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An Empirical Study on the Occupancy Detection Techniques Based on Context-Aware IoT System

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Proceedings of International Conference on Sustainable Expert Systems

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 176))

Abstract

Occupancy detection and behavior in buildings has a huge impact on cooling, heating, ventilation demand, building controls, and energy consumption in lighting appliances. The human factor is an important factor in real-time occupancy information and building energy management systems that offer great potential for maximizing energy efficiency and assessing energy flexibility. The occupancy predictive strategy provided a better quality of service and energy savings performance than reactive strategies. In this research paper, 20 papers based on context-aware IoT systems for occupancy detection are reviewed. The research works are categorized into the sensor, sensor fusion, Wi-Fi, LAN, radio frequency (RF) signals, machine learning, and so on. The research gaps and the challenges faced during the occupancy detection are listed for further enhancement in the occupancy detection methods. The research work is analyzed based on the performance metrics, classification methods, and the publication year. The analysis shows that the most frequently used performance metrics is accuracy, the most commonly used classification technique is the sensor, whereas most of the research papers are published in the year 2018.

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Authors and Affiliations

  1. Department of Computer Engineering & Information Technology, Veermata Jijabai Technological Institute, Matunga Mumbai, Maharastra, India

    Kavita Pankaj Shirsat & Girish P Bhole

Authors
  1. Kavita Pankaj Shirsat
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  2. Girish P Bhole
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Correspondence to Kavita Pankaj Shirsat .

Editor information

Editors and Affiliations

  1. Institute of Engineering, Tribhuvan University, Pulchowk, Nepal

    Subarna Shakya

  2. Intelligent Systems Research Centre, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Go Perception Laboratory, Cornell University, Ithaca, NY, USA

    Wang Haoxiang

  4. School of Information Technology, Deakin University, Geelong, VIC, Australia

    Zubair Baig

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Shirsat, K.P., Bhole, G.P. (2021). An Empirical Study on the Occupancy Detection Techniques Based on Context-Aware IoT System. In: Shakya, S., Balas, V.E., Haoxiang, W., Baig, Z. (eds) Proceedings of International Conference on Sustainable Expert Systems. Lecture Notes in Networks and Systems, vol 176. Springer, Singapore. https://doi.org/10.1007/978-981-33-4355-9_8

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  • DOI: https://doi.org/10.1007/978-981-33-4355-9_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-33-4354-2

  • Online ISBN: 978-981-33-4355-9

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