# Multi-Decision Dynamic Intelligent Routing Protocol for Delay-Tolerant Networks
## 4 Simulation Results Evaluation
### Simulation Result
Additionally, in scenarios with a lower number of messages in the network, MDDI outperforms DDNR. This is because, under favorable network conditions with suitable reinforcement learning environments, MDDI achieves a higher delivery rate during such periods. **Overhead:** The network overhead of the five protocols in Figures 6(b) and 7(b) all exhibit an increasing trend, while in Figure 7(b), they all show a decreasing trend. The performance of different protocols in terms of overhead under various network conditions is described in detail below. In Figure 7(b), DDNR performs less effectively than MDDI and SDDQ because it fails to maintain decision effectiveness when the network topology changes. In contrast, both MDDI and SDDQ use Q-values to decide whether to forward messages. Therefore, with sufficient cache resources, Q-values are updated dynamically with network changes, resulting in lower message loss and effectively reducing network overhead. Figure 6: Performance of protocols with different Buffer size. In Figure 6(b), epidemic and prophet exhibit a noticeable upward trend, with significantly higher network overhead compared with the other three algorithms. As message survival time increases, the number of network copies also grows. MDDI, DDNR, and SDDQ all employ strict decision criteria for routing and forwarding, which not only control the number of copies but also increase the probability of messages reaching their destination. Both MDDI and DDNR consider the remaining message survival time when deciding the next hop, which outperforms SDDQ, which relies solely on Q-values. Additionally, MDDI can adapt to network dynamics and learn from them when there is ample message survival time, resulting in lower network overhead compared with DDNR. In Figure 7(b), as the message survival interval increases and the total number of messages decreases, cache resources become more abundant. Consequently, the number of message copies on the network increases. Epidemic shows the most significant increase in network overhead, while Prophet, relying solely on encounter information, also forwards more message copies. SDDQ, when message survival intervals are short and network congestion is high, performs less effectively in deciding the next hop due to limited learning opportunities, resulting in higher network overhead compared with DDNR. However, both MDDI and DDNR consider message forwarding hops, ensuring successful message delivery with fewer intermediate relays when there are many messages in the network. When the total number of messages is low and network resources are abundant, MDDI demonstrates a distinct advantage in dynamic network environments. **Average delay:** In Figures 5(c) and 7(c), the average latency of the five protocols shows a general decreasing trend, while in Figure 6(c), it shows an increasing trend. The performance of different protocols in terms of average delay under various network conditions is described in detail below. Figure 7: Performance of protocols with different TTL. In Figure 6c, although Prophet and DDNR can determine the next forwarding node for messages based on historical information, they cannot find a suitable complete path for messages when the network topology changes. Therefore, their performance in terms of average delay is mediocre. MDDI and SDDQ consider the network's average delay in the discount factor when calculating the Q-value. This not only enables them to effectively find paths to forward messages to their destination nodes by continuously learning in the network but also reduces the overall average delay in the network. Dynamic multi-decision making in MDDI, compared with SDDQ, considers relay node selection more comprehensively and reasonably, resulting in MDDI having the best delay performance. In Figure 7c, as the message's lifetime increases, messages that are farther from the destination node are successfully delivered, increasing the transmission delay.