# Occupancy Counting with Burst and Intermittent Signals in Smart Buildings

## TL;DR

This paper proposes a passive WiFi-based occupancy counting method in smart buildings using probe requests and a multi-model Kalman filter, achieving accurate zone-level occupancy estimates without extensive sensor deployment.

## Contribution

It introduces a novel approach combining WiFi probe request analysis with an interacting multi-model Kalman filter for improved occupancy counting accuracy.

## Key findings

- WiFi probe requests can effectively estimate occupancy in smart buildings.
- The multi-model Kalman filter enhances localization accuracy.
- Numerical results demonstrate viability in real building environments.

## Abstract

Zone-level occupancy counting is a critical technology for smart buildings and can be used for several applications such as building energy management, surveillance, and public safety. Existing occupancy counting techniques typically require installation of large number of occupancy monitoring sensors inside a building as well as an established network. In this study, in order to achieve occupancy counting, we consider the use of WiFi probe requests that are continuously transmitted from WiFi enabled smart devices. To this end, WiFi Pineapple equipment are used for passively capturing ambient probe requests from WiFi devices such as smart phones and tablets, where no connectivity to a WiFi network is required. This information is then used to localize users within coarsely defined occupancy zones, and subsequently to obtain occupancy count within each zone at different time scales. An interacting multi-model Kalman filter technique is developed to improve occupancy counting accuracy. Our numerical results using WiFi data collected at a university building show that utilization of WiFi probe requests can be considered a viable solution tool for zone-level occupancy counting in smart buildings.