Resilient Sensor Placement for Kalman Filtering in Networked Systems: Complexity and Algorithms

TL;DR

This paper studies optimal sensor placement and attack strategies in networked systems to minimize or maximize the steady-state error covariance of Kalman filters, providing polynomial-time solutions for specific cases and NP-hardness results for resilient placement.

Contribution

It introduces polynomial-time algorithms for optimal sensor placement and attack strategies in certain network scenarios and analyzes the complexity of resilient sensor placement.

Findings

Optimal sensor placement for single-input systems can be computed in polynomial time.

Optimal sensor attack strategies can be efficiently determined in the considered network models.

Resilient sensor placement is NP-hard, with a pseudo-polynomial-time solution provided.

Abstract

Given a linear dynamical system affected by noise, we study the problem of optimally placing sensors (at design-time) subject to a sensor placement budget constraint in order to minimize the trace of the steady-state error covariance of the corresponding Kalman filter. While this problem is NP-hard in general, we consider the underlying graph associated with the system dynamics matrix, and focus on the case when there is a single input at one of the nodes in the graph. We provide an optimal strategy (computed in polynomial-time) to place the sensors over the network. Next, we consider the problem of attacking (i.e., removing) the placed sensors under a sensor attack budget constraint in order to maximize the trace of the steady-state error covariance of the resulting Kalman filter. Using the insights obtained for the sensor placement problem, we provide an optimal strategy (computed in polynomial-time) to attack the placed sensors. Finally, we consider the scenario where a system designer places the sensors under a sensor placement budget constraint, and an adversary then attacks the placed sensors subject to a sensor attack budget constraint. The resilient sensor placement problem is to find a sensor placement strategy to minimize the trace of the steady-state error covariance of the Kalman filter corresponding to the sensors that survive the attack. We show that this problem is NP-hard, and provide a pseudo-polynomial-time algorithm to solve it.