从传感器网络到处理网络：考虑计算与通信延迟的最优估计
From Sensor to Processing Networks: Optimal Estimation with Computation and Communication Latency
AI总结：
本文提出“处理网络”概念，通过形式化建模与解析分析，研究传感器在原始传输与本地预处理之间的最优计算-通信权衡，并证明考虑计算和通信延迟对估计精度有重要影响。
摘要：
本文研究在网络化系统（例如机器人集群、智能电网、传感器网络）中，存在通信和计算延迟的情况下，对感兴趣的时变现象进行监测的问题。边缘计算的最新进展使得处理可以分布在整个网络中，因此我们研究当传感器必须决定是传输原始数据（产生通信延迟）还是对其进行预处理（产生计算延迟）以便计算感兴趣现象状态的准确估计时，所产生的基本计算-通信权衡。我们提出两个关键贡献。首先，我们形式化了“处理网络”的概念。与“传感器和通信网络”不同——在后者中设计者关注的是设计合适的通信策略——在处理网络中，人们还可以控制计算在网络中发生的时间和位置。第二个贡献是针对单个传感器和多个同质传感器的情况，提供关于最优预处理延迟（即每个传感器上花费的最优计算时间）的解析结果。数值结果证实了我们的论断：考虑计算延迟（包括传感器端和估计器端）以及通信延迟会极大地影响估计精度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
