## 3 Methodology  
The proposed research methodology consists of training the proposed hybrid deep-learning model in a distributed manner and then deploying it on the edge devices. The edge or fog devices use its local data to update the pre-trained model and evaluate their own models. Moreover, all the users in the proposed model are considered trusted users due to the application of blockchain technology [1; 6; 7]. Due to the secured and flexible access control scheme, maintaining data integrity when communicating the data over IoT network is a challenging issue. Second, designing an adaptable security mechanism that can efficiently distinguish normal and attack instances in IIoT is also a challenging issue. The proposed model consists of an IoMT network consisting of various interconnected medical sensors, actuators, and machines, located at multiple fog nodes [8; 10; 14]. Third, developing a new framework for deploying blockchain and deep-learning techniques in current cloud-edge assisted industrial systems is of utmost importance with the integration of blockchain. The backup data is stored in the cloud whereas the meta-data are stored and hashed inside the blockchain. As such framework often faces issues related to scalability, due to different computing power of the participating edge nodes it is infeasible to store the complete block in the edge networks [4; 5]. Figure 2 represents the scenarios of our proposed framework. The integration of blockchain with the hybrid IOT and its application in fog computing are explained in Figure 2.  
This is based on the blockchain concept and can be utilized to perform peer-to-peer data transmission in a secured manner. This will be used to store transaction data for a long time and will not be fabricated or deleted from the blockchain. Furthermore, the transaction details are kept on the cloud server, which makes the data immutable and decentralized [12]. The data integrity can be achieved by the secure hash function SHA512. This hash function is included in the respective message digest and creates the fixed length of the unique fingerprint. The used SHA512 is resistant to collision and can be applied in real-time processing along with brute force attacks [13]. The message digest can be used in transaction blocks since it can circumvent the poisoning attack known as the avalanche effect [14]. Moreover, if we change one bit of data this can completely change the message digest. Thus, it preserves the IoT data integrity. Some of the information present in the created blocks are block index, previous hash, current block hash, current proof, data (Tvalue), (Tscore), and timestamp. Figure 3 represents different modules of the proposed system architecture and the flow of data through various organizations. Figure 3: Integration of the blockchain with the proposed model. Figure 2: System layers of the proposed model. As shown in Figures 4 and 5, the generated blockchain is maintained with the help of the hash function of the previous chain. Thus, the verifiability of the system has been enhanced. Moreover, in the blockchain, the verification of the integrity of the hash chain has been performed while conducting the generation of new blocks by utilizing the consensus mechanism [15]. 5G-Enabled IoMT Communication. One of the essential applications in 5G networks is smart healthcare. The general architecture and essential entities of the 5G intelligent health network are depicted in Figure 2, representing the smart antenna requirement for 5G-enabled network communication. Smart antennas benefit from several significant advancements in the current scenario that boost 5G [8]. Perfect signal and transmission capabilities are possible thanks to a well-coordinated RF beam. However, because the focus of interest diminishes with increased attenuation, the location remains an issue. The use of machine-to-machine connections (M2M) and the IoMT as the foundations of intelligent healthcare in 5G networks (IoMT) is predictable. There are two fundamental drawbacks to the strategies given.