存在传播延迟和非对称信道的自同步传感器网络分布式决策
Distributed Decision Through Self-Synchronizing Sensor Networks in the Presence of Propagation Delays and Asymmetric Channels
摘要：
在本文中，我们提出并分析了一种分布式算法，实现全局最优的决策，无论是估计或检测，通过线性耦合的积分器初始化与本地测量之间的自同步机制。我们建模的节点之间的相互作用作为一个有向图的权重（可能）依赖于无线电信道，我们提出特别注意的传播延迟发生在传感器之间的数据交换的效果，作为网络几何形状的函数。我们得到的必要和充分条件，建议的系统达成共识的全局最优决策统计。本文证明了一个重要结果：对于任意有界时滞条件，当且仅当有向图是拟强连通的，该算法以指数收敛速度达到一致性.我们提供了一个封闭的形式表达的全球共识，表明延迟的影响是，在一般情况下，在最后的决定中引入的偏见。最后，我们利用我们的封闭形式的表达式设计一个双步骤的共识机制，能够提供一个无偏的估计与最小的额外复杂性，而不需要知道或估计的信道参数。
英文摘要：
In this paper we propose and analyze a distributed algorithm for achieving globally optimal decisions, either estimation or detection, through a self-synchronization mechanism among linearly coupled integrators initialized with local measurements. We model the interaction among the nodes as a directed graph with weights (possibly) dependent on the radio channels and we pose special attention to the effect of the propagation delay occurring in the exchange of data among sensors, as a function of the network geometry. We derive necessary and sufficient conditions for the proposed system to reach a consensus on globally optimal decision statistics. One of the major results proved in this work is that a consensus is reached with exponential convergence speed for any bounded delay condition if and only if the directed graph is quasi-strongly connected. We provide a closed form expression for the global consensus, showing that the effect of delays is, in general, the introduction of a bias in the final decision. Finally, we exploit our closed form expression to devise a double-step consensus mechanism able to provide an unbiased estimate with minimum extra complexity, without the need to know or estimate the channel parameters.
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