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针对无人机辅助移动边缘计算(Mobile Edge Computing, MEC)网络中数据卸载的窃听问题，提出一种协同干扰下无人机辅助MEC网络节能安全任务卸载算法，即两层交替迭代算法(Two Layers Iteration Algorithm, TLIA)。引入了干扰辅助无人机以降低窃听信道的质量，并以系统总能耗最小化为优化目标，在满足用户服务质量与飞行速率的约束条件下，联合优化本地计算量、卸载计算量、用户发射功率和无人机轨迹。将非凸性优化问题解耦为任务卸载子问题与轨迹调度子问题，并采用所提TLIA算法进行求解。仿真结果表明，与固定轨迹算法、固定发射功率算法及无本地计算算法这3种传统基准算法相比，所提算法可以分别降低约25.9%、20.8%及10.1%的系统安全能耗，能够有效地增强MEC网络对物联网设备的支持能力。
Abstract：To address the eavesdropping issue in data offloading within unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC) networks, an energy-efficient secure task offloading algorithm under cooperative jamming is proposed, namely the two-layer alternating iteration algorithm(TLIA). By introducing a jamming-assisted UAV to degrade the quality of eavesdropping channels, the algorithm aims to minimize the total system energy consumption while jointly optimizing local computing workload, offloaded computing workload, user transmit power, and UAV trajectory, subject to the constraints on user quality of service and UAV flight dynamics. The non-convex optimization problem is decoupled into a task offloading subproblem and a trajectory scheduling subproblem, which are solved by the proposed TLIA algorithm. Simulation results demonstrate that compared to three conventional benchmarks—fixed trajectory, fixed transmit power, and no local computing algorithms—the proposed algorithm reduces the secure energy consumption by approximately 25.9%, 20.8%, and 10.1%, respectively. It can effectively enhance the MEC network's capability to support Internet of Things(IoT) devices.
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基本信息:
DOI：10.13682/j.issn.2095-6533.2025.03.001
中图分类号:V279;TN929.5
引用信息:
[1]胡晗,陈钻,郝书亭等.协同干扰下无人机辅助MEC网络节能安全任务卸载算法[J].西安邮电大学学报,2025,30(03):1-10.DOI:10.13682/j.issn.2095-6533.2025.03.001.
基金信息:
中国博士后科学基金项目(2023M744104); 国家自然科学基金项目(62471254)