# Sensor Selection Cost Optimization for Tracking Structurally Cyclic Systems: a P-Order Solution

## TL;DR

This paper presents a polynomial-time graph-theoretic method for optimizing sensor measurement selection to minimize costs while ensuring observability in large-scale structurally cyclic systems.

## Contribution

It introduces a novel polynomial-time solution for sensor selection cost optimization in structurally cyclic systems using graph theory.

## Key findings

- The proposed method guarantees minimal sensing cost under observability constraints.
- The solution operates with a computational complexity of O(m^3).
- Applicable to large-scale systems due to polynomial-time algorithm.

## Abstract

Measurements and sensing implementations impose certain cost in sensor networks. The sensor selection cost optimization is the problem of minimizing the sensing cost of monitoring a physical (or cyber- physical) system. Consider a given set of sensors tracking states of a dynamical system for estimation purposes. For each sensor assume different costs to measure different (realizable) states. The idea is to assign sensors to measure states such that the global cost is minimized. The number and selection of sensor measurements need to ensure the observability to track the dynamic state of the system with bounded estimation error. The main question we address is how to select the state measurements to minimize the cost while satisfying the observability conditions. Relaxing the observability condition for structurally cyclic systems, the main contribution is to propose a graph theoretic approach to solve the problem in polynomial time. Note that, polynomial time algorithms are suitable for large-scale systems as their running time is upper-bounded by a polynomial expression in the size of input for the algorithm. We frame the problem as a linear sum assignment with solution complexity of O(m3).