Unmanned aerial vehicle (UAV) has been widely applied in internet-of-things (IoT) scenarios while the security for UAV communications remains a challenging problem due to the broadcast nature of the line-of-sight (LoS) wireless channels. This article investigates the security problems for dual UAV-assisted mobile edge computing (MEC) systems, where one UAV is invoked to help the ground terminal devices (TDs) to compute the offloaded tasks and the other one acts as a jammer to suppress the vicious eavesdroppers. In our framework, minimum secure computing capacity maximization problems are proposed for both the time division multiple access (TDMA) scheme and non-orthogonal multiple access (NOMA) scheme by jointly optimizing the communication resources, computation resources, and UAVs' trajectories. The formulated problems are non-trivial and challenging to be solved due to the highly coupled variables. To tackle these problems, we first transform them into more tractable ones then a block coordinate descent based algorithm and a penalized block coordinate descent based algorithm are proposed to solve the problems for TDMA and NOMA schemes, respectively. Finally, numerical results show that the security computing capacity performance of the systems is enhanced by the proposed algorithms as compared with the benchmarks. Meanwhile, the NOMA scheme is superior to the TDMA scheme for security improvement.

中文翻译：

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无人机辅助 MEC 系统安全的联合资源和轨迹优化


无人机（UAV）已广泛应用于物联网（IoT）场景，但由于视距（LoS）无线信道的广播性质，无人机通信的安全性仍然是一个具有挑战性的问题。本文研究了双无人机辅助移动边缘计算（MEC）系统的安全问题，其中一架无人机被调用来帮助地面终端设备（TD）计算卸载的任务，另一架充当干扰机来抑制恶意行为窃听者。在我们的框架中，通过联合优化通信资源、计算资源和无人机轨迹，针对时分多址（TDMA）方案和非正交多址（NOMA）方案提出了最小安全计算能力最大化问题。由于高度耦合的变量，所提出的问题并不简单且难以解决。为了解决这些问题，我们首先将它们转化为更容易处理的问题，然后提出基于块坐标下降的算法和基于惩罚块坐标下降的算法分别解决TDMA和NOMA方案的问题。最后，数值结果表明，与基准相比，所提出的算法提高了系统的安全计算能力性能。同时，NOMA方案在安全性方面优于TDMA方案。