Smart Plants: Low-Cost Solution for Monitoring Indoor Environments

Zuniga, Agustin; Motlagh, Naser Hossein; Flores, Huber; Nurmi, Petteri

doi:10.1109/jiot.2022.3188475

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…While environmental sensors are becoming cheaper and easier to deploy, the less expensive variants often have lower accuracy and require either periodic manual re-calibration or software-based recalibration, which can be achieved using machine learning methods. Environmental sensors can also provide indications of the presence, movements, and even activities of occupants even if these can only be detected with a delay [11].…”

Section: A Sensing Infrastructurementioning

confidence: 99%

“…Smart spaces build on the increasing availability of sensors in physical spaces, e.g., for monitoring energy use, thermal comfort of occupants, social distancing, air quality, and general well-being [7], [8], [9]. These sensor-enabled devices enable various applications, which help, among other things, to reduce electricity usage, optimize Heating, Ventilation, and Air Conditioning (HVAC) use, improve the safety of the space, and offer services that support the occupants [10], [11]. Indeed, smart spaces aim to enhance the functions of these environments and overall elevate the user experience [12], [13].…”

Section: Introductionmentioning

confidence: 99%

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Digital Twins for Smart Spaces—Beyond IoT Analytics

Motlagh

Zaidan

Lovén

et al. 2024

IEEE Internet Things J.

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Smart spaces, physical spaces that are integrated with sensor-enabled IoT devices, are a powerful paradigm for optimizing the operations of the space and improving its quality for the occupants. Managing the applications and services running in the space is a complex task as the operations of the devices and services are dependent on the physical characteristics of the space, the occupants of the space, and the technologies that are being integrated. Digital twinning, the combination of physical representations with a virtual counterpart, is a potential technology for facilitating the management of smart space devices and services. While digital twins are increasingly adopted in industry, their use in everyday environments remains low due to difficulties in creating and linking the virtual representation with the physical environment. In this paper, we propose our vision for the adoption of digital twinning as a pathway to improve the functions of smart spaces. We derive a generic reference architecture that comprises four layers, covering the physical space, the sensing infrastructure, the network interfaces, and the underlying computational infrastructure. Next, we identify and address key requirements for the uptake of digital twins in smart space and assess their benefits using the ascendancy model of business analytics. Finally, to demonstrate the practicality of digital twinning, we present a proof-of-concept digital twin for the TellUs smart space at the University of Oulu in Finland and use it to highlight the potential benefits of different ascendancy levels.

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Section: A Sensing Infrastructurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Digital Twins for Smart Spaces—Beyond IoT Analytics

Motlagh

Zaidan

Lovén

et al. 2024

IEEE Internet Things J.

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Fire safety performance of functional vegetated green building systems: A comprehensive review

Ni,

Zhao,

Tam

et al. 2024

Journal of Building Engineering

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Indoor air quality monitoring and source apportionment using low-cost sensors

Higgins,

Kumar,

Morawska

2024

Environ. Res. Commun.

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Understanding of the various sources of indoor air pollution requires indoor air quality (IAQ) data that is usually lacking. Such data can be obtained using unobtrusive, low-cost sensors (LCS). The aim of this review is to examine the recent literature published on LCS for IAQ measurements and to determine whether these studies employed any methods to identify or quantify sources of indoor air pollution. Studies were reviewed in terms of whether any methods of source apportionment were employed, as well as the microenvironment type, geographical location, and several metrics relating to the contribution of outdoor pollutant ingress versus potential indoor pollutant sources. We found that out of 60 relevant studies, just four employed methods for source apportionment, all of which utilised receptor models. Most studies were undertaken in residential or educational environments. There is a lack of data on IAQ in other types of microenvironments and in locations outside of Europe and North America. There are inherent limitations with LCS in terms of producing data which can be utilised in source apportionment models. This applies to external pollution data, however IAQ can be even more challenging to measure due to its characteristics. The indoor environment is heterogeneous, with significant variability within the space as well as between different microenvironments and locations. Sensor placement, occupancy, and activity reports, as well as measurements in different microenvironments and locations, can contribute to understanding this variability. Outdoor pollutants can ingress into the space via the building envelope, however measurement of external pollution and environmental conditions, as well as recording details on the building fabric and ventilation conditions, can help apportion external contributions. Whether or not source apportionment models are employed on indoor data from LCS, there are parameters which, if carefully considered during measurement campaigns, can aid in source identification of pollutants.

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Smart Plants: Low-Cost Solution for Monitoring Indoor Environments

Cited by 7 publications

References 29 publications

Digital Twins for Smart Spaces—Beyond IoT Analytics

Digital Twins for Smart Spaces—Beyond IoT Analytics

Fire safety performance of functional vegetated green building systems: A comprehensive review

Indoor air quality monitoring and source apportionment using low-cost sensors

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