## 3 Background: State-of-the-Art IoT Applications and Medicine
The results of the study showed that the proposed system provides reliable and secure data storage and transmission, making it a promising approach for IoT-based healthcare applications [18]. The suggested database relies on distributed ledger technology (DLT), which is commonly employed to synchronize, replicate, and distribute information across various locations, organizations, sites, and decentralized networks. The findings indicated that utilizing blockchain with IoT systems amplified the burden on the downlink channel. Consequently, merging NB-IoT monitoring methods with blockchain technology has numerous advantages for various applications, particularly those that involve confidential data [18]. [PERSON] et al. conductedresearch on the possibilities of and obstacles against utilizing EC-boosted deep-transfer learning (DTL) to alleviate the impact of the COVID-19 pandemic [15]. The researchers were motivated to use DTL due to the substantial difficulties in applying DL to devices with limited computing resources. The effectiveness of DTL in EC is attributed to its training process, since DTL can be trained on one task and subsequently applied to another task. The study also showcased the current state of the art, recent technical basics, and the practical applications of DTL with EC to address any future pandemics. A framework for Beyond 5G (B5G) was suggested that facilitates high-speed and low-latency communications to aid in the diagnosis of COVID-19 [19]. The proposed platform consisted of a distributed DL model, in which each COVID-19 edge adopts a three-phase approach, with the DL structure operating globally while employing its own local DL structure. In this approach, the local DL structure runs on the edge nodes, while the global DL structure operates in the cloud. The local DL model is trained on the dataset available near the edge, which is then validated by domain experts before being applied  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author(s)** & **Journals** & **Application of IoT or Edge Devices** & **Methodology or Equipment** & **Main Findings** \\ \hline [PERSON] et al. [8] & IEEE Network & •  
to the DL model located in the cloud. The proposed platform was also enhanced using semantic visualization and feedback from experts [19]. [PERSON] et al. presented a B5G healthcare framework that incorporates a mass-surveillance system and AI to analyze chest-CT-scan or X-ray images, as well as establishing a monitoring system that manages mask wearing, body temperature, and social distancing [4]. The proposed framework employs three DL-based techniques, namely Deep Tree, ResNet50, and Inception v3. Additionally, the blockchain method was incorporated to enhance healthcare-information security [4]. The key features that ensure the effectiveness and performance of IoT-based systems for medical purposes include network adaptation, real-time healthcare-information processing, security, and efficient monitoring. The latest IoT and EC frameworks have demonstrated that combining EC and IoT is a valuable approach to developing technically sound medical networks. Additionally, DL is a powerful method for solving and diagnosing complex issues in real time when numerous sensors generate a significant amount of health data [20]. This is because DL can identify meaningful relationships between inputs and outputs without requiring human involvement on a real-time IoT platform. However, additional research is required to leverage the ability of emerging technologies to optimize the process of developing antiviral drugs, which has become a challenging and time-consuming task, despite some positive developments in vaccine development using conventional methods. Based on the capabilities and potential of the techniques introduced in prior research [1; 2; 3; 4; 5; 6; 9; 10], we were motivated to use these characteristics to design and develop an intelligent ecosystem for drug discovery, especially in clinical trials.