A Survey of Intelligent Network Slicing Management for Industrial IoT: Integrated Approaches for Smart Transportation, Smart Energy, and Smart Factory

被引:146
|
作者
Wu, Yulei [1 ]
Dai, Hong-Ning [2 ]
Wang, Haozhe [1 ]
Xiong, Zehui [3 ]
Guo, Song [4 ]
机构
[1] Univ Exeter, Coll Engn Math & Phys Sci, Exeter EX4 4QF, Devon, England
[2] Lingnan Univ, Dept Comp & Decis Sci, Hong Kong, Peoples R China
[3] Singapore Univ Technol & Design, Pillar Informat Syst Technol & Design, Singapore, Singapore
[4] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China
来源
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS | 2022年 / 24卷 / 02期
基金
中国国家自然科学基金; 英国工程与自然科学研究理事会;
关键词
Industrial Internet of Things; Network slicing; Smart manufacturing; Smart transportation; Computer architecture; Intelligent networks; Ultra reliable low latency communication; autonomous vehicle; smart energy; smart factory; orchestration and management; LOW-LATENCY; ARTIFICIAL-INTELLIGENCE; FUNCTION VIRTUALIZATION; ORCHESTRATION PLATFORM; COMPREHENSIVE SURVEY; RESOURCE-MANAGEMENT; WIRELESS NETWORKS; CORE NETWORK; 5G; INTERNET;
D O I
10.1109/COMST.2022.3158270
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Network slicing has been widely agreed as a promising technique to accommodate diverse services for the Industrial Internet of Things (IIoT). Smart transportation, smart energy, and smart factory/manufacturing are the three key services to form the backbone of IIoT. Network slicing management is of paramount importance in the face of IIoT services with diversified requirements. It is important to have a comprehensive survey on intelligent network slicing management to provide guidance for future research in this field. In this paper, we provide a thorough investigation and analysis of network slicing management in its general use cases as well as specific IIoT services including smart transportation, smart energy and smart factory, and highlight the advantages and drawbacks across many existing works/surveys and this current survey in terms of a set of important criteria. In addition, we present an architecture for intelligent network slicing management for IIoT focusing on the above three IIoT services. For each service, we provide a detailed analysis of the application requirements and network slicing architecture, as well as the associated enabling technologies. Further, we present a deep understanding of network slicing orchestration and management for each service, in terms of orchestration architecture, AI-assisted management and operation, edge computing empowered network slicing, reliability, and security. For the presented architecture for intelligent network slicing management and its application in each IIoT service, we identify the corresponding key challenges and open issues that can guide future research. To facilitate the understanding of the implementation, we provide a case study of the intelligent network slicing management for integrated smart transportation, smart energy, and smart factory. Some lessons learnt include: 1) For smart transportation, it is necessary to explicitly identify service function chains (SFCs) for specific applications along with the orchestration of underlying VNFs/PNFs for supporting such SFCs; 2) For smart energy, it is crucial to guarantee both ultra-low latency and extremely high reliability; 3) For smart factory, resource management across heterogeneous network domains is of paramount importance. We hope that this survey is useful for both researchers and engineers on the innovation and deployment of intelligent network slicing management for IIoT.
引用
收藏
页码:1175 / 1211
页数:37
相关论文
共 50 条
  • [21] Intelligent Rework Process Management System under Smart Factory Environment
    Jo, Da-Seol
    Kim, Tae-Woong
    Kim, Jun-Woo
    SUSTAINABILITY, 2020, 12 (23) : 1 - 17
  • [22] Deep Reinforcement Learning for Adaptive Network Slicing in 5G for Intelligent Vehicular Systems and Smart Cities
    Nassar, Almuthanna
    Yilmaz, Yasin
    IEEE INTERNET OF THINGS JOURNAL, 2022, 9 (01) : 222 - 235
  • [23] An Integrated Framework for Health State Monitoring in a Smart Factory Employing IoT and Big Data Techniques
    Yu, Wenjin
    Liu, Yuehua
    Dillon, Tharam
    Rahayu, Wenny
    Mostafa, Fahed
    IEEE INTERNET OF THINGS JOURNAL, 2022, 9 (03): : 2443 - 2454
  • [24] An IoT Smart System for Cold Supply Chain Storage and Transportation Management
    Alshdadi, Abdulrahman
    Kamel, Souad
    Alsolami, Eesa
    Lytras, Miltiadis D.
    Boubaker, Sahbi
    ENGINEERING TECHNOLOGY & APPLIED SCIENCE RESEARCH, 2024, 14 (02) : 13167 - 13172
  • [25] Energy-Net: A Deep Learning Approach for Smart Energy Management in IoT-Based Smart Cities
    Abdel-Basset, Mohamed
    Hawash, Hossam
    Chakrabortty, Ripon K.
    Ryan, Michael
    IEEE INTERNET OF THINGS JOURNAL, 2021, 8 (15) : 12422 - 12435
  • [26] Knowledge Management Models for the Smart Factory: A Comparative Analysis of Current Approaches
    Tinz, Patrick
    Tinz, Janik
    Zander, Stefan
    KMIS: PROCEEDINGS OF THE 11TH INTERNATIONAL JOINT CONFERENCE ON KNOWLEDGE DISCOVERY, KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT, VOL 3: KMIS, 2019, : 398 - 404
  • [27] Deep neural network and trust management approach to secure smart transportation data in sustainable smart cities
    Khan, Sohrab
    Khan, Sheharyar
    Sulaiman, Adel
    Al Reshan, Mana Saleh
    Alshahrani, Hani
    Shaikh, Asadullah
    ICT EXPRESS, 2024, 10 (05): : 1059 - 1065
  • [28] Intelligent Buildings in Smart Grids: A Survey on Security and Privacy Issues Related to Energy Management
    Llaria, Alvaro
    Dos Santos, Jessye
    Terrasson, Guillaume
    Boussaada, Zina
    Merlo, Christophe
    Curea, Octavian
    ENERGIES, 2021, 14 (09)
  • [29] A Survey on Beyond 5G Network Slicing for Smart Cities Applications
    Rafique, Wajid
    Barai, Joyeeta Rani
    Fapojuwo, Abraham O.
    Krishnamurthy, Diwakar
    IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2025, 27 (01): : 595 - 628
  • [30] Smart energy management: real-time prediction and optimization for IoT-enabled smart homes
    Karuna, G.
    Ediga, Poornima
    Akshatha, S.
    Anupama, P.
    Sanjana, T.
    Mittal, Aman
    Rajvanshi, Saurabh
    Habelalmateen, Mohammed I.
    COGENT ENGINEERING, 2024, 11 (01):