focus is on how structural, operational, and clinical variables impact patient perceptions of service quality, influencing their healthcare choices. Through the analysis of mobility trends with machine learning, significant differences between regions were uncovered, highlighting the influence of regional context on perceived quality. The integration of SHAP (SHapley Additive exPlanations) values into our analysis provided deeper insights into the logistic regression model, elucidating the specific contribution of each variable to perceived healthcare quality. This incorporation of SHAP values underscores the study's commitment to employing advanced, explainable AI techniques to enhance the interpretability and fairness of healthcare service evaluations. The choice of logistic regression elucidated the impact of specific variables on quality perception, offering essential insights for optimizing healthcare resource distribution and underscoring the importance of data-driven strategies to foster more equitable, efficient, and patient-centred healthcare systems. Contributing to the understanding of perceived quality dynamics within the healthcare context, the research paves the way for further investigations into enhancing accessibility and service quality, leveraging machine learning as a tool for improving healthcare services efficiency in diverse regional settings.

Personal protective equipment (P PE) rules must be respected and followed to the letter of healthcare professionals in Cameroon to avoid the transmission of infectious diseases. This research proposes solutions to the problems of non-adherence and non-compliance among healthcare workers in Cameroon towards PPE rules. Inadequate access to suitable PPE, discomfort when wearing PPE during poor quality, and a lack of training on correct PPE use are just a few of these obstacles amongst HCWs in Cameroon. Hospitals and healthcare facilities should prioritize offering thorough instruction on PPE use and pinpoint the need to adhere consistently to PPE rules to remove these obstacles. They should also ensure that all healthcare personnel have access to and a sufficient supply of the proper PPE. Through the organization of impromptu visits by surveillance teams to identify areas of support and training, there is a need for assistance from government bodies and public health groups in creating awareness of the security value of PPE use among healthcare professionals. There is a lack of PPE in health care settings in rural areas in Cameroon, thus a need for an adequate supply. Then can we put appropriate PPE policies and practices for HCWs in Cameroon, as they will keep to it.Healthcare professionals in Cameroon must comprehend that following PPE standards protects not just their selves from diseases but also their patients, coworkers, and communities. We are accountable for ensuring everyone's safety and well-being, and adhering to PPE regulations is an essential first step.

Despite the importance of influenza vaccination for healthcare workers (HCWs), their uptake remains challenging. This study aimed to measure influenza vaccine hesitancy among HCWs in Nakhon Phanom province, Thailand. A representative cross-sectional survey was conducted at six hospitals during August – September 2020 using a self-administered questionnaire. HCWs delaying getting influenza vaccines, accepting but unsure, or refusing with doubts were categorized as hesitant. Those accepting without any doubts were classified as non-hesitant. Vaccine hesitancy determinants were identified by multivariable logistic regression analysis. A total of 338 participants (97%) completed the questionnaires; most were female nurses working at district hospitals without pre-existing chronic medical conditions. Nearly 60% of participants (197/338) exhibited influenza vaccine hesitancy, and significant factors included age above 50 years adjusted odds ratio [aOR] 3.2, 95% CI 1.3-8.5), fair knowledge of influenza/vaccination (aOR 0.4, 95% CI 0.2-0.8), and negative influence of other HCW (High level– aOR 2.3, 95% CI 1.1-4.8; Moderate level- aOR 2.1, 95% CI 1.1-4.4). Imparting updated information to the HCW combined with positive guidance from influential peers may help reduce this hesitancy.

This study investigates the pivotal areas where Artificial Intelligence (AI) integration in healthcare warrants further exploration, leveraging insights from Boston Specialists, a forefront healthcare provider in AI adoption. Employing a qualitative case study approach, enriched by in-depth interviews and comprehensive literature reviews, we identify three main domains for potential AI expansion: scheduling efficiency, clinical documentation, and AI-driven medical research. Our results underscore the substantial enhancements AI brings to operational workflows and patient care, notably through improved scheduling systems, accuracy in medical documentation, and the facilitation of data-driven research methodologies. These findings suggest a critical need for healthcare institutions to further invest in AI technologies, focusing on these key areas to harness AI's full potential in personalizing care, optimizing efficiency, and advancing medical knowledge. The study concludes with a call for continued investigation into AI's integration within healthcare settings, emphasizing the importance of a supportive organizational infrastructure, ethical considerations, and data security in realizing AI's transformative impact on healthcare delivery and patient outcomes.

