Planned maintenance
A system upgrade is planned for 10/12-2024, at 12:00-13:00. During this time DiVA will be unavailable.
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Collecting Indoor Environmental Sensor Values for Machine Learning Based Smart Building Control
Mid Sweden University, Sweden.
Mid Sweden University, Sweden.
Mid Sweden University, Sweden.
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.ORCID iD: 0000-0002-5999-5976
2021 (English)In: IoTaIS 2020 - Proceedings: 2020 IEEE International Conference on Internet of Things and Intelligence Systems, Institute of Electrical and Electronics Engineers Inc. , 2021, p. 37-43Conference paper, Published paper (Refereed)
Abstract [en]

This research presents a solution for collecting indoor environmental sensor values and how the gathered sensor values then could be used for green building certification and in turn also machine learning based smart building control. We have created and implemented a proof of concept system consisting of a sensor collecting device using off the shelf hardware to complement the existing sensor information from buildings, as well as a cloud system for persistently storing this data for later usage. We have measured and evaluated our implemented system for our envisioned scenarios. In which we could observe that our proof-of-concept could scale to handle almost four sensor value updates per second at maximum stress, as well as having a latency for uploading a sensor value from our sensor of about 130 ms. Finally, we present our future and ongoing work based on these results which outlines our work for smart building control, green building certification, and the energy signature of buildings. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2021. p. 37-43
Keywords [en]
cloud, ecotechnology, energy signatures, green classification, industrial internet of things, Internet of Things, machine learning, sensors, smart buildings, Intelligent buildings, Building controls, Environmental sensor, Green buildings, Maximum stress, Off-the-shelf hardwares, Proof of concept, Sensor informations
National Category
Media and Communication Technology
Identifiers
URN: urn:nbn:se:ri:diva-52609DOI: 10.1109/IoTaIS50849.2021.9359717Scopus ID: 2-s2.0-85102210342ISBN: 9781728194486 (electronic)OAI: oai:DiVA.org:ri-52609DiVA, id: diva2:1539480
Conference
2020 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2020, 27 January 2021 through 28 January 2021
Available from: 2021-03-24 Created: 2021-03-24 Last updated: 2023-06-08Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Jennehag, Ulf

Search in DiVA

By author/editor
Jennehag, Ulf
By organisation
Industrial Systems
Media and Communication Technology

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 154 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf