{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T22:27:44Z","timestamp":1783722464560,"version":"3.55.0"},"reference-count":43,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T00:00:00Z","timestamp":1735862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61802252"],"award-info":[{"award-number":["61802252"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>With the increasing connectivity and automation on the Internet of Vehicles, safety, security, and privacy have become stringent challenges. In the last decade, several cryptography-based protocols have been proposed as intuitive solutions to protect vehicles from information leakage and intrusions. Before generating the encryption keys, a random number generator (RNG) plays an important component in cybersecurity. Several deep learning-based RNGs have been deployed to train the initial value and generate pseudo-random numbers. However, interference from actual unpredictable driving environments renders the system unreliable for its low-randomness outputs. Furthermore, dynamics in the training process make these methods subject to training instability and pattern collapse by overfitting. In this paper, we propose an Effective Pseudo-Random Number Generator (EPRNG) which exploits a deep convolution generative adversarial network (DCGAN)-based approach using our processed vehicle datasets and entropy-driven stopping method-based training processes for the generation of pseudo-random numbers. Our model starts from the vehicle data source to stitch images and add noise to enhance the entropy of the images and then inputs them into our network. In addition, we design an entropy-driven stopping method that enables our model training to stop at the optimal epoch so as to prevent overfitting. The results of the evaluation indicate that our entropy-driven stopping method can effectively generate pseudo-random numbers in a DCGAN. Our numerical experiments on famous test suites (NIST, ENT) demonstrate the effectiveness of the developed approach in high-quality random number generation for the IoV. Furthermore, the PRNGs are successfully applied to image encryption, and the performance metrics of the encryption are close to ideal values.<\/jats:p>","DOI":"10.3390\/info16010021","type":"journal-article","created":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T11:50:10Z","timestamp":1735905010000},"page":"21","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["EPRNG: Effective Pseudo-Random Number Generator on the Internet of Vehicles Using Deep Convolution Generative Adversarial Network"],"prefix":"10.3390","volume":"16","author":[{"given":"Chenyang","family":"Fei","sequence":"first","affiliation":[{"name":"School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7767-0553","authenticated-orcid":false,"given":"Xiaomei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6916-5639","authenticated-orcid":false,"given":"Dayu","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haomin","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zejie","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1109\/MNET.001.1900659","article-title":"Emerging technologies for 5G-IoV networks: Applications, trends, and opportunities","volume":"34","author":"Duan","year":"2020","journal-title":"IEEE Netw."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"109363","DOI":"10.1016\/j.comnet.2022.109363","article-title":"Implementing efficient attribute encryption in IoV under cloud environments","volume":"218","author":"Yang","year":"2022","journal-title":"Comput. Netw."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Rabadi, N.M. (2010, January 8\u201312). Implicit certificates support in IEEE 1609 security services for Wireless Access in Vehicular Environment (WAVE). Proceedings of the 7th IEEE International Conference on Mobile Ad-hoc and Sensor Systems (IEEE MASS 2010), San Francisco, CA, USA.","DOI":"10.1109\/MASS.2010.5663900"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Eckhoff, D., Sofra, N., and German, R. (2013, January 18\u201320). A performance study of cooperative awareness in ETSI ITS G5 and IEEE WAVE. Proceedings of the 10th Annual Conference on Wireless On-demand Network Systems and Services (WONS), Banff, AB, Canada.","DOI":"10.1109\/WONS.2013.6578347"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"370","DOI":"10.26599\/TST.2022.9010005","article-title":"LETRNG\u2014A lightweight and efficient true random number generator for GNU\/Linux systems","volume":"28","author":"Chen","year":"2022","journal-title":"Tsinghua Sci. