## 3 Proposed Framework
### The 5G Technology and Its Applications
#### 3.1.4 Proposed System Architecture  
Several physical servers combined in an FN configuration to cover a specific diameter region. The fog nodes (FN) can be wired or wirelessly connected [23; 24; 25]. The FN provides processors and sets up the equipment and network services as a small virtual data center. The processors, software, and network services made up the fog nodes (FN). Smart sensors (SS) collected data from the surrounding environment, which FN analyze and uses to understand decision-making better. The FN also provides a 5G network with a limited range of unicast wireless connections. Concurrent data transfer is enabled by the newly created 5G network protocols, which allow packets to be sent to all or a selected destination simultaneously [26]. The FN, a storage system for passive memory resident programs, can include the local database. For heavy IoMT applications, this means faster processing and loading times [27]. The Internet of Things in the Medical Field (IoMT) is a network safety, reliability, bandwidth, and optimization application that affects IoMIs, IoMT, and IoMT network safety deployment, reliability, bandwidth, and latency optimism. In this context, several FNs provide dynamic information as represented in Figure 8.  
In this research work, we considered a series of hospitals as a case study, where several inpatient wearable CPS, authentication servers, and base stations are interconnected. In a hospital, there are several units i.e., gastrology, cardiology, pulmonology, hematology, pathology, radiology, wards, patient reception centers, discharge lounges, and an emergency transportation center followed by private rooms to facilitate patients. These units, wards, and facilitation centers are interconnected to access, assess, store, and transmit the patient's data accompanied by other important credentials in the network. As shown in Figure 7, an AS employs the prognosticated information of the SML technique to contrive the validation process of legal patient wearable devices within its proximity region of an individual hospital in a decentralized environment rather than sending it to a particular AS, which manipulates the authentication process in a centralized environment. The entire Internet layer represents the actual user environment, where programs can be deployed without restriction. IoMT devices are divided into categories based on their location and function. This conserves energy, reduces consumption, lowers prices, and saves time. Software and hardware services help data centers integrate and process information. Peer-to-peer (P2P) TCP/IP can be used to communicate between IoMT  
Figure 7: Proposed Neural Network system for fog-computing. devices across short distances [28]. They can use FN via WiFi, ZigBee, and Bluetooth-like technologies if they are remotely apart. Figure 9 shows the system architecture of the proposed model. The system architecture consists of the blockchain, fog computing devices, and smart contracts. The proposed smart contracts check the access control policy using deep learning techniques and if the user has enough attributes, then access is provided, otherwise access is denied. Figure 10 describes the three-layer structure of the proposed framework. The first layer consists of the perception layer, the second layer consists of the fog layer, and the third layer consists of the Cloud layer. Each layer has its own function, and it supports different devices. Our proposed framework works on these three layers. IoMT and blockchain (BC) use for centralized communication paradigms which are part of the existing IoMT. Centralized Cloud servers will validate IoMT devices. As a result, the current IoMT solutions for sharing health-related data rely on Cloud computing (CC) and network resources for infrastructure and maintenance. A Wireless Body Area Network (WBAN) with devices that constantly expand and drop is deployed in the medical field. Figure 8: Proposed secure searchable encryption system using a DL-based intrusion detection system.