Federated learning based Covid-19 detection.

The world is affected by COVID-19, an infectious disease caused by the SARS-CoV-2 virus. Tests are necessary for everyone as the number of COVID-19 affected individual's increases. So, the authors developed a basic sequential CNN model based on deep and federated learning that focuses on user data security while simultaneously enhancing test accuracy. The proposed model helps users detect COVID-19 in a few seconds by uploading a single chest X-ray image. A deep learning-aided architecture that can handle client and server sides efficiently has been proposed in this work. The front-end part has been developed using StreamLit, and the back-end uses a Flower framework. The proposed model has achieved a global accuracy of 99.59% after being trained for three federated communication rounds. The detailed analysis of this paper provides the robustness of this work. In addition, the Internet of Medical Things (IoMT) will improve the ease of access to the aforementioned health services. IoMT tools and services are rapidly changing healthcare operations for the better. Hopefully, it will continue to do so in this difficult time of the COVID-19 pandemic and will help to push the envelope of this work to a different extent.

A novel authentication protocol to ensure confidentiality among the Internet of Medical Things in covid-19 and future pandemic scenario.

Diagnosing the patients remotely, controlling the medical equipment, and monitoring the quarantined patients are some of the necessary and frequent activities in COVID-19. Internet of Medical Things (IoMT) makes this works easy and feasible. Sharing information from patients and sensors associated with the patients to doctors is always an integral part of IoMT. Unauthorized access to such information may invite adversaries to disturb patients financially and mentally; furthermore, leaks in its confidentiality will lead to dangerous health concerns for patients. While ensuring authentication and confidentiality, We must focus on the constraints of IoMT, such as low energy consumption, deficient memory, and the dynamic nature of devices. Numerous protocols have been proposed for authentication in healthcare systems such as IoMT and telemedicine. However, many of these protocols were neither computationally efficient nor provided confidentiality, anonymity, and resistance against several attacks. In the proposed protocol, we have considered the most common scenario of IoMT and tried to overcome the limitations of existing works. Describing the system module and security analysis proves it is a panacea for COVID-19 and future pandemics.

iMnet: Intelligent RAT Selection Framework for 5G Enabled IoMT Network.

The COVID-19 outburst has encouraged the adoption of Internet of Medical Things (IoMT) network to empower the antiquated healthcare system and alleviate the health care costs. To realise the functionalities of the IoMT network, 5G heterogeneous networks emerged as an exemplary connectivity solution as it facilitates diversified service provisioning in the service delivery model at more convenient care. However, the crucial challenge for 5G heterogeneous wireless connectivity solution is to facilitate agile differentiated service provisioning. Lately, considerable research endeavour has been noted in this direction but multiservice consideration and battery optimisation have not been addressed. Motivated by the gaps in the existing literature, an intelligent radio access technology selection approach has been proposed to ensure Quality of Service provisioning in a multiservice scenario on the premise of battery optimisation. In particular, the proposed approach leverages the concept of Double Deep Reinforcement Learning to attain an optimal network selection policy. Eventually, the proposed approach corroborated by the rigorous simulations demonstrated a substantial improvement in the overall system utility. Subsequently, the performance evaluation underlines the efficacy of the proposed scheme in terms of convergence and complexity.

Designing Digital COVID-19 Screening: Insights and Deliberations.

Due to the global COVID-19 pandemic, public health control and screening measures have been introduced at healthcare facilities, including those housing our most vulnerable populations. These warning measures situated at hospital entrances are presently labour-intensive, requiring additional staff to conduct manual temperature checks and risk-assessment questionnaires of every individual entering the premises. To make this process more efficient, we present eGate, a digital COVID-19 health-screening smart Internet of Things system deployed at multiple entry points around a children's hospital. This paper reports on design insights based on the experiences of concierge screening staff stationed alongside the eGate system. Our work contributes towards social-technical deliberations on how to improve design and deploy of digital health-screening systems in hospitals. It specifically outlines a series of design recommendations for future health screening interventions, key considerations relevant to digital screening control systems and their implementation, and the plausible effects on the staff who work alongside them.

A survey on COVID-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects.

Extensive research has been conducted on healthcare technology and service advancements during the last decade. The Internet of Medical Things (IoMT) has demonstrated the ability to connect various medical apparatus, sensors, and healthcare specialists to ensure the best medical treatment in a distant location. Patient safety has improved, healthcare prices have decreased dramatically, healthcare services have become more approachable, and the operational efficiency of the healthcare industry has increased. This research paper offers a recent review of current and future healthcare applications, security, market trends, and IoMT-based technology implementation. This research paper analyses the advancement of IoMT implementation in addressing various healthcare concerns from the perspectives of enabling technologies, healthcare applications, and services. The potential obstacles and issues of the IoMT system are also discussed. Finally, the survey includes a comprehensive overview of different disciplines of IoMT to empower future researchers who are eager to work on and make advances in the field to obtain a better understanding of the domain.

QoS Review: Smart Sensing in Wake of COVID-19, Current Trends and Specifications With Future Research Directions.

