{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T16:53:17Z","timestamp":1783183997218,"version":"3.54.6"},"reference-count":47,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T00:00:00Z","timestamp":1690329600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Information-centric networking (ICN) has gained significant attention due to its in-network caching and named-based routing capabilities. Caching plays a crucial role in managing the increasing network traffic and improving the content delivery efficiency. However, caching faces challenges as routers have limited cache space while the network hosts tens of thousands of items. This paper focuses on enhancing the cache performance by maximizing the cache hit ratio in the context of software-defined networking\u2013ICN (SDN-ICN). We propose a statistical model that generates users\u2019 content preferences, incorporating key elements observed in real-world scenarios. Furthermore, we introduce a graph neural network\u2013double deep Q-network (GNN-DDQN) agent to make caching decisions for each node based on the user request history. Simulation results demonstrate that our caching strategy achieves a cache hit ratio 34.42% higher than the state-of-the-art policy. We also establish the robustness of our approach, consistently outperforming various benchmark strategies.<\/jats:p>","DOI":"10.3390\/fi15080251","type":"journal-article","created":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T01:50:37Z","timestamp":1690422637000},"page":"251","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Intelligent Caching with Graph Neural Network-Based Deep Reinforcement Learning on SDN-Based ICN"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5991-3697","authenticated-orcid":false,"given":"Jiacheng","family":"Hou","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianhao","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoye","family":"Lu","sequence":"additional","affiliation":[{"name":"David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4605-0500","authenticated-orcid":false,"given":"Amiya","family":"Nayak","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Lung, C.H., Wei, X., Chen, M., Chatterjee, S., and Zhang, Z. (2023). In-network Caching for ICN-based IoT (ICN-IoT): A Comprehensive Survey. IEEE Internet Things J.","DOI":"10.1109\/JIOT.2023.3274653"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Musa, S.S., Zennaro, M., Libsie, M., and Pietrosemoli, E. (2022). 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