Airborne Computing: A Toolkit for UAV-Assisted Federated Computing for Sustainable Smart Cities

被引:5
|
作者
Hayawi, Kadhim [1 ]
Anwar, Zahid [2 ]
Malik, Asad W. [3 ]
Trabelsi, Zouheir [4 ]
机构
[1] Zayed Univ, Coll Interdisciplinary Studies, Abu Dhabi, U Arab Emirates
[2] North Dakota State Univ, Dept Comp Sci, Fargo, ND 58108 USA
[3] Natl Univ Sci & Technol, Dept Comp, Islamabad 44000, Pakistan
[4] United Arab Emirates Univ, Coll Informat Technol, Abu Dhabi, U Arab Emirates
关键词
Task analysis; Smart cities; Edge computing; Autonomous aerial vehicles; Servers; Relays; Real-time systems; Fog computing; smart cities; task offloading; unmanned aerial vehicle (UAV) swarm; RESOURCE-SHARING FRAMEWORK; VEHICULAR NETWORKS; ARCHITECTURE;
D O I
10.1109/JIOT.2023.3292308
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Smart vehicles are equipped with onboard computing units designed to run in-vehicle applications. However, due to limited computing power, the onboard units are unable to execute compute-intensive tasks and those that require near real-time processing. Therefore tasks are offloaded to nearby fog/edge devices that have more powerful processors. However, the fog devices are static, placed at fixed locations such as intersections, and have a limited communication range. Therefore, they can only facilitate vehicles in their immediate vicinity and only limited areas of the city can be covered to provide services on demand. In this article, we propose an unmanned aerial vehicle (UAV)-based computing framework design termed Skywalker to provide computing in regions where there are no static fog units thereby extending coverage. Skywalker's contributions are threefold: 1) it allows for load-aware UAV placement and provisions a swarm of UAVs to fly to areas experiencing a gap in service where the size of the swarm is proportional to the demand; 2) it implements multiple scheduling algorithms that the UAVs swarm employs to divide up the task processing responsibility for individual UAVs within the swarm; and 3) a zone-based delivery mechanism is being proposed to facilitate the return of completed tasks, either through direct delivery or relay-based methods. The choice between these options depends on the distance covered by the requesting vehicle from the UAV swarm. The efficiency of the framework is compared with existing techniques and it is found that it can greatly extend coverage during peak traffic hours while providing low communication delay and consuming minimum energy.
引用
收藏
页码:18941 / 18950
页数:10
相关论文
共 50 条
  • [1] The Node Selection Strategy for Federated Learning in UAV-Assisted Edge Computing Environment
    Bai, Jingpan
    Chen, Yuan
    IEEE INTERNET OF THINGS JOURNAL, 2023, 10 (15): : 13908 - 13919
  • [2] A hierarchical federated learning incentive mechanism in UAV-assisted edge computing environment
    He, Guangxuan
    Li, Chunlin
    Song, Mingyang
    Shu, Yong
    Lu, Chengwei
    Luo, Youlong
    AD HOC NETWORKS, 2023, 149
  • [3] UAV-Assisted Task Offloading in Edge Computing
    Zhang, Junna
    Zhang, Guoxian
    Wang, Xinxin
    Zhao, Xiaoyan
    Yuan, Peiyan
    Jin, Hu
    IEEE INTERNET OF THINGS JOURNAL, 2025, 12 (05): : 5559 - 5574
  • [4] A Smart Content Caching and Replacement Scheme for UAV-Assisted Fog Computing Network
    Li, Xujie
    Shen, Jichang
    Sun, Ying
    Wang, Ziya
    Zheng, Xuedong
    2020 12TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS AND SIGNAL PROCESSING (WCSP), 2020, : 1040 - 1045
  • [5] Online Optimization for UAV-assisted Distributed Fog Computing in Smart Factories of Industry 4.0
    Lee, Gilsoo
    Saad, Walid
    Bennis, Mehdi
    2018 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2018,
  • [6] Machine Learning Driven UAV-assisted Edge Computing
    Zhang, Liang
    Jabbari, Bijan
    Ansari, Nirwan
    2022 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC), 2022, : 2220 - 2225
  • [7] Fog Computing for Sustainable Smart Cities: A Survey
    Perera, Charith
    Qin, Yongrui
    Estrella, Julio C.
    Reiff-Marganiec, Stephan
    Vasilakos, Athanasios V.
    ACM COMPUTING SURVEYS, 2017, 50 (03)
  • [8] Energy Efficiency Optimization in UAV-Assisted Communications and Edge Computing
    Yang, Yang
    Gursoy, M. Cenk
    PROCEEDINGS OF THE 21ST IEEE INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS (IEEE SPAWC2020), 2020,
  • [9] Analysis and prediction of UAV-assisted mobile edge computing systems
    Wang, Xiong
    Yang, Zhijun
    Ding, Hongwei
    Guan, Zheng
    MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2023, 20 (12) : 21267 - 21291
  • [10] Collaborative Service Provisioning for UAV-Assisted Mobile Edge Computing
    Qu, Yuben
    Wei, Zhenhua
    Qin, Zhen
    Wu, Tao
    Ma, Jinghao
    Dai, Haipeng
    Dong, Chao
    CHINESE JOURNAL OF ELECTRONICS, 2024, 33 (06) : 1504 - 1514