基于边缘智能的沉浸式元宇宙关键技术与展望

doi:10.11959/j.issn.2096-0271.2024001

Abstract

Abstract:

In recent years, applications such as 360 video, augmented reality, virtual reality, etc.have been rapidly developed and gradually formed a new mode of immersive metaverse experience.These new services and applications have common features such as high fidelity and immersive interaction.As a new paradigm, edge computing is increasingly ready to support these features.This paper firstly elucidates that the key to releasing the power of edge computing to support immersive metaverse experience is the processing of AI models and data for content generation.Then, this paper presents a generic framework to realize adaptive deep learning model deployment and data flow to support metaverse services and applications.

Key words: edge computing, artificial intelligence, metaverse, immersive experience

CLC Number:

TP37

Zhi WANG, Shutao XIA, Rui MAO. Edge-intelligence-based immersive metaverse:key technologies and prospects[J]. Big Data Research, 2024, 10(1): 35-45.

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References 34

[1]	JENSEN J F , KJEMS E , LEHMANN N ,et al. Virtual space:spatiality in virtual inhabited 3D worlds[M].[S.l.]: Springer Science ＆ Business Media, 2002.
[2]	ZAHARIADIS T , DARAS P , BALLESTEROS L B ,et al. Towards future 3D media internet[EB]. NEM Summit, 2008.
[3]	LAEEQ K. Metaverse:why,how and what[Z]. 2022.
[4]	NING H , WANG H , LIN Y ,et al. A survey on metaverse:the state-ofthe-art,technologies,applications,and challenges[EB]. arXiv preprint, 2021,arXiv:2111.09673.
[5]	BALLARDINI R M , HE K , ROOS T . AI-generated content:authorship and inventorship in the age of artificial intelligence[M]// Online distribution of content in the EU.[S.l.]: Edward Elgar Publishing, 2019: 117-135.
[6]	LEE L H , BRAUD T , ZHOU P ,et al. All one needs to know about metaverse:a complete survey on technological singularity,virtual ecosystem,and research agenda[EB]. arXiv preprint, 2021,arXiv:2110.05352.
[7]	FANG Z X , CAI L B , WANG G . MetaHuman creator the starting point of the metaverse[C]// Proceedings of 2021 International Symposium on Computer Technology and Information Science. Piscataway:IEEE Press, 2021: 154-157.
[8]	PAWAR C S , GANATRA A , NAYAK A ,et al. Use of machine learning services in cloud[M]// Computer networks,big data and IoT. Singapore: Springer, 2021: 43-52.
[9]	HU C , JIANG J , WANG Z ,et al. Decentralized federated learning:a segmented gossip approach[EB]. arXiv preprint, 2019,arXiv:1908.07782.
[10]	DHELIM S , KECHADI T , CHEN L ,et al. Edge-enabled metaverse:the convergence of metaverse and mobile edge computing[EB]. arXiv preprint, 2022,2022,arXiv:2205.02764.
[11]	WU C L , WANG Z , SUN L F . PAAS:a preference-aware deep reinforcement learning approach for 360° video streaming[C]// Proceedings of the 31st ACM Workshop on Network and Operating Systems Support for Digital Audio and Video. New York:ACM, 2021: 34-41.
[12]	WANG Y T , SU Z , ZHANG N ,et al. A survey on metaverse:fundamentals,security,and privacy[EB]. arXiv preprint, 2022,arXiv:2203.02662.
[13]	XU M , NG W C , LIM W Y B ,et al. A full dive into realizing the edge-enabled metaverse:visions,enabling technologies,and challenges[EB]. arXiv preprint, 2022,arXiv:2203.05471.
[14]	MACH P , BECVAR Z . Mobile edge computing:a survey on architecture and computation offloading[J]. IEEE Communications Surveys ＆ Tutorials, 2017,19(3): 1628-1656.
[15]	ZHANG J L , CHEN B , ZHAO Y C ,et al. Data security and privacy-preserving in edge computing paradigm:survey and open issues[J]. IEEE Access, 2018,6: 18209-18237.
