一种基于WSNs分簇与UAV飞行轨迹优化的UAV-WSNs数据收集方案

An Efficient Data-collection Scheme for UAV-WSNs Based on WSNs Clustering and Optimization of UAV Flight Trajectory

摘要: 针对无人机辅助无线传感器网络系统中数据收集的低效性问题，提出了一种能量高效的数据收集方案DPKM-PN （“Density Peak K-means” combined with “Pointer Networks”）。首先，基于密度峰值聚类方法和K-Means聚类方法建立了一种新的节点分簇算法；然后，联合优化了无人机对无线传感器网络各分簇的访问次序和悬停位置。实验结果显示，与最近提出的Ptr-A^*方案相比，DPKM-PN方案能够降低传感器节点约7.9%的能耗，降低系统总能耗量约为6.3%，有效提高了系统的数据收集效率。

Abstract: To tackle the low efficiency problem of data collection in UAV-assisted Wireless Sensor Networks (where UAV is the abbreviation for Unmanned Aerial Vehicle), an energy efficient data collection scheme named DPKM-PN ("Density Peak K-Means" combined with "Pointer Networks") is proposed. Firstly, a new node-clustering algorithm is established based on the Density-Peak-Clustering method and the K-Means-Clustering method; Then, the access sequence and hover positions of UAV to all the clusters of Wireless Sensor Networks are optimized. The experimental results show that, compared with the Ptr-A^* scheme proposed recently, the DPKM-PN scheme can reduce the energy consumption of the sensor nodes by about 7.9%, and the total energy consumption of the whole system by about 6.3%, effectively improving the data collection efficiency of the system.