QoS保障下的无人机传感网安全通信路径规划

在无线传感器网络中，无人机在传感器覆盖区域内定期巡游，以收集传感器感知的数据。由于无线信道的广播性质，信息更容易被地面上的非法节点窃听，无线通信安全受到挑战。通过无人机的轨迹规划和传感器功率控制可以在物理层保障无线通信的安全性。然而，现有研究无人机辅助无线通信路径规划的文献中，没有考虑到用户或节点需要最小通信时间来保证服务质量。针对此问题，在无线传感器网络通信系统中，加入最小通信时间约束，通过联合优化无人机的飞行轨迹、传感器的发送功率和无人机服务传感器的调度顺序，提出平均保密率最大化问题,并证明了其非凸性。为解决所提的非凸问题，将原问题分解为3个子问题，提出了一个应用块坐标下降、连续凸优化、迭代舍入方法的快速收敛迭代算法TPA。实验结果表明，所提算法TPA的平均保密率比未进行轨迹优化的基准算法提高了15.7%，比未进行功率控制的基准算法提高了159.8%。TPA与未加入最小通信时间约束的基准算法相比，在2种不同任务分布情况下，当无人机飞行周期大于70 s时，任务完成率平均提升44.6%和27.1%。

Security communication path planning ofUAV sensor network under QoS guarantee

In wireless sensor networks, unmanned aerial vehicles (UAVs) regularly cruise around the sensor-covered area to collect the data sensed by the sensors. Due to the broadcast nature of wireless channels, information is more likely to be eavesdropped by illegal nodes on the ground, which makes the security of wireless com-munication challenging. Through UAV trajectory planning and sensor power control, the security of wireless communication can be guaranteed in the physical layer. However, in the existing researches on UAV-assisted wireless communication trajectory planning, the minimum communication time required by sensors is not considered to ensure the quality of service. To solve this problem, the minimum communication time con-straint is added. By jointly optimizing the trajectory of UAV, the transmission power of sensors and the scheduling order of UAV serving sensors, the problem of maximizing the average secrecy rate is proposed. The non-convexity of this problem is also proved. In order to solve the proposed non-convex problem, the original problem is decomposed into three sub-problems, and a fast convergent iterative algorithm TPA is proposed, which uses block coordinate descending, continuous convex optimization and iterative rounding method. The experimental results show that TPA improves the average secrecy rate by 15.7% on average in comparison to the baseline scheme without trajectory optimization, and by 159.8% on average in comparison to the baseline scheme without power control. In comparison to the baseline scheme without the minimum communication time constraint, TPA improves the task completion rate on average by 44.6% and 27.1%, when the UAV flight period is greater than 70s under two different task distribution situations.