Real-Time Data Transmission Scheduling Algorithm for Wireless Sensor Networks Based on Deep Q-Learning
In the industrial environment, the data transmission of Wireless Sensor Networks (WSNs) usually has strict deadline requirements. Improving the reliability and real-time performance of data transmission has become one of the critical issues in WSNs research. One of the main methods to improve the ne...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/12/1877 |
| _version_ | 1797488002471034880 |
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| author | Aiqi Zhang Meiyi Sun Jiaqi Wang Zhiyi Li Yanbo Cheng Cheng Wang |
| author_facet | Aiqi Zhang Meiyi Sun Jiaqi Wang Zhiyi Li Yanbo Cheng Cheng Wang |
| author_sort | Aiqi Zhang |
| collection | DOAJ |
| description | In the industrial environment, the data transmission of Wireless Sensor Networks (WSNs) usually has strict deadline requirements. Improving the reliability and real-time performance of data transmission has become one of the critical issues in WSNs research. One of the main methods to improve the network performance of WSNs is to schedule the transmission process. An effective scheduling algorithm can meet the requirements of a strict industrial environment for network performance, which is of great research significance. Aiming at the problem of concurrent data transmission in WSNs, a real-time data transmission scheduling algorithm based on deep Q-learning is proposed. The algorithm comprehensively considers the influence of the remaining deadline, remaining hops, and unassigned time-slot nodes in the data transmission process, defines the reward function and action selection strategy of Q-learning, and guides the system state information transfer process. At the same time, deep learning and Q-learning are combined to solve the problem of disaster maintenance caused by the large scale of the system state. A multi-layer Stacked Auto Encoder (SAE) network model establishes the state-action mapping relationship, and the Q-learning algorithm updates it. Finally, according to the trained SAE network model, the data transmission scheduling strategy of the system in different states is obtained. The network performance of the proposed data transmission scheduling algorithm is analyzed and evaluated by simulation experiments. The simulation results show that compared with the commonly used heuristic algorithms, the proposed algorithm improves real-time performance and can better meet the data transmission requirements of high reliability and real-time WSNs. |
| first_indexed | 2024-03-09T23:55:54Z |
| format | Article |
| id | doaj.art-d42707263c074e4d9d4cb1d1e975c038 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T23:55:54Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-d42707263c074e4d9d4cb1d1e975c0382023-11-23T16:25:12ZengMDPI AGElectronics2079-92922022-06-011112187710.3390/electronics11121877Real-Time Data Transmission Scheduling Algorithm for Wireless Sensor Networks Based on Deep Q-LearningAiqi Zhang0Meiyi Sun1Jiaqi Wang2Zhiyi Li3Yanbo Cheng4Cheng Wang5School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaIn the industrial environment, the data transmission of Wireless Sensor Networks (WSNs) usually has strict deadline requirements. Improving the reliability and real-time performance of data transmission has become one of the critical issues in WSNs research. One of the main methods to improve the network performance of WSNs is to schedule the transmission process. An effective scheduling algorithm can meet the requirements of a strict industrial environment for network performance, which is of great research significance. Aiming at the problem of concurrent data transmission in WSNs, a real-time data transmission scheduling algorithm based on deep Q-learning is proposed. The algorithm comprehensively considers the influence of the remaining deadline, remaining hops, and unassigned time-slot nodes in the data transmission process, defines the reward function and action selection strategy of Q-learning, and guides the system state information transfer process. At the same time, deep learning and Q-learning are combined to solve the problem of disaster maintenance caused by the large scale of the system state. A multi-layer Stacked Auto Encoder (SAE) network model establishes the state-action mapping relationship, and the Q-learning algorithm updates it. Finally, according to the trained SAE network model, the data transmission scheduling strategy of the system in different states is obtained. The network performance of the proposed data transmission scheduling algorithm is analyzed and evaluated by simulation experiments. The simulation results show that compared with the commonly used heuristic algorithms, the proposed algorithm improves real-time performance and can better meet the data transmission requirements of high reliability and real-time WSNs.https://www.mdpi.com/2079-9292/11/12/1877real-timedata transmissiondeep Q-learningWireless Sensor Networks |
| spellingShingle | Aiqi Zhang Meiyi Sun Jiaqi Wang Zhiyi Li Yanbo Cheng Cheng Wang Real-Time Data Transmission Scheduling Algorithm for Wireless Sensor Networks Based on Deep Q-Learning Electronics real-time data transmission deep Q-learning Wireless Sensor Networks |
| title | Real-Time Data Transmission Scheduling Algorithm for Wireless Sensor Networks Based on Deep Q-Learning |
| title_full | Real-Time Data Transmission Scheduling Algorithm for Wireless Sensor Networks Based on Deep Q-Learning |
| title_fullStr | Real-Time Data Transmission Scheduling Algorithm for Wireless Sensor Networks Based on Deep Q-Learning |
| title_full_unstemmed | Real-Time Data Transmission Scheduling Algorithm for Wireless Sensor Networks Based on Deep Q-Learning |
| title_short | Real-Time Data Transmission Scheduling Algorithm for Wireless Sensor Networks Based on Deep Q-Learning |
| title_sort | real time data transmission scheduling algorithm for wireless sensor networks based on deep q learning |
| topic | real-time data transmission deep Q-learning Wireless Sensor Networks |
| url | https://www.mdpi.com/2079-9292/11/12/1877 |
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