UAV‐enabled computation migration for complex missions: A reinforcement learning approach. Issue 15 (1st September 2020)

• Record Type: Journal Article
• Authors: Zhu, Shichao; Gui, Lin; Cheng, Nan; Zhang, Qi; Sun, Fei; Lang, Xiupu
• Is Part Of: IET communications. Volume 14:Issue 15(2020)
• Page Start: 2472
• Page End: 2480
• Publication Date: 2020-09-01
• Subjects: decision making -- learning (artificial intelligence) -- autonomous aerial vehicles -- remotely operated vehicles -- Markov processes; UAV‐enabled computation migration -- complex missions -- reinforcement learning approach -- computation offloading -- remote areas -- traditional edge infrastructures -- unmanned aerial vehicle‐enabled edge -- near‐users edge computing service -- computation migration problem -- typical task‐flows -- proper UAV -- UAV‐ground communication data rate -- UAV location -- missions response time -- computation migration decision making problem -- advantage actor–critic reinforcement -- average response time; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2019.1188
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : The implementationof computation offloading is a challenging issue in the remote areas where traditional edge infrastructures are sparsely deployed. In this study, the authors propose a unmanned aerial vehicle (UAV)‐enabled edge computing framework, where a group of UAVs fly around to provide the near‐users edge computing service. They study the computation migration problem for the complex missions, which can be decomposed as some typical task‐flows considering the inter‐dependency of tasks. Each time a task appears, it should be allocated to a proper UAV for execution, which is defined as the computation migration or task migration. Since the UAV‐ground communication data rate is strongly associated with the UAV location, selecting a proper UAV to execute each task will largely benefit the missions response time. They formulate the computation migration decision making problem as a Markov decision process, in which the state contains the extracted observations from the environment. To cope with the dynamics of the environment, they propose an advantage actor–critic reinforcement learning approach to learn the near‐optimal policy on‐the‐fly. Simulation results show that the proposed approach has a desirable convergence property, and can significantly reduce the average response time of missions compared with the benchmark greedy method.