Security Enhancement for Deep Reinforcement Learning-Based Strategy in Energy-Efficient Wireless Sensor Networks
Energy efficiency and security issues are the main concerns in wireless sensor networks (WSNs) because of limited energy resources and the broadcast nature of wireless communication. Therefore, how to improve the energy efficiency of WSNs while enhancing security performance has attracted widespread...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/6/1993 |
| _version_ | 1797239286721937408 |
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| author | Liyazhou Hu Chao Han Xiaojun Wang Han Zhu Jian Ouyang |
| author_facet | Liyazhou Hu Chao Han Xiaojun Wang Han Zhu Jian Ouyang |
| author_sort | Liyazhou Hu |
| collection | DOAJ |
| description | Energy efficiency and security issues are the main concerns in wireless sensor networks (WSNs) because of limited energy resources and the broadcast nature of wireless communication. Therefore, how to improve the energy efficiency of WSNs while enhancing security performance has attracted widespread attention. In order to solve this problem, this paper proposes a new deep reinforcement learning (DRL)-based strategy, i.e., DeepNR strategy, to enhance the energy efficiency and security performance of WSN. Specifically, the proposed DeepNR strategy approximates the Q-value by designing a deep neural network (DNN) to adaptively learn the state information. It also designs DRL-based multi-level decision-making to learn and optimize the data transmission paths in real time, which eventually achieves accurate prediction and decision-making of the network. To further enhance security performance, the DeepNR strategy includes a defense mechanism that responds to detected attacks in real time to ensure the normal operation of the network. In addition, DeepNR adaptively adjusts its strategy to cope with changing network environments and attack patterns through deep learning models. Experimental results show that the proposed DeepNR outperforms the conventional methods, demonstrating a remarkable 30% improvement in network lifespan, a 25% increase in network data throughput, and a 20% enhancement in security measures. |
| first_indexed | 2024-04-24T17:49:08Z |
| format | Article |
| id | doaj.art-206782a834ee46aa881564928739b0a3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-24T17:49:08Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-206782a834ee46aa881564928739b0a32024-03-27T14:04:19ZengMDPI AGSensors1424-82202024-03-01246199310.3390/s24061993Security Enhancement for Deep Reinforcement Learning-Based Strategy in Energy-Efficient Wireless Sensor NetworksLiyazhou Hu0Chao Han1Xiaojun Wang2Han Zhu3Jian Ouyang4School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, ChinaChina Mobile Jianshe Co., Ltd. Zhejiang Branch, Hangzhou 310013, ChinaIndustrial Training Center, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaFaculty of Applied Sciences, Macao Polytechnic University, Macau 999078, ChinaIndustrial Training Center, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaEnergy efficiency and security issues are the main concerns in wireless sensor networks (WSNs) because of limited energy resources and the broadcast nature of wireless communication. Therefore, how to improve the energy efficiency of WSNs while enhancing security performance has attracted widespread attention. In order to solve this problem, this paper proposes a new deep reinforcement learning (DRL)-based strategy, i.e., DeepNR strategy, to enhance the energy efficiency and security performance of WSN. Specifically, the proposed DeepNR strategy approximates the Q-value by designing a deep neural network (DNN) to adaptively learn the state information. It also designs DRL-based multi-level decision-making to learn and optimize the data transmission paths in real time, which eventually achieves accurate prediction and decision-making of the network. To further enhance security performance, the DeepNR strategy includes a defense mechanism that responds to detected attacks in real time to ensure the normal operation of the network. In addition, DeepNR adaptively adjusts its strategy to cope with changing network environments and attack patterns through deep learning models. Experimental results show that the proposed DeepNR outperforms the conventional methods, demonstrating a remarkable 30% improvement in network lifespan, a 25% increase in network data throughput, and a 20% enhancement in security measures.https://www.mdpi.com/1424-8220/24/6/1993deep neural network (DNN)deep reinforcement learning (DRL)energy efficiencysecuritywireless sensor networks (WSNs) |
| spellingShingle | Liyazhou Hu Chao Han Xiaojun Wang Han Zhu Jian Ouyang Security Enhancement for Deep Reinforcement Learning-Based Strategy in Energy-Efficient Wireless Sensor Networks Sensors deep neural network (DNN) deep reinforcement learning (DRL) energy efficiency security wireless sensor networks (WSNs) |
| title | Security Enhancement for Deep Reinforcement Learning-Based Strategy in Energy-Efficient Wireless Sensor Networks |
| title_full | Security Enhancement for Deep Reinforcement Learning-Based Strategy in Energy-Efficient Wireless Sensor Networks |
| title_fullStr | Security Enhancement for Deep Reinforcement Learning-Based Strategy in Energy-Efficient Wireless Sensor Networks |
| title_full_unstemmed | Security Enhancement for Deep Reinforcement Learning-Based Strategy in Energy-Efficient Wireless Sensor Networks |
| title_short | Security Enhancement for Deep Reinforcement Learning-Based Strategy in Energy-Efficient Wireless Sensor Networks |
| title_sort | security enhancement for deep reinforcement learning based strategy in energy efficient wireless sensor networks |
| topic | deep neural network (DNN) deep reinforcement learning (DRL) energy efficiency security wireless sensor networks (WSNs) |
| url | https://www.mdpi.com/1424-8220/24/6/1993 |
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