More Web Proxy on the site http://driver.im/

research-article

BlueSentinel: a first approach using iBeacon for an energy efficient occupancy detection system

Authors:

Massimo De Marchi,

Alessandro A. Nacci,

Donatella SciutoAuthors Info & Claims

BuildSys '14: Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings

Pages 11 - 19

https://doi.org/10.1145/2676061.2674078

Published: 16 October 2018 Publication History

Abstract

In the last years, the concept of smart buildings has been proposed and proved to be an effective solution to tackle the problem of reducing the power consumption of complex (both residential and commercial) buildings, while providing the users with a very high level of comfort. In this context, knowing the exact position of users inside the buildings has been identified as a needed feature to optimize the behavior of the building itself. Recently, using the occupants mobile devices as sensors has been validated as an effective solution to have accurate occupancy detection systems, even if no energy efficient solution in therm of battery consumption has been found so far. On the contrary, with this work, we present BLUE-SENTINEL, an accurate and power efficient method to identify the occupants of each room of a smart building using mobile devices as source of information. The proposed approach faces the occupancy detection problem with a good accuracy by exploiting iBeacon, a very recent low-power technology proposed by Apple. In particular, since the iBeacon protocol is built upon Bluetooth Low Energy (BLE), it represents a very highly power-efficient solution. In addition to this, the iBeacon technology is characterized by a good level of compatibility and portability, supporting both iOS- and Android-based devices. The proposed approach has been validated in a real environment with a prototype system released as open source showing how this technology is suitable for the occupancy detection in a smart building.

References

[1]

Buildings energy data book. Technical report, US Department of Energy, August 2012.

[2]

Android beacon library. 2014.

[3]

Apple instrument. 2014.

[4]

Core location framework reference. 2014.

[5]

Flask microframework. 2014.

[6]

ibeacon for developers. 2014.

[7]

G. Anastasi, R. Bandelloni, M. Conti, F. Delmastro, E. Gregori, and G. Mainetto. Experimenting an indoor bluetooth-based positioning service. In ICDCS Workshops, pages 480--483, 2003.

[8]

B. Balaji, J. Xu, A. Nwokafor, R. Gupta, and Y. Agarwal. Sentinel: occupancy based hvac actuation using existing wifi infrastructure within commercial buildings. In SenSys, page 17, 2013.

[9]

P. Bolliger. Redpin - adaptive, zero-configuration indoor localization through user collaboration. In MELT, pages 55--60, 2008.

[10]

A. Bonetto, M. Ferroni, D. Matteo, A. Nacci, M. Mazzucchelli, D. Sciuto, and M. D. Santambrogio. Mpower: towards an adaptive power management system for mobile devices. In Proceedings of the 2012 IEEE 15th International Conference on Computational Science and Engineering, pages 318--325. IEEE Computer Society, 2012.

[11]

L. Breiman. Random forests. Machine learning, 45(1):5--32, 2001.

[12]

S. Dawson-Haggerty, A. Krioukov, J. Taneja, S. Karandikar, G. Fierro, N. Kitaev, and D. Culler. Boss: building operating system services. In Proceedings of the 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2013.

[13]

M. Ferroni, A. Cazzola, D. Matteo, A. A. Nacci, D. Sciuto, and M. D. Santambrogio. Mpower: gain back your android battery life! In Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication, pages 171--174. ACM, 2013.

[14]

T. S. S. Ferroni, Cazzola. On power and energy consumption modeling for smart mobile devices. In Proceedings of The 12th IEEE International Conference on Embedded and Ubiquitous Computing. IEEE Computer Society, 2014.

[15]

E. Fix and J. L. Hodges Jr. Discriminatory analysis-nonparametric discrimination: consistency properties. Technical report, DTIC Document, 1951.

[16]

S. Ghai, L. Thanayankizil, D. Seetharam, and D. Chakraborty. Occupancy detection in commercial buildings using opportunistic context sources. In Pervasive Computing and Communications Workshops (PERCOM Workshops), 2012 IEEE International Conference on, pages 463--466, March 2012.

[17]

C. Gomez, J. Oller, and J. Paradells. Overview and evaluation of bluetooth low energy: An emerging low-power wireless technology. Sensors, 12(9):11734--11753, 2012.

[18]

D. Hähnel, W. Burgard, D. Fox, K. P. Fishkin, and M. Philipose. Mapping and localization with rfid technology. In ICRA, pages 1015--1020, 2004.

