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On the Structural Perspective of Computational Effectiveness for Quantized Consensus in Layered UAV Networks

机译:分层无人机网络中量化共识计算有效性的结构视角

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Distributed computing tasks in small unmanned aerial vehicle (UAV) networks require effective data transmission schemes because of limited communication channels and transmission power. In this paper, we use distributed consensus as a canonical distributed computing task to study the effectiveness of the data transmission in digitized (quantized) channels for UAV networks. We show that layered structures are more effective than equivalent egalitarian structures in terms of the data transmission load required to reach consensus. In particular, we establish explicit relationships between simple structural characteristics and the performance of quantized consensus (e.g., consensus condition, consensus value, and transmission load to reach consensus) for broad classes of layered structures. We also provide analytical results on asymptotic and transient performance when additional local memories are used to further reduce the data transmission load to reach consensus.
机译:小型无人飞行器(UAV)网络中的分布式计算任务需要有效的数据传输方案,因为通信通道和传输功率有限。在本文中,我们将分布式共识作为规范的分布式计算任务来研究无人机网络在数字化(量化)通道中数据传输的有效性。我们证明,就达成共识所需的数据传输负载而言,分层结构比等效的平等结构更为有效。尤其是,我们为广泛的分层结构类别建立了简单的结构特征与量化共识(例如共识条件,共识值和达到共识的传输负载)的性能之间的显式关系。当使用其他本地内存进一步减少数据传输负载以达到共识时,我们还提供渐近和暂态性能的分析结果。

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