{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T18:07:19Z","timestamp":1764785239201,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T00:00:00Z","timestamp":1700179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation of China","award":["U19bB2014"],"award-info":[{"award-number":["U19bB2014"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s23229240","type":"journal-article","created":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T09:23:43Z","timestamp":1700213023000},"page":"9240","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Fast Anti-Jamming Algorithm Based on Imitation Learning for WSN"],"prefix":"10.3390","volume":"23","author":[{"given":"Wenhao","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Electronic Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhanyang","family":"Zhou","sequence":"additional","affiliation":[{"name":"The Sixty-Third Research Institute, National University of Defense Technology, Nanjing 210007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6366-3774","authenticated-orcid":false,"given":"Yingtao","family":"Niu","sequence":"additional","affiliation":[{"name":"The Sixty-Third Research Institute, National University of Defense Technology, Nanjing 210007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9947-9722","authenticated-orcid":false,"given":"Quan","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Communications Engineering, Army Engineering University of People\u2019s Liberation Army, Nanjing 210042, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huihui","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11974","DOI":"10.1109\/JIOT.2021.3132452","article-title":"Reliable task allocation for time-triggered IoT-WSN using discrete particle swarm optimization","volume":"9","author":"Baniabdelghany","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3037","DOI":"10.1109\/JIOT.2021.3094198","article-title":"IoT-enabled IEEE 802.15. 4 WSN monitoring infrastructure-driven fuzzy-logic-based crop pest prediction","volume":"9","author":"Sharma","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yadav, P., and Sharma, S.C. 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