T. Tran, Toan-Van Nguyen, Kyusung Shim, Beongku An
{"title":"物联网中支持认知无线电manet的最优QoS多播路由协议:深度q -学习方法","authors":"T. Tran, Toan-Van Nguyen, Kyusung Shim, Beongku An","doi":"10.1109/ICAIIC51459.2021.9415188","DOIUrl":null,"url":null,"abstract":"In this paper, we propose an optimal quality-of-service (QoS) multicast routing protocol (QMR) in Internet-of-Things (IoT) enabling cognitive radio mobile ad hoc networks (ICR-MANETs) based on deep Q-learning approach. To this end, we formulate a joint end-to-end queuing delay and link’s stability optimization problem. The formulated optimization is typically NP-complete. We then leverage a novel deep Q-learning method to solve this challenging problem, which arrives at optimal convergence $Q^{\\ast}$-values. Based on the obtained $Q^{\\ast}$-values, we propose a QoS multicast routing protocol to select the best set of neighbors associated with minimum $Q^{\\ast}$-values to establish the multicast tree to the multicast members. Simulation results show that the proposed QMR protocol outperforms the current state-of-the-art routing protocols, which emerges as an efficient multicast routing protocol in ICR-MANETs.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"An Optimal QoS Multicast Routing Protocol in IoT Enabling Cognitive Radio MANETs: A Deep Q-Learning Approach\",\"authors\":\"T. Tran, Toan-Van Nguyen, Kyusung Shim, Beongku An\",\"doi\":\"10.1109/ICAIIC51459.2021.9415188\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose an optimal quality-of-service (QoS) multicast routing protocol (QMR) in Internet-of-Things (IoT) enabling cognitive radio mobile ad hoc networks (ICR-MANETs) based on deep Q-learning approach. To this end, we formulate a joint end-to-end queuing delay and link’s stability optimization problem. The formulated optimization is typically NP-complete. We then leverage a novel deep Q-learning method to solve this challenging problem, which arrives at optimal convergence $Q^{\\\\ast}$-values. Based on the obtained $Q^{\\\\ast}$-values, we propose a QoS multicast routing protocol to select the best set of neighbors associated with minimum $Q^{\\\\ast}$-values to establish the multicast tree to the multicast members. Simulation results show that the proposed QMR protocol outperforms the current state-of-the-art routing protocols, which emerges as an efficient multicast routing protocol in ICR-MANETs.\",\"PeriodicalId\":432977,\"journal\":{\"name\":\"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-04-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAIIC51459.2021.9415188\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAIIC51459.2021.9415188","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Optimal QoS Multicast Routing Protocol in IoT Enabling Cognitive Radio MANETs: A Deep Q-Learning Approach
In this paper, we propose an optimal quality-of-service (QoS) multicast routing protocol (QMR) in Internet-of-Things (IoT) enabling cognitive radio mobile ad hoc networks (ICR-MANETs) based on deep Q-learning approach. To this end, we formulate a joint end-to-end queuing delay and link’s stability optimization problem. The formulated optimization is typically NP-complete. We then leverage a novel deep Q-learning method to solve this challenging problem, which arrives at optimal convergence $Q^{\ast}$-values. Based on the obtained $Q^{\ast}$-values, we propose a QoS multicast routing protocol to select the best set of neighbors associated with minimum $Q^{\ast}$-values to establish the multicast tree to the multicast members. Simulation results show that the proposed QMR protocol outperforms the current state-of-the-art routing protocols, which emerges as an efficient multicast routing protocol in ICR-MANETs.