Detection of Room Occupancy in Smart Buildings

Detection of Room Occupancy in Smart Buildings

Článek WoS

Recent advancements in occupancy and indoor environmental monitoring have encouraged the development of innovative solutions. This paper presents a~novel approach to room occupancy detection using Wi-Fi probe requests and machine learning techniques. We propose a~methodology that splits occupancy detection into two distinct subtasks: personnel presence detection, where the model predicts whether someone is present in the room, and occupancy level detection, which estimates the number of occupants on a~six-level scale (ranging from 1 person to up to 25 people) based on probe requests. To achieve this, we evaluated three types of neural networks: CNN (Convolutional Neural Network), LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit). Our experimental results show that the GRU model exhibits superior performance in both tasks. For personnel presence detection, the GRU model achieves an~accuracy of 91.8\%, outperforming the CNN and LSTM models with accuracies of 88.7\% and 63.8\%, respectively. This demonstrates the effectiveness of GRU in discerning room occupancy. Furthermore, for occupancy level detection, the GRU model achieves an~accuracy of~75.1\%, surpassing the CNN and LSTM models with accuracies of 47.1\% and 52.8\%, respectively. This research contributes to the field of occupancy detection by providing a~cost-effective solution that utilizes existing Wi-Fi infrastructure and demonstrates the potential of machine learning techniques in accurately classifying room occupancy.

Recent advancements in occupancy and indoor environmental monitoring have encouraged the development of innovative solutions. This paper presents a~novel approach to room occupancy detection using Wi-Fi probe requests and machine learning techniques. We propose a~methodology that splits occupancy detection into two distinct subtasks: personnel presence detection, where the model predicts whether someone is present in the room, and occupancy level detection, which estimates the number of occupants on a~six-level scale (ranging from 1 person to up to 25 people) based on probe requests. To achieve this, we evaluated three types of neural networks: CNN (Convolutional Neural Network), LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit). Our experimental results show that the GRU model exhibits superior performance in both tasks. For personnel presence detection, the GRU model achieves an~accuracy of 91.8\%, outperforming the CNN and LSTM models with accuracies of 88.7\% and 63.8\%, respectively. This demonstrates the effectiveness of GRU in discerning room occupancy. Furthermore, for occupancy level detection, the GRU model achieves an~accuracy of~75.1\%, surpassing the CNN and LSTM models with accuracies of 47.1\% and 52.8\%, respectively. This research contributes to the field of occupancy detection by providing a~cost-effective solution that utilizes existing Wi-Fi infrastructure and demonstrates the potential of machine learning techniques in accurately classifying room occupancy.

Occupancy detection;probe requests;Wi-Fi;energy savings;machine learning

Occupancy detection;probe requests;Wi-Fi;energy savings;machine learning

FRÝZA, T.; ZELENÝ, O.; BRAVENEC, T.

2025

17.06.2024

Czech Technical University in Prague

Brno

1805-9600

Radioengineering

33

3

Česká republika

432

441

10

https://www.radioeng.cz/fulltexts/2024/24_03_0432_0441.pdf