Detection of Room Occupancy in Smart Buildings

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F24%3APU151665" target="_blank" >RIV/00216305:26220/24:PU151665 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.radioeng.cz/fulltexts/2024/24_03_0432_0441.pdf" target="_blank" >https://www.radioeng.cz/fulltexts/2024/24_03_0432_0441.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.13164/re.2024.0432" target="_blank" >10.13164/re.2024.0432</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Detection of Room Occupancy in Smart Buildings

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Detection of Room Occupancy in Smart Buildings

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Radioengineering

ISSN

1805-9600

e-ISSN

—

Svazek periodika

33

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

10

Strana od-do

432-441

Kód UT WoS článku

001292738300010

EID výsledku v databázi Scopus

2-s2.0-85200274352