Computation-Communication Trade-offs and Sensor Selection in Real-time Estimation for Processing Networks

TL;DR

This paper investigates the trade-offs between computation and communication in networked systems for real-time state estimation, proposing optimal preprocessing strategies and sensor selection algorithms to enhance performance.

Contribution

It formulates a rigorous problem for optimal real-time estimation considering delays, and provides analytical and algorithmic solutions for homogeneous and heterogeneous sensor networks.

Findings

Optimal preprocessing can be computed analytically for homogeneous networks.

Sensor selection significantly improves estimation performance.

Preprocessing policies based on the proposed algorithms enhance network accuracy.

Abstract

Recent advances in electronics are enabling substantial processing to be performed at each node (robots, sensors) of a networked system. Local processing enables data compression and may mitigate measurement noise, but it is still slower compared to a central computer (it entails a larger computational delay). However, while nodes can process the data in parallel, the centralized computational is sequential in nature. On the other hand, if a node sends raw data to a central computer for processing, it incurs communication delay. This leads to a fundamental communication-computation trade-off, where each node has to decide on the optimal amount of preprocessing in order to maximize the network performance. We consider a network in charge of estimating the state of a dynamical system and provide three contributions. First, we provide a rigorous problem formulation for optimal real-time estimation in processing networks in the presence of delays. Second, we show that, in the case of a homogeneous network (where all sensors have the same computation) that monitors a continuous-time scalar linear system, the optimal amount of local preprocessing maximizing the network estimation performance can be computed analytically. Third, we consider the realistic case of a heterogeneous network monitoring a discrete-time multi-variate linear system and provide algorithms to decide on suitable preprocessing at each node, and to select a sensor subset when computational constraints make using all sensors suboptimal. Numerical simulations show that selecting the sensors is crucial. Moreover, we show that if the nodes apply the preprocessing policy suggested by our algorithms, they can largely improve the network estimation performance.