Sharma et al. [65] 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] investigated the importance of big data in smart health applications, which are critical in ensuring human safety. Seda Savaşcı Şen [67] 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]. 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] envisioned a connected ecosystem powered by the IoT and data. Mohamed Abdel-Basset et al. [70] 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] 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] 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] 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] 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] 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] 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] proposed a sophisticated health-based IoT solution that can improve COVID-19 administration and generate better results with less money. Anichur et al. [78], during COVID-19 of the smart industry, proposed the “EdgeSDN-I4COVID” architecture for intelligent and efficient management of IoT networks. Madhavan et al. [79] 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] that uses a lightweight block encryption mechanism to provide security for health and medical data. Abdur-Rahman and Hossain [81] 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] 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] 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]. 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] 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.