## 3 Proposed Framework
### Dataset
PoW had the highest efficiency, as demonstrated in Figure 14. The performance of the proposed framework was enhanced by generating random honest miners from the miner's group. We used the average time of 200 rounds of block creation as the simulation result without losing generality. With the help of the mining team, the development of concurrent multi-blocks can enhance the efficiency of PoN, as shown in Figure 15. The experimental results show that the proposed strategy is effective and efficient in providing secure data sharing and access control services for the IoT-based supply chain system. The proposed methodology resisted some malicious activities, such as impersonation and Sybil attacks. The process of joining a federation was fully decentralized, permissionless, and fair. It enabled any validator to engage in a mine for explicit reward sharing, which may change dynamically. Our proposed framework improved security and significantly reduced the computational resources needed to maintain a strict level of safety security in the blockchain, as shown in Figure 16.

Figure 11: Simulation results based on the bandwidth and number of rounds. Figure 12: Simulation results based on the bandwidth and number of rounds. Figure 14: Simulation results based on the bandwidth and number of rounds. Figure 13: Simulation results based on the bandwidth and number of rounds. Figure 17 represents the simulation results based on the true positive rate and the false positive rate. We classified the IoT data into different classes based on TP and TN. Figure 16: Simulation results based on the execution time and the cache hit rate. Figure 15.0 Simulation results based on the bandwidth and number of rounds. Figure 18 represents the simulation results based on the number of records and the execution time in seconds. The number of records were counted from 500 to 10,000, respectively. We compared our proposed framework with the benchmark model. From Figure 18 it is very clear that the execution time for the same number of rounds in case of our proposed framework was much less compared to the benchmark model. Figure 15.1 Simulation results based on the execution time and the cache hit rate. Figure 15.2 Simulation results based on the execution time and the cache hit rate. Figure 19 describes the simulation results based on the number of blocks and the processing time. We compared our proposed framework with the benchmark models. The performance achieved by our proposed framework was better than the benchmark models. #### Security Analysis

Registration against harmful actions is required for IoT nodes. Algorithms 1 and 2 illustrate how long it takes various IoT nodes to register. As can be seen in Figure 4, the real upload time for IoT sensor data with IPFS protected storage and the number of transactions is shown. As the number of transactions rises, so does the upload time. Block mining time, block creation time, and block access time are shown in Figures 5 and 6. As might be predicted, as the number of IoT sensor nodes grows, so does the elapsed time



Security Analysis and Computation Overhead Analysis

In this section, we will check and compare our proposed SML and CPBED model with comparable prototypes for storage overheads incurred by P \(G_{i}\), P \(G_{j}\), and TA. In the proposed scheme, every P Gi loMT network stores the identities of vicinity devices in their memory table such as P GID, P G, P G, GP, and TA, respectively. In contrast, the comparative schemes store these IDs and authentication parameters in a centralized or concerned trusted authority. During the session initiation and establishment, these parameters mostly matched in the centralized TA, or concerned TA, which created storage overhead in these memory constraint devices, because they have to process each request via hop count communication or a centralized location. As a result, the performance of these authentication schemes with communication metrics was degraded.