Yang Xinwei, Yu Tianqi, Chen Zhongyue, Yang Jianfeng, Hu Jianling, Wu Yingrui
School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China.
School of Electronic and Information Engineering, Wuxi University, Wuxi 214105, China.
Sensors (Basel). 2022 Apr 22;22(9):3236. doi: 10.3390/s22093236.
Flying ad hoc networks (FANETs) have been gradually deployed in diverse application scenarios, ranging from civilian to military. However, the high-speed mobility of unmanned aerial vehicles (UAVs) and dynamically changing topology has led to critical challenges for the stability of communications in FANETs. To overcome the technical challenges, an Improved Weighted and Location-based Clustering (IWLC) scheme is proposed for FANET performance enhancement, under the constraints of network resources. Specifically, a location-based K-means++ clustering algorithm is first developed to set up the initial UAV clusters. Subsequently, a weighted summation-based cluster head selection algorithm is proposed. In the algorithm, the remaining energy ratio, adaptive node degree, relative mobility, and average distance are adopted as the selection criteria, considering the influence of different physical factors. Moreover, an efficient cluster maintenance algorithm is proposed to keep updating the UAV clusters. The simulation results indicate that the proposed IWLC scheme significantly enhances the performance of the packet delivery ratio, network lifetime, cluster head changing ratio, and energy consumption, compared to the benchmark clustering methods in the literature.
飞行自组织网络（FANETs）已逐渐部署在从民用到军事的各种应用场景中。然而，无人机（UAVs）的高速移动性和动态变化的拓扑结构给FANETs中的通信稳定性带来了严峻挑战。为了克服这些技术挑战，在网络资源的约束下，提出了一种改进的基于加权和位置的聚类（IWLC）方案，以提高FANET的性能。具体而言，首先开发了一种基于位置的K均值++聚类算法来建立初始无人机集群。随后，提出了一种基于加权求和的簇头选择算法。在该算法中，考虑到不同物理因素的影响，采用剩余能量比、自适应节点度、相对移动性和平均距离作为选择标准。此外，还提出了一种高效的集群维护算法来不断更新无人机集群。仿真结果表明，与文献中的基准聚类方法相比，所提出的IWLC方案显著提高了数据包传输率、网络寿命、簇头变化率和能耗等性能。
