# Inverse Coupled Simulated Annealing for Enhanced OSPF Convergence in IoT Networks
## 3 Simulation and Evaluation
### Results, Analysis, and Discussion  
The simulation platform for this experiment is based on the Ubuntu 16.04 operating system, running on hardware equipped with a 12 th Gen Intel(R) Core(TM) i7-12700H processor and 16 GB of RAM. The experiment utilizes the NS3.33 network simulation software to conduct the simulations. Through these simulations, performance evaluations were carried out on four key metrics: fault detection time (FDT), fault detection accuracy (FDA), routing overhead (RO), and packet delivery rate (PDR). The experiment compared the performance of the OSPF-ICSA algorithm, the AODV protocol [7; 8], the OSPF protocol, the BFD protocol [15; 16], and the routing convergence algorithm based on load centrality (LC) [28; 30]. The experiments set up two network topologies based on the literature [1; 2; 4; 5; 12]. The algorithm and topology parameters are shown in Table 1.  
Testing was conducted under two fault scenarios, randomly introducing between 1 and 20 link failures, and 1 and 5 node failures. The experimental results are as follows. Figures 5 and 6 present a comparative experimental analysis of fault detection times under link failure and node failure scenarios for two different scales of topologies. As shown in the figures, the BFD algorithm demonstrates the best performance in terms of fault detection time, with the shortest detection time. However, the detection speed of OSPF-ICSA is also impressive, with an average fault detection time of approximately 4 s. Compared to the AODV protocol, OSPF-ICSA reduces fault detection time by an average of 74% and 37.38% in the two scenarios, respectively. In comparison to the traditional OSPF protocol, OSPF-ICSA achieves reductions of 86.24% and 87.37%, respectively. Additionally, compared to the load centrality (LC) algorithm, fault detection time is reduced  
by an average of 34.73% and 40.14%, respectively. These results indicate that OSFP-ICSA significantly enhances fault detection efficiency, second only to the BFD algorithm. Its lower fault detection time is attributed to the OSFP-ICSA algorithm's ability to respond flexibly and rapidly in the network environment. When network conditions deteriorate, OSFP-ICSA minimizes fault detection time through the reverse coupled annealing algorithm, significantly reducing the detection duration. Topology 1 in Figure 5 shows that, with one link failure, the detection time is 12 s. This occurs because both annealing algorithms reach a dynamic equilibrium at the 12 s mark. Subsequently, as the number of link failures increases, the fault detection time continues to decrease until it reaches its minimum value. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Name** & **Topology 1** & **Topology 2** \\ \hline UOSA Maximum Temperature \(T_{m}^{u}\) & 100 & 100 \\ UOSA Termination Temperature \(T_{k}^{u}\) & 0 & 0 \\ UOSA Heating Weight \(\beta^{u}\) & 1.2 & 1.2 \\ UOSA Annealing Weight \(\delta^{u}\) & 0.8 & 0.8 \\ UOSA Inner Loop Iterations \(M_{1}^{u}\) & 1 & 1 \\ DOSA Maximum Temperature \(T_{m_{f}}^{d}\) & 100 & 100 \\ DOSA Termination Temperature \(T_{k}^{d}\) & 0 & 0 \\ DOSA Heating Weight \(\beta^{d}\) & 1.2 & 1.2 \\ DOSA Annealing Weight \(\delta^{d}\) & 0.8 & 0.8 \\ DOSA Inner Loop Iterations \(M_{1}^{d}\) & 1 & 1 \\ Threshold \(\epsilon\) & 0.8 & 0.8 \\ Number of Backbone Nodes & 40 & 80 \\ Number of Access Networks & 13 & 19 \\ Number of Flows & 16 & 26 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Algorithm and topology parameter configuration table. Figure 5: Comparison of link failure count vs. fault detection time. Figure 6: Comparison of node failure count vs. fault detection time. Figures 7 and 8 display the fault detection accuracy of five algorithms under link failure and node failure scenarios for two different scales of topologies. It can be observed that the accuracy of the OSPF-ICSA algorithm is nearly identical to that of the BFD algorithm in both scenarios, with both approaching 100%.