Acta Scientific Computer Sciences

Research Article Volume 4 Issue 8

A Machine Learning Approach for Occupancy Detection in Smart Building

Shashi Shekhar Kumar1 and Upendra Pratap Singh2*

1Department of Information Technology, IIIT Allahabad, Prayagraj, India
2Department of Computer Science and Engineering, SOA University, Bhubaneshwar, India

*Corresponding Author: Upendra Pratap Singh, Department of Computer Science and Engineering, SOA University, Bhubaneshwar, India.

Received: May 16, 2022; Published: July 18, 2022

Reprints View PDF Related Articles

Abstract

The IoT has emerged as one of the fastest-growing areas of research. Community of IoT is exploring this area of study to enhance innovative building features for reliable comfort in every prospect. One of the intelligent building features is occupancy detection, which can detect the Occupancy of a particular room or premises in real-time. Different IoT sensors implemented in an intelligent building can generate enormous data. For simulation purposes, we have used python along with apache spark framework, which will analyze the data generated and consequently, we can realize the other features like humidity, Co2 level of the room. The key contribution of this paper is to enhance the accuracy of the machine learning model for better occupancy prediction. With machine learning, the approach building feature can adjust automatically.

Keywords: Smart Building; Big Data; Occupancy Detection; Cyber-Physical System; Internet of Things

References

  1. A Corna., et al. “Occupancy detection via ibeacon on android devices for smart building management”. in 2015 Design, Automation and Test in Europe Conference and Exhibition (DATE). IEEE (2015): 629-632.
  2. LM Candanedo and V Feldheim. “Accurate occupancy detection of an office room from light, temperature, humidity and Co2 measurements using statistical learning models”. Energy and Buildings 112 (2016): 28-39.
  3. Q Hua., et al. “Occupancy detection in smart buildings using support vector regression method”. in 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) 02 (2016): 77-80.
  4. Y Jeon., et al. “Iot-based occupancy detection system in indoor residential environments”. Building and Environment 132 (2018): 181-204.
  5. H Elkhoukhi., et al. “Towards a real-time occupancy detection approach for smart buildings”. Procedia Computer Science 134 (2018): 114-120.
  6. H Zhao., et al. “Thermal-sensor-based occupancy detection for smart buildings using machine-learning methods”. ACM Transactions on Design Automation of Electronic Systems (TODAES)4 (2018): 1-21.
  7. E Hailemariam., et al. “Real-time occupancy detection using decision trees with multiple sensor types”. in Proceedings of the 2011 Symposium on Simulation for Architecture and Urban Design (2011): 141-148.
  8. H Saha., et al. “Occupancy sensing in buildings: A review of data analytics approaches”. Energy and Buildings (2019).
  9. K Akkaya., et al. “Iot-based occupancy monitoring techniques for energy-efficient smart buildings”. in 2015 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) (2015): 58-63.
  10. Y Ji., et al. “Occupancy detection technology in the building based on iot environment sensors”. in Proceedings of the 8th International Conference on the Internet of Things (2018): 1-4.
  11. W Shen and G Newsham. “Smart phone based occupancy detection in office buildings”. in 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD) (2016): 632-636.
  12. M Azam., et al. “Occupancy estimation using wifi motion detection via supervised machine learning algorithms”. in 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) (2019): 1-5.
  13. Y Gao., et al. “Occupancy detection in smart housing using both aggregated and appliance-specific power consumption data”. in 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) (2018): 1296-1303.
  14. C Feng., et al. “Deep learning-based real-time building occupancy detection using ami data”. IEEE Transactions on Smart Grid (2020): 1-1.
  15. T Ekwevugbe., et al. “Real-time building occupancy sensing using neural-network based sensor network”. in 2013 7th IEEE International Conference on Digital Ecosystems and Technologies (DEST) (2013): 114-119.
  16. A Parise., et al. “Indoor occupancy prediction using an iot platform”. in 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS) (2019): 26-31.
  17. MR Bashir and AQ Gill. “Towards an iot big data analytics framework: Smart buildings systems”. in 2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS) (2016): 1325-1332.
  18. OP Qwerty. “History of the goofy layout of keyboards”. Ph.D. dissertation, Podunk IN, (1996).
  19. R Swearingen. “Morpholoty and syntax of british sailors’ English”. New York NY, Tech. Rep. (1985).
  20. T Upsilon. “Obscure greek letters and their meanings in mathematics and the sciences”. in Proceedings of the seventh international trivia conference, V. W. Xavier, Ed. Philadelphia PA: Last Resort Publishers, (1987): 129-158.
  21. J Tetazoo. “A brief guide to recreational pyromania”. (2005/06/12).
  22. R Huo., et al. “A comprehensive survey on blockchain in industrial internet of things: Motivations, research progresses, and future challenges”. IEEE Communications Surveys and Tutorials (2022).
  23. E Khaoula., et al. “Machine learning and the internet of things for smart buildings: A state of the art survey”. in 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET). IEEE (2022): 1-10.
  24. S Zhu., et al. “Survey of testing methods and testbed development concerning internet of things”. Wireless Personal Communications1 (2022): 165-194.
  25. J J P Abad´ıa., et al. “A systematic survey of internet of things frameworks for smart city applications”. Sustainable Cities and Society (2022): 103949.
  26. G F Huseien and K W Shah. “A review on 5g technology for smart energy management and smart buildings in singapore”. Energy and AI 7 (2022): 100116.

Citation

Citation: Shashi Shekhar Kumar and Upendra Pratap Singh. “A Machine Learning Approach for Occupancy Detection in Smart Building". Acta Scientific Computer Sciences 4.8 (2022): 78-81.

Copyright

Copyright: © 2022 Shashi Shekhar Kumar and Upendra Pratap Singh. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




Metrics

Acceptance rate35%
Acceptance to publication20-30 days

Indexed In




News and Events


  • Certification for Review
    Acta Scientific certifies the Editors/reviewers for their review done towards the assigned articles of the respective journals.
  • Submission Timeline for Upcoming Issue
    The last date for submission of articles for regular Issues is April 30th, 2024.
  • Publication Certificate
    Authors will be issued a "Publication Certificate" as a mark of appreciation for publishing their work.
  • Best Article of the Issue
    The Editors will elect one Best Article after each issue release. The authors of this article will be provided with a certificate of "Best Article of the Issue".
  • Welcoming Article Submission
    Acta Scientific delightfully welcomes active researchers for submission of articles towards the upcoming issue of respective journals.

Contact US





+91 9182824667

     info@acta-scientific.com

Creative Commons License Open Access by Acta Scientific is licensed under a Creative Commons Attribution 4.0 International License
Based on a work at https://actascientific.com

ff

Acta-Publications

ff

© 2021 Acta Scientific, All rights reserved.