Healthcare professionals constantly face situations that reflect ecosystems’ degradation. These can negatively affect their mental health. Research suggests that hope and resilience can play an important role in this scenario, since they are related to/predict mental health in highly heterogeneous samples (considering geography, age, profession, health, etc.). In this context, the aims of the present study are: to characterize and explore the relationship between hope, resilient coping and mental health of Portuguese healthcare professionals. Using Google Forms, 276 healthcare professionals answered to the GHQ-28, the (adult) Trait Hope Scale, and the Brief Resilient Coping Scale (cross-sectional study). The minimum and maximum possible scores were reached, with the exception of the maximum score of GHQ-28-Total. Regarding Hope, 19.6% scored below the midpoint (M=43.46, SD=11.97); 29.3% revealed low resilience (M=14.93, SD=4.05); and the average of 4 of the 5 Mental Health scores (exception: Severe Depression) indicates the probability of a psychiatric case. Hope correlated with Social Dysfunction and GHQ-28-Total; resilient coping proved to be a (weak) predictor of 4 of the 5 GHQ-28 indicators (exception: Severe depression). The results support the need to promote the sample's mental health, hope and resilient coping. They also suggest that stimulating resilient coping may contribute to improving healthcare professionals’ mental health.

The study conducts a comparative life cycle assessment (LCA) analysis to assess the environmental impact of two different manufacturing processes used to produce transparent dental aligners. The former method consists of thermoforming a polymeric disc over 3D printed, customized models, while the second, more innovative approach involves the direct printing of aligners using additive manufacturing (AM), specifically applying digital light processing (DLP) technology. The analysis results highlight how adopting direct printing through AM brings significant advantages in terms of environmental sustainability, thanks to the substantial reduction in raw materials and electricity consumption. These drops translate into decreased potential environmental impacts across all impact categories considered within the EF 3.1 method. Furthermore, lowering the amount of raw materials needed in the direct printing process contributes to a notable decrease in the overall volume of waste generated, emphasising the environmental benefits of this innovative technique.

BACKGROUND: Body mass index (BMI) is calculated by dividing a person's height in meters by their weight in kilograms and is always expressed in kg/m². BMI is a reliable risk indicator for various diseases that can develop due to a higher percentage of body fat. There is a possibility of developing certain medical conditions including heart disease, high blood pressure, type 2 diabetes, gallstones, breathing problems and some malignancies that increase with BMI. OBJECTIVE: This study seeks to identify the trends in the Body Mass Index (BMI) of women visiting Better Life Primary Health Care Centre in Ondo City, Ondo State, Nigeria for various healthcare services. METHODOLOGY: Seventy (70) women who participated in this study were randomly selected using systematic random sampling. Their necessary information was obtained using a prepared record sheet to collect their data. Results were analysed using SPSS version 21. RESULTS: The results show mean age of the respondents is 30.50 + 6.52921 21 (30%) were between 31 – 35 years, 21 (30%) were traders, 36 (51%) had tertiary education, 39 (55.7%) were between 151 – 160 cm in height. 32 (45.7%) were between 60 – 80 kg in weight and 30 (42.9%) had normal weight, respectively. CONCLUSION: Body Mass Index (BMI) calculation is an essential tool that can help in identifying one of the significant public health issues that leads to obesity; a predisposing factor to various medical conditions like hypertension, musculoskeletal issues, heart diseases, cancers, and lots more. It is therefore important to incorporate BMI calculation into routine checks to prevent or reduce health burdens that may arise from obesity.

Questionnaires are useful instruments for gathering responses to specific factual questions. However, the problems of questionnaire responses impede the effective use of the questionnaire. Some of these problems including non-responses, non-completion, issues of judgement, and social desirability such as information that the respondent is not willing to disclose need to be dealt with. The situation is more compounded in a scenario where an information security practice study in a typical hospital consists of broad categories of respondents and the survey findings are to be relied on to actually address information security compliance issues. This study, therefore, shares "pitfalls" to watch when preparing a questionnaire to measure the information security practice level in a hospital which is characterized by different respondents with varying domain knowledge such as knowledge in information security, information communication technology, and that of the domain knowledge of healthcare. A synergy of a conventional pretesting method and behaviour coding were therefore used to pretest the questionnaire. Questionnaire problems including a lack of understanding of the healthcare information systems’ structure of all hospitals, unclear questions, the insignificant difference between questions, problematic questions, inadequate questions, and complex terms were among the identified pitfalls to watch. Out of a total of 118 questionnaire items that were used in the pretesting, a total of 50 questionnaire items (representing 42%) were identified to have problems after the pretesting was conducted with a total of 36 respondents in behavior coding and 21 respondents in conventional pretesting.

