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BuildSense: Accurate, Cost-aware, Fault-tolerant Monitoring with Minimal Sensor Infrastructure

Published: 09 August 2019 Publication History

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.

References

[1]
Azad Ali, Abdelmajid Khelil, Neeraj Suri, and Mohammadreza Mahmudimanesh. 2015. Adaptive hybrid compression for wireless sensor networks. ACM Trans. Sens. Netw. 11, 4 (2015), 1--36.
[2]
Bharathan Balaji, Hidetoshi Teraoka, Rajesh Gupta, and Yuvraj Agarwal. 2013. Zonepac: Zonal power estimation and control via HVAC metering and occupant feedback. In Proceedings of the 5th ACM Workshop on Embedded Systems for Energy-Efficient Buildings. ACM, 1--8.
[3]
Bharathan Balaji, Jian Xu, Anthony Nwokafor, Rajesh Gupta, and Yuvraj Agarwal. 2013. Sentinel: Occupancy based HVAC actuation using existing WiFi infrastructure within commercial buildings. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems. ACM, 17.
[4]
Nikhil Bansal and Kirk Pruhs. 2012. Weighted geometric set multi-cover via quasi-uniform sampling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 7501. LNCS, 145--156.
[5]
C. T. S. Beckett, R. Cardell-Oliver, D. Ciancio, and C. Huebner. 2017. Measured and simulated thermal behaviour in rammed earth houses in a hot-arid climate. Part B: Comfort. J. Build. Eng. 13 (2017), 146--158.
[6]
Mihaela Cardei, My T. Thai, Yingshu Li, and Weili Wu. 2005. Energy-efficient target coverage in wireless sensor networks. In Proceedings of the 24th Joint Conference of the IEEE Computer and Communications Societies (INFOCOM’05), Vol. 3. IEEE, 1976--1984.
[7]
Rachel Cardell-Oliver and Chayan Sarkar. 2016. Robust sensor data collection over a long period using virtual sensing. In Proceedings of the Workshop on Time Series Analytics and Applications (TSAA’16). 2--7.
[8]
Rachel Cardell-Oliver and Chayan Sarkar. 2017. BuildSense: Long-term, fine-grained building monitoring with minimal sensor infrastructure. In Proceedings of the 4th ACM International Conference on Systems for Energy-efficient Built Environments (BuildSys’17). ACM, New York, NY, Article 9, 10 pages.
[9]
D. Chen. 2016. AccuRate and the Chenath engine for residential house energy rating. Retrieved from https://hstar.com.au/Home/Chenath.
[10]
Declan T. Delaney, Gregory M. P. O’Hare, and Antonio G. Ruzzelli. 2009. Evaluation of energy-efficiency in lighting systems using sensor networks. In Proceedings of the 1st ACM Workshop on Embedded Sensing Systems for Energy-efficiency in Buildings. ACM, 61--66.
[11]
Amol Deshpande, Carlos Guestrin, Samuel R. Madden, Joseph M. Hellerstein, and Wei Hong. 2004. Model-driven data acquisition in sensor networks. In Proceedings of the 30th International Conference on Very Large Data bases, Vol. 30. VLDB Endowment, 588--599.
[12]
Wan Du, Zikun Xing, Mo Li, Bingsheng He, Lloyd Hock Chye Chua, and Haiyan Miao. 2015. Sensor placement and measurement of wind for water quality studies in urban reservoirs. ACM Trans. Sens. Netw. 11, 3 (Feb. 2015), 1--27.
[13]
Varick L. Erickson, Miguel Á. Carreira-Perpiñán, and Alberto E. Cerpa. 2011. OBSERVE: Occupancy-based system for efficient reduction of HVAC energy. In Proceedings of the 10th International Conference on Information Processing in Sensor Networks (IPSN’11). IEEE, 258--269.
[14]
Anca D. Galasiu and Jennifer A. Veitch. 2006. Occupant preferences and satisfaction with the luminous environment and control systems in daylit offices: A literature review. Energy Build. 38, 7 (2006), 728--742.
[15]
Deepak Ganesan, Răzvan Cristescu, and Baltasar Beferull-Lozano. 2006. Power-efficient sensor placement and transmission structure for data gathering under distortion constraints. ACM Trans. Sens. Netw. 2, 2 (May 2006), 155--181.
[16]
Ali Ghahramani, Farrokh Jazizadeh, and Burcin Becerik-Gerber. 2014. A knowledge-based approach for selecting energy-aware and comfort-driven HVAC temperature set points. Energy Build. 85 (2014), 536--548.
[17]
Carlos Guestrin, Peter Bodik, Romain Thibaux, Mark Paskin, and Samuel Madden. 2004. Distributed regression: An efficient framework for modeling sensor network data. In Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks (IPSN’04). IEEE, 1--10.
[18]
Himanshu Gupta, Vishnu Navda, Samir Das, and Vishal Chowdhary. 2008. Efficient gathering of correlated data in sensor networks. ACM Trans. Sens. Netw. 4, 1 (2008), 4.
[19]