[19]

M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The weka data mining software: an update. ACM SIGKDD explorations newsletter, 11(1):10--18, 2009.

[20]

M. Hazas and A. Ward. A novel broadband ultrasonic location system. In Ubicomp, pages 264--280, 2002.

[21]

Y. Jiang, X. Pan, K. Li, Q. Lv, R. P. Dick, M. Hannigan, and L. Shang. Ariel: automatic wi-fi based room fingerprinting for indoor localization. In UbiComp, pages 441--450, 2012.

[22]

A. LaMarca, Y. Chawathe, S. Consolvo, J. Hightower, I. E. Smith, J. Scott, T. Sohn, J. Howard, J. Hughes, F. Potter, J. Tabert, P. Powledge, G. Borriello, and B. N. Schilit. Place lab: Device positioning using radio beacons in the wild. In Pervasive, pages 116--133, 2005.

[23]

J.-S. Lee, Y.-W. Su, and C.-C. Shen. A Comparative Study of Wireless Protocols: Bluetooth, UWB, ZigBee, and Wi-Fi. In Industrial Electronics Society, 2007. IECON 2007. 33rd Annual Conference of the IEEE, pages 46--51, Nov. 2007.

[24]

R. Melfi, B. Rosenblum, B. Nordman, and K. Christensen. Measuring building occupancy using existing network infrastructure. In IGCC, pages 1--8, 2011.

[25]

A. Nacci, F. Trovò, F. Maggi, M. Ferroni, A. Cazzola, D. Sciuto, and M. D. Santambrogio. Adaptive and flexible smartphone power modeling. Mobile Networks and Applications, 18(5):600--609, 2013.

[26]

N. T. Nguyen, R. Zheng, and Z. Han. Umli: An unsupervised mobile locations extraction approach with incomplete data. In WCNC, pages 2119--2124, 2013.

[27]

L. M. Ni, Y. Liu, Y. C. Lau, and A. P. Patil. Landmarc: Indoor location sensing using active rfid. Wireless Networks, 10(6):701--710, 2004.

[28]

V. Otsason, A. Varshavsky, A. LaMarca, and E. de Lara. Accurate gsm indoor localization. In Ubicomp, pages 141--158, 2005.

[29]

L. Pei, R. Chen, J. Liu, T. Tenhunen, H. Kuusniemi, and Y. Chen. Inquiry-based bluetooth indoor positioning via rssi probability distributions. In Proceedings of the 2010 Second International Conference on Advances in Satellite and Space Communications, SPACOMM '10, pages 151--156, Washington, DC, USA, 2010. IEEE Computer Society.

[30]

J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.

[31]

B. Ur, E. McManus, M. P. Y. Ho, and M. L. Littman. Practical trigger-action programming in the smart home. Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, 2014.

[32]

R. Want, A. Hopper, V. Falcao, and J. Gibbons. The active badge location system. ACM Trans. Inf. Syst., 10(1):91--102, 1992.

[33]

A. Ward, A. Jones, and A. Hopper. A new location technique for the active office. IEEE Personal Commun., 4(5):42--47, 1997.

[34]

T. Weng and Y. Agarwal. From buildings to smart buildingssensing and actuation to improve energy efficiency. IEEE Design & Test of Computers, 29(4):36--44, 2012.

[35]

T. Weng, A. Nwokafor, and Y. Agarwal. Buildingdepot 2.0: An integrated management system for building analysis and control. In Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, pages 1--8. ACM, 2013.

[36]

I. H. Witten and E. Frank. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2005.

Cited By

Pannuto PKempke BChuo LBlaauw DDutta P(2018)HarmoniumACM Transactions on Sensor Networks10.1145/318575214:2(1-29)Online publication date: 28-Jun-2018
https://dl.acm.org/doi/10.1145/3185752
He SShin K(2017)Geomagnetism for Smartphone-Based Indoor LocalizationACM Computing Surveys10.1145/313922250:6(1-37)Online publication date: 6-Dec-2017
https://dl.acm.org/doi/10.1145/3139222
Han JPan SSinha MNoh HZhang PTague PWhitehouse KDutta PNoh H(2017)SensetributeProceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments10.1145/3137133.3137152(1-10)Online publication date: 8-Nov-2017
https://dl.acm.org/doi/10.1145/3137133.3137152
Show More Cited By