Introduction: Blood transfusion involves the transfer of blood from donors to patients. A blood transfusion is carried out every 2 seconds in the US. It is made up of about 29000 units of red blood cells and is transfused every day in the US. When blood transfusion is done correctly, it can result in the saving of lives and the improvement of healthcare. However, it may also lead to immediate, late, delayed, and chronic complications. No previous studies have been conducted in Qatar to address this issue. Methods: This is a cross-sectional study intended to determine the knowledge, attitude, and practice toward blood transfusion among healthcare providers at Hamad Medical Corporation (HMC), which is the principal healthcare provider in Qatar. Participants between 18 and 25 years of age were selected for the research study. A 10-item online questionnaire that people can fill out on their own will be used to get the data needed for the analysis and meet the study's goals. Results: the analysis has indicated that facing negative reactions after blood transfusion and being worried about getting affected by any infection have a small positive association, with the specific values coming in at r = 0.317, p = 0.000. Fever after blood transfusion and feeling like refusing blood transfusion have a significant and moderate positive correlation, with the specific values coming in at r = 0.630 and p = 0.000. Conclusion: The findings of this study have helped us figure out how healthcare providers feel, what they know, and what they do during a blood transfusion.

Smart primary healthcare building facility services capture a new level of process and operational data through advanced monitoring, enabling experts to use the building facilities to produce significant and efficient healthcare service delivery within the individual spheres of influence. This study assessed the impact of IoT services on achieving smart primary healthcare building facilities in the rural area of South Africa to enhance primary healthcare delivery. The study identified three (3) basic constructs of IoT services that comprised the application of IoT location recognition and tracking services, the application of the IoT high-speed communication network-based services, and the application of IoT-based services. The study is quantitative, and a questionnaire was used to collect data from the project managers and healthcare practitioners working with the primary healthcare agency in South Africa. The study found a variable degree of impact between the three (3) IoT constructs and the achievement of primary healthcare building facility services in South Africa. The study recommends adopting IoT essential services for achieving smart primary healthcare building facility services in the rural areas of South Africa and other developing countries facing similar primary healthcare delivery challenges.

Recent reports have it that over 85\% of data breaches are still caused by the human element, of which healthcare is one of the suitable organizations mostly targeted by cybercriminals. The work of healthcare staff is often associated with high workloads, high emergency cases, and a broad range of psychological, social, and cultural factors. The significance of these factors could undermine conscious care information security (IS) practice leading to serious violations. This study comprehensively examined the correlation between the psycho-social-cultural factors, work factors with IS and privacy behaviour in a hospital that has fully adopted electronic health records (EHR) management system. The findings are to facilitate the decision-making process towards improving the cyber-security practice in healthcare. A quantitative approach was adopted where we collected responses from 212 healthcare staff through an online questionnaire survey. A broad range of constructs was selected from psychological, social, cultural perception and work factors based on earlier review work. These were therefore related to some security practices, to assess the IS knowledge, attitude and behaviour gaps among healthcare staff in a comprehensive way. From the study, IS self-reported conscious care behaviour (ISCCB) risk was relatively higher as compared to information security knowledge (ISK) risks and information security attitude (ISA) risk. Furthermore, the study revealed that work emergency has a positive correlation with ISCCB (r=1.95, p-value =0.001) risk. Conscientiousness also had positive correlation with ISCCB risk (r=0.157, p-value=0.05) however agreeableness negatively correlated with ISK risk (r=-0.166, p-value =0.05), and ISA risk (r=-0.140, p-value =0.05). Based on these findings, intrinsic and extrinsic motivation methods combined with cutting-edge technologies can be explored to discourage IS risks behaviours while enhancing conscious care security practice.

