Optimization

B. Geißler, Alexander Martin, A. Morsi, Maximilian Walther, O. Kolb, Jens M. Lang, Lisa Wagner
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Abstract

—Mobile edge computing (MEC) has become a promising technology for real-time communications. Mobile devices can reduce the energy consumption and prolong the lifetime significantly via offloading the computing tasks to the MEC server. Moreover, physical layer security techniques can ensure the secure transmission of the offloading data. This paper investigates a MEC system that consists of an access point, multiple mobile devices and a malicious eavesdropper. The tasks allocation, local central processor’s frequency, offloading power, and offloading timeslots are optimized jointly to minimize the total energy consumption of the system. A difference of convex algorithm based scheme is proposed to solve the joint optimization problem. Moreover, a Karush Kuhn Tucker conditions based algorithm is also proposed to reduce the computational complexity. Numerical results show that the proposed algorithms are very effective. Moreover, the power consumption for secure offloading decreases with the increase of the distance between the mobile devices and the eavesdropper.
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优化
-移动边缘计算(MEC)已成为一项前景广阔的实时通信技术。移动设备可以通过将计算任务转移到 MEC 服务器来降低能耗并显著延长使用寿命。此外,物理层安全技术还能确保加载数据的安全传输。本文研究的 MEC 系统由一个接入点、多个移动设备和一个恶意窃听者组成。为了使系统的总能耗最小,对任务分配、本地中央处理器的频率、加载功率和加载时隙进行了联合优化。为解决联合优化问题,提出了一种基于差分凸算法的方案。此外,还提出了一种基于 Karush Kuhn Tucker 条件的算法,以降低计算复杂度。数值结果表明,所提出的算法非常有效。此外,随着移动设备与窃听者之间距离的增加,安全加载的功耗也在降低。
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