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"13841","DOI":"10.1007\/s11042-019-08592-z","article-title":"FPGA-based generic RO TRNG architecture for image confusion","volume":"79","author":"Sivaraman","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1109\/TDMR.2024.3394576","article-title":"Guidelines for the design of random Telegraph Noise-based true random number generators","volume":"24","author":"Zanotti","year":"2024","journal-title":"IEEE Trans. Device Mater. Reliab."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4448","DOI":"10.1007\/s12274-022-4109-9","article-title":"A flexible and stretchable bionic true random number generator","volume":"15","author":"Wan","year":"2022","journal-title":"Nano Res."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yang, Y.G., and Zhao, Q.Q. (2016). Novel pseudo-random number generator based on quantum random walks. Sci. Rep., 6.","DOI":"10.1038\/srep20362"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2431","DOI":"10.1109\/TCAD.2021.3096464","article-title":"A lightweight full entropy TRNG with on-chip entropy assurance","volume":"40","author":"Chen","year":"2021","journal-title":"IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Okada, K., Endo, K., Yasuoka, K., and Kurabayashi, S. (2023). Learned pseudo-random number generator: WGAN-GP for generating statistically robust random numbers. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0287025"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Pasqualini, L., and Parton, M. (2020). Pseudo random number generation through reinforcement learning and recurrent neural networks. Algorithms, 13.","DOI":"10.3390\/a13110307"},{"key":"ref_13","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"117929","DOI":"10.1016\/j.eswa.2022.117929","article-title":"Multi-camera vehicle counting using edge-AI","volume":"207","author":"Ciampi","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/0167-2789(93)90126-L","article-title":"Chaotic traveling waves in a coupled map lattice","volume":"68","author":"Kaneko","year":"1993","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_16","unstructured":"Li, J., Madry, A., Peebles, J., and Schmidt, L. (2017). Towards understanding the dynamics of generative adversarial networks. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1109\/18.42194","article-title":"Estimation of the entropy and information of absolutely continuous random variables","volume":"35","author":"Mokkadem","year":"1989","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_18","first-page":"163","article-title":"A statistical test suite for random and pseudorandom number generators for cryptographic applications","volume":"Volume 800","author":"Rukhin","year":"2001","journal-title":"NIST Special Publication 800-22"},{"key":"ref_19","unstructured":"John, W. (2024, August 15). A Pseudorandom Number Sequence Test Program. Available online: https:\/\/www.fourmilab.ch\/random\/."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1109\/JIOT.2016.2572638","article-title":"Toward sensor-based random number generation for mobile and IoT devices","volume":"3","author":"Wallace","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_21","first-page":"6453","article-title":"Quantum true random number generation on IBM\u2019s cloud platform","volume":"34","author":"Kumar","year":"2022","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"112296","DOI":"10.1016\/j.chaos.2022.112296","article-title":"Chaotical PRNG based on composition of logistic and tent maps using deep-zoom","volume":"161","author":"Valle","year":"2022","journal-title":"Chaos Solitons Fractals"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"104911","DOI":"10.1016\/j.micpro.2023.104911","article-title":"Efficient and secure chaotic PRNG for color image encryption","volume":"101","author":"Albahrani","year":"2023","journal-title":"Microprocess. Microsyst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1692","DOI":"10.1016\/j.chaos.2009.03.068","article-title":"True random number generation from mobile telephone photo based on chaotic cryptography","volume":"42","author":"Zhao","year":"2009","journal-title":"Chaos Solitons Fractals"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"15929","DOI":"10.1007\/s11042-018-7015-0","article-title":"A parallelizable chaos-based true random number generator based on mobile device cameras for the android platform","volume":"78","author":"Yeoh","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1109\/TIFS.2018.2850770","article-title":"Machine learning cryptanalysis of a quantum random number generator","volume":"14","author":"Truong","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_27","unstructured":"De Bernardi, M., Khouzani, M.H.R., and Malacaria, P. (2018). Pseudo-random number generation using generative adversarial networks. ECML PKDD 2018 Workshops, Proceedings of the Nemesis 2018, UrbReas 2018, SoGood 2018, IWAISe 2018, and Green Data Mining 2018, Dublin, Ireland, 10\u201314 September 2018, Springer. Proceedings 18."