College of Information Science and Technology, Nanjing Forestry University, Nanjing 210000, China.
Sensors (Basel). 2023 Jul 4;23(13):6131. doi: 10.3390/s23136131.
Delay tolerant networks (DTNs), are characterized by their difficulty in establishing end-to-end paths and and large message propagation delays. To control network overhead costs, reduce message delays, and improve delivery rates in DTNs, it is essential to not only delete messages that have reached their destination but also to more precisely determine appropriate relay nodes. Based on the above goals, this paper constructs a multi-copy relay node selection router algorithm based on Q-lambda reinforcement learning with reference to the idea of community division (QLCR). In community division, if a node has the highestdegree, it is considered the core node, and nodes with similar interests and structural properties are divided into a community. Node degree refers to the number of nodes associated with the node, indicating its importance in the network. Structural similarity determines the distance between nodes. The selection of relay nodes considers node degree, interests, and structural similarity. The Q-lambda reinforcement learning algorithm enables each node to learn from the entire network, setting corresponding reward values based on encountered nodes meeting the specified conditions. Through iterative processes, the node with the most cumulative reward value is chosen as the final relay node. Experimental results demonstrate that the proposed algorithm achieves a high delivery rate while maintaining low network overhead and delay.
延迟容忍网络（DTN）的特点是难以建立端到端路径和较大的消息传播延迟。为了控制网络开销成本、降低消息延迟并提高 DTN 中的投递率，不仅要删除已到达目的地的消息，还要更准确地确定合适的中继节点。基于上述目标，本文构建了一种基于 Q-λ强化学习的多副本中继节点选择路由器算法，该算法借鉴了社区划分的思想（QLCR）。在社区划分中，如果一个节点的度值最高，则认为它是核心节点，并且具有相似兴趣和结构属性的节点被划分到一个社区中。节点度是指与节点相关联的节点数量，表明其在网络中的重要性。结构相似度决定了节点之间的距离。中继节点的选择考虑了节点度、兴趣和结构相似度。Q-λ强化学习算法使每个节点都可以从整个网络中学习，根据遇到的满足指定条件的节点设置相应的奖励值。通过迭代过程，选择累积奖励值最高的节点作为最终的中继节点。实验结果表明，所提出的算法在保持低网络开销和延迟的同时，实现了高投递率。
