Abstract
Buildings can achieve energy-efficiency by using solar passive design, energy-efficient structures and materials, or by optimizing their operational energy use. In each of these areas, efficiency can be improved if the physical properties of the building along with its dynamic behavior can be captured using low-cost embedded sensor devices. This opens up a new challenge of installing and maintaining the sensor devices for different types of buildings. In this article, we propose BuildSense, a sensing framework for fine-grained, long-term monitoring of buildings using a mix of physical and virtual sensors. It not only reduces the deployment and management cost of sensors but can also guarantee accurate and fault-tolerant data coverage for long-term use. We evaluate BuildSense using sensor measurements from two rammed-earth houses that were custom-designed for a challenging hot-arid climate so almost no artificial heating or cooling is required. We demonstrate that BuildSense can significantly reduce the cost of permanent physical sensors while still achieving fit-for-purpose accuracy, fault-tolerance, and stability. Overall, we were able to reduce the cost of a building sensor network by 60% to 80% by replacing physical sensors with virtual ones while still maintaining accuracy of ≤1.0°C and fault-tolerance of two or more predictors per virtual sensor.
| Original language | English |
|---|---|
| Article number | A36 |
| Number of pages | 23 |
| Journal | ACM Transactions on Sensor Networks |
| Volume | 15 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Aug 2019 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
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SDG 12 Responsible Consumption and Production
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Dive into the research topics of 'BuildSense: accurate, cost-aware, fault-tolerant monitoring with minimal sensor infrastructure'. Together they form a unique fingerprint.Datasets
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Rammed-Earth Houses Thermal Performance Measurements
Cardell-Oliver, R. (Creator), Beckett, C. (Contributor), Ciancio, D. (Supervisor) & Hubner, C. (Contributor), The University of Western Australia, 29 Nov 2018
Dataset
File -
Nierstein bei Mainz Vineyard Soil Data
Cardell-Oliver, R. (Creator) & Hubner, C. (Data Collector), The University of Western Australia, 16 Jul 2019
Dataset
File
Projects
- 1 Finished
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Investigation of the Thermal Performance of Rammed Earth Residential Buildings and Implications in Energy Saving Design
Ciancio, D. (Investigator 01), Cardell-Oliver, R. (Investigator 02), Huebner, C. (Investigator 03), Mahony, L. (Investigator 04) & Carpenter, D. (Investigator 05)
ARC Australian Research Council
1/01/14 → 1/04/18
Project: Research
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