Resilient Distributed Recovery of Large Fields

TL;DR

This paper introduces a resilient distributed algorithm for large field recovery that withstands measurement attacks, enabling agents to accurately recover their targeted field components despite adversarial corruption.

Contribution

It proposes a novel consensus+innovations distributed algorithm that ensures convergence to true field values under attack conditions, with specific conditions on attacker strength and network connectivity.

Findings

Algorithm successfully recovers field components despite measurement attacks.

Convergence depends on network connectivity and attack severity.

Numerical examples demonstrate effectiveness of the proposed method.

Abstract

This paper studies the resilient distributed recovery of large fields under measurement attacks, by a team of agents, where each measures a small subset of the components of a large spatially distributed field. An adversary corrupts some of the measurements. The agents collaborate to process their measurements, and each is interested in recovering only a fraction of the field. We present a field recovery consensus+innovations type distributed algorithm that is resilient to measurement attacks, where an agent maintains and updates a local state based on its neighbors states and its own measurement. Under sufficient conditions on the attacker and the connectivity of the communication network, each agent's state, even those with compromised measurements, converges to the true value of the field components that it is interested in recovering. Finally, we illustrate the performance of our algorithm through numerical examples.