# Multi-Decision Dynamic Intelligent Routing Protocol for Delay-Tolerant Networks
## 1 Introduction
[10] presents the Community Trend Message Locking (CTML) routing protocol. The protocol locks messages to specific communities based on their content and trends, improving message delivery efficiency in DTNs. However, these routing protocols struggle to adapt quickly to node mobility and changes in network topology. In recent years, researchers have increasingly recognized the application of reinforcement learning [11] in DTNs. Q-learning [12] is a value iteration-based reinforcement learning algorithm that learns a value function called the Q-function. The double Q-learning proposed in [13] is an enhanced version of the Q-learning algorithm. Existing routing algorithms based on reinforcement learning models often depend on the reward function, which significantly influences the effectiveness of the learning strategy. Depending on different routing optimization criteria, tailored reward functions are designed for use during the training and learning processes. Ref. [14] evaluates the probability of encounter between nodes using the Q-learning algorithm for packet casting in vehicular opportunity networks and decides the choice of next-hop node together with relative velocity. Ref. [15] introduces an adaptive routing protocol based on improved double Q-learning, where the algorithm determines the next-hop node for data packets based on a hybrid Q-value. Although the above-proposed Q-learning-based DTN protocol has good results, the algorithm is difficult to learn sufficiently under the extreme conditions of the network to ensure that the best route can be guaranteed to be found in all situations. Ref. [16] proposes a delay-tolerant network routing algorithm called Double Q-learning Routing (DQLR); it utilizes a double Q-learning algorithm to address the issue of packet delivery delay in DTNs. Ref. [17] proposes a Double Q-learning-based routing protocol for opportunistic networks called Off-Policy Reinforcement-based Adaptive Learning (ORAL). The protocol utilizes a weighted double Q-estimator to make routing decisions, addressing the limitations of existing protocols. Table 1 compares the types and drawbacks of the DTN routing protocols mentioned above. On the basis of the above, we employ a reinforcement learning algorithm, double Q-learning, to adapt to the rapid changes in the network, in addition to defining the relationships between nodes in the DTN and synthesizing the information attributes. By performing multi-decision routing for the forwarding nodes based on the above factors, the nodes can more effectively decide whether the next hop will successfully forward the message to the destination node. Most existing routing protocols lack real-time awareness during network transmission and struggle with efficient message forwarding. In response, we designed a Multi-Decision Dynamic Intelligence (MDDI) routing protocol. This protocol combines reinforcement learning algorithms with node relationships and message attributes to achieve a multi-decision routing selection. Its goal is to maintain good message transmission performance in various states of the network. In MDDI, the Q-values in the Q-tables within the nodes are continuously adjusted with network changes, and a double Q-updating strategy is used to avoid overestimation and provide more accurate Q-values for evaluating the performance of the nodes. Meanwhile, in order to adapt to each state of the network, we also take the node relationship into account to decide the best next hop when routing based on the node interaction information as well as the message attributes to provide the overall performance of the DTNs. The main contributions of this paper are as follows:  
(1) Real-time sensing of network performance. In calculating the reward value and the discount factor, we use the average delay of the network, the average number of hops, and the message attributes of the nodes after forwarding the message as the learning training content. (2) Introducing node relationships to improve routing decisions.