# An Accelerating Approach for Blockchain Information Transmission Based on NDN
## 5 Simulation Results and Discussions
### NDN Network with Acceleration Strategy  
In this section, we will introduce an acceleration strategy to optimize the NDN network, and compare the performance of the Bitcoin system before and after the acceleration strategy is introduced. The network topology used in this section is the same as the previous one. In the previous simulation, we set \(P\) = 0.15 based on the real network data, and calculated the number of secondary nodes based on the \(P\) value. However, there are peaks and underestimations of traffic in the network, the value of \(P\) will also change and affect the number of secondary nodes. Because changes in the number of secondary nodes can affect the performance of the AITS strategy, we selected a set of \(P\) values for simulation experiments and compared them with the original network, the value of these \(P\) has a 5% fluctuation relative to the real network value, as shown in Figure 5. The original algorithm in Figures 5-7 represents that the blockchain uses the NDN network for information transmission, but no additional information acceleration strategy is applied. It can be seen that in complex networks, as the transmission path in the network becomes longer, the node receiving delay increases significantly, but the use of the AITS strategy (accelerated information transmission strategy) can significantly reduce this delay. The simulation results show that compared with the original algorithm, the AITS strategy can significantly reduce the transmission delay, and the reduction is determined by the number of secondary nodes, the higher the \(P\) value, the higher the number of secondary nodes in the network, and the better the performance of the AITS strategy. Whether it has good scalability is also an important factor in evaluating the information acceleration strategy proposed in this paper. In order to prove the scalability of the AITS strategy, we designed a set of simulation experiments. We created three sets of simulation networks, the first group has 20 nodes, of which six are miner nodes; the second group has 150 nodes, of which 20 are miner nodes; the third group has 250 nodes, of which 30 are Miner nodes. These three sets of simulation networks are all established in the same way and have the same external parameters: all nodes in the network are connected with each other with probability \(P\), and interact with each other according to their roles. In each network, the number of secondary nodes is 15% of the number of network nodes. The simulation results are shown in Figure 6. It can be seen that the AITS strategy has good performance no matter whether the network is simple or complex, and as the network scale becomes larger, AITS performs better in a complex network environment. The simulation results further prove the superiority of the AITS strategy proposed in this paper in complex environments. Figure 5: Transmission delay in original algorithm and AITS. Another advantage of the AITS strategy is reducing the data overhead, which means reducing the redundant transmission of the data. Since the secondary node stores the data packets sent by the miner node, it distributes directly from the secondary node to the destination when it needs to distribute to another node on the path, which reduces a large amount of data overhead. We select 20 miner nodes to observe the packet overhead sent when it broadcasts information to the entire network. As shown in Figure 7, after the introduction of the AITS strategy, the data overhead of all miner nodes has been significantly reduced, the reduction depends on the distance between the miner node and the secondary node. The closer to the secondary node, the fewer data packets are repeatedly requested, and the data overhead is reduced. The simulation results proved this. The simulation results proved the excellent performance of AITS in reducing data overhead in complex network conditions. Figure 6: Performance comparison of AITS and original algorithm under different network scales.