[16]	朱锐, 王宏志, 崔双双 ,等. 面向元宇宙的云边端协同大数据管理[J]. 大数据, 2023,9(1): 63-77.
	ZHU R , WANG H Z , CUI S S ,et al. Cloud-edge-end collaborative big data management for metaverse[J]. Big Data Research, 2023,9(1): 63-77.
[17]	周知, 于帅, 陈旭 . 边缘智能:边缘计算与人工智能融合的新范式[J]. 大数据, 2019,5(2): 53-63.
	ZHOU Z , YU S , CHEN X . Edge intelligence:a new nexus of edge computing and artificial intelligence[J]. Big Data Research, 2019,5(2): 53-63.
[18]	NOMA T , ZHAO L , BADLER N I . Design of a virtual human presenter[J]. IEEE Computer Graphics and Applications, 2000,20(4): 79-85.
[19]	MA G , WANG Z , ZHANG M ,et al. Understanding performance of edge content caching for mobile video streaming[J]. IEEE Journal on Selected Areas in Communications, 2017,35(5): 1076-1089.
[20]	JIANG J Y , LUO Z Y , HU C H ,et al. Joint model and data adaptation for cloud inference serving[C]// Proceedings of 2021 IEEE Real-Time Systems Symposium. Piscataway:IEEE Press, 2021: 279-289.
[21]	SHAKER N , TOGELIUS J , NELSON M J . Procedural content generation in games[M]. Cham: Springer, 2016.
[22]	CRESWELL A , WHITE T , DUMOULIN V ,et al. Generative adversarial networks:an overview[J]. IEEE Signal Processing Magazine, 2018,35(1): 53-65.
[23]	WANG Z , WU S , XIE W ,et al. NeRF:neural radiance fields without known camera parameters[EB]. arXiv preprint, 2021,arXiv:2102.07064.
[24]	CHOUDHARY T , MISHRA V , GOSWAMI A ,et al. A comprehensive survey on model compression and acceleration[J]. Artificial Intelligence Review, 2020,53(7): 5113-5155.
[25]	CHEN C , YIN Y C , SHANG L F ,et al. bert2BERT:towards reusable pretrained language models[EB]. arXiv preprint, 2021,arXiv:2110.07143.
[26]	POLINO A , PASCANU R , ALISTARH D ,et al. Model compression via distillation and quantization[EB]. arXiv preprint, 2018,arXiv:1802.05668.
[27]	TANG C , OUYANG K , WANG Z ,et al. Mixed-precision neural network quantization via learned layer-wise importance[C]// Proceedings of European Conference on Computer Vision.[S.l.:s.n.], 2022.
[28]	HE Z L , LI H S , WANG Z ,et al. Adaptive compression for online computer vision:an edge reinforcement learning approach[J]. ACM Transactions on Multimedia Computing,Communications,and Applications, 2021,17(4): 1-23.
[29]	LI H S , HU C H , JIANG J Y ,et al. JALAD:joint accuracy-and latency-aware deep structure decoupling for edge-cloud execution[C]// Proceedings of 2018 IEEE 24th International Conference on Parallel and Distributed Systems. Piscataway:IEEE Press, 2019: 671-678.
[30]	GOIAN H S , AL-JARRAH O Y , MUHAIDAT S ,et al. Popularity-based video caching techniques for cache-enabled networks:a survey[J]. IEEE Access, 2019,7: 27699-27719.
[31]	NARAYANAN A , VERMA S , RAMADAN E ,et al. DeepCache:a deep learning based framework for content caching[C]// Proceedings of the 2018 Workshop on Network Meets AI ＆ ML. New York:ACM, 2018: 48-53.
[32]	YE J H , LI Z C , WANG Z ,et al. Joint cache size scaling and replacement adaptation for small content providers[C]// Proceedings of IEEE INFOCOM 2021- IEEE Conference on Computer Communications. Piscataway:IEEE Press, 2021: 1-10.
[33]	ZHOU S J , WANG Z , HU C H ,et al. Caching in dynamic environments:a near-optimal online learning approach[J]. IEEE Transactions on Multimedia, 2023,25: 792-804.
[34]	朱文武, 王智 . 数据驱动的网络多媒体边缘内容分发[J]. 中国科学(信息科学), 2021,51(3): 468-504.
	ZHU W W , WANG Z . Data-driven multimedia edge network and content delivery[J]. Scientia Sinica (Informationis), 2021,51(3): 468-504.

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