IRSML: An intelligent routing algorithm based on machine learning in software defined wireless networking
In software-defined wireless networking (SDWN), the optimal routing technique is one of the effective solutions to improve its performance. This routing technique is done by many different methods, with the most common using integer linear programming problem (ILP), building optimal routing metrics....
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| Format: | Article |
| Language: | English |
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Electronics and Telecommunications Research Institute (ETRI)
2022-10-01
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| Series: | ETRI Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.4218/etrij.2021-0212 |
| _version_ | 1811330194140561408 |
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| author | Thuy-Van T. Duong Le Huu Binh |
| author_facet | Thuy-Van T. Duong Le Huu Binh |
| author_sort | Thuy-Van T. Duong |
| collection | DOAJ |
| description | In software-defined wireless networking (SDWN), the optimal routing technique is one of the effective solutions to improve its performance. This routing technique is done by many different methods, with the most common using integer linear programming problem (ILP), building optimal routing metrics. These methods often only focus on one routing objective, such as minimizing the packet blocking probability, minimizing end-to-end delay (EED), and maximizing network throughput. It is difficult to consider multiple objectives concurrently in a routing algorithm. In this paper, we investigate the application of machine learning to control routing in the SDWN. An intelligent routing algorithm is then proposed based on the machine learning to improve the network performance. The proposed algorithm can optimize multiple routing objectives. Our idea is to combine supervised learning (SL) and reinforcement learning (RL) methods to discover new routes. The SL is used to predict the performance metrics of the links, including EED quality of transmission (QoT), and packet blocking probability (PBP). The routing is done by the RL method. We use the Q-value in the fundamental equation of the RL to store the PBP, which is used for the aim of route selection. Concurrently, the learning rate coefficient is flexibly changed to determine the constraints of routing during learning. These constraints include QoT and EED. Our performance evaluations based on OMNeT++ have shown that the proposed algorithm has significantly improved the network performance in terms of the QoT, EED, packet delivery ratio, and network throughput compared with other wellknown routing algorithms. |
| first_indexed | 2024-04-13T15:58:05Z |
| format | Article |
| id | doaj.art-1fe49cc88b7b4de8b11309e70e606323 |
| institution | Directory Open Access Journal |
| issn | 1225-6463 |
| language | English |
| last_indexed | 2024-04-13T15:58:05Z |
| publishDate | 2022-10-01 |
| publisher | Electronics and Telecommunications Research Institute (ETRI) |
| record_format | Article |
| series | ETRI Journal |
| spelling | doaj.art-1fe49cc88b7b4de8b11309e70e6063232022-12-22T02:40:37ZengElectronics and Telecommunications Research Institute (ETRI)ETRI Journal1225-64632022-10-0144573374510.4218/etrij.2021-021210.4218/etrij.2021-0212IRSML: An intelligent routing algorithm based on machine learning in software defined wireless networkingThuy-Van T. DuongLe Huu BinhIn software-defined wireless networking (SDWN), the optimal routing technique is one of the effective solutions to improve its performance. This routing technique is done by many different methods, with the most common using integer linear programming problem (ILP), building optimal routing metrics. These methods often only focus on one routing objective, such as minimizing the packet blocking probability, minimizing end-to-end delay (EED), and maximizing network throughput. It is difficult to consider multiple objectives concurrently in a routing algorithm. In this paper, we investigate the application of machine learning to control routing in the SDWN. An intelligent routing algorithm is then proposed based on the machine learning to improve the network performance. The proposed algorithm can optimize multiple routing objectives. Our idea is to combine supervised learning (SL) and reinforcement learning (RL) methods to discover new routes. The SL is used to predict the performance metrics of the links, including EED quality of transmission (QoT), and packet blocking probability (PBP). The routing is done by the RL method. We use the Q-value in the fundamental equation of the RL to store the PBP, which is used for the aim of route selection. Concurrently, the learning rate coefficient is flexibly changed to determine the constraints of routing during learning. These constraints include QoT and EED. Our performance evaluations based on OMNeT++ have shown that the proposed algorithm has significantly improved the network performance in terms of the QoT, EED, packet delivery ratio, and network throughput compared with other wellknown routing algorithms.https://doi.org/10.4218/etrij.2021-0212intelligent routingreinforcement learningsdwnsupervised learning |
| spellingShingle | Thuy-Van T. Duong Le Huu Binh IRSML: An intelligent routing algorithm based on machine learning in software defined wireless networking ETRI Journal intelligent routing reinforcement learning sdwn supervised learning |
| title | IRSML: An intelligent routing algorithm based on machine learning in software defined wireless networking |
| title_full | IRSML: An intelligent routing algorithm based on machine learning in software defined wireless networking |
| title_fullStr | IRSML: An intelligent routing algorithm based on machine learning in software defined wireless networking |
| title_full_unstemmed | IRSML: An intelligent routing algorithm based on machine learning in software defined wireless networking |
| title_short | IRSML: An intelligent routing algorithm based on machine learning in software defined wireless networking |
| title_sort | irsml an intelligent routing algorithm based on machine learning in software defined wireless networking |
| topic | intelligent routing reinforcement learning sdwn supervised learning |
| url | https://doi.org/10.4218/etrij.2021-0212 |
| work_keys_str_mv | AT thuyvantduong irsmlanintelligentroutingalgorithmbasedonmachinelearninginsoftwaredefinedwirelessnetworking AT lehuubinh irsmlanintelligentroutingalgorithmbasedonmachinelearninginsoftwaredefinedwirelessnetworking |