## 3 Background: State-of-the-Art IoT Applications and Medicine  
The healthcare system has been the focus of many studies on the potential of IoT and EC technologies. Table 1 provides an overview of the current state-of-the-art applicationsof IoT and EC that can be leveraged to develop a drug-discovery ecosystem. One example is the use of IoT sensors to collect data for the predictive maintenance of devices and equipment, which can help to develop a resiliency plan for the industry. [PERSON] et al. proposed using DL for IoTs in an EC ecosystem [8]. Edge computing is a computing architecture that is designed to bring computation and data storage closer to where they are needed, such as IoT devices. Their aim is to reduce latency and improve performance by minimizing data transmission to and from a centralized cloud server. Instead, computational resources are distributed to local trusted edge servers, allowing the faster processing of data in real time. Additionally, EC offers benefits such as increased privacy, scalability, and reduced bandwidth usage [8]. [PERSON] et al. proposed an end-to-end IoT platform that utilizes mobile EC to collect and analyze real-time data on the physical conditions of human subjects for healthcare purposes. The platform employs a representation-learning technique to extract valuable insights from large amounts of data with varying dimensions, which are obtained from IoT-EDs that are equipped with specific monitoring equipment [17]. The data collected are then used for the real-time observation and analysis of symptoms. This platform aims to improve the accuracy of diagnoses and to offer personalized treatments for patients, thereby enhancing the overall quality of healthcare services [3]. The proposed IoT-based platform can help address the limitations of traditional monitoring systems by leveraging EC and specific monitoring equipment to collect and analyze the real-time data obtained. The extracted information is then transferred to the cloud for further analysis and processing. Overall, this platform can provide more actionable insights and improve the tracking and monitoring of healthcare ecosystems. The study conducted by [PERSON] et al. proposed a real-time-monitoring platform that utilized mixed reality (MR) and augmented reality (AR) devices to visualize locations that pose a high risk of infection, including shopping malls, stations, airports, and restaurants. In this system, a single spatial database is used to store and aggregate public information related to these high-risk locations, including the overcrowding and congestion levels of shops and floors. This aim of this platform is to assist in the monitoring and control of the spread of infectious diseases by providing real-time information about crowded places and identifying potential sites of high levels of infection. The use of MR and AR devices allows a more immersive and interactive experience, enabling users to navigate through virtual representations of real-world environments and identify potential sources of infection [2]. The system provides key factors that can help the industry to survey the feasibility of wearable AR equipment for detecting and monitoring activity spaces in intelligent ecosystems without infringing on privacy [2]. [PERSON] et al. proposed a novel approach that combines blockchain technology and shareable databases for a reliable monitoring platform over narrowband Internet of things (NB-IoT) connectivity. The proposed system aims to address the challenges of data security, data integrity, and data availability in IoT-based healthcare applications. The authors proposed a blockchain-based distributed database system that enables the secure and reliable sharing of medical data among different healthcare providers, while also ensuring data privacy and confidentiality. The system utilizes the low power, low cost, and wide coverage features of NB-IoT to transmit medical data to the cloud, where it is stored securely and can be accessed by authorized users.