IoMT Based Big Data Framework for COVID-19: Comparison
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Please note this is a comparison between Version 2 by Catherine Yang and Version 1 by Ali Hassan Sodhro.

The Internet of Medical Things (IoMT) is transforming modern healthcare systems by merging technological, economical, and social opportunities and has recently gained traction in the healthcare domain. The severely contagious respiratory syndrome coronavirus called COVID-19 has emerged as a severe threat to public health. COVID-19 is a highly infectious virus that is spread by person-to-person contact.

  • IoMT
  • Internet of Medical Things
  • COVID-19 outbreak detection

1. Introduction

The Internet of Medical Things could establish a network of interconnected healthcare devices having sensors for diagnosing and monitoring patients’ health status [1]. These sensors build smart devices, which help patients and healthcare workers to communicate smartly even from distinct locations. This massive connectivity permits devices and sensors to detect, analyze, and connect. Therefore, these devices alert patients, doctors, and other health workers by interacting with them automatically, to provide services smartly [2]. These smart applications are growing exponentially, and, as a result, there is an incredible rise in the number of interconnected IoMT devices that increase the data traffic over the network [3]. The diverse sensors, devices, and applications become the source of big data [4]. Big data frameworks incorporate conventional devices and components to acquire, store, and analyze various types of data by utilizing equal handling ability to accomplish complex changes and analysis [5]. However, structuring and developing a big data framework for a particular task is not an inconsequential or simple function [6]. Subsequently, the data acquired from several heterogeneous and self-ruling sources with evolving and complex collections is continuously developing [7]. Besides, the ascent of big data applications, where data acquisition has increased exponentially, has led to the ability to commonly utilize equipment and software platforms to store, analyze, and maintain within an acceptable measure of time [8]. In some countries, COVID-19 has been combated using IoMT technology in conjunction with other approaches. The use of IoMT enhances the protection of front-line personnel, boost efficacy by reducing the disease’s toll on people’s lives, and reduce fatality rates [8].
The extraction of values from big data sources must be analyzed for predictions to prevent and cure diseases [9]. The COVID-19 pandemic, which began in late 2019 [10], has been a focus of medical research [11]. COVID-19 is a virus that infects humans and causes respiratory illnesses [12]. Because of the profoundly contiguous nature of COVID-19, it is difficult to examine COVID-19 patients physically [13,14][13][14]. Several researchers have proposed solutions for this problem. Fangyu Li et al. [15] offered a framework plan for a COVID-19 patient monitoring platform that is end-to-end, non-invasive, and uses WiFi. Aman et al. [4] surveyed the IoMT technology, application, architecture, and security developments for COVID-19. Qiong Jia [16] provided a big data framework, comprising prevention mechanisms, prevention, response, and recovery. Nasajpour [7] presented a study to define the role of IoT in monitoring and diagnosing the COVID-19 symptoms. Swayamsiddha [17] proposed a framework—Cognitive IoT (CIoT)—for remote patient monitoring of COVID-19 patients. Abdel-Basset [18] used disruptive and emerging technologies for COVID-19 analysis, such as IoT, IoMT, AI, big data, autonomous robots, drone technology, virtual reality (VR), and blockchain.

