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Fusing Sensors for Occupancy Sensing in Smart Buildings

  • Nabeel Nasir
  • Kartik Palani
  • Amandeep Chugh
  • Vivek Chil Prakash
  • Uddhav Arote
  • Anand P. Krishnan
  • Krithi Ramamritham
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8956)

Abstract

Understanding occupant-building interactions helps in personalized energy and comfort management. However, occupant identification using affordable infrastructure, remains unresolved. Our analysis of existing solutions revealed that for a building to have real-time view of occupancy state and use it intelligently, there needs to be a smart fusion of affordable, not-necessarily-smart, yet accurate enough sensors. Such a sensor fusion should aim for minimalistic user intervention while providing accurate building occupancy data. We describe an occupant detection system that accurately monitors the occupants’ count and identities in a shared office space, which can be scaled up for a building. Incorporating aspects from data analytics and sensor fusion with intuition, we have built a Smart-Door using inexpensive sensors to tackle this problem. It is a scalable, plug-and-play software architecture for flexibly realizing smart-doors using different sensors to monitor buildings with varied occupancy profiles. Further, we show various smart-energy applications of this occupancy information: detecting anomalous device behaviour and load forecasting of plug-level loads.

Keywords

Smart Door Smart Building Energy Saving User Comfort Electrical Energy 

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References

  1. 1.
    Agarwal, Y., Balaji, B., Gupta, R., Lyles, J., Wei, M., Weng, T.: Occupancy-driven energy management for smart building automation. In: Proceedings of the 2Nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, BuildSys 2010, pp. 1–6. ACM, New York (2010), http://doi.acm.org/10.1145/1878431.1878433 Google Scholar
  2. 2.
    Balaji, B., Xu, J., Nwokafor, A., Gupta, R., Agarwal, Y.: Sentinel: Occupancy based hvac actuation using existing wifi infrastructure within commercial buildings. In: Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, SenSys 2013, pp. 17:1–17:14. ACM, New York (2013), http://doi.acm.org/10.1145/2517351.2517370
  3. 3.
    Emmerich, S., Persily, A.: State-Of-The-Art Review of Co2 Demand Controlled Ventilation Technology and Application. Diane Publishing Company (2001), http://books.google.co.in/books?id=1hrONzju3IYC
  4. 4.
    Erickson, V.L., Carreira-Perpiñán, M.A., Cerpa, A.E.: Occupancy modeling and prediction for building energy management. ACM Trans. Sen. 10(3), 42:1–42:28 (2014), http://doi.acm.org/10.1145/2594771 CrossRefGoogle Scholar
  5. 5.
    Floerkemeier, C., Lampe, M.: Issues with rfid usage in ubiquitous computing applications. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 188–193. Springer, Heidelberg (2004), http://dx.doi.org/10.1007/978-3-540-24646-6_13 CrossRefGoogle Scholar
  6. 6.
    Ghai, S., Thanayankizil, L., Seetharam, D., Chakraborty, D.: Occupancy detection in commercial buildings using opportunistic context sources. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 463–466 (March 2012)Google Scholar
  7. 7.
    Hnat, T.W., Griffiths, E., Dawson, R., Whitehouse, K.: Doorjamb: unobtrusive room-level tracking of people in homes using doorway sensors. In: Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems, pp. 309–322. ACM (2012)Google Scholar
  8. 8.
    Labs, K.: Kinect sdk xna [Programming Guide] (2011), http://www.kosaka-lab.com/tips/2011/06/kinect-sdk-xna.php
  9. 9.
    Lee, K.C., Ho, J., Yang, M.H., Kriegman, D.: Video-based face recognition using probabilistic appearance manifolds. In: Proceedings of 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. I–313–I–320 (June 2003)Google Scholar
  10. 10.
    Padmanabh, K., Malikarjuna V, A., Sen, S., Katru, S.P., Kumar, A., Vuppala, S.K., Paul, S.: isense: A wireless sensor network based conference room management system. In: Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, BuildSys 2009, pp. 37–42. ACM, New York (2009), http://doi.acm.org/10.1145/1810279.1810288 CrossRefGoogle Scholar
  11. 11.
    Palani, K., Nasir, N., Prakash, V.C., Chugh, A., Gupta, R., Ramamritham, K.: Putting smart meters to work: Beyond the usual. In: Proceedings of the 5th International Conference on Future Energy Systems, e-Energy 2014, pp. 237–238. ACM, New York (2014), http://doi.acm.org/10.1145/2602044.2602084 Google Scholar
  12. 12.
    van der Veen, J., van der Waaij, B., Meijer, R.: Sensor data storage performance: Sql or nosql, physical or virtual. In: 2012 IEEE 5th International Conference on Cloud Computing (CLOUD), pp. 431–438 (June 2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Nabeel Nasir
    • 1
  • Kartik Palani
    • 1
  • Amandeep Chugh
    • 1
  • Vivek Chil Prakash
    • 1
  • Uddhav Arote
    • 1
  • Anand P. Krishnan
    • 1
  • Krithi Ramamritham
    • 1
  1. 1.Department of Computer ScienceIndian Institute of Technology BombayMumbaiIndia

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