},{"key":"ref_28","first-page":"222","article-title":"A novel method to generate pseudo-random sequence based on GAN","volume":"7","author":"Ji","year":"2022","journal-title":"J. Netw. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"27445","DOI":"10.1007\/s11042-021-10979-w","article-title":"A novel image encryption algorithm based on least squares generative adversarial network random number generator","volume":"80","author":"Man","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_30","first-page":"31","article-title":"NPCR and UACI randomness tests for image encryption","volume":"1","author":"Wu","year":"2011","journal-title":"Cyber J. Multidiscip. J. Sci. Technol. J. Sel. Areas Telecommun. JSAT"},{"key":"ref_31","first-page":"337","article-title":"Key generation method based on generative adversarial network and its application in low-light-level image encryption","volume":"43","author":"Li","year":"2022","journal-title":"Acta Armamentarii"},{"key":"ref_32","unstructured":"Saad, M.M., Rehmani, M.H., and O\u2019Reilly, R. (2024). Early stopping criteria for training generative adversarial networks in biomedical imaging. arXiv, Available online: https:\/\/arxiv.org\/abs\/2405.20987."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"12292","DOI":"10.1109\/JIOT.2023.3332947","article-title":"QKBAKA: A quantum-key-based authentication and key agreement scheme for the internet of vehicles","volume":"11","author":"Shi","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Kim, H., Kwon, Y., Sim, M., Lim, S., and Seo, H. (2021). Generative adversarial networks-based pseudo-random number generator for embedded processors. Information Security and Cryptology\u2014ICISC 2020, Proceedings of the 23rd International Conference, Seoul, Republic of Korea, 2\u20134 December 2020, Springer. Proceedings 23.","DOI":"10.1007\/978-3-030-68890-5_12"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bonomi, F., Milito, R., Zhu, J., and Addepalli, S. (2012, January 17). Fog computing and its role in the internet of things. Proceedings of the first edition of the MCC Workshop on Mobile Cloud Computing, Helsinki, Finland.","DOI":"10.1145\/2342509.2342513"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"8804","DOI":"10.1109\/JIOT.2019.2923611","article-title":"AKM-IoV: Authenticated key management protocol in fog computing-based internet of vehicles deployment","volume":"6","author":"Wazid","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012, January 16\u201321). Are we ready for autonomous driving? The KITTI Vision Benchmark Suite. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Moysis, L., Volos, C., Jafari, S., Munoz-Pacheco, J.M., Kengne, J., Rajagopal, K., and Stouboulos, I. (2020). Modification of the logistic map using fuzzy numbers with application to pseudorandom number generation and image encryption. Entropy, 22.","DOI":"10.3390\/e22040474"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., Madhavan, V., and Darrell, T. (2020, January 14\u201319). BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Liang, D., Zhang, S., Huang, X., Li, B., and Hu, S. (2016, January 27\u201330). Traffic-Sign Detection and Classification in the Wild. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.232"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2015","DOI":"10.1002\/sec.1458","article-title":"A secure image encryption algorithm based on chaotic maps and SHA-3","volume":"9","author":"Ye","year":"2016","journal-title":"Secur. Commun. Netw."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3507","DOI":"10.1016\/j.cnsns.2010.01.004","article-title":"A fast image encryption and authentication scheme based on chaotic maps","volume":"15","author":"Yang","year":"2010","journal-title":"Commun. Nonlinear Sci. Numer. Simul."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Luo, J., and Yu, W. (2022, January 5\u20137). An image encryption method based on random number matrix iterations. Proceedings of the 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Beijing, China.","DOI":"10.1109\/CISP-BMEI56279.2022.9980116"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/1\/21\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:22:42Z","timestamp":1759918962000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/1\/21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,3]]},"references-count":43,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["info16010021"],"URL":"https:\/\/doi.org\/10.3390\/info16010021","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,3]]}}}