{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T17:20:32Z","timestamp":1778692832448,"version":"3.51.4"},"reference-count":28,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T00:00:00Z","timestamp":1674172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021YFB3101500"],"award-info":[{"award-number":["2021YFB3101500"]}]},{"name":"National Key Research and Development Program of China","award":["62102042"],"award-info":[{"award-number":["62102042"]}]},{"name":"National Natural Science Foundation of China under grant","award":["2021YFB3101500"],"award-info":[{"award-number":["2021YFB3101500"]}]},{"name":"National Natural Science Foundation of China under grant","award":["62102042"],"award-info":[{"award-number":["62102042"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The vehicular ad hoc network (VANET) constitutes a key technology for realizing intelligent transportation services. However, VANET is characterized by diverse message types, complex security attributes of communication nodes, and rapid network topology changes. In this case, how to ensure safe, efficient, convenient, and comfortable message services for users has become a challenge that should not be ignored. To improve the flexibility of routing matching multiple message types in VANET, this paper proposes a secure intelligent message forwarding strategy based on deep reinforcement learning (DRL). The key supporting elements of the model in the strategy are reasonably designed in combination with the scenario, and sufficient training of the model is carried out by deep Q networks (DQN). In the strategy, the state space is composed of the distance between candidate and destination nodes, the security attribute of candidate nodes and the type of message to be sent. The node can adaptively select the routing scheme according to the complex state space. Simulation and analysis show that the proposed strategy has the advantages of fast convergence, well generalization ability, high transmission security, and low network delay. The strategy has flexible and rich service patterns and provides flexible security for VANET message services.<\/jats:p>","DOI":"10.3390\/s23031204","type":"journal-article","created":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T06:52:41Z","timestamp":1674197561000},"page":"1204","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Deep Reinforcement Learning-Based Intelligent Security Forwarding Strategy for VANET"],"prefix":"10.3390","volume":"23","author":[{"given":"Boya","family":"Liu","sequence":"first","affiliation":[{"name":"School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China"},{"name":"Beijing Electronic Science and Technology Institute, Beijing 100070, China"},{"name":"National Engineering Laboratory of Mobile Network Security, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoai","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China"},{"name":"National Engineering Laboratory of Mobile Network Security, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3310-926X","authenticated-orcid":false,"given":"Guosheng","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China"},{"name":"National Engineering Laboratory of Mobile Network Security, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenyu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China"},{"name":"National Engineering Laboratory of Mobile Network Security, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5466-1627","authenticated-orcid":false,"given":"Peiliang","family":"Zuo","sequence":"additional","affiliation":[{"name":"Beijing Electronic Science and Technology Institute, Beijing 100070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,20]]},"reference":[{"key":"ref_1","unstructured":"Cyber Security Administration of the Ministry of Industry and Information Technology (2022, December 14). White Paper on Network Security of Internet of Vehicles, Available online: chrome-extension:\/\/efaidnbmnnnibpcajpcglclefindmkaj\/https:\/\/www.apec.org\/docs\/default-source\/groups\/ppsti\/the-2nd-apec-white-paper-on-the-internet-of-vehicles-edition-2.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2582","DOI":"10.1109\/JIOT.2019.2948315","article-title":"Highly Anonymous Mobility-Tolerant Location-Based Onion Routing for VANETs","volume":"7","author":"Haghighi","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2409","DOI":"10.1109\/TITS.2019.2918255","article-title":"Intersection Fog-Based Distributed Routing for V2V Communication in Urban Vehicular Ad Hoc Networks","volume":"21","author":"Sun","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2934","DOI":"10.1109\/TVT.2019.2895274","article-title":"Link Stability Based Optimized Routing Framework for Software Defined Vehicular Networks","volume":"68","author":"Sudheera","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Farooq, W., Khan, M., and Rehman, S. (2017, January 10\u201314). AMVR: A multicast routing protocol for autonomous military vehicles communication in VANET. Proceedings of the 2017 14th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, Pakistan.","DOI":"10.1109\/IBCAST.2017.7868128"},{"key":"ref_6","unstructured":"Network 5.0 Industry and Technology Innovation Alliance (2021). Network 5.0 Technology White Paper (2.0), Network 5.0 Industry and Technology Innovation Alliance. Available online: http:\/\/network5.cn\/english.php."