Deep reinforcement learning-based resource reservation algorithm for emergency Internet-of-things slice
Based on the requirements of ultra-low latency services for emergency Internet-of-things (EIoT) applications,a multi-slice network architecture for ultra-low latency emergency IoT was designed,and a general methodology framework based on resource reservation,sharing and isolation for multiple slices...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2020-09-01
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| Series: | Tongxin xuebao |
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| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020200/ |
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| author | Guolin SUN Ruijie OU Guisong LIU |
| author_facet | Guolin SUN Ruijie OU Guisong LIU |
| author_sort | Guolin SUN |
| collection | DOAJ |
| description | Based on the requirements of ultra-low latency services for emergency Internet-of-things (EIoT) applications,a multi-slice network architecture for ultra-low latency emergency IoT was designed,and a general methodology framework based on resource reservation,sharing and isolation for multiple slices was proposed.In the proposed framework,real-time and automatic inter-slice resource demand prediction and allocation were realized based on deep reinforcement learning (DRL),while intra-slice user resource allocation was modeled as a shape-based 2-dimension packing problem and solved with a heuristic numerical algorithm,so that intra-slice resource customization was achieved.Simulation results show that the resource reservation-based method enable EIoT slices to explicitly reserve resources,provide a better security isolation level,and DRL could guarantee accuracy and real-time updates of resource reservations.Compared with four existing algorithms,dueling deep Q-network (DQN) performes better than the benchmarks. |
| format | Article |
| id | doaj-art-8289da41752d4c0e988b360bb5ba67d6 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2020-09-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-8289da41752d4c0e988b360bb5ba67d62025-01-14T07:19:49ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2020-09-014182059737076Deep reinforcement learning-based resource reservation algorithm for emergency Internet-of-things sliceGuolin SUNRuijie OUGuisong LIUBased on the requirements of ultra-low latency services for emergency Internet-of-things (EIoT) applications,a multi-slice network architecture for ultra-low latency emergency IoT was designed,and a general methodology framework based on resource reservation,sharing and isolation for multiple slices was proposed.In the proposed framework,real-time and automatic inter-slice resource demand prediction and allocation were realized based on deep reinforcement learning (DRL),while intra-slice user resource allocation was modeled as a shape-based 2-dimension packing problem and solved with a heuristic numerical algorithm,so that intra-slice resource customization was achieved.Simulation results show that the resource reservation-based method enable EIoT slices to explicitly reserve resources,provide a better security isolation level,and DRL could guarantee accuracy and real-time updates of resource reservations.Compared with four existing algorithms,dueling deep Q-network (DQN) performes better than the benchmarks.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020200/emergency IoTdeep reinforcement learningresource reservationultra-low latency communication |
| spellingShingle | Guolin SUN Ruijie OU Guisong LIU Deep reinforcement learning-based resource reservation algorithm for emergency Internet-of-things slice Tongxin xuebao emergency IoT deep reinforcement learning resource reservation ultra-low latency communication |
| title | Deep reinforcement learning-based resource reservation algorithm for emergency Internet-of-things slice |
| title_full | Deep reinforcement learning-based resource reservation algorithm for emergency Internet-of-things slice |
| title_fullStr | Deep reinforcement learning-based resource reservation algorithm for emergency Internet-of-things slice |
| title_full_unstemmed | Deep reinforcement learning-based resource reservation algorithm for emergency Internet-of-things slice |
| title_short | Deep reinforcement learning-based resource reservation algorithm for emergency Internet-of-things slice |
| title_sort | deep reinforcement learning based resource reservation algorithm for emergency internet of things slice |
| topic | emergency IoT deep reinforcement learning resource reservation ultra-low latency communication |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020200/ |
| work_keys_str_mv | AT guolinsun deepreinforcementlearningbasedresourcereservationalgorithmforemergencyinternetofthingsslice AT ruijieou deepreinforcementlearningbasedresourcereservationalgorithmforemergencyinternetofthingsslice AT guisongliu deepreinforcementlearningbasedresourcereservationalgorithmforemergencyinternetofthingsslice |