## 3 Proposed Framework
### Dataset
 & **Time** & **Accuracy** \\ \hline Access Control & [1] & NA & 18 & Yes & Yes & 80\% \\  & [2] & 60 & NA & Yes & Yes & 80\% \\  & [3] & 250 & NA & Yes & Yes & 80\% \\  & [4] & 100 & NA & Yes & NA & 92\% \\  & [5] & 100 & 8 & Yes & Yes & NA \\ Access Control Types & [6] & 50 & 1 & No & Yes & NA \\  & [7] & NA & 1 & No & NA & NA \\  & [8] & NA & 14 & No & Yes & 86\% \\  & [9] & NA & 1 & No & Yes & 75\% \\ Framework & [10] & 100 & 2 & No & Yes & NA \\  & [11] & NA & 150 & No & NA & 90\% \\ Security & [12] & 50 & 1 & Yes & Yes & 93\% \\  & [13] & 200 & NA & Yes & Yes & 79\% \\  & [14] & 100 & NA & Yes & No & NA \\  & [15] & 50 & 6 & Yes & No & 60\% \\  & [16] & 100 & 30 & Yes & NA & 91\% \\ Data Storage & [17] & NA & 45 & Yes & Yes & 70\% \\  & [18] & NA & 4 & No & Yes & 80\% \\  & [19] & 100 & 4 & No & Yes & NA \\  & [20] & 40 & 9 & No & Yes & 80\% \\  & [21] & 200 & 23 & No & NA & NA \\  & [22] & 100 & 1 & No & NA & 70\% \\  & [23] & 50 & 3 & No & Yes & 80\% \\  & [24] & 150 & 3 & No & Yes & NA \\  & [25] & NA & 3 & No & Yes & 73 Figure 10 shows the effect of the number of rounds and the error rate. We compared our proposed framework to the models that are already out there. We can see from Figure 11 that our new framework is more efficient than the benchmark models. In Figure 12, we ran simulations based on how many transactions and how accurate they were. In the first simulation, the honest node behavior was set to make real blocks and real votes when the nodes were chosen as miners, while malicious nodes made fake blocks and fake votes together when nodes were chosen as miners. As shown in Figure 13, in this simulation, we changed the percentage of the malicious nodes so that we could see how well the proposed framework worked and how well the benchmark model worked based on the bandwidth and number of rounds. It was set to 0 to 300 nodes. Figure 13 shows how it looked. The accuracy of PoIP was better when there were a lot of nodes, such as 500 or more. As soon as the percentage of malicious nodes is more than 50%, the accuracy will go down a lot. If the proposed framework is better than the others because the positive value is used to pick honest miners, then it is better than the other frameworks. The percentage of a malicious node in real blockchains does not go above 50% in this kind of case. It is important to see how quickly one block creation works so we ran a second simulation with a different number of nodes to see how well it worked. We used the average time of 100 (or 50) rounds of block generation as the simulation results, but we did not t lose generality with this method. PoW was the most efficient, as shown in Figure 14. The performance of the proposed framework was better when random honest miners were chosen from the group of miners. Our simulation results were based on the average time of 200 rounds of block creation. This way, we did not t lose the generality of our results. There is a group of people who work together to make PoN more efficient, as shown in Figure 15. The development of concurrent multi-blocks can help. In tests, it was found that the proposed strategy was both effective and efficient at providing secure data sharing and access control for the IoT-based supply chain system. The proposed method stopped some bad things from happening, such as impersonation and Sybil attacks. People could join a federation without permission, and the process was completely fair. It allowed anyone who was a validator to become involved in a mine for a specific reward, which may change over time. If you look at Figure 16, you can see that our proposed framework increased security and significantly reduced the. To test the efficiency with one block creation, we ran a second simulation with a different number of nodes. We used the average time of 100 (or 50) rounds of block generation as the simulation results without losing generality.