A Cost-Effective, Scalable, and Portable IoT Data Infrastructure for Indoor Environment Sensing

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dc.contributor.authorAnik, Sheiken
dc.contributor.authorGao, Xinghuaen
dc.contributor.authorMeng, Naen
dc.contributor.authorAgee, Philipen
dc.contributor.authorMcCoy, Andrew P.en
dc.date.accessioned2022-02-12T15:07:09Zen
dc.date.available2022-02-12T15:07:09Zen
dc.date.issued2022-05-15en
dc.date.updated2022-02-12T15:06:47Zen
dc.description.abstractThe vast number of facility management systems, home automation systems, and the ever-increasing number of Internet of Things (IoT) devices are in constant need of environmental monitoring. Indoor environment data can be utilized to improve indoor facilities and better occupants’ working and living experience, however, such data are scarce because many existing facility monitoring technologies are expensive and proprietary for certain building systems. With the aim of addressing the indoor environment data availability issue, the authors designed and prototyped a cost-effective, distributed, scalable, and portable indoor environmental data collection system, Building Data Lite (BDL). BDL is based on Raspberry Pi computers and multiple changeable arrays of sensors, such as sensors of temperature, humidity, light, motion, sound, vibration, and multiple types of gases. The system includes a distributed sensing network and a centralized server. The server provides a web-based graphical user interface that enables users to access the collected data over the Internet. To evaluate the BDL system’s functionality, cost effectiveness, scalability, and portability, the research team conducted a case study in an affordable housing community where the system prototype is deployed to 12 households. The results indicate that the system is functioning as designed, costs $73 per zone and provides 12 types of indoor environment data, is easy to scale up, and is fully portable. This research contributes to the body of knowledge by proposing an innovative way for establishing a distributed wireless IoT data infrastructure for indoor environment sensing in new or existing buildings.en
dc.description.notes32 pages, 15 figuresen
dc.description.versionAccepted versionen
dc.format.extentPages 104027en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1016/j.jobe.2022.104027en
dc.identifier.eissn2352-7102en
dc.identifier.issn2352-7102en
dc.identifier.orcidMcCoy, Andrew [0000-0002-3827-0458]en
dc.identifier.orcidAgee, Philip [0000-0001-6299-3042]en
dc.identifier.orcidGao, Xinghua [0000-0002-3531-8137]en
dc.identifier.urihttp://hdl.handle.net/10919/108325en
dc.identifier.volume49en
dc.language.isoenen
dc.relation.urihttps://www.sciencedirect.com/science/article/abs/pii/S2352710222000407en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjecteess.SYen
dc.subjectcs.SYen
dc.subject0905 Civil Engineeringen
dc.subject1201 Architectureen
dc.subject1202 Buildingen
dc.titleA Cost-Effective, Scalable, and Portable IoT Data Infrastructure for Indoor Environment Sensingen
dc.title.serialJournal of Building Engineeringen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dcterms.dateAccepted2022-01-08en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Computer Scienceen
pubs.organisational-group/Virginia Tech/Architecture and Urban Studiesen
pubs.organisational-group/Virginia Tech/Architecture and Urban Studies/Building Constructionen
pubs.organisational-group/Virginia Tech/Architecture and Urban Studies/Myers-Lawson School of Constructionen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Architecture and Urban Studies/CAUS T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

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