Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

被引:54
|
作者
Xu, Minrui [1 ]
Du, Hongyang [1 ]
Niyato, Dusit [1 ]
Kang, Jiawen [2 ,3 ]
Xiong, Zehui [4 ]
Mao, Shiwen [5 ]
Han, Zhu [6 ,7 ]
Jamalipour, Abbas [8 ]
Kim, Dong In [9 ]
Shen, Xuemin [10 ]
Leung, Victor C. M. [11 ,12 ]
Poor, H. Vincent [13 ]
机构
[1] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore
[2] Guangdong Univ Technol, Sch Automat, Key Lab Intelligent Informat Proc & Syst Integrat, Minist Educ, Guangzhou 510006, Guangdong, Peoples R China
[3] Guangdong Univ Technol, Guangdong Hong Kong Macao Joint Lab Smart Discrete, Guangzhou 510006, Guangdong, Peoples R China
[4] Singapore Univ Technol & Design, Pillar Informat Syst Technol & Design, Singapore 487372, Singapore
[5] Auburn Univ, Dept Elect & Comp Engn, Auburn, AL 36849 USA
[6] Univ Houston, Dept Elect & Comp Engn, Houston, TX 77004 USA
[7] Kyung Hee Univ, Dept Comp Sci & Engn, Seoul 446701, South Korea
[8] Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW 2006, Australia
[9] Sungkyunkwan Univ, Dept Elect & Comp Engn, Suwon 16419, South Korea
[10] Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L 3G1, Canada
[11] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518061, Peoples R China
[12] Univ British Columbia, Dept Elect & Comp Engn, Vancouver, BC V6T 1Z4, Canada
[13] Princeton Univ, Dept Elect & Comp Engn, Princeton 08544, NJ USA
来源
关键词
Computational modeling; Servers; Biological system modeling; Artificial intelligence; Generative AI; Surveys; Mobile handsets; AIGC; generative AI; mobile edge networks; communication and networking; AI training and inference; Internet technology; RESOURCE-ALLOCATION; SEMANTIC COMMUNICATIONS; DNN INFERENCE; INTELLIGENCE; BLOCKCHAIN; SMART; CHALLENGES; ENVIRONMENTS; CONVERGENCE; COMPRESSION;
D O I
10.1109/COMST.2024.3353265
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, fine-tuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGC-driven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks.
引用
收藏
页码:1127 / 1170
页数:44
相关论文
共 50 条
  • [31] Collaborative DNNs Inference with Joint Model Partition and Compression in Mobile Edge-Cloud Computing Networks
    Tang, Yaxin
    Li, Xiuhua
    Li, Hui
    Yang, Zhengyi
    Wang, Xiaofei
    Leung, Victor C. M.
    2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024, 2024,
  • [32] Cost-Minimized Computation Offloading of Online Multifunction Services in Collaborative Edge-Cloud Networks
    Feng, Chuan
    Han, Pengchao
    Zhang, Xu
    Zhang, Qihan
    Zong, Yue
    Liu, Yejun
    Guo, Lei
    IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, 2023, 20 (01): : 292 - 304
  • [33] EdgeMatrix: A Resources Redefined Edge-Cloud System for Prioritized Services
    Ren, Yuanming
    Shen, Shihao
    Ju, Yanli
    Wang, Xiaofei
    Wang, Wenyu
    Leung, Victor C. M.
    IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (IEEE INFOCOM 2022), 2022, : 610 - 619
  • [34] Students health physique information sharing in publicly collaborative services over edge-cloud networks
    Liu, Ping
    Shi, Dai
    Zang, Bin
    Liu, Xiang
    JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS, 2024, 13 (01):
  • [35] Unleashing the Power of Generative AI in Agriculture 4.0 for Smart and Sustainable Farming
    Sai, Siva
    Kumar, Sanjeev
    Gaur, Aanchal
    Goyal, Shivam
    Chamola, Vinay
    Hussain, Amir
    COGNITIVE COMPUTATION, 2025, 17 (01)
  • [36] Energy-Efficient Offloading in Mobile Edge Computing with Edge-Cloud Collaboration
    Long, Xin
    Wu, Jigang
    Chen, Long
    ALGORITHMS AND ARCHITECTURES FOR PARALLEL PROCESSING, ICA3PP 2018, PT III, 2018, 11336 : 460 - 475
  • [37] Efficient Computation Resource Management in Mobile Edge-Cloud Computing
    Zhang, Yongmin
    Lan, Xiaolong
    Li, Yue
    Cai, Lin
    Pan, Jianping
    IEEE INTERNET OF THINGS JOURNAL, 2019, 6 (02) : 3455 - 3466
  • [38] Hierarchical Edge-Cloud Computing for Mobile Blockchain Mining Game
    Jiang, Suhan
    Li, Xinyi
    Wu, Jie
    2019 39TH IEEE INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS 2019), 2019, : 1327 - 1336
  • [39] Towards Software Defined ICN based Edge-Cloud Services
    Ravindran, Ravishankar
    Liu, Xuan
    Chakraborti, Asit
    Zhang, Xinwen
    Wang, Guoqiang
    PROCEEDINGS OF THE 2013 IEEE 2ND INTERNATIONAL CONFERENCE ON CLOUD NETWORKING (CLOUDNET), 2013, : 227 - 235
  • [40] Empowering generative AI through mobile edge computing
    Laha Ale
    Ning Zhang
    Scott A. King
    Dajiang Chen
    Nature Reviews Electrical Engineering, 2024, 1 (7): : 478 - 486