Q-learning based computation offloading for multi-UAV-enabled cloud-edge computing networks

Unmanned aerial vehicles (UAVs) have been recently considered as a flying platform to provide wide coverage and relaying services for mobile users (MUs). Mobile edge computing (MEC) is developed as a new paradigm to improve quality of experience of MUs in future networks. Motivated by the high flexi...

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Vydáno v:IET communications Ročník 14; číslo 15; s. 2481 - 2490
Hlavní autoři: Wang, Meng, Shi, Shuo, Gu, Shushi, Gu, Xuemai, Qin, Xue
Médium: Journal Article
Jazyk:angličtina
Vydáno: The Institution of Engineering and Technology 15.09.2020
Témata:
autonomous aerial vehicles
cellular radio
cloud computing
complexity reduction
computation resources
controllability
edge servers
efficient Q‐learning based computation offloading algorithm
flying platform
integer programming
learning (artificial intelligence)
mixed‐integer nonlinear programming problem
mobile computing
mobile edge computing
mobile users
MU energy consumption
multiUAV‐enabled cloud‐edge computing networks
multiUAV‐enabled MEC system
multi‐robot systems
MUs‐Edge‐Cloud three‐layer network architecture
nonlinear programming
optimisation problem
QCOA
quality of experience
random offloading
relay networks (telecommunication)
relaying services
remote cloud centre
Special Section: UAV-Enabled Mobile Edge Computing
traversal offloading
two‐layer network architecture
UAV
unmanned aerial vehicles
flying platform
QCOA
integer programming
nonlinear programming
relaying services
multi-robot systems
remote cloud centre
UAV
edge servers
optimisation problem
efficient Q-learning based computation offloading algorithm
mobile computing
MU energy consumption
controllability
mixed-integer nonlinear programming problem
complexity reduction
mobile users
cloud computing
learning (artificial intelligence)
MUs-Edge-Cloud three-layer network architecture
multiUAV-enabled cloud-edge computing networks
relay networks (telecommunication)
computation resources
multiUAV-enabled MEC system
unmanned aerial vehicles
random offloading
quality of experience
autonomous aerial vehicles
traversal offloading
two-layer network architecture
mobile edge computing
cellular radio
ISSN:1751-8628, 1751-8636
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Shrnutí:Unmanned aerial vehicles (UAVs) have been recently considered as a flying platform to provide wide coverage and relaying services for mobile users (MUs). Mobile edge computing (MEC) is developed as a new paradigm to improve quality of experience of MUs in future networks. Motivated by the high flexibility and controllability of UAVs, in this study, the authors study a multi-UAV-enabled MEC system, in which UAVs have computation resources to offer computation offloading opportunities for MUs, aiming to reduce MUs' total consumptions in terms of time and energy. Considering the rich computation resource in the remote cloud centre, they propose the MUs-Edge-Cloud three-layer network architecture, where UAVs play the role of flying edge servers. Based on this framework, they formulate the computation offloading issue as a mixed-integer non-linear programming problem, which is difficult to obtain an optimal solution in general. To address this, they propose an efficient Q-learning based computation offloading algorithm (QCOA) to reduce the complexity of optimisation problem. Numerical results show that the proposed QCOA outperforms benchmark offloading policies (e.g. random offloading, traversal offloading). Furthermore, the proposed three-layer network architecture achieves a 5% benefits compared with the traditional two-layer network architecture in terms of MUs' energy and time consumptions.
ISSN:1751-8628
1751-8636
DOI:10.1049/iet-com.2019.1184