## 3 System Design
### Algorithm
#### 3.3.1 Relay Node Selection
In order to verify the scalability of the proposed scheme under different network topologies, as shown in Figure 7, we use a real-world topology from The Internet Topology Zoo [38] dataset, specifically the INTEGRA and AGIS topologies, as the underlying physical topologies for our simulation experiments. The default bandwidth between backbone nodes in the topology is set to 55 Mbps and 155 Mbps, with a delay of 4 ms. ICN routers are incrementally deployed on top of the underlying physical network. The entire network is composed of a mix of traditional network devices and SDN network devices. We added two data source points (S1, S2) and two data receiver points (C1, C2) at the network edge, distinguished by different colors. The access nodes for the data source (receiver) points are ICN edge routers, with an access bandwidth of 300 Mbps and a delay of 1 ms. The maximum queue length for each router is set to 100 packets, and each packet has a payload size of 1000 bytes. To validate the scalability of our solution for ICN transmission, we continuously increase the sending rate of the data source and compare the performance between using default ICN transmission and using users' idle bandwidth-assisted ICN transmission. Our scalability metrics are average throughput and average packet loss rate. The main objective is to verify the support of the proposed solution for high bandwidth demand scenarios. In addition, we also compared the impact of the incentive mechanism on system performance. The introduction of the incentive mechanism is beneficial for aggregating more underlying physical link resources. ### Throughout

Throughouttput is one of the most important metrics for measuring data transmission performance. In this section, we use throughput as a scalability metric to evaluate the performance improvement brought by utilizing users' idle bandwidth and the incentive mechanism. We first incrementally deploy an ICN overlay network on the INTEGRA and AGIS topologies, with user-side ICN router nodes serving as backup nodes for ICN network transmission. We conducted ten rounds of simulations, where data source nodes (S1, S2) send data to data receiver nodes (C1, C2) and measured the stable throughput of the network at different sending rates. As shown in Figure 8, in both network topologies, when the sending rate is low, the advantage of the proposed solution is not evident as the ICN network transmission does not utilize the users' idle bandwidth. However, as the sending rate increases, using the users' idle bandwidth effectively improves network throughput compared to native ICN network transmission, resulting in a maximum throughput improvement of 19.4%. This enhancement further improves the scalability of the solution in high bandwidth demand scenarios. Furthermore, we compared the impact of using a blockchain incentive mechanism on system performance. The use of the incentive mechanism extends the online time of user nodes and increases the bandwidth resources contributed by user nodes and the number of user nodes, which is beneficial for aggregating more underlying physical link resources. In this experiment, we used the same settings as the previous one and introduced the blockchain incentive mechanism. We compared the network throughput before and after the introduction of the incentive mechanism, as shown in \[pathstatus(node_{i})=(\alpha\times Bandwidth_{i}+\beta/RTT_{i})\times sigmoid (p_{i}-p_{0}) \tag{1}\]1 It can be observed that at lower sending rates, the incentive mechanism does not bring significant advantages and even slightly decreases the throughput compared to before the incentive mechanism was introduced, which is due to the increased overhead of node selection with too many user nodes. However, as the sending rate increases, the end-to-end throughput of the system improves more with the effect of the incentive mechanism, as ICN regular nodes can choose the user node with the best state for ICN packet forwarding.