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6647","DOI":"10.1109\/JIOT.2020.2975084","article-title":"TROVE: A Context-Awareness Trust Model for VANETs Using Reinforcement Learning","volume":"7","author":"Guo","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"15554","DOI":"10.1109\/JSEN.2021.3056463","article-title":"Intelligent Dynamic Spectrum Access Using Deep Reinforcement Learning for VANETs","volume":"21","author":"Wang","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1683","DOI":"10.1007\/s00779-012-0600-8","article-title":"ALCA: Agent learning-based clustering algorithm in vehicular ad hoc networks","volume":"17","author":"Kumar","year":"2013","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ji, X., Xu, W., Zhang, C., Yun, T., Zhang, G., Wang, X., Wang, Y., and Liu, B. (2019, January 7\u20139). Keep forwarding path freshest in VANET via applying reinforcement learning. Proceedings of the 2019 IEEE First International Workshop on Network Meets Intelligent Computations (NMIC), Dallas, TX, USA.","DOI":"10.1109\/NMIC.2019.00008"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1111\/coin.12261","article-title":"Routing using reinforcement learn-ing in vehicular ad hoc networks","volume":"36","author":"Saravanan","year":"2020","journal-title":"Comput. Intell."},{"key":"ref_12","unstructured":"Sun, Y., Lin, Y., and Tang, Y. (2017). Communications, Signal Processing, and Systems, Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems (ICCSP 2017), Harbin, China, 14\u201317 July 2017, Springer. Lecture Notes in Electrical Engineering."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Roh, B., Han, M.H., Ham, J.H., and Kim, K.I. (2020). Q-LBR: Q-learning based load balancing routing for UAV-assisted VANET. Sensors, 20.","DOI":"10.3390\/s20195685"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.15837\/ijccc.2020.5.3928","article-title":"V2V routing in VANET based on heuristic Q-learning","volume":"15","author":"Yang","year":"2020","journal-title":"Int. J. Comput. Commun."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Nahar, A., and Das, D. (2020, January 15\u201319). Adaptive Reinforcement Routing in Software Defined Vehicular Networks. Proceedings of the 2020 International Wireless Communications and Mobile Computing (IWCMC), Limassol, Cyprus.","DOI":"10.1109\/IWCMC48107.2020.9148237"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4087","DOI":"10.1109\/TVT.2018.2789466","article-title":"UAV relay in VANETs against smart jamming with reinforcement learning","volume":"67","author":"Xiao","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2143","DOI":"10.1007\/s11277-018-5809-z","article-title":"Reinforcement Learning Based Mobility Adaptive Routing for Vehicular Ad-Hoc Networks","volume":"101","author":"Wu","year":"2018","journal-title":"Wirel. Pers. Commun."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1358","DOI":"10.1109\/LCOMM.2020.3048250","article-title":"Adaptive UAV-Assisted Geographic Routing With Q-Learning in VANET","volume":"25","author":"Jiang","year":"2021","journal-title":"IEEE Commun. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"10166","DOI":"10.1109\/ACCESS.2021.3050625","article-title":"MDPRP: A Q-learning Approach for the Joint Control of Beaconing Rate and Trans-mission Power in VANETs","volume":"9","year":"2021","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jabbar, W., and Malaney, R. (2020\u201316, January 18). Mobility Models and the Performance of Location-based Routing in VANETs. Proceedings of the 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), Victoria, BC, Canada.","DOI":"10.1109\/VTC2020-Fall49728.2020.9348864"},{"key":"ref_21","unstructured":"Mahalakshmi., G., Uma, E., Senthilnayaki, B., Devi, A., Rajeswary, C., and Dharanyadevi, P. (2021, January 3\u20134). Trust Score Evaluation Scheme for Secure Routing in VANET. Proceedings of the 2021 IEEE International Conference on Mobile Networks and Wireless Communications (ICMNWC), Tumkur, India."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"16558","DOI":"10.1109\/TITS.2021.3134686","article-title":"ARPLR: An All-Round and Highly Privacy-Preserving Location-Based Routing Scheme for VANETs","volume":"23","author":"Wang","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, D., Yu, F.R., Yang, R., and Tang, H. (2018, January 25). A deep reinforcement learning-based trust management scheme for software-defined vehicular networks. Proceedings of the 8th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications(DIVANet), Montreal, QC, Canada.","DOI":"10.1145\/3272036.3272037"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Khan, M.U., Hosseinzadeh., M., and Mosavi, A. (2022). An Intersection-Based Routing Scheme Using Q-Learning in Vehicular Ad Hoc Networks for Traffic Management in the Intelligent Transportation System. Mathematics, 10.","DOI":"10.3390\/math10203731"},{"key":"ref_25","first-page":"3104","article-title":"A Diversified Message Type Forwarding Strategy Based on Reinforcement Learning in VANET","volume":"16","author":"Xu","year":"2022","journal-title":"KSII Trans. Internet Inf. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lansky., J., Rahmani., A.M., and Hosseinzadeh, M. (2022). Reinforcement Learning-Based Routing Protocols in Vehicular Ad Hoc Networks for Intelligent Transport System (ITS): A Survey. Mathematics, 10.","DOI":"10.3390\/math10244673"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1109\/TNSE.2021.3139005","article-title":"Cluster-Based Malicious Node Detection for False Downstream Data in Fog Computing-Based VANETs","volume":"9","author":"Gu","year":"2022","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_28","first-page":"1","article-title":"A review of deep reinforcement learning","volume":"41","author":"Liu","year":"2018","journal-title":"Chin. J. Comput."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1204\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:12:02Z","timestamp":1760119922000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1204"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,20]]},"references-count":28,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23031204"],"URL":"https:\/\/doi.org\/10.3390\/s23031204","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,20]]}}}