An intelligent task offloading algorithm (iTOA) for UAV edge computing network
Open accessAbstract
Unmanned Aerial Vehicle (UAV) has emerged as a promising technology for the support of human activities, such as target tracking, disaster rescue, and surveillance. However, these tasks require a large computation load of image or video processing, which imposes enormous pressure on the UAV computation platform. To solve this issue, in this work, we propose an intelligent Task Offloading Algorithm (iTOA) for UAV edge computing network. Compared with existing methods, iTOA is able to perceive the network’s environment intelligently to decide the offloading action based on deep Monte Calor Tree Search (MCTS), the core algorithm of Alpha Go. MCTS will simulate the offloading decision trajectories to acquire the best decision by maximizing the reward, such as lowest latency or power consumption. To accelerate the search convergence of MCTS, we also proposed a splitting Deep Neural Network (sDNN) to supply the prior probability for MCTS. The sDNN is trained by a self-supervised learning manager. Here, the training data set is obtained from iTOA itself as its own teacher. Compared with game theory and greedy search-based methods, the proposed iTOA improves service latency performance by 33% and 60%, respectively.
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UAVs still face challenges in performing computationally intensive tasks because of limited processing power and battery lifetime.
“Despite current advances, UAVs still face challenges in performing tasks that are computationally intensive because of a UAV's limited processing power and battery lifetime [121].”
From A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges (2022) · cites this paper as [121]
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Flexible Computation Offloading at the Edge for Autonomous Drones with Uncertain Flight Times
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- Authors
- Chen, J., Chen, S., Luo, S., Wang, Q., Cao, B., & Li, X.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Beijing University of Posts and Telecommunications (CN)
- University of Hong Kong (HK)
- Corresponding author
- Cao, Bin
- Venue
- Digital Communications and Networks
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 6
- Issue
- 4
- Pages
- 433-443
- ISSN
- 2352-8648 (ISSN-L)
- OpenAlex
- W3023570908
- Cited by
- 73
- References
- 53
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 18 · 2023: 17 · 2022: 21 · 2021: 9 · 2020: 2
- FWCI
- 5.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Digital Communications and Networks (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Engineering › Aerospace Engineering › UAV Applications and Optimization
- Physical Sciences › Engineering › Aerospace Engineering › Satellite Communication Systems
- Keywords
- task offloading, UAV edge computing, self-supervised learning, service latency, power consumption, image and video processing
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