2. IoT or IoMT including COVID-19

Ruby Dwivedi et al. [19] review the role of existing IoT applications that are participating to improve the healthcare system and also investigate the current state of research representing the efficacy of IoMT aids to patients and the medical assisting system. Furthermore, it also provides a brief look at technologies that supplement IoMT and the challenges that come with implementing a smart healthcare system. An extensive survey about emerging virtual smart health monitoring and its challenges is provided by Elliot Mbunge et al. [23][20]. Sujith et al. [24][21] suggested a smart health monitoring framework that combines blockchain, deep learning, and machine learning technologies to create a cost-effective and real-time health monitoring system. Rahman et al. [25][22] proposed a smart health framework that assists in automatic community-wide symptom severity monitoring, as well as individualized monitoring, paving the way for early disease outbreak surveillance in a smart and connected community. In the context of the IoHT area, a complete analysis of the various IoT device authentication techniques and identity management systems, with an emphasis on current achievements, open difficulties, and future possibilities is provided by Moustafa Mamdouh et al. [26][23]. D.Campos- Ferreira et al. [27][24] examined the various problems that must be overcome in order to reach an ideal situation for both containing the current COVID-19 epidemic and preventing future pandemics. According to the author, IoMT can connect and remotely monitor patients, lowering the danger of exposure to healthcare professionals. Kashani et al. [28][25] looked into the remote monitoring requirements for telemedicine, especially in developing countries and concluded that the IoT has the potential to decrease the pressure on sanitary systems while also offering personalized health care to improve people’s quality of life. However, Weiping Ding et al. [29][26] provide a comparative examination of the various types of data, emerging technology, and methodologies employed in the diagnosis and prediction of COVID-19. The authors suggested that IoMT technology has been efficiently used in the healthcare system to improve worldwide efforts in epidemic monitoring, infection tracking, illness detection, vaccine and medicine development, resource allocation, and outbreak prediction. XuranLi et al. [30][27] examine the advantages of this architecture and how blockchain-enabled IoMT might be of use. In the COVID-19 pandemic, the authors also discuss how blockchain-enabled IoMT can aid in infectious disease prevention, location sharing, contact tracing, and injectable drug supply chain management. To comprehend and explain the skepticism and resistance to IoMT among clinical users, Nastaran Hajiheydari et al. [31][28] established an integrative theoretical paradigm that incorporates system, information, and individual positive and negative variables. Jiuchuan Guo [32][29] devised a method that allows patients and their family to remotely record their medical data and daily circumstances, alleviating the strain of having to go to a central hospital. Bayin et al. [33][30] developed an IoMT-based cost-effective and time-efficient method for healthcare staff to assess and keep a record of infection in COVID-19 patients, with excellent usability. Intawong et al. [34][31] use a bottom-up method to explore COVID-19 issues and control mechanisms by examining the invention of three real-time application technologies, as well as their implementation. Hemant Jain et al. [35][32] presented a 5G network slice architecture for digital real-time healthcare systems that capture biometric data and send it via the 5G network slice, as well as an integrated analytics framework. The IoMT architecture, technologies, applications, and security advances that have been made to IoMT in countering COVID-19 were emphasized by Aman et al. [4]. Greco et al. [36][33] covered early health monitoring systems based on wearable sensors to the current trends in fog/edge computing for smart health, the general usage of IoT technologies in health care. By laying out a strategy for combating the COVID-19 epidemic, R.P. Singh et al. [37][34] aimed to study, discuss, and emphasize the overall applicability of the well-proven IoT philosophy. The introduction of IoT reduces healthcare expenses and improves the treatment outcome of infected patients, according to the findings. Lim & Rahmani [38][35] performed a systematic analysis of the current cutting-edge ontology of IoT resolutions that are utilized in the health sector, recognize the associated obstacles, and suggest a federated edge-cloud semantic IoT architecture to promote HC-PH collaborations for individual and population health and well-being. Volkov et al. [39][36] analyzed existing methodologies and technology in digital twins, the IoT, and telemedicine to examine present healthcare concerns. The key characteristics of contemporary platforms that support telehealth applications were reviewed. In addition, the notion of a smart healthcare platform was developed, focusing on activities linked to enabling the development of mobile health applications, such as arranging user data access, management, and sharing. The technical foundations of machine learning, big data analysis, cloud computing, and IoT in clinical medicine were investigated by Zhao-xia Lu et al. [40][37]. The uses of IoT and AI in clinical medicine are thoroughly summarized, the major hurdles are assessed, and future trends and advances were also discussed in numerous studies. Alharbi et al. [41][38] examine current attempts to address the COVID-19 problem using technology improvements, including IoMT. M. Hasnain et al. [42][39] offer an overview of recent research on frontline medical worker safety concerns and how technology is being used to combat the COVID-19 epidemic. Many different types of wearable health equipment to monitor oxygen saturation, temperature, heart rate, and respiration rate, as well as respiratory support systems, such as oxygen treatment and ventilators, are widely used to support coronavirus patients were discussed by Md. Milon et al. [43][40]. Vafea et al. [44][41] looked at the new technologies that are being employed in COVID-19 research, diagnosis, and treatment. During the COVID-19 outbreak, Monaghesh and Hajizadeh [45][42] conducted a comprehensive evaluation on the utility of telehealth services in disease prevention, diagnosis, treatment, and management. Mbunge et al. [46][43] reviewed robotics, IoMT, smart applications, 5G technologies, blockchain, telemedicine, big data, artificial intelligence (AI), geospatial technology, and additive manufacturing as emerging technologies for combating COVID-19, focusing on features, challenges, and domiciliation country. Anand et al. [47][44] examined emerging technology for dealing with pandemic threats. Virtual reality, 5G, artificial intelligence, the Internet of Healthcare Things, wearable sensing, mobile APK, drone facilities, and blockchain are examples of upcoming technologies that can address these important concerns. Vikram Puri et al. [48][45] suggested a decentralized healthcare architecture powered by artificial intelligence (AI) that accesses and authenticates Internet of Things (IoT) devices while also instilling confidence and transparency in patient health records. The applications of IoT in healthcare are defined by Juneja et al. [49][46], as well as how these applications can be employed with various sensors. In an IoMT-enabled COVID-19 situation for patient home monitoring, Basudeb Bera et al. [50][47] combined fog computing and blockchain technologies to create a more secure solution. Alam and Rahmani [51][48] investigated using medical data and decision support systems, COVID-19 identification, and lung area segmentation detection, as well as IoMT application-centric settings and concluded that such a system may benefit all IoMT stakeholders.

