{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:52:08Z","timestamp":1778082728032,"version":"3.51.4"},"reference-count":41,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["92367104"],"award-info":[{"award-number":["92367104"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Research Foundation, Singapore and Infocomm Media Development Authority under its Future Communications Research & Development Programme"},{"name":"Ministry of Education, Singapore, under its Academic Research Fund Tier 2","award":["MOE-T2EP20221-0017"],"award-info":[{"award-number":["MOE-T2EP20221-0017"]}]},{"name":"Hong Kong RGC General Research","award":["152244\/21E"],"award-info":[{"award-number":["152244\/21E"]}]},{"name":"Hong Kong RGC General Research","award":["152169\/22E"],"award-info":[{"award-number":["152169\/22E"]}]},{"name":"Hong Kong RGC General Research","award":["152228\/23E"],"award-info":[{"award-number":["152228\/23E"]}]},{"name":"Hong Kong RGC General Research","award":["162161\/24E"],"award-info":[{"award-number":["162161\/24E"]}]},{"name":"Research Impact Fund","award":["R5011-23F"],"award-info":[{"award-number":["R5011-23F"]}]},{"name":"Research Impact Fund","award":["R5060-19"],"award-info":[{"award-number":["R5060-19"]}]},{"name":"Collaborative Research Fund","award":["C1042-23GF"],"award-info":[{"award-number":["C1042-23GF"]}]},{"name":"NSFC\/RGC Collaborative Research Scheme","award":["CRS_HKUST602\/24"],"award-info":[{"award-number":["CRS_HKUST602\/24"]}]},{"name":"Areas of Excellence Scheme","award":["AoE\/E-601\/22-R"],"award-info":[{"award-number":["AoE\/E-601\/22-R"]}]},{"name":"InnoHK"},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["ECCS-2302469"],"award-info":[{"award-number":["ECCS-2302469"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CMMI-2222810"],"award-info":[{"award-number":["CMMI-2222810"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004405","name":"Toyota Motor Corporation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004405","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Amazon"},{"name":"Japan Science and Technology Agency (JST) Adopting Sustainable Partnerships for Innovative Research Ecosystem","award":["JPMJAP2326"],"award-info":[{"award-number":["JPMJAP2326"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Cogn. Commun. Netw."],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/tccn.2025.3571753","type":"journal-article","created":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T13:16:30Z","timestamp":1747746990000},"page":"994-1011","source":"Crossref","is-referenced-by-count":5,"title":["Mixture of Experts-Enabled Parallel Scheduling and Processing for Vehicular Generative AI Services"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1561-3577","authenticated-orcid":false,"given":"Gaochang","family":"Xie","sequence":"first","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunication, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4440-941X","authenticated-orcid":false,"given":"Zehui","family":"Xiong","sequence":"additional","affiliation":[{"name":"Singapore University of Technology and Design, Tampines, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2825-8463","authenticated-orcid":false,"given":"Renchao","family":"Xie","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunication, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8606-5985","authenticated-orcid":false,"given":"Xiumei","family":"Deng","sequence":"additional","affiliation":[{"name":"Singapore University of Technology and Design, Tampines, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9831-2202","authenticated-orcid":false,"given":"Song","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8972-8094","authenticated-orcid":false,"given":"Mohsen","family":"Guizani","sequence":"additional","affiliation":[{"name":"Machine Learning Department, Mohammad Bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6606-5822","authenticated-orcid":false,"given":"Zhu","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2025.3527719"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2024.3355179"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2024.3396276"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.3390\/app14177782"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2023.3316615"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TGCN.2021.3127923"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3555802"},{"key":"ref8","article-title":"Optimizing generative AI networking: A dual perspective with multi-agent systems and mixture of experts","author":"Zhang","year":"2024","journal-title":"arXiv:2405.12472"},{"key":"ref9","article-title":"Fusion of mixture of experts and generative artificial intelligence in mobile edge metaverse","author":"Liu","year":"2024","journal-title":"arXiv:2404.03321"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.003.2400046"},{"key":"ref11","first-page":"2664","article-title":"Uni-perceiver-MoE: Learning sparse generalist models with conditional MoEs","volume-title":"Proc. Adv. Neural Inf. Proc. Syst. (NeurIPS)","author":"Zhu"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3038558"},{"key":"ref13","article-title":"Towards MoE deployment: Mitigating inefficiencies in mixture-of-expert (MoE) inference","author":"Huang","year":"2023","journal-title":"arXiv:2303.06182"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2023.3299851"},{"key":"ref15","first-page":"1","article-title":"Outrageously large neural networks: The sparsely-gated mixture-of-experts layer","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Shazeer"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2024.3392809"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/IOTM.001.2400045"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TGCN.2021.3093821"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2023.3315014"},{"issue":"120","key":"ref20","first-page":"1","article-title":"Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity","volume":"23","author":"Fedus","year":"2022","journal-title":"J. Mach. Learn. Res."},{"key":"ref21","first-page":"1","article-title":"GShard: Scaling giant models with conditional computation and automatic sharding","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Lepikhin"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2024.3385639"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3091130"},{"key":"ref24","article-title":"Generative AI-enhanced multi-modal semantic communication in Internet of Vehicles: System design and methodologies","author":"Lu","year":"2024","journal-title":"arXiv:2409.15642"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-024-05892-6"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3492326"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3360183"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2931370"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3295451"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2024.3416318"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOMWKSHPS57453.2023.10225799"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.2200252"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2024.3442292"},{"key":"ref34","first-page":"224","article-title":"SiDA: Sparsity-inspired data-aware serving for efficient and scalable large mixture-of-experts models","volume-title":"Proc. Mach. Learn. Syst. (MLSys)","author":"Du"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM52923.2024.10901084"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/WACVW60836.2024.00106"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3427834"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3153089"},{"key":"ref39","first-page":"18332","article-title":"DeepSpeed-MoE: Advancing mixture-of-experts inference and training to power next-generation AI scale","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Rajbhandari"},{"key":"ref40","first-page":"6265","article-title":"Base layers: Simplifying training of large, sparse models","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Lewis"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3265432"}],"container-title":["IEEE Transactions on Cognitive Communications and Networking"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/6687307\/11304002\/11007552-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6687307\/11304002\/11007552.pdf?arnumber=11007552","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T19:00:49Z","timestamp":1766170849000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11007552\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":41,"URL":"https:\/\/doi.org\/10.1109\/tccn.2025.3571753","relation":{},"ISSN":["2332-7731","2372-2045"],"issn-type":[{"value":"2332-7731","type":"electronic"},{"value":"2372-2045","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}