{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T06:10:06Z","timestamp":1770963006375,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T00:00:00Z","timestamp":1770595200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Open Project Program of Guangxi Key Laboratory of Digital Infrastructure","award":["GXDIOP2024008"],"award-info":[{"award-number":["GXDIOP2024008"]}]},{"name":"Project for Enhancing Young and Middle-aged Teacher\u2019s Research Basis Ability in Colleges of Guangxi","award":["2025KY0250"],"award-info":[{"award-number":["2025KY0250"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62461057"],"award-info":[{"award-number":["62461057"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61861013"],"award-info":[{"award-number":["61861013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017691","name":"Guangxi Key Research and Development Program","doi-asserted-by":"publisher","award":["AB25069178"],"award-info":[{"award-number":["AB25069178"]}],"id":[{"id":"10.13039\/501100017691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>With the proliferation of 5G, wireless networks, and other infrastructure, 360\u00b0 video streaming has experienced rapid development. Efficient scheduling of 360\u00b0 video streams relies on accurate feedback of user-side Quality of Experience (QoE), necessitating the construction of more precise QoE assessment methods. However, the existing tile-based QoE assessment methods for 360\u00b0 video streaming have several limitations. First, full-reference video quality assessment methods require additional overhead to transmit the source video as a reference. Second, many learning-based methods are too complex. This high complexity results in heavy computational costs during training, reducing their practicality in scenarios requiring frequent model adaptation. Third, some methods rely only on simple indicators like bitrate and stall duration or spatial features in isolation. They ignore the spatio-temporal coupling inherent in 360\u00b0 videos, which reduces the QoE assessment accuracy. To sum up, there is a lack of a lightweight QoE assessment method that can effectively integrate multidimensional influencing factors like the spatio-temporal features of 360\u00b0 video and network state. A matching QoE assessment dataset is also missing. Therefore, focusing on tile-based 360\u00b0 video streaming, this paper proposes a QoE assessment method named STGCN360. This method comprehensively considers multidimensional influencing factors, including network state and the spatio-temporal features of the video stream. To reduce complexity, it limits spatio-temporal graph modeling to the key tiles within the user\u2019s viewport, avoiding the need to process all tiles. Then, a spatio-temporal graph convolutional network (STGCN) is employed to train the QoE assessment model. Furthermore, we integrate multi-source heterogeneous datasets through feature engineering, enabling the simultaneous representation of both video quality and multidimensional factors to support the training of STGCN360. The experimental results indicate that, compared to the existing methods, STGCN360 enables more accurate QoE assessment for 360\u00b0 video streaming, improving accuracy by approximately 30.79% to 32.07%. Simultaneously, the training time cost is significantly reduced, with training efficiency improved by approximately 3.7 to 5.1 times.<\/jats:p>","DOI":"10.3390\/info17020174","type":"journal-article","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T14:05:15Z","timestamp":1770645915000},"page":"174","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["STGCN360: A QoE Assessment Method with Spatio-Temporal Graph Convolutional Networks for Tile-Based 360\u00b0 Video Streaming"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1379-440X","authenticated-orcid":false,"given":"Shijia","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5383-5736","authenticated-orcid":false,"given":"Yong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China"}]},{"given":"Danqing","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Digital Media, Geely University of China, Chengdu 610000, China"}]},{"given":"Xuan","family":"Lei","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Digital Infrastructure, Guangxi Zhuang Autonomous Region Information Center, Nanning 530000, China"}]},{"given":"Junqi","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China"}]},{"given":"Yuming","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11457","DOI":"10.1109\/JIOT.2024.3520626","article-title":"Emergency Communications in Post-Disaster Scenarios: IoT-Enhanced Airship and Buffer Support","volume":"12","author":"He","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4299","DOI":"10.1109\/TNSE.2025.3571278","article-title":"Enhanced ISAC Framework for Moving Target Assisted by Beyond-Diagonal RIS: Accurate Localization and Efficient