Smart Sensing has shown notable contributions in the healthcare industry and revamps immense advancement. With this, the present smart sensing applications such as the Internet of Medical Things (IoMT) applications are elongated in the COVID-19 outbreak to facilitate the victims and alleviate the extensive contamination frequency of this pathogenic virus. Although, the existing IoMT applications are utilized productively in this pandemic, but somehow, the Quality of Service (QoS) metrics are overlooked, which is the basic need of these applications followed by patients, physicians, nursing staff, etc. In this review article, we will give a comprehensive assessment of the QoS of IoMT applications used in this pandemic from 2019 to 2021 to identify their requirements and current challenges by taking into account various network components and communication metrics. To claim the contribution of this work, we explored layer-wise QoS challenges in the existing literature to identify particular requirements, and set the footprint for future research. Finally, we compared each section with the existing review articles to acknowledge the uniqueness of this work followed by the answer of a question why this survey paper is needed in the presence of current state-of-the-art review papers.

Current Technologies for Detection of COVID-19: Biosensors, Artificial Intelligence and Internet of Medical Things (IoMT): Review.

Despite the fact that COVID-19 is no longer a global pandemic due to development and integration of different technologies for the diagnosis and treatment of the disease, technological advancement in the field of molecular biology, electronics, computer science, artificial intelligence, Internet of Things, nanotechnology, etc. has led to the development of molecular approaches and computer aided diagnosis for the detection of COVID-19. This study provides a holistic approach on COVID-19 detection based on (1) molecular diagnosis which includes RT-PCR, antigen-antibody, and CRISPR-based biosensors and (2) computer aided detection based on AI-driven models which include deep learning and transfer learning approach. The review also provide comparison between these two emerging technologies and open research issues for the development of smart-IoMT-enabled platforms for the detection of COVID-19.

A Novel Approach to Combat COVID-19 using Smart-ID-Cards

COVID-19, A Pandemic with its increasing pace has spread across the globe. The medical care system was badly hit by it as the number of patients fared the number of available hospital beds and other facilities required to treat patients. To rescue, various Internet of Things (IoT) based devices were proposed to combat COVID-19 by offering a helping hand to the medical care system. The pace at which the death rate was increasing, it became the need to combat the root cause of COVID-19, the root cause being the quick spread. ID-Card though not so famous IoMT (Internet of Medical Things) device can be made to work smart, smart enough to monitor the home isolated patients, to keep a check on a precautionary distance measure and much more. The study aims to explore and discuss the state-of-the-art of various IoT to control the novel Coronavirus (COVID-19) spread by tracing out positive patients and stopping this chain by tracing symptoms just a click away. The IoMT Smart-ID-Card is proposed to easefully detect, monitor, and combat COVID-19. © 2022 IEEE.

Adopting 5G-Enabled E-Healthcare for Collaborative Pandemic Management

The spread of the COVID-19 pandemic had a huge impact on personal lives, society, and economies all over the world. Many countries are still struggling with the rising and falling numbers of COVID-19 cases. The drastic effects of the pandemic have brought sharp focus on healthcare and the need for rapid technology adoption and strong collaborative digital healthcare solutions for dealing with the health crisis. 5G networks can play a vital role in transforming the critical components of healthcare ecosystem by providing cost effective, high connectivity to the patients and healthcare workers. This research article investigates and highlights the technical aspects 5G technology, its effective utilization for collaborative e-health services, and the 5G-based solutions. It also presents a detailed discussion on challenges of 5G implementation and possible solutions. In the end, it discusses the future research directions for 5G-enabled e-collaboration in decreasing the health-based challenges and issues in future pandemic outbreaks.

Fog-cloud architecture-driven Internet of Medical Things framework for healthcare monitoring.

The new coronavirus disease (COVID-19) has increased the need for new technologies such as the Internet of Medical Things (IoMT), Wireless Body Area Networks (WBANs), and cloud computing in the health sector as well as in many areas. These technologies have also made it possible for billions of devices to connect to the internet and communicate with each other. In this study, an Internet of Medical Things (IoMT) framework consisting of Wireless Body Area Networks (WBANs) has been designed and the health big data from WBANs have been analyzed using fog and cloud computing technologies. Fog computing is used for fast and easy analysis, and cloud computing is used for time-consuming and complex analysis. The proposed IoMT framework is presented with a diabetes prediction scenario. The diabetes prediction process is carried out on fog with fuzzy logic decision-making and is achieved on cloud with support vector machine (SVM), random forest (RF), and artificial neural network (ANN) as machine learning algorithms. The dataset produced in WBANs is used for big data analysis in the scenario for both fuzzy logic and machine learning algorithm. The fuzzy logic gives 64% accuracy performance in fog and SVM, RF, and ANN have 89.5%, 88.4%, and 87.2% accuracy performance respectively in the cloud for diabetes prediction. In addition, the throughput and delay results of heterogeneous nodes with different priorities in the WBAN scenario created using the IEEE 802.15.6 standard and AODV routing protocol have been also analyzed. Fog-Cloud architecture-driven for IoMT networks • An IoMT framework is designed with important components and functions such as fog and cloud node capabilities. •Real-time data has been obtained from WBANs in Riverbed Modeler for a more realistic performance analysis of IoMT. •Fuzzy logic and machine learning algorithms (RF, SVM, and ANN) are used for diabetes predictions. •Intra and Inter-WBAN communications (IEEE 802.15.6 standard) are modeled as essential components of the IoMT framework with all functions.