A Fast Anti-Jamming Algorithm Based on Imitation Learning for WSN
Wireless sensor networks (WSNs), integral components underpinning the infrastructure of the internet of things (IoT), confront escalating threats originating from attempts at malicious jamming. Nevertheless, the limited nature of the hardware resources in distributed, low-cost WSNs, such as those fo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/22/9240 |
| _version_ | 1797457760774782976 |
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| author | Wenhao Zhou Zhanyang Zhou Yingtao Niu Quan Zhou Huihui Ding |
| author_facet | Wenhao Zhou Zhanyang Zhou Yingtao Niu Quan Zhou Huihui Ding |
| author_sort | Wenhao Zhou |
| collection | DOAJ |
| description | Wireless sensor networks (WSNs), integral components underpinning the infrastructure of the internet of things (IoT), confront escalating threats originating from attempts at malicious jamming. Nevertheless, the limited nature of the hardware resources in distributed, low-cost WSNs, such as those for computing power and storage, poses a challenge when implementing complex and intelligent anti-jamming algorithms like deep reinforcement learning (DRL). Hence, in this paper a rapid anti-jamming method is proposed based on imitation learning in order to address this issue. First, on-network nodes obtain expert anti-jamming trajectories using heuristic algorithms, taking historical experiences into account. Second, an RNN neural network that can be used for anti-jamming decision making is trained by mimicking these expert trajectories. Finally, the late-access network nodes receive anti-jamming network parameters from the existing nodes, allowing them to obtain a policy network directly applicable to anti-jamming decision making and thus avoiding redundant learning. Experimental results demonstrate that, compared with traditional Q-learning and random frequency-hopping (RFH) algorithms, the imitation learning-based algorithm empowers late-access network nodes to swiftly acquire anti-jamming strategies that perform on par with expert strategies. |
| first_indexed | 2024-03-09T16:27:35Z |
| format | Article |
| id | doaj.art-9043697cecec485a9f494db3f4eb9c11 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T16:27:35Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9043697cecec485a9f494db3f4eb9c112023-11-24T15:05:53ZengMDPI AGSensors1424-82202023-11-012322924010.3390/s23229240A Fast Anti-Jamming Algorithm Based on Imitation Learning for WSNWenhao Zhou0Zhanyang Zhou1Yingtao Niu2Quan Zhou3Huihui Ding4School of Electronic Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaThe Sixty-Third Research Institute, National University of Defense Technology, Nanjing 210007, ChinaThe Sixty-Third Research Institute, National University of Defense Technology, Nanjing 210007, ChinaCollege of Communications Engineering, Army Engineering University of People’s Liberation Army, Nanjing 210042, ChinaSchool of Electronic Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaWireless sensor networks (WSNs), integral components underpinning the infrastructure of the internet of things (IoT), confront escalating threats originating from attempts at malicious jamming. Nevertheless, the limited nature of the hardware resources in distributed, low-cost WSNs, such as those for computing power and storage, poses a challenge when implementing complex and intelligent anti-jamming algorithms like deep reinforcement learning (DRL). Hence, in this paper a rapid anti-jamming method is proposed based on imitation learning in order to address this issue. First, on-network nodes obtain expert anti-jamming trajectories using heuristic algorithms, taking historical experiences into account. Second, an RNN neural network that can be used for anti-jamming decision making is trained by mimicking these expert trajectories. Finally, the late-access network nodes receive anti-jamming network parameters from the existing nodes, allowing them to obtain a policy network directly applicable to anti-jamming decision making and thus avoiding redundant learning. Experimental results demonstrate that, compared with traditional Q-learning and random frequency-hopping (RFH) algorithms, the imitation learning-based algorithm empowers late-access network nodes to swiftly acquire anti-jamming strategies that perform on par with expert strategies.https://www.mdpi.com/1424-8220/23/22/9240imitation learninganti-jamming communicationwireless sensor network |
| spellingShingle | Wenhao Zhou Zhanyang Zhou Yingtao Niu Quan Zhou Huihui Ding A Fast Anti-Jamming Algorithm Based on Imitation Learning for WSN Sensors imitation learning anti-jamming communication wireless sensor network |
| title | A Fast Anti-Jamming Algorithm Based on Imitation Learning for WSN |
| title_full | A Fast Anti-Jamming Algorithm Based on Imitation Learning for WSN |
| title_fullStr | A Fast Anti-Jamming Algorithm Based on Imitation Learning for WSN |
| title_full_unstemmed | A Fast Anti-Jamming Algorithm Based on Imitation Learning for WSN |
| title_short | A Fast Anti-Jamming Algorithm Based on Imitation Learning for WSN |
| title_sort | fast anti jamming algorithm based on imitation learning for wsn |
| topic | imitation learning anti-jamming communication wireless sensor network |
| url | https://www.mdpi.com/1424-8220/23/22/9240 |
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