Energy-Efficient Computational Offloading for Secure NOMA-Enabled Mobile Edge Computing Networks
Computational offloading and nonorthogonal multiple access (NOMA) are two promising technologies for alleviating the problems of limited battery capacity, insufficient computational capability, and massive deployment of terminal equipment in the Internet of Things (IoT) era. However, offloading data...
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Published in | Wireless communications and mobile computing Vol. 2022; pp. 1 - 11 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
Oxford
Hindawi
27.04.2022
John Wiley & Sons, Inc |
Subjects | |
Online Access | Get full text |
ISSN | 1530-8669 1530-8677 |
DOI | 10.1155/2022/5230594 |
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Abstract | Computational offloading and nonorthogonal multiple access (NOMA) are two promising technologies for alleviating the problems of limited battery capacity, insufficient computational capability, and massive deployment of terminal equipment in the Internet of Things (IoT) era. However, offloading data may be threatened by malicious eavesdroppers, which leads to more energy consumptions to avoid being eavesdropped. In this work, we study the energy-efficient way of computational offloading under the condition of certain security requirement in a secure NOMA-enabled mobile-edge computing (MEC) networks, where K end users are intended to offload their data to the N-antenna access point (AP) through the same resource block under the threat of an eavesdropper. We first address energy-efficient local resource allocation by minimizing sum-energy consumption of end users, subject to CPU frequencies, offloading bits, secrecy offloading rate, and transmit power. We then optimize the local resources to obtain the minimum computation latency of task for each end user, with the constraint of certain energy budget. The solutions to the above two optimization problems are given and demonstrated numerically by a 3-user scenario. |
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AbstractList | Computational offloading and nonorthogonal multiple access (NOMA) are two promising technologies for alleviating the problems of limited battery capacity, insufficient computational capability, and massive deployment of terminal equipment in the Internet of Things (IoT) era. However, offloading data may be threatened by malicious eavesdroppers, which leads to more energy consumptions to avoid being eavesdropped. In this work, we study the energy-efficient way of computational offloading under the condition of certain security requirement in a secure NOMA-enabled mobile-edge computing (MEC) networks, where K end users are intended to offload their data to the N-antenna access point (AP) through the same resource block under the threat of an eavesdropper. We first address energy-efficient local resource allocation by minimizing sum-energy consumption of end users, subject to CPU frequencies, offloading bits, secrecy offloading rate, and transmit power. We then optimize the local resources to obtain the minimum computation latency of task for each end user, with the constraint of certain energy budget. The solutions to the above two optimization problems are given and demonstrated numerically by a 3-user scenario. Computational offloading and nonorthogonal multiple access (NOMA) are two promising technologies for alleviating the problems of limited battery capacity, insufficient computational capability, and massive deployment of terminal equipment in the Internet of Things (IoT) era. However, offloading data may be threatened by malicious eavesdroppers, which leads to more energy consumptions to avoid being eavesdropped. In this work, we study the energy-efficient way of computational offloading under the condition of certain security requirement in a secure NOMA-enabled mobile-edge computing (MEC) networks, where K end users are intended to offload their data to the N -antenna access point (AP) through the same resource block under the threat of an eavesdropper. We first address energy-efficient local resource allocation by minimizing sum-energy consumption of end users, subject to CPU frequencies, offloading bits, secrecy offloading rate, and transmit power. We then optimize the local resources to obtain the minimum computation latency of task for each end user, with the constraint of certain energy budget. The solutions to the above two optimization problems are given and demonstrated numerically by a 3-user scenario. |
Author | Wang, Haiping |
Author_xml | – sequence: 1 givenname: Haiping orcidid: 0000-0002-2949-170X surname: Wang fullname: Wang, Haiping organization: School of Mechatronics and Mould Engineering Taizhou Vocational College of Science and Technology Zhejiang 318020 China |
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Cites_doi | 10.1109/ACCESS.2019.2925808 10.1109/COMST.2017.2745201 10.1109/JIOT.2017.2750180 10.1109/2.708447 10.1007/BF01130406 10.1109/TVT.2019.2917890 10.1109/TCOMM.2021.3070620 10.1109/ACCESS.2020.3015786 10.1109/ACCESS.2020.3011694 10.1109/ACCESS.2017.2696032 10.1109/JSYST.2020.3009723 10.1109/LCOMM.2021.3114772 10.1002/j.1538-7305.1975.tb02040.x 10.1109/ACCESS.2020.2969214 10.1109/TCOMM.2019.2949994 10.1109/TNSM.2020.3020249 10.1109/JIOT.2019.2904741 10.1109/TVT.2020.2996254 10.1109/ACCESS.2020.2975310 10.1109/JSAC.2018.2864426 10.23919/JCC.2020.10.005 10.1109/TVT.2019.2912227 10.1109/JSAC.2017.2760478 10.1109/JIOT.2021.3103391 10.1109/TCOMM.2020.3038875 10.1109/TWC.2020.2979456 |
ContentType | Journal Article |
Copyright | Copyright © 2022 Haiping Wang. Copyright © 2022 Haiping Wang. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
Copyright_xml | – notice: Copyright © 2022 Haiping Wang. – notice: Copyright © 2022 Haiping Wang. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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SubjectTerms | Computation offloading Computational efficiency Cryptography Eavesdropping Edge computing End users Energy budget Energy consumption Energy efficiency Internet of Things Mobile computing Network latency Nonorthogonal multiple access Optimization Resource allocation Servers Unmanned aerial vehicles |
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Title | Energy-Efficient Computational Offloading for Secure NOMA-Enabled Mobile Edge Computing Networks |
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