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Placement of unmanned aerial vehicles as communication relays in two-tiered multi-agent system: Clustering based methods

机译:无人驾驶车辆作为两层多助手系统中的通信继电器的位置:基于聚类的方法

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摘要

The network performance and the unmanned aerial vehicle (UAV) number are important objectives when UAVs are placed as communication relays to enhance the multi-agent information exchange. The problem is a non-deterministic polynomial hard (NP-hard) multi-objective optimization problem, instead of generating a Pareto solution, this work focuses on considering both objectives at the same level so as to achieve a balanced solution between them. Based on the property that agents connected to the same UAV are a cluster, two clustering-based algorithms, M-K-means (MKM) and modified fast search and find density of peaks (MFSFDP) methods, are first proposed. Since the former algorithm requires too much computational time and the latter one requires too many relays, an algorithm for the balanced network performance and relay number (BPN) is proposed by discretizing the area to avoid missing the optimal relay positions and defining a new local density function to reflect the network performance metric. Simulation results demonstrate that the proposed algorithms are feasible and effective. Comparisons between these algorithms show that the BPN algorithm uses fewer relay UAVs than the MFSFDP and classic set-covering based algorithm, and its computational time is far less than the MKM algorithm.
机译:当UAV被放置为通信继电器时,网络性能和无人驾驶飞行器(UAV)数量是重要的目标,以增强多代理信息交换。问题是非确定性多项式硬(NP-Hard)的多目标优化问题,而不是产生帕累托解决方案,这项工作侧重于考虑同一水平的两个目标,以便在它们之间实现平衡的解决方案。基于连接到同一UAV的代理的属性是群集,首先提出了两个基于聚类的算法,M-K均值(MKM)和修改的快速搜索以及查找峰值(MFSFDP)方法的密度。由于前算法需要太多的计算时间并且后者需要太多的继电器,因此通过离散化区域来提出用于平衡网络性能和中继数(BPN)的算法,以避免缺少最佳中继位置并定义新的局部密度。函数以反映网络性能度量标准。仿真结果表明,所提出的算法是可行和有效的。这些算法之间的比较表明,BPN算法使用比MFSFDP和基于经典集合覆盖的算法更少的继电器UV,其计算时间远远低于MKM算法。

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