Communication","volume":"12","author":"Wang","year":"2025","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9577","DOI":"10.1109\/TVT.2025.3539335","article-title":"Delay Minimization for NOMA-MEC Offloading in ABS-Aided Maritime Communication Networks","volume":"74","author":"He","year":"2025","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1729","DOI":"10.1109\/TMC.2025.3605205","article-title":"Fully Anonymous Broadcast Signcryption for Secure Health Data Transmission in WBANs","volume":"25","author":"Liang","year":"2026","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1849","DOI":"10.1109\/TWC.2024.3513221","article-title":"Viewport Prediction, Bitrate Selection, and Beamforming Design for THz-Enabled 360\u00b0 Video Streaming","volume":"24","author":"Setayesh","year":"2025","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_6","unstructured":"Rodriguez, A., and Rodriguez, A. (2026, January 12). In Five Years, VR Could Be as Big in the US as Netflix. 2021. Quartz. Available online: https:\/\/qz.com\/1298512\/vr-could-be-as-big-in-the-us-as-netflix-in-five-years-study-shows."},{"key":"ref_7","unstructured":"CISCO (2026, January 12). Cisco Visual Networking Index: Global Mobile Data Traffic FORECAST Update. Available online: http:\/\/media.mediapost.com\/uploads\/CiscoForecast.pdf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.comcom.2021.06.029","article-title":"A survey on 360-degree video: Coding, quality of experience and streaming","volume":"177","author":"Chiariotti","year":"2021","journal-title":"Comput. Commun."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1109\/JSTSP.2019.2956716","article-title":"Probabilistic Tile Visibility-Based Server-Side Rate Adaptation for Adaptive 360-Degree Video Streaming","volume":"14","author":"Zou","year":"2020","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1109\/TMM.2023.3277286","article-title":"Achieving QoE Fairness in Bitrate Allocation of 360\u00b0 Video Streaming","volume":"26","author":"Li","year":"2024","journal-title":"IEEE Trans. Multimed."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"108011","DOI":"10.1016\/j.comcom.2024.108011","article-title":"A review on machine learning based user-centric multimedia streaming techniques","volume":"231","author":"Ghosh","year":"2025","journal-title":"Comput. Commun."},{"key":"ref_12","unstructured":"ITU (2026, January 12). ITU-T. P.1203: Parametric Bitstream-Based Quality Assessment of Progressive Download and Adaptive Audiovisual Streaming Services Over Reliable Transport. Available online: https:\/\/www.itu.int\/rec\/T-REC-P.1203."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1240","DOI":"10.1587\/transcom.2018ANI0003","article-title":"Methods for Adaptive Video Streaming and Picture Quality Assessment to Improve QoS\/QoE Performances","volume":"E102.B","author":"Kanai","year":"2019","journal-title":"IEICE Trans. Commun."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.neucom.2019.12.082","article-title":"Deep learning based QoE evaluation for internet video","volume":"386","author":"Yue","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, H., Hu, H., Gao, G., Wen, Y., and Guan, K. (2018, January 23\u201327). Deepqoe: A Unified Framework for Learning to Predict Video QoE. Proceedings of the 2018 IEEE International Conference on Multimedia and Expo (ICME), San Diego, CA, USA.","DOI":"10.1109\/ICME.2018.8486523"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1109\/TCSVT.2019.2895223","article-title":"Streaming Video QoE Modeling and Prediction: A Long Short-Term Memory Approach","volume":"30","author":"Eswara","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"19507","DOI":"10.1109\/ACCESS.2022.3149592","article-title":"A Survey on Multimedia Services QoE Assessment and Machine Learning-Based Prediction","volume":"10","author":"Kougioumtzidis","year":"2022","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1109\/TBC.2021.3136748","article-title":"Tile-Based Panoramic Video Quality Assessment","volume":"68","author":"Jiang","year":"2022","journal-title":"IEEE Trans. Broadcast."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1109\/TMM.2020.2990075","article-title":"Panoramic Video Quality Assessment Based on Non-Local Spherical CNN","volume":"23","author":"Yang","year":"2021","journal-title":"IEEE Trans. Multimed."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2880","DOI":"10.1109\/TVCG.2025.3549179","article-title":"Adaptive Score Alignment Learning for Continual Perceptual Quality Assessment of 360-Degree Videos in Virtual Reality","volume":"31","author":"Zhou","year":"2025","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TMM.2020.3044458","article-title":"Quality Assessment for Omnidirectional Video: A Spatio-Temporal Distortion Modeling Approach","volume":"24","author":"Gao","year":"2022","journal-title":"IEEE Trans. Multimed."