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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| Published in: | IET communications Vol. 14; no. 15; pp. 2481 - 2490 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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The Institution of Engineering and Technology
15.09.2020
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| ISSN: | 1751-8628, 1751-8636 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Qin, Xue Wang, Meng Gu, Shushi Gu, Xuemai Shi, Shuo |
| Author_xml | – sequence: 1 givenname: Meng surname: Wang fullname: Wang, Meng organization: 1School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, People's Republic of China – sequence: 2 givenname: Shuo surname: Shi fullname: Shi, Shuo email: crcss@hit.edu.cn organization: 2Peng Cheng Laboratory, Shenzhen 518055, People's Republic of China – sequence: 3 givenname: Shushi orcidid: 0000-0002-3897-5407 surname: Gu fullname: Gu, Shushi organization: 3School of Electronics and Information Engineering, Harbin Institute of Technology (Shenzen), Shenzen 518055, People's Republic of China – sequence: 4 givenname: Xuemai surname: Gu fullname: Gu, Xuemai organization: 2Peng Cheng Laboratory, Shenzhen 518055, People's Republic of China – sequence: 5 givenname: Xue surname: Qin fullname: Qin, Xue organization: 4School of Computer Science, Texas A&M University at Corpus Christi, TX 78412, USA |
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| Cites_doi | 10.1007/s12083-019-00752-0 10.1109/COMST.2017.2705720 10.1109/TVT.2018.2790421 10.1109/JIOT.2019.2894257 10.1109/TVT.2018.2816244 10.1109/JIOT.2018.2875246 10.1109/INFCOM.2012.6195685 10.1109/JIOT.2018.2868616 10.1109/TVT.2018.2867191 10.1109/JSAC.2018.2864426 10.1109/TC.2014.2366735 10.1109/TVT.2019.2917890 10.1109/TCOMM.2016.2611512 10.1109/CC.2018.8543056 10.1109/JIOT.2018.2878876 10.1109/TVT.2018.2890773 10.1109/TWC.2019.2928539 10.1109/TVT.2019.2923214 10.1109/COMST.2017.2745201 10.1109/MCOM.2016.7470933 10.1109/TWC.2019.2900035 10.1109/JIOT.2018.2875917 10.1109/TMC.2018.2871147 10.1109/JIOT.2018.2876513 10.1109/TAES.2011.5937283 10.1109/TNET.2015.2487344 |
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| Keywords | 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 |
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| Snippet | Unmanned aerial vehicles (UAVs) have been recently considered as a flying platform to provide wide coverage and relaying services for mobile users (MUs).... |
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| SubjectTerms | 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 |
| Title | Q-learning based computation offloading for multi-UAV-enabled cloud-edge computing networks |
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