{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T22:33:47Z","timestamp":1783377227644,"version":"3.54.6"},"reference-count":33,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T00:00:00Z","timestamp":1776902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["L232051"],"award-info":[{"award-number":["L232051"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The inherent redundancy in vehicle sensor data, coupled with constrained onboard resources and stringent latency requirements, renders traditional bit-oriented transmission paradigms inefficient for autonomous-driving perception tasks. Semantic communication offers a promising direction by shifting the focus from bit-level fidelity to task-level information delivery. In this paper, we propose a unified framework that integrates hierarchical transmission and online scheduling for Internet of Vehicles (IoV)-oriented collaborative perception. The proposed hierarchy separates information into two complementary layers: a coarse metadata layer (object bounding boxes) for latency-critical awareness, and fine-grained visual semantics (multi-scale region-of-interest (ROI) patches) for perception-intensive tasks. We formulate an online scheduling problem that jointly exploits Age of Information (AoI) and Channel State Information (CSI) to dynamically decide what to transmit and at what fidelity under per-frame budget constraints. To address cross-scheme fairness, we report resource utilization under a fixed kbps\/fps physical budget and evaluate robustness using a combination of a lightweight task-proxy metric and COCO-style Average Recall (AR100) under ROI-only evaluation. The hierarchical transmission architecture, combined with AoI awareness, reduces global semantic staleness by approximately 78%. The Lyapunov-based online scheduler enables intelligent, signal-to-noise ratio (SNR)-adaptive switching between coarse and fine semantic levels, ensuring robust perception under varying channel quality. Under strict physical-budget constraints and unreliable channel conditions, joint source-channel coding (JSCC) exhibits significantly stronger task robustness than conventional schemes: at 0 dB SNR, the task-proxy detection rate improves by nearly 47 percentage points over the uncoded baseline.<\/jats:p>","DOI":"10.3390\/s26092606","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T12:04:42Z","timestamp":1776945882000},"page":"2606","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Hierarchical Semantic Transmission and Lyapunov-Optimized Online Scheduling for the Internet of Vehicles"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2705-5828","authenticated-orcid":false,"given":"Le","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5563-662X","authenticated-orcid":false,"given":"Yani","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenzhao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Penghao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6257-7732","authenticated-orcid":false,"given":"Shujun","family":"Han","sequence":"additional","affiliation":[{"name":"School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xu, R., Xiang, H., Tu, Z., Xia, X., Yang, M.-H., and Ma, J. (2022). V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer. arXiv.","DOI":"10.1007\/978-3-031-19842-7_7"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1109\/COMST.2022.3223224","article-title":"Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges","volume":"25","author":"Yang","year":"2023","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1109\/TCCN.2019.2919300","article-title":"Deep Joint Source-Channel Coding for Wireless Image Transmission","volume":"5","author":"Bourtsoulatze","year":"2019","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2663","DOI":"10.1109\/TSP.2021.3071210","article-title":"Deep Learning Enabled Semantic Communication Systems","volume":"69","author":"Xie","year":"2021","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gan, J., Sheng, Y., Zhang, H., Liang, L., Ye, H., Guo, C., and Jin, S. (2026). SComCP: Task-Oriented Semantic Communication for Collaborative Perception. IEEE Trans. Veh. Technol., 1\u201315. early access.","DOI":"10.1109\/TVT.2026.3654968"},{"key":"ref_6","unstructured":"Jiang, L., Zhu, Y., Yin, H., and Li, B. (2025). Hyperion: Low-Latency Ultra-HD Video Analytics via Collaborative Vision Transformer Inference. arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Guo, C., Xi, J., He, Z., Liu, J., and Yang, J. (2024). A Knowledge Base Driven Task-Oriented Image Semantic Communication Scheme. Remote Sens., 16.","DOI":"10.3390\/rs16214044"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, C., Ji, M., Wu, Q., Fan, P., and Fan, Q. (2024). Optimizing Age of Information in Internet of Vehicles over Error-Prone Channels. Sensors, 24.","DOI":"10.3390\/s24247966"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1109\/TWC.2023.3278460","article-title":"Minimizing the AoI in Resource-Constrained Multi-Source Relaying Systems: Dynamic and Learning-Based Scheduling","volume":"23","author":"Zakeri","year":"2024","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1183","DOI":"10.1109\/JSAC.2021.3065072","article-title":"Age of Information: An Introduction and Survey","volume":"39","author":"Yates","year":"2021","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_11","unstructured":"Zhang, G., Hu, Q., Cai, Y., and Yu, G. (June, January 28). Adaptive CSI Feedback for Deep Learning-Enabled Image Transmission. Proceedings of the IEEE International Conference on Communications (ICC), Rome, Italy."