Occupancy Detection in Smart Home Space Using Interoperable Building Automation Technologies
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10250539" target="_blank" >RIV/61989100:27240/22:10250539 - isvavai.cz</a>
Result on the web
<a href="http://hcisj.com/articles/issue_view.php?wr_id=402&page=1" target="_blank" >http://hcisj.com/articles/issue_view.php?wr_id=402&page=1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.22967/HCIS.2022.12.047" target="_blank" >10.22967/HCIS.2022.12.047</a>
Alternative languages
Result language
angličtina
Original language name
Occupancy Detection in Smart Home Space Using Interoperable Building Automation Technologies
Original language description
To detect whether people are occupying individual rooms in a smart home, a range of sensors and building automation technologies can be employed. For these technologies to function in tandem and exchange useful data in a smart home environment, they must be interoperable. The article presents a new interoperable solution which combines existing decentralized KNX building automation technology with a KNX/LabVIEW software application gateway using visible light communication to track occupancy in a room. The article also describes a novel KNX/IoT software application gateway which uses an MQTT protocol for interoperability between KNX technology and IBM Watson IoT platform. We conducted an experiment with the originally designed solution to detect occupancy in an office room. We used KNX and BACnet building automation technology to produce an interoperable KNX/BACnet hardware gateway which allowed the application of artificial neural network mathematical methods for CO2 waveform prediction. The best results in detecting occupancy in a room were R = 0.9548 (Levenberg-Marquardt algorithm), R = 0.9872 (Bayesian regularization algorithm), and R = 0.8409 (scaled conjugate gradient algorithm), which correspond to the results obtained by other authors and a minimum system prediction accuracy of 96%.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
<a href="/en/project/EF16_019%2F0000867" target="_blank" >EF16_019/0000867: Research Centre of Advanced Mechatronic Systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Human-centric Computing and Information Sciences
ISSN
2192-1962
e-ISSN
2192-1962
Volume of the periodical
12
Issue of the periodical within the volume
47
Country of publishing house
KR - KOREA, REPUBLIC OF
Number of pages
17
Pages from-to
1-17
UT code for WoS article
000869908300001
EID of the result in the Scopus database
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