Intelligent routing strategy in the Internet of things based on deep reinforcement learning
At the era of the Internet of things,networking mode that connects everything would bring tremendous increase in the data volume and challenge the traditional routing protocols.The limitations of the existing routing protocols was analyzed when facing the data explosion and then the routing selectio...
| Published in: | 物联网学报 |
|---|---|
| Main Authors: | , , |
| Format: | Article |
| Language: | Chinese |
| Published: |
China InfoCom Media Group
2019-06-01
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| Subjects: | |
| Online Access: | http://www.wlwxb.com.cn/thesisDetails#10.11959/j.issn.2096-3750.2019.00097 |
| _version_ | 1849865264111288320 |
|---|---|
| author | Ruijin DING Feifei GAO Ling XING |
| author_facet | Ruijin DING Feifei GAO Ling XING |
| author_sort | Ruijin DING |
| collection | DOAJ |
| container_title | 物联网学报 |
| description | At the era of the Internet of things,networking mode that connects everything would bring tremendous increase in the data volume and challenge the traditional routing protocols.The limitations of the existing routing protocols was analyzed when facing the data explosion and then the routing selection problem was re-modeled as a Markov decision process.On this basis,the deep reinforcement learning technique was utilized to choose the next-hop router for data transmission task in order to shorten the transmission path length while network congestion was avoided.The simulation results demonstrate that the congestion probability can be reduced significantly and the network throughput can be enhanced by the proposed strategy. |
| format | Article |
| id | doaj-art-fb4bff22bbdc41feb3a45bd005b0bbbb |
| institution | Directory of Open Access Journals |
| issn | 2096-3750 |
| language | zho |
| publishDate | 2019-06-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| spelling | doaj-art-fb4bff22bbdc41feb3a45bd005b0bbbb2025-08-20T01:17:05ZzhoChina InfoCom Media Group物联网学报2096-37502019-06-013566359644553Intelligent routing strategy in the Internet of things based on deep reinforcement learningRuijin DINGFeifei GAOLing XINGAt the era of the Internet of things,networking mode that connects everything would bring tremendous increase in the data volume and challenge the traditional routing protocols.The limitations of the existing routing protocols was analyzed when facing the data explosion and then the routing selection problem was re-modeled as a Markov decision process.On this basis,the deep reinforcement learning technique was utilized to choose the next-hop router for data transmission task in order to shorten the transmission path length while network congestion was avoided.The simulation results demonstrate that the congestion probability can be reduced significantly and the network throughput can be enhanced by the proposed strategy.http://www.wlwxb.com.cn/thesisDetails#10.11959/j.issn.2096-3750.2019.00097deep reinforcement learning;routing;internet of things;network congestion |
| spellingShingle | Ruijin DING Feifei GAO Ling XING Intelligent routing strategy in the Internet of things based on deep reinforcement learning deep reinforcement learning;routing;internet of things;network congestion |
| title | Intelligent routing strategy in the Internet of things based on deep reinforcement learning |
| title_full | Intelligent routing strategy in the Internet of things based on deep reinforcement learning |
| title_fullStr | Intelligent routing strategy in the Internet of things based on deep reinforcement learning |
| title_full_unstemmed | Intelligent routing strategy in the Internet of things based on deep reinforcement learning |
| title_short | Intelligent routing strategy in the Internet of things based on deep reinforcement learning |
| title_sort | intelligent routing strategy in the internet of things based on deep reinforcement learning |
| topic | deep reinforcement learning;routing;internet of things;network congestion |
| url | http://www.wlwxb.com.cn/thesisDetails#10.11959/j.issn.2096-3750.2019.00097 |
| work_keys_str_mv | AT ruijinding intelligentroutingstrategyintheinternetofthingsbasedondeepreinforcementlearning AT feifeigao intelligentroutingstrategyintheinternetofthingsbasedondeepreinforcementlearning AT lingxing intelligentroutingstrategyintheinternetofthingsbasedondeepreinforcementlearning |