# Building sensor coverage in couture: balancing cost, coverage, and comfort

## TL;DR

This paper introduces a new method for placing air quality sensors in buildings to balance cost, coverage, and comfort using mathematical models.

## Contribution

The novel contribution is the use of integer linear programming with a bi-level optimization framework to balance cost and coverage in sensor placement.

## Key findings

- The proposed method efficiently identifies optimal sensor configurations.
- The integration of ILP with the Leader-Follower framework reduces computational complexity.
- The approach ensures reliable coverage and cost-effectiveness in building sensor layouts.

## Abstract

The layout and performance of air quality sensors are crucial for building control systems and occupant comfort. Most previous studies have primarily focused on sensor placement and density. However, aspects such as accuracy, multifunctionality, and cost-effectiveness have received relatively little attention. To address these gaps, this study proposes two integer linear programming (ILP) models to optimize sensor placement. One model is designed to optimize cost, while the other focuses on maximizing coverage. To achieve an optimal balance between cost and coverage, a Leader-Follower (bi-level) approach is employed. This approach enables attaining the required coverage at the lowest possible cost or maximizing coverage within a fixed budget. Measurement accuracy at different locations is also incorporated to support energy efficiency and occupant satisfaction. The integration of the ILP models with the Leader-Follower framework reduces computational complexity and runtime while preserving solution accuracy. The results demonstrate that the proposed method can efficiently and rapidly identify optimal sensor configurations while ensuring both reliable coverage and cost-effectiveness.