## 3 Related Work  
The distributed nature of the cloud environment makes it the best choice to store health-related data and provides a flexible means of remote patient monitoring by various experts. Therefore, the combination of cloud and IoT technologies is promising for the real-time processing of signals, which generally increases the complexity of the architecture  
Figure 1: Advantages of IoT-dependent health care. required to receive and send information. In order to minimize the complexity between cloud and IoT technology, a framework is suggested to handle real-time IoT statistics, with unrelated scientifically based information verified in the cloud environment using tools such as hybrid services [23]. A framework based on service management for IoT devices is suggested for the cloud environment that consists of three types of layers and five crucial elements to serve consumers and gather information from patients, smart homes, smart healthcare devices, social networks, etc. The next level includes a service provider that provides physical assets, visualization, supervision service and security and privacy features. The last layer is the intermediate layer, which will help to manage services between consumers and providers, relying on available assets. The authors of [24] suggested an IoT machine learning architecture for prior detection of human heart disease based on three-tier frameworks to gather data from sensors embedded in wearable devices. The suggested architecture is executed using Mahat (Apache) and Hbase (Apache) for data prognosis and cloud storage to help identify heart illness in the early phase. The authors of [25] designed a framework to track patients suffering from arthritis and identify them in early stages. The proposed framework is based on three stages, the first of which involves gathering data from sensors, followed by information storage in the cloud and optimization of the collected data containing patient information such as uric acid levels and data on swelling in the body. Open stack Apache red displacement was used to design the model. The authors of [26] designed and visualized sensor devices for household applications such as smart rooms for patients suffering from chronic diseases. No real-time data were considered in the experiment, as the proposed approach is prohibitively expensive. The framework of this approach can be assumed, and sensors can be used in place of cameras to minimize the total cost of the experiment. The outline of the proposed IoT framework is depicted in Figure 2.  
The authors of [27] suggested a fuzzy ant tree classifier comprising a combination of of ant colony development, fuzzy regulation and a settlement tree, which was tested for an application involving organization of medical history. A thorough review of medical history can help to address issues that arise in elderly patients, supporting diagnosis and decision making with respect to pain management, medication, etc. The role of IoT in the proposed framework is to gather real-time information from sensors and identify health issues by verifying behavioral trends and physical need at home. Few researchers have conducted studies involving the support of elderly people using IoT tracking devices. Services for elderly people are often based on social platforms, enabling tracking using IoT devices. Sudden falls in elderly patients due to muscle pain can lead to death in some cases. To overcome such issues, an algorithm known as the fall identification algorithm was suggested to identify their condition and the specified area where the maximum chances of accident arise. Open issues include cloud integration, big data handling, security, etc. Data received by IoT sensors can be effectively stored in cloud servers, and IoT devices can be used to transmit data to different cloud subsystems for disease identification. Cloud and big data integration may solve problems associated with medical data management.