## 5 Simulation Results and Analysis
Figure 6: Comparison of awareness EFV and the awareness overhead of DRNSM-APINT. Figure 6d shows the awareness overhead associated with various reporting node scenarios across different path lengths. The graph reveals that an increase in the number of reporting nodes leads to a reduction in segment length, subsequently decreasing the awareness overhead caused by accumulation of awareness data within the segments. However, it is noticeable that when there are three or four reporting nodes, the difference in awareness overhead between the two scenarios is small, indicating that the same awareness effect can be achieved by reducing the number of reporting nodes. Figure 7 shows the performance comparison of the optimal reporting scheme selected by the DRNSM-APINT and other different reporting node selection algorithms. The FHINT method, based on the official INT specification [11], conducts periodic reporting after a fixed number of hops. The SRINT and EEDA method are the same as before. Figure 7a presents a comparison of EFV across four methods. Specifically, the DRNSM-APINT algorithm selects varying numbers of reporting nodes across different paths. Therefore, the EFV values presented in the figure represent the optimal solutions achieved by the DRNSM-APINT algorithm in various scenarios. From the figure, it can be seen that the FHINT method has the highest EFV, while the SRINT and EEDA methods have smaller EFV. The EFV of DRNSM-APINT algorithm is lower than that of the comparison algorithm under various path scenarios. Taking the 20 hops as an example, DRNSM-APINT can reduce EFV by 17.1% compared to FHINT, 12.3% compared to SRINT, and 8.2% compared to EEDA. This indicates that the DRNSM-APINT method can maintain better awareness performance across different path scenarios. Figure 7b compares the average awareness time of segments obtained through different methods. It can be seen that the awareness time reported periodically by the FHINT method is generally large, but the fluctuation is small. The SRINT method is limited by the size of the onboard awareness data, resulting in differences in the number of service hops and significant fluctuations in awareness time. EEDA aims to maximize reporting capability,  
Figure 7: Performance comparison across various methods. which may result in an increase in some segment links, thereby increasing awareness time. The awareness time of DRNSM-APINT is significantly lower than other compared methods. Taking the 20 hops as an example, DRNSM-APINT reduces awareness time by 17.4% compared to FHINT, 12.4% compared to SRINT, and 13% compared to EEDA, indicating that the proposed method can significantly reduce awareness time. Meanwhile, the DRNSM-APINT has relatively small time fluctuations, indicating that the algorithm has good robustness in complex dynamic scenes. Figure 6(c) presents a comparison of the awareness overhead associated with various methods. From the figure, there is no significant difference among the four methods when the service path is short. However, as the path length increases, the overhead of the FHINT method gradually increases, while the overhead of the SRINT and DRNSM-APINT algorithms is relatively close, and the EEDA method is the smallest. This is because DRNSM-APINT adopts active-passive joint telemetry method, which introduces additional telemetry packet headers and increases the awareness overhead. EEDA sacrifices some awareness time to achieve low overhead measurement and reporting, while DRNSM-APINT still has lower overhead than the other two methods. Figure 6(d) compares the total number of reporting nodes selected by the four methods across varying numbers of services. From the figure, it can be seen that the number of reporting nodes of EEDA is the largest. This is due to the fact that the method strives to select as many idle nodes with abundant resources as possible for reporting, leading to a substantial and fluctuating number of reporting nodes. The number of reporting nodes for FHINT and SRINT methods is relatively small.