A Survey on Mobility of Edge Computing Networks in IoT: State-of-the-Art, Architectures, and Challenges

被引:53
作者
Abkenar, Forough Shirin [1 ]
Ramezani, Parisa [1 ]
Iranmanesh, Saeid [2 ]
Murali, Sarumathi [1 ]
Chulerttiyawong, Donpiti [1 ]
Wan, Xinyu [1 ]
Jamalipour, Abbas [1 ]
Raad, Raad [2 ]
机构
[1] Univ Sydney, Sch Elect & Informat Engn, WiNG Lab, Sydney, NSW 2006, Australia
[2] Univ Wollongong, Sch Elect Comp & Telecommun Engn, Wollongong, NSW 2522, Australia
关键词
Mobile edge computing nodes; aerial nodes; ground vehicular nodes; spatial nodes; maritime vessels; architectures; challenges; applications; VEHICULAR FOG; RESOURCE-ALLOCATION; DATA-COLLECTION; ENERGY; INTERNET; OPTIMIZATION; EFFICIENT; COMPUTATION; COMMUNICATION; INTELLIGENCE;
D O I
10.1109/COMST.2022.3211462
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
computing leverages computing resources closer to the end-users at the edge of the network, rather than distant cloud servers in the centralized IoT architecture. Edge computing nodes (ECNs), experience less transmission latency and usually save on energy while network overheads are mitigated. The ECNs can be fixed or mobile in their positions. We will focus on mobile ECNs in this survey. This paper presents a comprehensive survey on mobile ECNs and identifies some open research questions. In particular, mobile ECNs are classified into four categories, namely aerial, ground vehicular, spatial, and maritime nodes. For each specific group, any mutual basic terms used in the state-of-the-art are described, different types of nodes employed in the group are reviewed, the general network architecture is introduced, the existing methods and algorithms are studied, and the challenges that the group is scrimmaging against are explored. Moreover, the integrated architectures are surveyed, wherein two different categories of the aforementioned nodes jointly play the role of ECNs in the network. Finally, the research gaps, that are yet to be filled in the area of mobile ECNs, are discussed along with directions for future research and investigation in this promising area.
引用
收藏
页码:2329 / 2365
页数:37
相关论文
共 170 条
[61]   Deep-Learning-Based Joint Resource Scheduling Algorithms for Hybrid MEC Networks [J].
Jiang, Feibo ;
Wang, Kezhi ;
Dong, Li ;
Pan, Cunhua ;
Xu, Wei ;
Yang, Kun .
IEEE INTERNET OF THINGS JOURNAL, 2020, 7 (07) :6252-6265
[62]  
Junyong Wei, 2019, 2019 IEEE International Conference on Smart Internet of Things (SmartIoT). Proceedings, P85, DOI 10.1109/SmartIoT.2019.00022
[63]   Unmanned Aerial Vehicles in Agriculture: A Review of Perspective of Platform, Control, and Applications [J].
Kim, Jeongeun ;
Kim, Seungwon ;
Ju, Chanyoung ;
Son, Hyoung Il .
IEEE ACCESS, 2019, 7 (105100-105115) :105100-105115
[64]  
Kimachia K., IOV PIONEERING UNION
[65]  
Klaimi J, 2018, INT WIREL COMMUN, P452, DOI 10.1109/IWCMC.2018.8450313
[66]   A computer vision approach for trajectory classification [J].
Kontopoulos, Ioannis ;
Makris, Antonios ;
Zissis, Dimitris ;
Tserpes, Konstantinos .
2021 22ND IEEE INTERNATIONAL CONFERENCE ON MOBILE DATA MANAGEMENT (MDM 2021), 2021, :163-168
[67]  
Kuiper E., 2006, P 2 INT C WIRELESS M, P33, DOI 10.1109/icwmc.2006.63
[68]  
Kuntao Cui, 2019, 2019 IEEE International Conference on Smart Internet of Things (SmartIoT). Proceedings, P92, DOI 10.1109/SmartIoT.2019.00023
[69]   Resource Allocation for Vehicular Fog Computing Using Reinforcement Learning Combined With Heuristic Information [J].
Lee, Seung-seob ;
Lee, SuKyoung .
IEEE INTERNET OF THINGS JOURNAL, 2020, 7 (10) :10450-10464
[70]   MobEyes: Smart mobs for urban monitoring with a vehicular sensor network [J].
Lee, Uichin ;
Zhou, Biao ;
Gerla, Mario ;
Magistretti, Eugenio ;
Bellavista, Paolo ;
Corradi, Antonio .
IEEE WIRELESS COMMUNICATIONS, 2006, 13 (05) :52-57