Delay-tolerant networks (DTNs) are wireless mobile networks, which suffer from frequent disruption, high latency, and lack of a complete path from source to destination. The intermittent connectivity in DTNs makes it difficult to efficiently deliver messages. Research results have shown that the routing protocol based on reinforcement learning can achieve a reasonable balance between routing performance and cost. However, due to the complexity, dynamics, and uncertainty of the characteristics of nodes in DTNs, providing a reliable multihop routing in DTNs is still a particular challenge. In this paper, we propose a Fuzzy-logic-based Double -Learning Routing (FDQLR) protocol that can learn the optimal route by combining fuzzy logic with the Double -Learning algorithm. In this protocol, a fuzzy dynamic reward mechanism is proposed, and it uses fuzzy logic to comprehensively evaluate the characteristics of nodes including node activity, contact interval, and movement speed. Furthermore, a hot zone drop mechanism and a drop mechanism are proposed, which can improve the efficiency of message forwarding and buffer management of the node. The simulation results show that the fuzzy logic can improve the performance of the FDQLR protocol in terms of delivery ratio, delivery delay, and overhead. In particular, compared with other related routing protocols of DTNs, the FDQLR protocol can achieve the highest delivery ratio and the lowest overhead.

中文翻译：

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时滞网络中基于模糊逻辑的双向学习路由

延迟容忍网络（DTN）是无线移动网络，经常遭受中断，高延迟以及缺少从源到目的地的完整路径的困扰。DTN中的间歇性连接使得很难有效地传递消息。研究结果表明，基于强化学习的路由协议可以在路由性能和成本之间达到合理的平衡。但是，由于DTN中节点特性的复杂性，动态性和不确定性，在DTN中提供可靠的多跳路由仍然是一个特殊的挑战。在本文中，我们提出了一个基于模糊逻辑双-学习路由（FDQLR）协议，可以通过模糊逻辑与双结合学习的最佳路线-学习算法。在该协议中，提出了一种模糊动态奖励机制，该机制使用模糊逻辑对节点的活动，接触间隔和运动速度等特征进行综合评估。此外，提出了一种热区丢弃机制和丢弃机制，可以提高节点的消息转发和缓冲区管理效率。仿真结果表明，模糊逻辑可以在传递比率，传递延迟和开销方面改善FDQLR协议的性能。特别是，与DTN的其他相关路由协议相比，FDQLR协议可以实现最高的传递比率和最低的开销。