This paper investigates the use of a networked system ($e.g.$, swarm of robots, smart grid, sensor network) to monitor a time-varying phenomenon of interest in the presence of communication and computation latency. Recent advances in edge computing have enabled processing to be spread across the network, hence we investigate the fundamental computation-communication trade-off, arising when a sensor has to decide whether to transmit raw data (incurring communication delay) or preprocess them (incurring computational delay) in order to compute an accurate estimate of the state of the phenomenon of interest. We propose two key contributions. First, we formalize the notion of $processing$ $network$. Contrarily to $sensor$ $and$ $communication$ $networks$, where the designer is concerned with the design of a suitable communication policy, in a processing network one can also control when and where the computation occurs in the network. The second contribution is to provide analytical results on the optimal preprocessing delay ($i.e.$, the optimal time spent on computations at each sensor) for the case with a single sensor and multiple homogeneous sensors. Numerical results substantiate our claims that accounting for computation latencies (both at sensor and estimator side) and communication delays can largely impact the estimation accuracy.

中文翻译：

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从传感器到处理网络：计算和通信延迟的最佳估计

本文研究了使用网络系统（$eg$、机器人群、智能电网、传感器网络）来监控存在通信和计算延迟时感兴趣的时变现象。边缘计算的最新进展使处理能够在整个网络中传播，因此我们研究了基本的计算-通信权衡，当传感器必须决定是传输原始数据（导致通信延迟）还是预处理它们（导致计算延迟）以计算对感兴趣现象状态的准确估计。我们提出了两个关键贡献。首先，我们将 $processing$ $network$ 的概念形式化。与 $sensor$ $and$ $communication$ $networks$ 相反，设计师关心的是设计合适的通信策略，在处理网络中，还可以控制计算在网络中的时间和地点。第二个贡献是针对具有单个传感器和多个同类传感器的情况提供最佳预处理延迟（$ie$，在每个传感器的计算上花费的最佳时间）的分析结果。数值结果证实了我们的说法，即考虑计算延迟（在传感器和估计器端）和通信延迟可以在很大程度上影响估计精度。