Status update frequency optimization for In-vehicle LAN route based on deep Q-learning
The popularization of the automated fleet requires a reliable transmission of In-vehicle Local Area Network (LAN) with high mobility under complex environment. Therefore, In-vehicle communication system needs to collect the network status of the vehicles in real time to ensure the availability of ro...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | Results in Control and Optimization |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666720722000376 |
| _version_ | 1828175604518748160 |
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| author | Yaowen Qi Qilong Huang Li Yang Cangqi Zhou Chengsheng Pan |
| author_facet | Yaowen Qi Qilong Huang Li Yang Cangqi Zhou Chengsheng Pan |
| author_sort | Yaowen Qi |
| collection | DOAJ |
| description | The popularization of the automated fleet requires a reliable transmission of In-vehicle Local Area Network (LAN) with high mobility under complex environment. Therefore, In-vehicle communication system needs to collect the network status of the vehicles in real time to ensure the availability of routing decisions. The high update frequency of the network status will increase the availability of the routing decisions. However, it will also incur congestion of the In-vehicle LAN. Therefore, it is of great practical interest to optimize the network status update frequency for the In-vehicle LAN to achieve the balance between route availability and network congestion. We consider this important problem in this paper and make the following contributions. Firstly, we analyze the network routing feature information to establish the multi-feature dynamic queue model and age of multi-feature information model, which describes the relationship between the network status and the routing availability. Secondly, this status update frequency selection problem is formulated as a multi-objective constrained optimization and solved by deep Q-learning with feature selection mechanism to improve the timeliness of In-vehicle LAN. Numerical results demonstrate that comparing with the existing algorithms, the drop rate of In-vehicle LAN decreases about 7.68% and the convergence speed of the proposed method improves about 8.3%. |
| first_indexed | 2024-04-12T04:29:51Z |
| format | Article |
| id | doaj.art-4b8169a14b78431a924ecfbb17a923a8 |
| institution | Directory Open Access Journal |
| issn | 2666-7207 |
| language | English |
| last_indexed | 2024-04-12T04:29:51Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Control and Optimization |
| spelling | doaj.art-4b8169a14b78431a924ecfbb17a923a82022-12-22T03:47:58ZengElsevierResults in Control and Optimization2666-72072022-09-018100163Status update frequency optimization for In-vehicle LAN route based on deep Q-learningYaowen Qi0Qilong Huang1Li Yang2Cangqi Zhou3Chengsheng Pan4School of Automation, Nanjing University of Science and Technology, Nanjing, 210094, ChinaSchool of Automation, Nanjing University of Science and Technology, Nanjing, 210094, China; Corresponding author.School of Automation, Nanjing University of Science and Technology, Nanjing, 210094, ChinaSchool of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, ChinaSchool of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, ChinaThe popularization of the automated fleet requires a reliable transmission of In-vehicle Local Area Network (LAN) with high mobility under complex environment. Therefore, In-vehicle communication system needs to collect the network status of the vehicles in real time to ensure the availability of routing decisions. The high update frequency of the network status will increase the availability of the routing decisions. However, it will also incur congestion of the In-vehicle LAN. Therefore, it is of great practical interest to optimize the network status update frequency for the In-vehicle LAN to achieve the balance between route availability and network congestion. We consider this important problem in this paper and make the following contributions. Firstly, we analyze the network routing feature information to establish the multi-feature dynamic queue model and age of multi-feature information model, which describes the relationship between the network status and the routing availability. Secondly, this status update frequency selection problem is formulated as a multi-objective constrained optimization and solved by deep Q-learning with feature selection mechanism to improve the timeliness of In-vehicle LAN. Numerical results demonstrate that comparing with the existing algorithms, the drop rate of In-vehicle LAN decreases about 7.68% and the convergence speed of the proposed method improves about 8.3%.http://www.sciencedirect.com/science/article/pii/S2666720722000376Network optimizationAge of informationStatus updateQ-learning |
| spellingShingle | Yaowen Qi Qilong Huang Li Yang Cangqi Zhou Chengsheng Pan Status update frequency optimization for In-vehicle LAN route based on deep Q-learning Results in Control and Optimization Network optimization Age of information Status update Q-learning |
| title | Status update frequency optimization for In-vehicle LAN route based on deep Q-learning |
| title_full | Status update frequency optimization for In-vehicle LAN route based on deep Q-learning |
| title_fullStr | Status update frequency optimization for In-vehicle LAN route based on deep Q-learning |
| title_full_unstemmed | Status update frequency optimization for In-vehicle LAN route based on deep Q-learning |
| title_short | Status update frequency optimization for In-vehicle LAN route based on deep Q-learning |
| title_sort | status update frequency optimization for in vehicle lan route based on deep q learning |
| topic | Network optimization Age of information Status update Q-learning |
| url | http://www.sciencedirect.com/science/article/pii/S2666720722000376 |
| work_keys_str_mv | AT yaowenqi statusupdatefrequencyoptimizationforinvehiclelanroutebasedondeepqlearning AT qilonghuang statusupdatefrequencyoptimizationforinvehiclelanroutebasedondeepqlearning AT liyang statusupdatefrequencyoptimizationforinvehiclelanroutebasedondeepqlearning AT cangqizhou statusupdatefrequencyoptimizationforinvehiclelanroutebasedondeepqlearning AT chengshengpan statusupdatefrequencyoptimizationforinvehiclelanroutebasedondeepqlearning |