文章摘要
Jienan Chen,Siyu Chen,Siyu Luo,Qi Wang,Bin Cao,Xiaoqian Li.[J].重庆邮电大学新办英文刊,2020,(4):433-443
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An intelligent task offloading algorithm (iTOA) for UAV edge computing network
Received: October 08, 2019  Revised: March 15, 2020
DOI:https://doi.org/10.1016/j.dcan.2020.04.008
中文关键词: 
英文关键词: Unmanned aerial vehicles (UAVs);Mobile edge computing (MEC);Intelligent task offloading algorithm (iTOA);Monte Carlo tree search (MCTS);Deep reinforcement learning;Splitting deep neural network (sDNN)
基金项目:
AuthorInstitutionE-mail
Jienan Chen The National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China  
Siyu Chen The National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China  
Siyu Luo The National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China  
Qi Wang The National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China  
Bin Cao State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China caobin65@163.com 
Xiaoqian Li Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong  
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中文摘要:
      
英文摘要:
      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.