Green IoT Event Detection for Carbon-Emission Monitoring in Sensor Networks
This research addresses the intersection of low-power microcontroller technology and binary classification of events in the context of carbon-emission reduction. The study introduces an innovative approach leveraging microcontrollers for real-time event detection in a homogeneous hardware/firmware m...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/1/162 |
| _version_ | 1797358160293396480 |
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| author | Cormac D. Fay Brian Corcoran Dermot Diamond |
| author_facet | Cormac D. Fay Brian Corcoran Dermot Diamond |
| author_sort | Cormac D. Fay |
| collection | DOAJ |
| description | This research addresses the intersection of low-power microcontroller technology and binary classification of events in the context of carbon-emission reduction. The study introduces an innovative approach leveraging microcontrollers for real-time event detection in a homogeneous hardware/firmware manner and faced with limited resources. This showcases their efficiency in processing sensor data and reducing power consumption without the need for extensive training sets. Two case studies focusing on landfill CO<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>2</mn></msub></semantics></math></inline-formula> emissions and home energy usage demonstrate the feasibility and effectiveness of this approach. The findings highlight significant power savings achieved by minimizing data transmission during non-event periods (94.8–99.8%), in addition to presenting a sustainable alternative to traditional resource-intensive AI/ML platforms that comparatively draw and produce 20,000 times the amount of power and carbon emissions, respectively. |
| first_indexed | 2024-03-08T14:57:50Z |
| format | Article |
| id | doaj.art-ead355f602094b978619921ee441498a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T14:57:50Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ead355f602094b978619921ee441498a2024-01-10T15:08:50ZengMDPI AGSensors1424-82202023-12-0124116210.3390/s24010162Green IoT Event Detection for Carbon-Emission Monitoring in Sensor NetworksCormac D. Fay0Brian Corcoran1Dermot Diamond2SMART Infrastructure Facility, Engineering and Information Sciences, University of Wollongong, Wollongong, NSW 2522, AustraliaSchool of Mechanical and Manufacturing Engineering, Faculty of Engineering and Computing, Dublin City University, Glasnevin, D09 V209 Dublin, IrelandInsight Centre for Data Analytics, Dublin City University, Glasnevin, D09 V209 Dublin, IrelandThis research addresses the intersection of low-power microcontroller technology and binary classification of events in the context of carbon-emission reduction. The study introduces an innovative approach leveraging microcontrollers for real-time event detection in a homogeneous hardware/firmware manner and faced with limited resources. This showcases their efficiency in processing sensor data and reducing power consumption without the need for extensive training sets. Two case studies focusing on landfill CO<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>2</mn></msub></semantics></math></inline-formula> emissions and home energy usage demonstrate the feasibility and effectiveness of this approach. The findings highlight significant power savings achieved by minimizing data transmission during non-event periods (94.8–99.8%), in addition to presenting a sustainable alternative to traditional resource-intensive AI/ML platforms that comparatively draw and produce 20,000 times the amount of power and carbon emissions, respectively.https://www.mdpi.com/1424-8220/24/1/162chemical sensingelectricity meteringevent detectionTinyMLIoTgreen |
| spellingShingle | Cormac D. Fay Brian Corcoran Dermot Diamond Green IoT Event Detection for Carbon-Emission Monitoring in Sensor Networks Sensors chemical sensing electricity metering event detection TinyML IoT green |
| title | Green IoT Event Detection for Carbon-Emission Monitoring in Sensor Networks |
| title_full | Green IoT Event Detection for Carbon-Emission Monitoring in Sensor Networks |
| title_fullStr | Green IoT Event Detection for Carbon-Emission Monitoring in Sensor Networks |
| title_full_unstemmed | Green IoT Event Detection for Carbon-Emission Monitoring in Sensor Networks |
| title_short | Green IoT Event Detection for Carbon-Emission Monitoring in Sensor Networks |
| title_sort | green iot event detection for carbon emission monitoring in sensor networks |
| topic | chemical sensing electricity metering event detection TinyML IoT green |
| url | https://www.mdpi.com/1424-8220/24/1/162 |
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