# Multi-Decision Dynamic Intelligent Routing Protocol for Delay-Tolerant Networks
## 4 Simulation Results Evaluation
### Simulation Environment  
The proposed model in this paper will be simulated and analyzed using the Opportunistic Network Environment Simulator (ONE) [18], which is a simulation tool designed for use in Delay-Tolerant Networking (DTN) environments. The default parameter settings applied in this paper are presented in Table 5 below. The simulation environment includes 126 nodes divided into six groups: two pedestrian groups, one car group, and three tram groups. The pedestrian and car groups consist of 40 nodes each, while the tram groups consist of 2 nodes each. Regarding node movement speeds, the pedestrian groups move at speeds ranging from 0.5-1.5 m/s, the car group moves at speeds ranging from 2.7-13.9 m/s, and the tram groups move at speeds ranging from 7-10 m/s. In terms of motion models, the pedestrian and car groups utilize map-based shortest path motion models, whereas the tram groups use predetermined map-based motion models. In terms of waiting times, the pedestrian groups wait for 0-30 s, the car group waits for 0-60 s, and the tram groups wait for 0-120 s. The communication medium for pedestrian and car groups is a Bluetooth interface with a speed of 250 kb/s and a range of 50 m. The subway train group uses a Bluetooth interface with a transmission range of 800 m and a speed of 20 MB/second. Each experiment is conducted ten times by altering the random seed values for node distribution.  
When evaluating network performance in this paper, several key metrics are employed to assess the quality of the network. These metrics include message delivery rate, network overhead, and average latency [19].  
**Message delivery rate:** It measures the success rate of message transmission within the network during the simulation.  
**End-to-end average latency:** It calculates the average time it takes for successfully delivered messages to travel from the source node to the destination node during the simulation.  
**Network overhead:** It is another crucial metric used to evaluate network performance.  
In the double-Q learning algorithm, the learning rate and discount factor constants are crucial parameters for updating Q-values. Consequently, we initially determine the optimal values for these two parameters through experimentation and assess their impact on network performance using the message delivery rate. Figure 5 shows the variation of the delivery rate in MDDI for different \(\alpha\) and \(\gamma^{*}\). In Figure 5a, we observe that as the value of \(\alpha\) varies within the range of 0 to 1, the message delivery rate initially increases and then decreases. The message delivery rate reaches its maximum when \(\alpha\) is set to 0.9. On the other hand, Figure 5b illustrates the behavior of the message delivery rate as the discount factor constant changes within the range of 0 to 1. The message delivery rate exhibits a fluctuating pattern of increase followed by a decrease, with the highest rate occurring at \(\gamma^{*}\) = 0.8.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Simulation time & 43,200 s \\ Simulation map & Helsinki (4500 km \(\times\) 3400 km) \\ Number of nodes & 126 \\ Buffer size & 5 M–50 M \\ Message size & 500 k \\ Moving speed & 0.5 m/s–1.5 m/s \\ Transmission range & 30 m \\ Message generation interval & 25 s–35 s \\ Message TTL & 30 min–90 min \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation parameters setting.  
Based on these simulation results, we can conclude that for the MDDI during the simulation, the optimal learning rate is 0.9 and the discount factor constant is 0.8 to achieve the best network performance.