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Islam, M.T., Rothenberg, C.E., and Gomes, P.H. (2023, January 19\u201323). Predicting XR Services QoE with ML: Insights from In-band Encrypted QoS Features in 360-VR. Proceedings of the 2023 IEEE 9th International Conference on Network Softwarization (NetSoft), Madrid, Spain.","DOI":"10.1109\/NetSoft57336.2023.10175481"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1109\/JSTSP.2023.3300529","article-title":"A Quality-of-Experience Database for Adaptive Omnidirectional Video Streaming","volume":"17","author":"Liu","year":"2023","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Karim, S., He, H., Laghari, A.A., and Madiha, H. (2020). Quality of Service (QoS): Measurements of Video Streaming. arXiv.","DOI":"10.1007\/s11042-020-09959-3"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sawabe, A., and Iwai, T. (2021, January 14\u201323). A QoS Model to Identify Required QoS for Guaranteeing Quality of Internet Video Streaming Services. Proceedings of the ICC 2021\u2014IEEE International Conference on Communications, Montreal, QC, Canada.","DOI":"10.1109\/ICC42927.2021.9500711"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3491432","article-title":"A Bayesian Quality-of-Experience Model for Adaptive Streaming Videos","volume":"18","author":"Duanmu","year":"2023","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"26833","DOI":"10.1007\/s11042-021-10934-9","article-title":"An automated model for the assessment of QoE of adaptive video streaming over wireless networks","volume":"80","author":"Taha","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Eswara, N., Reddy, D.S.V., Chakraborty, S., Sethuram, H.P., Kuchi, K., Kumar, A., and Channappayya, S.S. (2017, January 14\u201316). A linear regression framework for assessing time-varying subjective quality in HTTP streaming. Proceedings of the 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Montreal, QC, Canada.","DOI":"10.1109\/GlobalSIP.2017.8308598"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7957","DOI":"10.1007\/s11042-015-2711-5","article-title":"QoE prediction model for mobile video telephony","volume":"75","author":"Jana","year":"2016","journal-title":"Multimed. Tools Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5944","DOI":"10.1109\/TCSVT.2022.3164467","article-title":"Blindly Assess Quality of In-the-Wild Videos via Quality-Aware Pre-Training and Motion Perception","volume":"32","author":"Li","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/TBC.2024.3443544","article-title":"Enhancing QoE for Multi-Device Video Delivery: A Novel Dataset and Model Perspective","volume":"71","author":"Yang","year":"2025","journal-title":"IEEE Trans. Broadcast."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1109\/COMST.2016.2619982","article-title":"QoE in Video Transmission: A User Experience-Driven Strategy","volume":"19","author":"Zhao","year":"2017","journal-title":"IEEE Commun. Surv. Tutorials"},{"key":"ref_33","first-page":"2198","article-title":"Viewport-Based CNN: A Multi-Task Approach for Assessing 360\u00b0 Video Quality","volume":"44","author":"Xu","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","first-page":"587","article-title":"Logarithmic laws in service quality perception: Where microeconomics meets psychophysics and quality of experience","volume":"52","author":"Reichl","year":"2013","journal-title":"Telecommun. Syst."},{"key":"ref_35","unstructured":"Barkowsky, M., Staelens, N., and Janowski, L. (October, January 30). Open collaboration on hybrid video quality models\u2014VQEG joint effort group hybrid. Proceedings of the IEEE 15th International Workshop on Multimedia Signal Processing (MMSP), Pula, Italy."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"103526","DOI":"10.1016\/j.jvcir.2022.103526","article-title":"A brief survey on adaptive video streaming quality assessment","volume":"86","author":"Zhou","year":"2022","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"60267","DOI":"10.1109\/ACCESS.2022.3180491","article-title":"Measuring, Modeling and Integrating Time-Varying Video Quality in End-to-End Multimedia Service Delivery: A Review and Open Challenges","volume":"10","author":"Hewage","year":"2022","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1109\/TBC.2022.3164332","article-title":"An End-to-End No-Reference Video Quality Assessment Method with Hierarchical Spatiotemporal Feature Representation","volume":"68","author":"Shen","year":"2022","journal-title":"IEEE Trans. Broadcast."