## 3 System Design
### Algorithm
#### 3.3.1 Relay Node Selection
\[pathstatus(node_{i})=(\alpha\times Bandwidth_{i}+\beta/RTT_{i})\times sigmoid (p_{i}-p_{0}) \tag{1}\]2 Comparison of average throughput testing. \[pathstatus(node_{i})=(\alpha\times Bandwidth_{i}+\beta/RTT_{i})\times sigmoid (p_{i}-p_{0}) \tag{1}\]3 Simulated physical topologies. ### Packet Loss Rate

Packet loss rate refers to the proportion of data packets that fail to reach their destination due to network congestion, network device failures, or poor transmission link quality. The packet loss rate is typically expressed as a percentage and is an important indicator of network quality. In this section, we use the packet loss rate as a scalability metric to evaluate system performance improvements. We conducted ten rounds of experimental simulations, where data source nodes (S1, S2) sent data to receiver nodes (C1, C2). Packet captures were used to measure the end-to-end packet loss rate. As shown in Figure 9, in both network topologies, compared to native ICN network transmission, utilizing users' idle bandwidth resources effectively reduces packet loss rate in high bandwidth transmission scenarios. This is because when ICN regular routing nodes experience high network load, they can switch to user nodes for ICN packet forwarding, thereby reducing packet loss rate. Similar to the throughput testing experiments, we also compared the further improvement of system performance with the introduction of an incentive mechanism. The incentive mechanism provides a wider range of node selection, thereby aggregating more underlying link resources. As shown in the figure, compared to the solution without the incentive mechanism, this solution can further reduce the end-to-end packet loss rate, especially at high sending rates. The introduction of the incentive mechanism allows ICN regular nodes to select the best performing user nodes for ICN packet forwarding, thus improving packet loss rate performance. Based on the experimental results, we can conclude that utilizing users' idle bandwidth resources and implementing an incentive mechanism can effectively improve system scalability, reduce packet loss rate, and enhance transmission quality. ### Delay and Bandwidth Utilization

We leverage ICN technology to aggregate users' idle bandwidth, while some application-layer methods, such as NKN overlay network, utilize DHT algorithms to maintain the global network topology and message routing. However, as the network scales, updating and maintaining the DHT routing table consumes significant computational and network resources. This undoubtedly increases the workload on user nodes. Moreover, DHT-based routing cannot consider the underlying physical network topology and network load between nodes, which may result in longer routing paths or increased end-to-end latency; bandwidth resource utilization is also low. In this experiment, we compare the performance of solutions that aggregate idle bandwidth using ICN network technology and application-layer methods. The primary evaluation metric is end-to-end latency and the bandwidth resource utilization, and delay comparison experiments are conducted under different backbone link transmission

\[pathstatus(node_{i})=(\alpha\times Bandwidth_{i}+\beta/RTT_{i})\times sigmoid (p_{i}-p_{0}) \tag{1}\]4 Comparison of average packet loss rate testing. delays. Bandwidth resource utilization is measured under different time sequences, where resource utilization is defined as the proportion of available bandwidth of the selected relay node to the available bandwidth of all nodes. The results, in both network topologies, as shown in Figure 10, indicate that the end-to-end latency based on NKN is unstable, which is determined by the characteristics of the DHT routing algorithm. In contrast, ICN is a network technology that uses ICN to aggregate idle bandwidth for network transmission provides more stable end-to-end latency and has a competitive advantage in terms of latency compared to the NKN solution.