C. Huebner, R. Cardell-Oliver, R. Becker, K. Spohrer, K. Jotter, and T. Wagenknecht. 2010. Wireless soil moisture sensor networks for environmental monitoring and irrigation. In EGU General Assembly Conference Abstracts, Vol. 12. 2539. ftp://ftp.gfz-potsdam.de/home/cegit/egu/pdf/EGU2010-2539.pdf.
[20]
Hongbo Jiang, Shudong Jin, and Chonggang Wang. 2011. Prediction or not? An energy-efficient framework for clustering-based data collection in wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 22, 6 (2011), 1064--1071.
[21]
Andreas Krause, Carlos Guestrin, Anupam Gupta, and Jon Kleinberg. 2006. Near-optimal sensor placements: Maximizing information while minimizing communication cost. In Proceedings of the 5th International Conference on Information Processing in Sensor Networks. ACM, 2--10.
[22]
Andrew Krioukov, Stephen Dawson-Haggerty, Linda Lee, Omar Rehmane, and David Culler. 2011. A living laboratory study in personalized automated lighting controls. In Proceedings of the 3rd ACM Workshop on Embedded Sensing Systems for Energy-efficiency in Buildings. ACM, 1--6.
[23]
Chong Luo, Feng Wu, Jun Sun, and Chang Wen Chen. 2009. Compressive data gathering for large-scale wireless sensor networks. In Proceedings of the 15th International Conference on Mobile Computing and Networking. ACM, 145--156.
[24]
Ali Marjovi, Adrian Arfire, and Alcherio Martinoli. 2017. Extending urban air quality maps beyond the coverage of a mobile sensor network: Data sources, methods, and performance evaluation. In Proceedings of the International Conference on Embedded Wireless Systems and Networks. 12--23. Retrieved from http://dl.acm.org/citation.cfm?id=3108012.
[25]
S. Mini, Siba K. Udgata, and Samrat L. Sabat. 2014. Sensor deployment and scheduling for target coverage problem in wireless sensor networks. IEEE Sens. J. 14, 3 (2014), 636--644.
[26]
U.S. Department of Energy (DOE). 2008. Buildings Energy Data Book. Retrieved from http://www.c2es.org/technology/overview/buildings.
[27]
Devika Pisharoty, Rayoung Yang, Mark W. Newman, and Kamin Whitehouse. 2015. Thermocoach: Reducing home energy consumption with personalized thermostat recommendations. In Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-efficient Built Environments. ACM, 201--210.
[28]
Maher Rebai, Hichem Snoussi, Faicel Hnaien, Lyes Khoukhi, et al. 2015. Sensor deployment optimization methods to achieve both coverage and connectivity in wireless sensor networks. Comput. Op. Res. 59 (2015), 11--21.
[29]
Silvia Santini and Kay Romer. 2006. An adaptive strategy for quality-based data reduction in wireless sensor networks. In Proceedings of the 3rd International Conference on Networked Sensing Systems (INSS’06). 29--36.
[30]
Chayan Sarkar, Vijay S. Rao, and R. Venkatesha Prasad. 2014. No-sense: Sense with dormant sensors. In Proceedings of the 20th National Conference on Communications (NCC’14). IEEE, 1--6.
[31]
Chayan Sarkar, Vijay S. Rao, R. Venkatesha Prasad, Sankar Narayan Das, Sudip Misra, and Athanasios Vasilakos. 2016. VSF: An energy-efficient sensing framework using virtual sensors. IEEE Sens. J. 16, 12 (2016), 5046--5059.
[32]
Chayan Sarkar, Akshay Uttama Nambi S. N., and R. Venkatesha Prasad. 2016. iLTC: Achieving individual comfort in shared spaces. In Proceedings of the International Conference on Embedded Wireless Systems and Networks (EWSN’16). ACM.
[33]
Mina Sartipi and Robert Fletcher. 2011. Energy-efficient data acquisition in wireless sensor networks using compressed sensing. In Proceedings of the Data Compression Conference (DCC’11). IEEE, 223--232.
[34]
Di Tian and Nicolas D. Georganas. 2003. A node scheduling scheme for energy conservation in large wireless sensor networks. Wirel. Commun. Mobile Comput. 3, 2 (2003), 271--290.
[35]
Xiaopei Wu, Mingyan Liu, and Yue Wu. 2012. In-situ soil moisture sensing: Optimal sensor placement and field estimation. ACM Trans. Sens. Netw. 8, 4, Article 33 (Sept. 2012), 30 pages.
[36]
Liu Xiang, Jun Luo, and Athanasios Vasilakos. 2011. Compressed data aggregation for energy efficient wireless sensor networks. In Proceedings of the 8th IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON’11). IEEE, 46--54.
[37]
Sunhee Yoon and Cyrus Shahabi. 2007. The clustered AGgregation (CAG) technique leveraging spatial and temporal correlations in wireless sensor networks. ACM Trans. Sens. Netw. 3, 1 (2007), 3--es.
[38]
Xiaohan Yu and Seung Jun Baek. 2017. Energy-efficient collection of sparse data in wireless sensor networks using sparse random matrices. ACM Trans. Sens. Netw. 13, 3, Article 22 (Aug. 2017), 36 pages.

