Review of Research Progress, Trends and Gap in Occupancy Sensing for Sophisticated Sensory Operation

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 765)

Abstract

With the adoption of increasing number of occupancy sensor in building premises, there is a growing concern about the inclusion of the smarter features for catering up sophisticated demands of information processing in Internet-of-Things (IoT). Although, there are various commercially available occupancy sensors, but there is a bigger deal of trade-off between the existing offered featured and actual demands of the user that is quite dynamic. Therefore, we reviewed the most potential research work carried out towards incorporating various features of occupancy sensor in present times in order to investigate the degree of effectiveness in existing research contribution with respect to problems, techniques, advantages, and limitation. This is the first reported review manuscript in occupancy sensing that offers a quick view of existing research trends as well as brief of potential research gap with respect to open-end problems that are yet to be solved in future studies.

Keywords

Occupancy sensor PIR sensor Occupation estimation Object detection Energy Accuracy 

References

  1. 1.
    Krarti, M.: Energy Audit of Building Systems: An Engineering Approach, 2nd edn. CRC Press, Boca Raton (2016)Google Scholar
  2. 2.
    Benya, J.R., Leban, D.J.: Lighting Retrofit, and Relighting: A Guide to Energy Efficient Lighting. Wiley, Hoboken (2011)Google Scholar
  3. 3.
    Fraden, J.: Handbook of Modern Sensors: Physics, Designs, and Applications. Springer, Heidelberg (2015)Google Scholar
  4. 4.
    Yasuura, H., Kyung, C.-M., Liu, Y., Lin, Y.-L.: Smart Sensors at the IoT Frontier. Springer, Heidelberg (2017)Google Scholar
  5. 5.
    Pritoni, M., Wooley, J.M., Modera, M.P.: Do occupancy-responsive learning thermostats save energy? A field study in university residence halls. Elsevier J. Energy Buildings 127, 469–478 (2016)CrossRefGoogle Scholar
  6. 6.
    Rafsanjani, H.N., Ahn, C.R., Alahmad, M.: A review of approaches for sensing, understanding, and improving occupancy-related energy-use behaviors in commercial buildings. J. Energies 8, 10996–11029 (2015)CrossRefGoogle Scholar
  7. 7.
    Kjærgaard, M.B., Lazarova-Molnar, S., Jradi, M.: Poster abstract: towards a categorization framework for occupancy sensing systems. In: Proceedings of the Sixth ACM International Conference on Future Energy Systems (e-Energy), pp. 215–216. Association for Computing Machinery (2015).  https://doi.org/10.1145/2768510.2770947
  8. 8.
    Kleiminger, W., Staake, T., Santini, S.: Occupancy Detection from Electricity Consumption Data. ACM, New York (2013)CrossRefGoogle Scholar
  9. 9.
    Zhang, J., Liu, G., Dasu, A.: Review of literature on terminal box control, occupancy sensing technology and multi-zone Demand Control Ventilation (DCV). Technical report of U.S. Department of Energy (2012)Google Scholar
  10. 10.
    Eedara, P., Li, H., Janakiraman, N., Tungala, N.R.A., Chamberland, J.F., Huff, G.H.: Occupancy estimation with wireless monitoring devices and application-specific antennas. IEEE Trans. Sig. Process. 65(8), 2123–2135 (2017)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Iyer, B., Pathak, N.P., Ghosh, D.: Dual-Input Dual-Output RF sensor for indoor human occupancy and position monitoring. IEEE Sens. J. 15(7), 3959–3966 (2015)CrossRefGoogle Scholar
  12. 12.
    Liu, P., Nguang, S.K., Partridge, A.: Occupancy inference using pyroelectric infrared sensors through hidden markov models. IEEE Sens. J. 16(4), 1062–1068 (2016)CrossRefGoogle Scholar
  13. 13.
    Li, B., Li, S., Nallanathan, A., Nan, Y., Zhao, C., Zhou, Z.: Deep sensing for next-generation dynamic spectrum sharing: more than detecting the occupancy state of primary spectrum. IEEE Trans. Commun. 63(7), 2442–2457 (2015)CrossRefGoogle Scholar
  14. 14.
    Avestruz, A.T., Cooley, J.J., Vickery, D., Paris, J., Leeb, S.B.: Dimmable solid state ballast with integral capacitive occupancy sensor. IEEE Trans. Ind. Electronics 59(4), 1739–1750 (2012)CrossRefGoogle Scholar
  15. 15.
    Cooley, J.J., Avestruz, A.T., Leeb, S.B.: A retrofit capacitive sensing occupancy detector using fluorescent lamps. IEEE Trans. Industr. Electronics 59(4), 1898–1911 (2012)CrossRefGoogle Scholar
  16. 16.
    George, B., Zangl, H., Bretterklieber, T., Brasseur, G.: A combined inductive-capacitive proximity sensor for seat occupancy detection. IEEE Trans. Instrum. Meas. 59(5), 1463–1470 (2010)CrossRefGoogle Scholar
  17. 17.
    Hossain, K., Champagne, B.: Wideband spectrum sensing for cognitive radios with correlated subband occupancy. IEEE Sig. Process. Lett. 18(1), 35–38 (2011)CrossRefGoogle Scholar
  18. 18.
    Mary Reena, K.E., Mathew, A.T., Jacob, L.: An occupancy based cyber-physical system design for intelligent building automation. Math. Prob. Eng. 2015, 15 (2015)CrossRefGoogle Scholar
  19. 19.
    Vidal, C., F-Sánchez, C., Díaz, J., Pérez, J.: A model-driven engineering process for autonomic sensor-actuator networks. Int. J. Distrib. Sens. Netw. 11(3), 684892 (2015)CrossRefGoogle Scholar