Index Terms

BlueSentinel: a first approach using iBeacon for an energy efficient occupancy detection system
1. Computer systems organization
  1. Embedded and cyber-physical systems
  2. Real-time systems
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

Recommendations

BlueSentinel: a first approach using iBeacon for an energy efficient occupancy detection system
BuildSys '14: Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings

In the last years, the concept of smart buildings has been proposed and proved to be an effective solution to tackle the problem of reducing the power consumption of complex (both residential and commercial) buildings, while providing the users with a ...
Experiences with using iBeacons for Indoor Positioning
ISEC '16: Proceedings of the 9th India Software Engineering Conference

iBeacon is a protocol standardized by Apple based on Bluetooth Low Energy which allows broadcasting devices (e.g. beacons) to emit signals and a compatible receiver (e.g. smartphone) to capture and interpret the same. iBeacon seems useful in indoor ...
Different Approaches to Indoor Localization Based on Bluetooth Low Energy Beacons and Wi-Fi
Computational Collective Intelligence
Abstract
Thanks to Global Navigation Satellite Systems, the position of a smartphone equipped with the particular receiver can be determined with an accuracy of a few meters outdoors, having a clear view of the sky. These systems, however, are not usable ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

BuildSys '14: Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings

November 2014

241 pages

ISBN:9781450331449

DOI:10.1145/2674061

General Chair:
Mani Srivastava
University of California, Los Angeles

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 October 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SenSys '14

Sponsor:

SenSys '14: The 12th ACM Conference on Embedded Network Sensor Systems

November 3 - 6, 2014

Tennessee, Memphis

Acceptance Rates

Overall Acceptance Rate 148 of 500 submissions, 30%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

14
Total Citations
View Citations
28
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 03 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Pannuto PKempke BChuo LBlaauw DDutta P(2018)HarmoniumACM Transactions on Sensor Networks10.1145/318575214:2(1-29)Online publication date: 28-Jun-2018
https://dl.acm.org/doi/10.1145/3185752
He SShin K(2017)Geomagnetism for Smartphone-Based Indoor LocalizationACM Computing Surveys10.1145/313922250:6(1-37)Online publication date: 6-Dec-2017
https://dl.acm.org/doi/10.1145/3139222
Han JPan SSinha MNoh HZhang PTague PWhitehouse KDutta PNoh H(2017)SensetributeProceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments10.1145/3137133.3137152(1-10)Online publication date: 8-Nov-2017
https://dl.acm.org/doi/10.1145/3137133.3137152
Kalyanaraman AHong DSoltanaghaei EWhitehouse K(2017)Forma TrackProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/31309261:3(1-21)Online publication date: 11-Sep-2017
https://dl.acm.org/doi/10.1145/3130926
Sun WPaek JChoi SGollakotta SZhou X(2017)CV-TrackProceedings of the 4th ACM Workshop on Hot Topics in Wireless10.1145/3127882.3127886(1-5)Online publication date: 16-Oct-2017
https://dl.acm.org/doi/10.1145/3127882.3127886
Griffiths EKalyanaraman ARanjan JWhitehouse K(2017)An Empirical Design Space Analysis of Doorway Tracking Systems for Real-World EnvironmentsACM Transactions on Sensor Networks10.1145/308915713:4(1-34)Online publication date: 8-Sep-2017
https://dl.acm.org/doi/10.1145/3089157
Shen WNewsham GGunay B(2017)Leveraging existing occupancy-related data for optimal control of commercial office buildingsAdvanced Engineering Informatics10.1016/j.aei.2016.12.00833:C(230-242)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1016/j.aei.2016.12.008
Kempke BPannuto PDutta PXing G(2016)HarmoniumProceedings of the 15th International Conference on Information Processing in Sensor Networks10.5555/2959355.2959370(1-12)Online publication date: 11-Apr-2016
https://dl.acm.org/doi/10.5555/2959355.2959370
Kempke BPannuto PCampbell BDutta P(2016)SurePointProceedings of the 14th ACM Conference on Embedded Network Sensor Systems CD-ROM10.1145/2994551.2994570(137-149)Online publication date: 14-Nov-2016
https://dl.acm.org/doi/10.1145/2994551.2994570
Sangoboye FKjærgaard M(2016)PLCountProceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments10.1145/2993422.2993575(147-156)Online publication date: 16-Nov-2016
https://dl.acm.org/doi/10.1145/2993422.2993575
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents