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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\u2010logic\u2010based Double <jats:italic>Q<\/jats:italic>\u2010Learning Routing (FDQLR) protocol that can learn the optimal route by combining fuzzy logic with the Double <jats:italic>Q<\/jats:italic>\u2010Learning 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.<\/jats:p>","DOI":"10.1155\/2021\/8890772","type":"journal-article","created":{"date-parts":[[2021,1,28]],"date-time":"2021-01-28T01:20:09Z","timestamp":1611796809000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Fuzzy\u2010Logic\u2010Based Double <i>Q<\/i>\u2010Learning Routing in Delay\u2010Tolerant Networks"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3109-2553","authenticated-orcid":false,"given":"Jiagao","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0380-1087","authenticated-orcid":false,"given":"Fan","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8964-7115","authenticated-orcid":false,"given":"Yahang","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7911-0584","authenticated-orcid":false,"given":"Hongyu","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0824-6203","authenticated-orcid":false,"given":"Linfeng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,1,27]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2019.2916663"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2017.2683200"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2018.1701171"},{"key":"e_1_2_10_4_2","doi-asserted-by":"crossref","unstructured":"SakaiK. 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