3. IoT or IoMT Framework for COVID-19

Sharma et al. [65][49] implemented a remote access IoT-based model with a bio wearable sensor system for early detection of COVID-19 utilizing an ontology method and biomedical signals, including ECG, PPG, temperature, and accelerometer. Renugadevi et al. [66][50] investigated the importance of big data in smart health applications, which are critical in ensuring human safety. Seda Savaşcı Şen [67][51] presented an Internet of Things-based surveillance system for coronavirus pandemics in particular. Symptoms of the Coronavirus, such as respiration rate, body temperature, blood pressure, oxygen saturation, and heart rate, may be tracked, and the proposed IoT software may be used to indicate the social distance between people. In IoT adoption, the relevance of the risk–trust relationship was emphasized by Arffi et al. [68][52]. The finding suggests that performance expectations have no bearing on the intention to use the Internet of Things for eHealth. For smart monitoring, proactive prevention and control, and mitigation of COVID-19 and related outbreaks, Deepti Gupta et al. [69][53] envisioned a connected ecosystem powered by the IoT and data. Mohamed Abdel-Basset et al. [70][54] presented an IoMT-based approach for limiting the development of COVID-19 outbreaks while ensuring the safety of healthcare workers and maintaining patients’ physical and psychological wellness. Adarsh Kumar et al. [71][55] explored drone-based systems, as well as COVID-19 pandemic settings, and architecture was provided for coping with pandemic events in real-time and simulation-based situations. This took place in isolated and heavily congested pandemic regions where either wireless or internet connection is a big worry or the odds of COVID-19 spreading are high. In a push-pull data fetching mechanism, its architecture leverages wearable sensors to capture observations in body area networks (BANs). Otoom et al. [72][56] proposed an Internet of Things-based framework for collecting real-time symptom data from users in order to detect suspected coronavirus cases early, monitor the treatment response of those who have already recovered from the virus, and better understand the virus’s nature by collecting and analyzing relevant data. Swayamsiddha et al. [17] advocated for the employment of Cognitive IoMT disruptive technology in smart healthcare and in the fight against the COVID-19 pandemic, as well as outlining the primary benefits and application areas. Singh et al. [73][57] explored the possibility of using the IoMT strategy to combat the continuing COVID-19 epidemic while treating orthopedic patients. The many clouds and connected network-based services of IoMT include data sharing, report monitoring, patient tracking, information collection and analysis, hygiene medical care, and so on. To prevent and guard against COVID-19, P. Singh et al. [74][58] create a quality-of-service framework based on the Internet of Things with the help of fog. It forecasts COVID-19 infection based on the user’s symptoms using real-time health data processing provides users, their guardian, and doctors/experts with an emergency alarm, medical reports, and important precautions. It uses patient IoT devices to collect sensitive data from hospitals/quarantine shelters in order to take the decisions or necessary actions. It also conveys a message of warning to government health organizations, ordering them to control the spread of chronic illness and take appropriate action as soon as possible. Ameni Kallel et al. [75][59] employing a framework that incorporates machine learning (ML), cloud, fog, and Internet of Things (IoT) technologies, offer a new smart COVID-19 illness monitoring and prognostic system. Khowaja et al. [76][60] underlined the necessity of integrating technologies to assist in dealing with COVID-19 and offered a hypothetical framework that connects smart sensors with the Internet of Medical Things to cover the gamut of best practices in an automated manner. Poongodi et al. [77][61] proposed a sophisticated health-based IoT solution that can improve COVID-19 administration and generate better results with less money. Anichur et al. [78][62], during COVID-19 of the smart industry, proposed the “EdgeSDN-I4COVID” architecture for intelligent and efficient management of IoT networks. Madhavan et al. [79][63] used an IoMT-based framework for a web-based service that uses chest X-ray images to diagnose and classify different types of pneumonia or COVID-19. In a cloud-based IoT environment, a remote health monitoring model is proposed by Akhbarifar et al. [80][64] that uses a lightweight block encryption mechanism to provide security for health and medical data. Abdur-Rahman and Hossain [81][65] developed an edge IoMT system that employs deep learning to detect a variety of COVID-19 symptoms and delivers reports and warnings for medical decision support. During the COVID-19 pandemic, Zhang [82][66] proposed a revolutionary IoMT platform that enabled remote health monitoring and decision-making concerning emotion, providing convenient and continuous emotion-aware healthcare services. Jikui Liu et al. [83][67] created a system for remote monitoring of cardiopulmonary health using the IoMT. It is a remote monitoring device that can help with the follow-up and treatment of COVID-19 patients who have been discharged. Rinku, a system for remotely validating COVID-19 symptoms, was proposed by Rodriguez et al. [84][68]. Rinku can handle data from several patients at the same time and provide useful information on the intensity of the symptoms reported, which could aid healthcare professionals in making management decisions to maximize their clinical resources. Yonghang Tai et al. [85][69] propose a novel paradigm for COVID-19 diagnostic integration and introduces a new line of inquiry into the integration of XR and deep learning for IoMT deployment.

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