# Multi-Decision Dynamic Intelligent Routing Protocol for Delay-Tolerant Networks
## 4 Simulation Results Evaluation
### Simulation Result  
In this section, we will conduct a network performance evaluation of the proposed MDDI protocol. In addition to comparing it with the typical Epidemic [20] and Prophet [3] routing protocols, Epidemic [20] is based on flooding, with which nodes transmit data packets to every encountered node, resulting in high overhead. We will also compare it with the single decision based on double Q-values (SDDQ) proposed in Section 2 and a dynamic decision based on node relationships (DDNR) proposed in Section 3.1. The values of \(\alpha\) and \(\gamma^{*}\) in SDDQ are the same as the values in MMDI. We will assess the impact of variations in network scenarios across three aspects: node caching, message survival duration, and message generation interval, on the network performance of the routing protocols. **Delivery ratio:** The five protocols in Figures 5(a), 6(a), and 7(a) all show an upward trend in delivery rates. The performance of different protocols in terms of delivery rates under various network conditions is described in detail below. In Figure 5(a), MDDI exhibits the most significant advantage, while the Epidemic algorithm performs the worst in terms of delivery rate. The delivery rates of SDDQ and Prophet are comparable, and MDDI outperforms DDNR in terms of delivery performance. SDDQ achieves adaptive learning and can adapt well to the dynamic changes in the network. However, under conditions of limited node cache space, making decisions solely based on Q-values can lead to inaccurate estimations, resulting in average delivery rates in low-cache conditions. DDNR, on the other hand, makes forwarding decisions based on the dynamic attributes of nodes and messages, hence performing well in terms of delivery rates under various cache states. The proposed MDDI not only adapts to dynamic changes in the network through adaptive learning but also dynamically makes decisions based on the attributes of nodes and messages, resulting in the best performance. In Figure 6(a), the delivery rates of the five protocols all exhibit an upward trend and eventually stabilize at a relatively high level. This is because the longer the message's lifetime, the wider the range over which the message is forwarded in the network. Therefore, the majority of messages can be delivered to their destination nodes within their lifetime. DDNR's delivery rate increases as the message's lifetime increases because it takes into account the impact of the message's remaining lifetime when selecting forwarding nodes. Consequently, it achieves a high delivery rate even in scenarios with low message lifetimes. As the message's lifetime increases, both MDDI and SDDQ perform well across various message lifetime states. This is because they quickly find suitable next hops for messages through reinforcement learning. MDDI, which also incorporates node relationships into its decision-making, can find more efficient relay nodes compared with SDDQ, resulting in superior delivery performance. Figure 5: Performance of delivery ratio under different \(\alpha\) and \(\gamma^{*}\) in MDDI protocol. In Figure 7(a), at lower message generation intervals, there is a large volume of messages in the network, resulting in high buffer occupancy and an increased risk of congestion, leading to a higher rate of message loss. Consequently, the delivery rates of all five protocols are relatively low during this period. As the message interval increases, the number of messages in the network decreases, and the pressure on the buffers decreases as well. Therefore, in such scenarios, the delivery performance of the Epidemic protocol, which does not limit the number of copies, surpasses that of Prophet. SDDQ performs less effectively in congested network conditions due to its poor learning environment, resulting in a lower delivery rate. However, both MDDI and DDNR maintain high delivery rates even when node buffers are limited because they consider the quality of interactions between nodes, making their node selections more appropriate.