论文标题
扩散网络的采样算法
A Sampling Algorithm for Diffusion Networks
论文作者
论文摘要
在本文中，我们提出了一种自适应扩散网络的抽样机制，该机制基于每个节点的邻域中的均方误差而适应性地更改采样节点的量。它显示了瞬态期间快速收敛，并且在稳态下采样节点的数量显着减少。除了降低计算成本外，所提出的机制也可以用作审查技术，从而通过减少节点之间的通信量来节省能量。我们还提出了一个理论分析，以获得以稳定状态采样的网络节点数量的下限和上限。
In this paper, we propose a sampling mechanism for adaptive diffusion networks that adaptively changes the amount of sampled nodes based on mean-squared error in the neighborhood of each node. It presents fast convergence during transient and a significant reduction in the number of sampled nodes in steady state. Besides reducing the computational cost, the proposed mechanism can also be used as a censoring technique, thus saving energy by reducing the amount of communication between nodes. We also present a theoretical analysis to obtain lower and upper bounds for the number of network nodes sampled in steady state.
