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

Shirsat, Kavita; Bhole, Girish P.

doi:10.1007/978-981-33-4355-9_8

Cited by 3 publications

(4 citation statements)

References 23 publications

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“… Using an indirect method (without violating privacy, e.g. a camera) to determine the occupancy of monitored spaces [38] , [39] within the IoT [40] , [41] . Using the SVM method to predict the course of the

concentration with great accuracy for the measured data in an interval of 1 day (accuracy was better than 98%) see Table 1 and Table 2 [40] , [37] .…”

Section: Discussionmentioning

confidence: 99%

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A innovative wavelet transformation method optimization in the noise-canceling application within intelligent building occupancy detection monitoring

Vaňuš

Kubíček

Vilímek

et al. 2023

Heliyon

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concentration with great accuracy for the measured data in an interval of 1 day (accuracy was better than 98%) see Table 1 and Table 2 [40] , [37] .…”

Section: Discussionmentioning

confidence: 99%

“…Using an indirect method (without violating privacy, e.g. a camera) to determine the occupancy of monitored spaces [38] , [39] within the IoT [40] , [41] .…”

Section: Discussionmentioning

confidence: 99%

A innovative wavelet transformation method optimization in the noise-canceling application within intelligent building occupancy detection monitoring

Vaňuš

Kubíček

Vilímek

et al. 2023

Heliyon

View full text Add to dashboard Cite

“…However, there is a limitation in applying them to energy poverty households as they cannot accurately detect occupancy when there is no movement. Previous studies have researched methods to detect occupancy by combining various sensors to overcome this limitation [27,28]. Additional sensors to detect light, noise, CO 2 , etc., and their applicability will be discussed in Section 4 based on field surveys.…”

Section: Sensor Nodementioning

confidence: 99%

IoT-Based Indoor Thermal Environment and Occupancy Monitoring for Energy Poverty Care

Yun,

Ryu,

Seo

et al. 2024

Energies

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Energy poverty, defined as difficulty meeting the minimum requirements for a thermal environment, is becoming a significant social issue. To provide efficient welfare services, information provision and monitoring are required. However, characteristics of energy poverty, such as inconsistent residential patterns, small living spaces, and limited electricity and telecommunication resources, lead to a lack of information. This research introduces the empirical results of the development of the system. Based on the feedback from welfare workers and experts supporting energy poverty, a monitoring system combining various sensors was prototyped. This system measures temperature, humidity, illuminance, air velocity, CO2, black bulb temperature, occupancy, and noise and generates indicators for occupancy and thermal environment monitoring. Applicability assessment was conducted across 55 energy poverty households in Korea during the duration of cooling and heating. Subjects were living in spaces averaging 6.3 sqm within buildings over 43 years old and renting on a monthly or weekly basis. Electricity and communication are partially supplied. Based on the actual measurement data and field surveys, the configuration of an energy poverty monitoring system was proposed. In particular, the applicability of the simple methodology for the determination of black bulb temperature, metabolic rate, and clothing insulation required for a thermal environment evaluation was assessed. The proposed system can be efficiently used for taking care of energy poverty where the installation of conventional monitoring systems is restricted.

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“…Building energy management systems have enormous promise for improving energy efficiency and assessing energy flexibility. Shirsat and Bhole assess occupancy detection in buildings using real-time occupancy data [47]. In terms of service quality and energy savings, the occupancy prediction technique outperformed reactive tactics.…”

Section: A Application-based Categorizationmentioning

confidence: 99%

Human Sensing by Using Radio Frequency Signals: A Survey on Occupancy and Activity Detection

Shahbazian

Trubitsyna

2023

IEEE Access

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Applications for human sensing, also known as (human) occupancy detection, include energy management systems for intelligent buildings, intruder detection, e-health systems, the identification of everyday activity, and the monitoring of vital signs. These applications require intelligent decisionmaking that relies on human sensing. Multiple technologies based on vision, sensors, or radio signals can be used to detect occupancy. Vision-based systems use a multitude of cameras to recognize the human presence, but they are restricted by light availability, line-of-sight coverage, expensive equipment, and privacy concerns. Sensor-based techniques refer to a prospective method that employs various combinations of sensors. These solutions are static and necessitate costly equipment installation and maintenance. Due to technical advancements, radio-based signals, such as WiFi, have been integrated into various forms of infrastructure, including homes, offices, and constructions. Due to how human body movements affect wireless signal propagation, it is possible to detect human motions by analyzing the received wireless signals (such as reflection, diffraction, and scattering). Due to its low cost and non-intrusive nature, wirelessbased human activity detection has received substantial attention and become a key topic of study. This article reviews the underlying principles, methodologies, and system architectures of radio-frequency-based occupancy detection systems. We classify the reviewed research studies based on the technical measures and applications they employ. In addition to focusing on the security aspects of occupancy detection and discussing future trends and difficulties, we also discuss practical considerations.

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

Cited by 3 publications

References 23 publications

A innovative wavelet transformation method optimization in the noise-canceling application within intelligent building occupancy detection monitoring

A innovative wavelet transformation method optimization in the noise-canceling application within intelligent building occupancy detection monitoring

IoT-Based Indoor Thermal Environment and Occupancy Monitoring for Energy Poverty Care

Human Sensing by Using Radio Frequency Signals: A Survey on Occupancy and Activity Detection

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