Background: To determine vitamin D supplementation frequency among infants, factors that influence adherence, and reasons for discontinuation of initiated vitamin D. Methods: This cross-sectional study was conducted using a questionnaire administered to the mothers via a face-to-face interview on 560 infants aged from 1 to 24 months admitted to outpatient clinics from June to December 2017. Results: A total of 351 infants were administered vitamin D, and the rate of supplementation in the first year of life was 83%, while it was only 28% between 13 and 24 months. The rate of vitamin D supplementation was higher among infants who were exclusively formula-fed (p<.05). When the data were analyzed using logistic regression analysis, only visit family physicians seems to be a statistically significant independent variable in increasing supplementation (p<.05). Compared with family refusal, the rate of discontinuation of vitamin D by the healthcare providers was higher after the first year of life (p<.05). The rates of vitamin D discontinuation by healthcare providers, especially by nurses who considered the duration of supplementation adequate, were statistically significantly higher when compared with the fontanel closure and other reasons (p<0.05). Conclusions: The rate of vitamin D supplementation was higher among families who visited family physicians, which suggests the importance of well-baby visits. Since vitamin D supplementation was less common among exclusively breastfed infants, mothers should be educated. Healthcare professionals need further education about the importance of vitamin D supplementation and indications for discontinuation.

Health literacy is an indicator of a society’s ability to make better health judgement for themselves and the people around them. This study investigates the prevalence of health literacy among Malaysian adults and provides an overall picture of the current health literacy state of the society. The study also highlights socio-demographics markers of communities with limited health literacy which may warrant future intervention. A population based self-administered survey using the Health Literacy Survey Malaysian Questionnaire18 (HLS-M-Q18) instrument was conducted as part of the National Health Morbidity Survey 2019 in Malaysia. The nationwide survey utilized two-staged stratified random sampling method. A sample of 9478 individuals aged 18 and above participated in the study. The health literacy score was divided into three levels; limited, sufficient and excellent. Findings showed that majority Malaysian population has sufficient health literacy level, albeit leaning towards the lower end of the category with an average score of 35.5. The limited health literacy groups are associated with respondents with older age, lower education level and lower household income. Overall health literacy state for Malaysia is categorized at a lower sufficient level. Health literacy improvements should focus on communities with limited health literacy level.

Background: Disability and caste are two different forms of oppression, however Dalits and people with disabilities commonly face similar types of marginalities. Dalit women with disabilities may experience double discrimination because of the intersectionality of disability and caste. This study examines whether the disability and caste identity of women together affects and compounds the utilization of maternal healthcare services. Methods: A cross-sectional survey was conducted using a semi-structured questionnaire among a total of 354 Dalit and non-Dalit women, with and without a disability aged between 15 – 49 years. Maternal healthcare service utilization was assessed by Ante-Natal Care (ANC), health facility (HF) delivery, and Post-Natal Care (PNC) during the last pregnancy. Logistic regression was performed to detect the predictors of service utilization and identify whether disability and caste were associated with service utilization. First, disability and caste were fitted separately in models. Secondly, the intersectionality of disability and caste was tested by the inclusion of disability*caste interaction term. Finally, the confounding effect of socio-demographic factors was investigated. Results: Out of surveyed women, 73% had 4+ ANC visits, 65% had HF delivery and 29% had a PNC visit during their last pregnancy. Women with a disability had lower odds of HF delivery (OR 0.50, CI 0.30 – 0.84) and PNC (OR 0.47, CI 0.25 – 0.88) than women without a disability. Adjustment for women’s age and household wealth explained associations in HF delivery by women with disabilities. There was no association between caste and service utilization. Disability overrode caste and there was no other evidence of effect modification by women’s caste status in the utilization. However, a weak interaction effect in the utilization of ANC services was found in the caste group by their education (OR 0.19, 95% CI 0.05 – 0.74). Conclusions: Disabled women – whether Dalit or non-Dalit - had lower rates of utilizing all maternal healthcare services than non-disabled women. However, Dalit women with disabilities were more likely to receive PNC than non-Dalit women with disabilities. Increasing equity in maternal healthcare service utilization requires that traditional approaches to service development and program intervention to be re-examined and more nuanced interventions considered to ensure improved access and outcome among all vulnerable groups.