4.7 Article

Joint Computation Offloading and Trajectory Design for Aerial Computing

期刊

IEEE WIRELESS COMMUNICATIONS
卷 28, 期 5, 页码 88-94

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MWC.011.2100073

关键词

Process control; Performance gain; Trajectory; Computational efficiency; Software reliability; Servers; Telecommunication network reliability

资金

  1. National Natural Science Foundation of China [61771374, 61771373, 61801360, 62001393]
  2. Natural Science Basic Research Program of Shaanxi [2020JC-15, 2020JM-109]
  3. Fundamental Research Funds for the Central Universities [31020200QD010]
  4. Special Funds for Central Universities Construction of World-Class Universities (Disciplines) and Special Development Guidance [0639021GH0201024]
  5. Xi'an Unmanned System Security and Intelligent Communications ISTC Center

向作者/读者索取更多资源

Multiple drones are used for efficient multi-modal multi-task processes, proposing an aerial computing framework that integrates air-ground services to provide edge computing, achieving better performance in terms of average task completion time.
Aerial computing is a key enabling technique to provide seamless telecommunication and IT services for emerging computation-craving applications. As a drone employed in an aerial computing platform is limited in computation, caching, communication, and control capabilities, researchers have utilized multiple drones for efficient multi-modal multi-task processes, which also bring great challenges in massive data transmission and dynamic network management. Motivated by recent advancement of device-to-device communication and software defined networking technologies, we propose an air-ground integrated aerial computing framework where a cloudlet server and multiple mobile edge servers are mounted on drones to provide reliable and efficient edge computing services for ground devices. The joint implementation of computation offloading and trajectory design for the proposed framework is investigated. Extensive numerical results demonstrate that our aerial computing framework achieves better performance in terms of average task completion time than traditional ones.

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