Decentralized Observability with Limited Communication between Sensors

TL;DR

This paper investigates decentralized observability in sensor networks with limited communication, providing conditions for state retrieval, strategies for enhancing connectivity, and applying the approach to brain dynamics modeling.

Contribution

It offers necessary and sufficient conditions for observability, strategies to improve sensor communication, and extends the framework to cost-aware communication scenarios.

Findings

Conditions for sensor and system state observability are established.

A linear update strategy effectively encodes measurements for state retrieval.

Application to brain dynamics demonstrates practical utility.

Abstract

In this paper, we study the problem of jointly retrieving the state of a dynamical system, as well as the state of the sensors deployed to estimate it. We assume that the sensors possess a simple computational unit that is capable of performing simple operations, such as retaining the current state and model of the system in its memory. We assume the system to be observable (given all the measurements of the sensors), and we ask whether each sub-collection of sensors can retrieve the state of the underlying physical system, as well as the state of the remaining sensors. To this end, we consider communication between neighboring sensors, whose adjacency is captured by a communication graph. We then propose a linear update strategy that encodes the sensor measurements as states in an augmented state space, with which we provide the solution to the problem of retrieving the system and sensor states. The present paper contains three main contributions. First, we provide necessary and sufficient conditions to ensure observability of the system and sensor states from any sensor. Second, we address the problem of adding communication between sensors when the necessary and sufficient conditions are not satisfied, and devise a strategy to this end. Third, we extend the former case to include different costs of communication between sensors. Finally, the concepts defined and the method proposed are used to assess the state of an example of approximate structural brain dynamics through linearized measurements.