## 3 Proposed Framework
### Dataset
The sabotage threat is not going to be addressed in this work; in practice, this risk can be mitigated, for example by enhancing the physical security of the device deployed, but it cannot be completely removed given the assumption that the adversary has physical access so they can destroy the device or shut it down. A more serious security challenge, in this case, is information leakage. Take, for example, drones that are frequently flown into hostile territories. These devices are very likely to be shot at if discovered, which can cause financial losses. However, if a drone is captured by an adversary, this can have more dire consequences. Extracting information from such a device (e.g., origin, mission, and likely destinations) is much more valuable to the enemy than destroying it. Another threat, in this case, is false alarms, caused by noise or environment variations. The latter may trigger undue tamper response mechanisms such as powering down the system or the deletion of sensitive data, which undermines the system's availability and disrupts its operation. The identification of threat mechanisms is explained in algorithms 1, 2 and 3. Our proposed algorithm only allows authorized users to access the EMR through fog computing

### Modeling of the Analysis Process

The analysis of previously recorded connected network data is the second major phase in the 5G inspection process. We tested the blockchain network in the simulation with various analysts operating at the same time. Algorithm 3 shows how the analytical procedure was coded. Every analysis must analyze Nominalise inputs. To begin, the analysts must retrieve acquisition information from the blockchain, download a copy of the raw data electronic medical records (EMR), and validate the hash value of the EMR. The process obtains the connect length, produces certain indicators, and runs the Add Analysis transaction if the check is successful. Lastly, the raw data EMR's local copy is erased. For secondary communication, multicasting is supported. With a high-speed connection and low latency, the QoS of inter primary and primary communication increases. Fog-IoMT supports flux/ubiquitous applications, heterogeneity, and secondary connections compared to previous communications systems. Because of the distance between the components, secondary and primary communication is separated. The FNs' scalability has increased or decreased. The local connection is controlled by primary communication, while secondary communication controls the external connections. A subset of key communication is the T2T relationship. The applications were vetted and authorized on the blockchain. To avoid congestion, a fog-IoMT IoMT layer on a secure channel was utilized. This was also performed to communicate with TFNT. ### Attack Risk Factor

The stake value was calculated using the actual cost of various values estimated from exchanges as follows. Where the current value of a token in the staking queue, that is modified every block interval. is the total number of tokens staked during the current block interval and is the number of tokens adequate for mining the transactions. Agreement nodes in PoIP are referred to as miners since they are responsible for validating, confirming, and constructing honest blocks. Miners can invest their original tokens or receive tokens assigned by other existing shareholders. To calculate the security risk of PoIP systems, we assumed that the number of different tokens in the stake pool was the same and added a \"generic\" token; its price is the total of a token value in the PoIP staking blacklist as mentioned in Equation (6). Where n is number of tokens used for computation in PoIP, represents the value of token i in the PoIP stake blacklist where and. To initiate an attack on the PoIP blockchain system, intruders need to possess a sufficient amount of and hold the majority of the stake value.