{"title":"支持V2V移动边缘计算的智能交通DQN","authors":"Xiaoming Guo, Xiao Hong","doi":"10.1109/SMARTCOMP58114.2023.00048","DOIUrl":null,"url":null,"abstract":"The paper introduces a deep reinforcement learning model for a special scenario in future smart transportation. The scenario describes a mobile edge computing platform hosted by a group of self-organized connected vehicles for sharing computation resources. The presented DQN model is to solve the trade-offs between the computing capability and the traffic state. Results show the existence of the trade-off and the need for future research in a few areas.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DQN for Smart Transportation Supporting V2V Mobile Edge Computing\",\"authors\":\"Xiaoming Guo, Xiao Hong\",\"doi\":\"10.1109/SMARTCOMP58114.2023.00048\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper introduces a deep reinforcement learning model for a special scenario in future smart transportation. The scenario describes a mobile edge computing platform hosted by a group of self-organized connected vehicles for sharing computation resources. The presented DQN model is to solve the trade-offs between the computing capability and the traffic state. Results show the existence of the trade-off and the need for future research in a few areas.\",\"PeriodicalId\":163556,\"journal\":{\"name\":\"2023 IEEE International Conference on Smart Computing (SMARTCOMP)\",\"volume\":\"77 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE International Conference on Smart Computing (SMARTCOMP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SMARTCOMP58114.2023.00048\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SMARTCOMP58114.2023.00048","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
DQN for Smart Transportation Supporting V2V Mobile Edge Computing
The paper introduces a deep reinforcement learning model for a special scenario in future smart transportation. The scenario describes a mobile edge computing platform hosted by a group of self-organized connected vehicles for sharing computation resources. The presented DQN model is to solve the trade-offs between the computing capability and the traffic state. Results show the existence of the trade-off and the need for future research in a few areas.
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