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Volume 43 Issue 12
Dec.  2021
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Wanming HAO, Jiwei SUN, Gangcan SUN, Zhengyu ZHU, Yiqing ZHOU. Secure Energy-efficient Resource Allocation in Mobile Edge Computing Based on Non-Orthogonal Multiple Access[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3580-3587. doi: 10.11999/JEIT200872
Citation: Wanming HAO, Jiwei SUN, Gangcan SUN, Zhengyu ZHU, Yiqing ZHOU. Secure Energy-efficient Resource Allocation in Mobile Edge Computing Based on Non-Orthogonal Multiple Access[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3580-3587. doi: 10.11999/JEIT200872

Secure Energy-efficient Resource Allocation in Mobile Edge Computing Based on Non-Orthogonal Multiple Access

doi: 10.11999/JEIT200872
Funds:  The National Natural Science Foundation of China (62101499, 61801434), The Innovation Demonstration Project of Henan Province (201111212300), The Science and Technology Innovation Major Project of Zhengzhou (2019CXZX0037)
  • Received Date: 2020-10-09
  • Rev Recd Date: 2021-04-17
  • Available Online: 2021-05-10
  • Publish Date: 2021-12-21
  • In order to improve the security of Non-Orthogonal Multiple Access (NOMA) based Mobile Edge Computation (MEC) system when computation tasks are partially offloading, the physical layer security of MEC network in the presence of eavesdroppers is considered, and the security interruption probability is used to measure the security performance of computation offloading. Considering the transmit power constraint, local task calculation constraint and secret outage probability constraint, and then, the energy consumption weight factor is introduced to balance the transmission energy consumption and the calculated energy consumption. After that, the sum of system weighted energy consumption is finally achieved. In the case of satisfying two user priorities, to reduce the system overhead, a joint task offloading and resource allocation mechanism is proposed to archive the optimal solution of the transformed problem through an iterative optimization algorithm based on bisection search, and the optimal computing task offloading and power allocation are obtained. Simulation results show that the proposed algorithm can effectively reduce the energy consumption of the system.
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