Cited By

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  • (2023)Data-driven virtual sensing for spatial distribution of temperature and humidityJournal of Building Engineering10.1016/j.jobe.2022.10572667(105726)Online publication date: May-2023
  • (2022)Data-driven simulation for energy and local comfort optimization: Case study of a laboratoryJournal of Building Engineering10.1016/j.jobe.2022.10461254(104612)Online publication date: Aug-2022

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  1. BuildSense: Accurate, Cost-aware, Fault-tolerant Monitoring with Minimal Sensor Infrastructure

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      Published In

      cover image ACM Transactions on Sensor Networks
      ACM Transactions on Sensor Networks  Volume 15, Issue 3
      August 2019
      324 pages
      ISSN:1550-4859
      EISSN:1550-4867
      DOI:10.1145/3335317
      Issue’s Table of Contents
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 09 August 2019
      Accepted: 01 May 2019
      Revised: 01 March 2019
      Received: 01 May 2018
      Published in TOSN Volume 15, Issue 3

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      Author Tags

      1. Energy-efficient building
      2. sensing as a service
      3. sensor data estimation
      4. virtual sensing

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      Funding Sources

      • Universities Australia under grants UWiN - Underground Wireless Sensor Networks
      • Human Research Ethics Office of the University of Western Australia
      • Australian Research Council and Western Australian Department of Housing
      • ASPM-Advanced Wireless Sensor Networks for Soil Parameter Monitoring

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      Cited By

      View all
      • (2023)Data-driven virtual sensing for spatial distribution of temperature and humidityJournal of Building Engineering10.1016/j.jobe.2022.10572667(105726)Online publication date: May-2023
      • (2022)Data-driven simulation for energy and local comfort optimization: Case study of a laboratoryJournal of Building Engineering10.1016/j.jobe.2022.10461254(104612)Online publication date: Aug-2022

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