  20. 20.
    Hua, Z.-X., Chen, X.: Multisensor track occupancy detection model based on chaotic neural networks. Int. J. Distrib. Sens. Netw. 11(7), 896340 (2015)CrossRefGoogle Scholar
  21. 21.
    Man, D., Yang, W., Xuan, S., Du, X.: Thwarting nonintrusive occupancy detection attacks from smart meters. Secur. Commun. Netw. 2017, 9 (2017)CrossRefGoogle Scholar
  22. 22.
    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, pp. 1–6 (2010)Google Scholar
  23. 23.
    Hammoud, A., Deriaz, M., Konstantas, D.: UltraSense: a self-calibrating ultrasound-based room occupancy sensing system. Procedia Comput. Sci. 109, 75–83 (2017)CrossRefGoogle Scholar
  24. 24.
    Shih, O., Lazik, P., Rowe, A.: AURES: a wide-band ultrasonic occupancy sensing platform. In: Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments, pp. 157–166 (2016)Google Scholar
  25. 25.
    Schoofs, A., Delaney, D.T., MP O’Hare, G., Ruzzelli, A.G.: COPOLAN: non-invasive occupancy profiling for preliminary assessment of HVAC fixed timing strategies. In: Proceedings of the Third ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, pp. 25–30 (2011)Google Scholar
  26. 26.
    Chen, Z., Zhao, R., Zhu, Q., Masood, M.K., Soh, Y.C., Mao, K.: Building occupancy estimation with environmental sensors via CDBLSTM. IEEE Trans. Ind. Electronics PP(99), 1 (2017)Google Scholar
  27. 27.
    Depatla, S., Muralidharan, A., Mostofi, Y.: Occupancy estimation using only WiFi power measurements. IEEE J. Sel. Areas Commun. 33(7), 1381–1393 (2015)CrossRefGoogle Scholar
  28. 28.
    Ebadat, A., Bottegal, G., Varagnolo, D., Wahlberg, B., Johansson, K.H.: Regularized deconvolution-based approaches for estimating room occupancies. IEEE Trans. Autom. Sci. Eng. 12(4), 1157–1168 (2015)CrossRefGoogle Scholar
  29. 29.
    Lam, A.H., Yuan, Y., Wang, D.: An occupant-participatory approach for thermal comfort enhancement and energy conservation in buildings. In: Proceedings of the 5th International Conference on Future Energy Systems, pp. 133–143 (2014)Google Scholar
  30. 30.
    Forouzanfar, M., Mabrouk, M., Rajan, S., Bolic, M., Dajani, H.R., Groza, V.Z.: Event recognition for contactless activity monitoring using phase-modulated continuous wave Radar. IEEE Trans. Biomed. Eng. 64(2), 479–491 (2017)CrossRefGoogle Scholar
  31. 31.
    Mikkelsen, L., Buchakchiev, R., Madsen, T., Schwefel, H.P.: Public transport occupancy estimation using WLAN probing. In: 2016 8th International Workshop on Resilient Networks Design and Modeling (RNDM), Halmstad, pp. 302–308 (2016)Google Scholar
  32. 32.
    Munir, S., et al.: Real-time fine grained occupancy estimation using depth sensors on ARM embedded platforms. In: 2017 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), Pittsburgh, PA, pp. 295–306 (2017)Google Scholar
  33. 33.
    Nagarathinam, S., Iyer, S.R., Vasan, A., Sarangan, V., Sivasubramaniam, A.: On the utility of occupancy sensing for managing HVAC energy in large zones. In: Proceedings of the ACM Sixth International Conference on Future Energy Systems, pp. 219–220 (2015)Google Scholar
  34. 34.
    Lu, J., Sookoor, T., Srinivasan, V., Gao, G., Holben, B., Stankovic, J., Field, E., Whitehouse, K.: The smart thermostat: using occupancy sensors to save energy in homes. In: Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, pp. 211–224. ACM (2010)Google Scholar
  35. 35.
    Tyndall, A., Cardell-Oliver, R., Keating, A.: Occupancy estimation using a low-pixel count thermal imager. IEEE Sens. J. 1(10), 3784–3791 (2016)CrossRefGoogle Scholar
  36. 36.
    Scott, J., Brush, A.J.B., Krumm, J., Meyers, B., Hazas, M., Hodges, S., Villar, N.: PreHeat: controlling home heating using occupancy prediction. In: Proceedings of the 13th International Conference on Ubiquitous Computing, pp. 281–290. ACM (2011)Google Scholar
  37. 37.
    Yang, Y., Hao, J., Luo, J., Pan, S.J.: CeilingSee: device-free occupancy inference through lighting infrastructure based LED sensing. In: 2017 IEEE International Conference on Pervasive Computing and Communications (PerCom), Kona, HI, pp. 247–256 (2017)Google Scholar
  38. 38.
    de Bakker, C., van de Voort, T., van Duijhoven, J., Rosemann, A.: Assessing the energy use of occupancy-based lighting control strategies in open-plan offices. In: 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC), Calabria, Italy, pp. 476–481 (2017)Google Scholar
  39. 39.
    Steyer, S., Tanzmeister, G., Wollherr, D.: Object tracking based on evidential dynamic occupancy grids in urban environments. In: 2017 IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA, USA, pp. 1064–1070 (2017)Google Scholar
  40. 40.
    Nesa, N., Banerjee, I.: IoT-based sensor data fusion for occupancy sensing using dempster-shafer evidence theory for smart buildings. IEEE Internet of Things J. PP(99), 1 (2017)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electronics and Instrumentation EngineeringBMS College of EngineeringBangaloreIndia

Personalised recommendations