标题： 针对空中窃听者的无人机启用安全MEC的鲁棒任务卸载 显示英文标题

标题： Robust Task Offloading for UAV-enabled Secure MEC Against Aerial Eavesdropper

摘要： 无人机（UAV）被认为是未来第六代通信网络中多接入边缘计算（MEC）的有前景的候选方案。 然而，空中窃听无人机（EUAVs）对数据卸载构成了重大安全威胁。 在本文中，我们研究了一个具有多个服务无人机（SUAVs）的鲁棒MEC场景，以应对来自EUAV的潜在窃听，在实际应用中考虑了诸如任务复杂性等随机参数。 具体来说，该问题被建模为优化SUAV的部署位置、地面用户（GUs）与SUAV之间的连接关系以及卸载比例。 在任务复杂性不确定的情况下，相应的机会约束是在不确定性集下构建的，这很难处理。 因此，我们首先通过K均值算法优化SUAV的预部署。 然后，采用分布鲁棒优化方法，并利用条件风险价值将机会约束转化为凸形式，可以通过凸工具包求解。 最后，仿真结果表明，在考虑不确定性的情况下，仅消耗比理想情况多5%的能量，这验证了所提出算法的鲁棒性。 显示英文摘要

摘要： Unmanned aerial vehicles (UAVs) are recognized as a promising candidate for the multi-access edge computing (MEC) in the future sixth generation communication networks. However, the aerial eavesdropping UAVs (EUAVs) pose a significant security threat to the data offloading. In this paper, we investigate a robust MEC scenario with multiple service UAVs (SUAVs) towards the potential eavesdropping from the EUAV, in which the random parameters such as task complexities are considered in the practical applications. In detail, the problem is formulated to optimize the deployment positions of SUAVs, the connection relationships between GUs and SUAVs, and the offloading ratios. With the uncertain task complexities, the corresponding chance constraints are constructed under the uncertainty set, which is tricky to deal with. Therefore, we first optimize the pre-deployment of SUAVs by the K-means algorithm. Then, the distributionally robust optimization method is employed, and the conditional value at risk is utilized to transform the chance constraints into convex forms, which can be solved via convex toolkits. Finally, the simulation results show that with the consideration of uncertainties, just 5% more energy is consumed compared with the ideal circumstance, which verifies the robustness of the proposed algorithms.