Abstract
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 Q‐Learning Routing (FDQLR) protocol that can learn the optimal route by combining fuzzy logic with the Double Q‐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.
Highlights
Nowadays, with the improvement of network technologies, the application scenarios are expanding
The main contributions of this paper are as follows: (1) We propose a Fuzzy-logic-based Double Q-Learning Routing (FDQLR) protocol that combines the fuzzy logic with a Double Q-Learning algorithm to improve the decision of the best hop in the routing of Delay-tolerant networks (DTNs)
Based on the DQLR protocol, we present an enhanced routing protocol called Fuzzy-logic-based Double Q -Learning Routing (FDQLR), which uses a Double Q-Learning algorithm to select the hop nodes with an unbiased estimation, and utilizes fuzzy logic to comprehensively evaluate the characteristics such as node activity, contact interval, and movement speed, which directly affect the performance of message delivery
Summary
Jiagao Wu ,1,2 Fan Yuan ,1,2 Yahang Guo ,1,2 Hongyu Zhou ,1,2 and Linfeng Liu 1,2. Received 6 April 2020; Revised 19 December 2020; Accepted 11 January 2021; Published 27 January 2021. We propose a Fuzzy-logic-based Double Q -Learning Routing (FDQLR) protocol that can learn the optimal route by combining fuzzy logic with the Double Q-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. 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. Compared with other related routing protocols of DTNs, the FDQLR protocol can achieve the highest delivery ratio and the lowest overhead
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