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yuan, H., Xu, W., Wang, Y., and Wang, X. (2024). Channel-Blind Joint Source-Channel Coding for Wireless Image Transmission. Sensors, 24.","DOI":"10.3390\/s24124005"},{"key":"ref_13","unstructured":"Yuan, Y., Lu, Q., Kong, D., Wang, S., and Zhang, X. (2025, January 24\u201327). Deep Joint Source Channel Coding via Attention Networks for Wireless Image Transmission. Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC), Milan, Italy."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cao, H., Han, S., Meng, R., Xu, X., and Zhang, P. (2025). Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite\u2013Ground Scenario. Sensors, 25.","DOI":"10.3390\/s25010269"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Song, M., Ma, N., Dong, C., Xu, X., and Zhang, P. (2023). Deep Joint Source-Channel Coding for Wireless Image Transmission with Adaptive Models. Electronics, 12.","DOI":"10.3390\/electronics12224637"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"31127","DOI":"10.1038\/s41598-025-16753-4","article-title":"A Star Modulation Network for Wireless Image Semantic Transmission","volume":"15","author":"Li","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_17","unstructured":"Lu, M., Liu, G., Liang, L., Guo, C., Ye, H., and Jin, S. (2025). Cross-Modal Semantic Communication for Heterogeneous Collaborative Perception. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1109\/LCOMM.2025.3639610","article-title":"Region-of-Interest-Guided Deep Joint Source-Channel Coding for Image Transmission","volume":"30","author":"Choi","year":"2026","journal-title":"IEEE Commun. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2637","DOI":"10.1109\/TNET.2018.2873606","article-title":"Scheduling Policies for Minimizing Age of Information in Broadcast Wireless Networks","volume":"26","author":"Kadota","year":"2018","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2268","DOI":"10.1109\/TWC.2019.2963667","article-title":"Age of Information Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective","volume":"19","author":"Chen","year":"2020","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Long, Y., Gong, S., Sun, S., Lee, G., Li, L., and Niyato, D. (2024). Lyapunov-Guided Deep Reinforcement Learning for Semantic-Aware AoI Minimization in UAV-Assisted Wireless Networks. arXiv.","DOI":"10.1109\/WCNC61545.2025.10978137"},{"key":"ref_22","unstructured":"Fang, H., Li, X., Guo, C., Liang, L., and Jin, S. (2026). AoI-Driven Queue Management and Power Control in V2V Networks: A GNN-Enhanced MARL Approach. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"10784","DOI":"10.1109\/TVT.2023.3257770","article-title":"Semantic Communication-Based Dynamic Resource Allocation in D2D Vehicular Networks","volume":"72","author":"Su","year":"2023","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6900","DOI":"10.1109\/TCOMM.2024.3405355","article-title":"Adaptive Resource Allocation for Semantic Communication Networks","volume":"72","author":"Wang","year":"2024","journal-title":"IEEE Trans. Commun."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kim, J.-W., Kim, J.-W., and Lee, J. (2024). Intelligent Resource Allocation Scheme Using Reinforcement Learning for Efficient Data Transmission in VANET. Sensors, 24.","DOI":"10.3390\/s24092753"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3814","DOI":"10.1109\/TCOMM.2026.3657402","article-title":"Joint Task Scheduling and Resource Allocation for Semantic-Aware VEC: A Lyapunov-Guided Multi-Objective Reinforcement Learning Approach","volume":"74","author":"Lin","year":"2026","journal-title":"IEEE Trans. Commun."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6790","DOI":"10.1109\/TWC.2025.3626670","article-title":"Hybrid-Action DRL-Based Resource Allocation for Semantic-Aware Computation Offloading in Vehicular Edge Networks","volume":"25","author":"Chen","year":"2026","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_28","unstructured":"Ultralytics (2026, March 12). Ultralytics YOLOv8. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft COCO: Common Objects in Context. Proceedings of the European Conference on Computer Vision (ECCV), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Du, B., Du, H., Niyato, D., and Li, R. (2025). Task-Oriented Semantic Communication in Large Multimodal Models-Based Vehicle Networks. arXiv.","DOI":"10.1109\/TMC.2025.3564543"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Neely, M.J. (2010). Stochastic Network Optimization with Application to Communication and Queueing Systems, Morgan & Claypool Publishers.","DOI":"10.1007\/978-3-031-79995-2"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., Madhavan, V., and Darrell, T. (2020, January 13\u201319). BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Shi, J., Pang, H., Wang, M., Zhou, J., Tian, D., Zheng, C., and Liu, Z. (2025, January 6\u201318). Multi-Modal Vehicle-Infrastructure Collaborative Perception via Deformable Attention Mechanism. Proceedings of the 2025 IEEE International Annual Conference on Complex Systems and Intelligent Science (CSIS-IAC), Shenzhen, China.","DOI":"10.1109\/CSIS-IAC65538.2025.11161374"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/9\/2606\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T12:19:41Z","timestamp":1778156381000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/9\/2606"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,23]]},"references-count":33,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["s26092606"],"URL":"https:\/\/doi.org\/10.3390\/s26092606","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,23]]}}}