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UAV-Enabled Computing Power Networks : Design and Performance Analysis Under Energy Constraints

  • Yiqin DENG
  • , Zhengru FANG
  • , Senkang HU
  • , Yanan MA
  • , Xiaoyu GUO
  • , Haixia ZHANG
  • , Yuguang FANG*
  • *Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

This paper presents an innovative framework that boosts computing power by utilizing ubiquitous computing power distribution and enabling higher computing node accessibility via adaptive UAV positioning, establishing a UAV-enabled Computing Power Network (UAV-CPN). In a UAV-CPN, a UAV functions as a dynamic relay, outsourcing computing tasks from the request zone to an expanded service zone with diverse computing nodes, including vehicle onboard units, edge servers, and dedicated powerful nodes. This approach has the potential to alleviate communication bottlenecks and overcome the 'island effect' observed in multi-access edge computing. A significant challenge is to quantify computing power performance under complex dynamics of communication and computing. To address this challenge, we introduce task completion probability to capture the capability of UAV-CPNs for task computing. We further enhance UAV-CPN performance under a hybrid energy architecture by jointly optimizing UAV altitude and transmit power, where fuel cells and batteries collectively power both UAV propulsion and communication systems. Extensive evaluations show significant performance gains, highlighting the importance of balancing communication and computing capabilities, especially under dual-energy constraints. These findings underscore the potential of UAV-CPNs to significantly boost computing power.

Original languageEnglish
Pages (from-to)9563-9577
Number of pages15
JournalIEEE Transactions on Mobile Computing
Volume25
Issue number7
Early online date19 Jan 2026
DOIs
Publication statusPublished - 1 Jul 2026

Bibliographical note

Publisher Copyright:
© 2002-2012 IEEE.

Funding

The research work described in this paper was conducted in the JC STEM Lab of Smart City funded by The Hong Kong Jockey Club Charities Trust under Contract 2023-0108. The work described in this paper was also partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CityU 11216324). The work of Yiqin Deng was also supported in part by the National Natural Science Foundation of China under Grant No. 62301300, in part by the Shandong Province Science Foundation under Grant No. ZR2023QF053.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Computing power networks
  • edge computing
  • low-altitude economy
  • task completion probability
  • unmanned aerial vehicle (UAV)

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