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1109\/LSP.2020.3048607","article-title":"Semantic Information Oriented No-Reference Video Quality Assessment","volume":"28","author":"Wu","year":"2021","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2244","DOI":"10.1109\/TCSVT.2018.2868063","article-title":"Blind Video Quality Assessment with Weakly Supervised Learning and Resampling Strategy","volume":"29","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ghosh, M., and Singhal, C. (2021, January 13\u201316). M-3R: A Memory Based Approach for Streaming QoE Prediction under 3R settings. Proceedings of the 2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), Hyderabad, India.","DOI":"10.1109\/ANTS52808.2021.9936944"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ghosh, M., Singhal, D.C., and Wayal, R. (2022). DeSVQ: Deep Learning Based Streaming Video QoE Estimation. ICDCN\u201922: Proceedings of the 23rd International Conference on Distributed Computing and Networking, Association for Computing Machinery.","DOI":"10.1145\/3491003.3491023"},{"key":"ref_43","first-page":"1-1","article-title":"AhG8: Suggested testing procedure for 360-degree video","volume":"D0027","author":"Zakharchenko","year":"2016","journal-title":"Proceedings of the JVET-D0027"},{"key":"ref_44","first-page":"1408","article-title":"Weighted-to-Spherically-Uniform Quality Evaluation for Omnidirectional Video","volume":"24","author":"Sun","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Chen, S., Zhang, Y., Li, Y., Chen, Z., and Wang, Z. (2018, January 23\u201327). Spherical Structural Similarity Index for Objective Omnidirectional Video Quality Assessment. Proceedings of the 2018 IEEE International Conference on Multimedia and Expo (ICME), San Diego, CA, USA.","DOI":"10.1109\/ICME.2018.8486584"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Schatz, R., Zabrovskiy, A., and Timmerer, C. (2019, January 5\u20137). Tile-based Streaming of 8K Omnidirectional Video: Subjective and Objective QoE Evaluation. Proceedings of the 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX), Berlin, Germany.","DOI":"10.1109\/QoMEX.2019.8743230"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"204585","DOI":"10.1109\/ACCESS.2020.3037253","article-title":"Impact of the Impairment in 360-Degree Videos on Users VR Involvement and Machine Learning-Based QoE Predictions","volume":"8","author":"Anwar","year":"2020","journal-title":"IEEE Access"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1109\/JSTSP.2019.2956631","article-title":"QoE Evaluation Methods for 360-Degree VR Video Transmission","volume":"14","author":"Fei","year":"2020","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4241","DOI":"10.1109\/TCSVT.2021.3050157","article-title":"Modeling the Perceptual Quality for Viewport-Adaptive Omnidirectional Video Streaming Considering Dynamic Quality Boundary Artifact","volume":"31","author":"Zou","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1109\/TCSVT.2021.3057368","article-title":"Viewport-Based Omnidirectional Video Quality Assessment: Database, Modeling and Inference","volume":"32","author":"Meng","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Mahmoud, M., Rizou, S., Panayides, A.S., Lazaridis, P.I., Karagiannidis, G.K., Kantartzis, N.V., and Zaharis, Z.D. (2025, January 27\u201330). STAV360: A Dataset for Subjective Tile-based Assessment of 360\u00b0 Videos. Proceedings of the 12th International Conference on Information Technology (ICIT), Amman, Jordan.","DOI":"10.1109\/ICIT64950.2025.11049277"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Roy, A., Roy, K.K., Ali, A.A., Amin, M.A., and Rahman, A.K.M.M. (2021, January 18\u201322). Unified Spatio-Temporal Modeling for Traffic Forecasting using Graph Neural Network. Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China.","DOI":"10.1109\/IJCNN52387.2021.9533319"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00530-025-01920-4","article-title":"MADRL-based bitrate allocation for QoE fairness in 360\u00b0 video streaming with viewport prediction","volume":"31","author":"Liu","year":"2025","journal-title":"Multimed. Syst."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Casas, P., and Wassermann, S. (2017, January 22\u201324). Improving QoE prediction in mobile video through machine learning. Proceedings of the 2017 8th International Conference on the Network of the Future (NOF), London, UK.","DOI":"10.1109\/NOF.2017.8251212"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"26503","DOI":"10.1109\/JIOT.2024.3398548","article-title":"MADRL-Based Rate Adaptation for 360\u00b0 Video Streaming with Multiviewpoint Prediction","volume":"11","author":"Wang","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Liang, Y., Sun, H., Zhang, X., Xie, Q., Liu, G., Liu, Z., Tan, Z., and Liu, Y. (2025). Lightweight Multifactor Authentication and Key Agreement Scheme in Vehicle-to-Grid Networks. IEEE Trans. Veh. Technol., 1\u201318.","DOI":"10.1109\/TVT.2025.3621240"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/17\/2\/174\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T05:17:13Z","timestamp":1770959833000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/17\/2\/174"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,9]]},"references-count":56,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["info17020174"],"URL":"https:\/\/doi.org\/10.3390\/info17020174","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,9]]}}}