Deep Graph Reinforcement Learning Based Intelligent Traffic Routing Control for Software-Defined Wireless Sensor Networks
Software-defined wireless sensor networks (SDWSN), where the data and control planes are decoupled, are more suited to handling big sensor data and effectively monitoring dynamic environments and events. To overcome the limitations of using static routing tables under high traffic intensity, such as...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/4/1951 |
| _version_ | 1797483075754524672 |
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| author | Ru Huang Wenfan Guan Guangtao Zhai Jianhua He Xiaoli Chu |
| author_facet | Ru Huang Wenfan Guan Guangtao Zhai Jianhua He Xiaoli Chu |
| author_sort | Ru Huang |
| collection | DOAJ |
| description | Software-defined wireless sensor networks (SDWSN), where the data and control planes are decoupled, are more suited to handling big sensor data and effectively monitoring dynamic environments and events. To overcome the limitations of using static routing tables under high traffic intensity, such as network congestion, high packet loss rate, low throughput, etc., it is critical to design intelligent traffic routing control for the SDWSNs. In this paper we propose a deep graph reinforcement learning (DGRL) model-based intelligent traffic control scheme for SDWSNs, which combines graph convolution with deterministic policy gradient. The model fits well for the task of intelligent routing control for the SDWSN, as the process of data forwarding can be regarded as the sampling of continuous action space and the traffic data has strong graph features. The intelligent control policies are made by the SDWSN controller and implemented at the sensor nodes to optimize the data forwarding process. Simulation experiments performed on the Omnet++ platform show that, compared with the existing traffic routing algorithms for SDWSNs, the proposed intelligent routing control method can effectively reduce packet transmission delay, increase packet delivery ratio, and reduce the probability of network congestion. |
| first_indexed | 2024-03-09T22:41:43Z |
| format | Article |
| id | doaj.art-86b0ab59788d4104853be51613ae52ae |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:41:43Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-86b0ab59788d4104853be51613ae52ae2023-11-23T18:36:45ZengMDPI AGApplied Sciences2076-34172022-02-01124195110.3390/app12041951Deep Graph Reinforcement Learning Based Intelligent Traffic Routing Control for Software-Defined Wireless Sensor NetworksRu Huang0Wenfan Guan1Guangtao Zhai2Jianhua He3Xiaoli Chu4School of Information Science & Engineering, East China University of Science and Technology, Shanghai 200237, ChinaSchool of Information Science & Engineering, East China University of Science and Technology, Shanghai 200237, ChinaInstitute of Image Communication and Information Processing, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UKDepartment of Electronic and Electrical Engineering, University of Sheffield, Sheffield S1 3JD, UKSoftware-defined wireless sensor networks (SDWSN), where the data and control planes are decoupled, are more suited to handling big sensor data and effectively monitoring dynamic environments and events. To overcome the limitations of using static routing tables under high traffic intensity, such as network congestion, high packet loss rate, low throughput, etc., it is critical to design intelligent traffic routing control for the SDWSNs. In this paper we propose a deep graph reinforcement learning (DGRL) model-based intelligent traffic control scheme for SDWSNs, which combines graph convolution with deterministic policy gradient. The model fits well for the task of intelligent routing control for the SDWSN, as the process of data forwarding can be regarded as the sampling of continuous action space and the traffic data has strong graph features. The intelligent control policies are made by the SDWSN controller and implemented at the sensor nodes to optimize the data forwarding process. Simulation experiments performed on the Omnet++ platform show that, compared with the existing traffic routing algorithms for SDWSNs, the proposed intelligent routing control method can effectively reduce packet transmission delay, increase packet delivery ratio, and reduce the probability of network congestion.https://www.mdpi.com/2076-3417/12/4/1951software-defined wireless sensor networkintelligent routing controldeep reinforcement learninggraph convolutional network |
| spellingShingle | Ru Huang Wenfan Guan Guangtao Zhai Jianhua He Xiaoli Chu Deep Graph Reinforcement Learning Based Intelligent Traffic Routing Control for Software-Defined Wireless Sensor Networks Applied Sciences software-defined wireless sensor network intelligent routing control deep reinforcement learning graph convolutional network |
| title | Deep Graph Reinforcement Learning Based Intelligent Traffic Routing Control for Software-Defined Wireless Sensor Networks |
| title_full | Deep Graph Reinforcement Learning Based Intelligent Traffic Routing Control for Software-Defined Wireless Sensor Networks |
| title_fullStr | Deep Graph Reinforcement Learning Based Intelligent Traffic Routing Control for Software-Defined Wireless Sensor Networks |
| title_full_unstemmed | Deep Graph Reinforcement Learning Based Intelligent Traffic Routing Control for Software-Defined Wireless Sensor Networks |
| title_short | Deep Graph Reinforcement Learning Based Intelligent Traffic Routing Control for Software-Defined Wireless Sensor Networks |
| title_sort | deep graph reinforcement learning based intelligent traffic routing control for software defined wireless sensor networks |
| topic | software-defined wireless sensor network intelligent routing control deep reinforcement learning graph convolutional network |
| url | https://www.mdpi.com/2076-3417/12/4/1951 |
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