Occupancy Estimation in Semi-Public Spaces using Sensor Fusion and Context Awareness

Gaonkar, Pradnya; Bapat, Jyotsna; Das, Debabrata; Rao, Subrahmanya Vrk

doi:10.1109/tensymp46218.2019.8971134

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…More details about challenges are in technical challenges section 5.1. Heat, air quality sensors, temperature, humidity, light, CO2, Wi-Fi probe-sensing, camera, PIR, millimeter-wave radio, power meters, BLE beacons sensors LBNL Building 59 [96], RO-BOD [149], LANGEVIN [77] [141], [161], [44], [120], [158], [135], [108], [41], [125], [170], [49] Activity (sensing, recognition, detection)…”

Section: Iot With Ai In Smart Buildingsmentioning

confidence: 99%

Sensing within Smart Buildings: A Survey

2023

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Increasingly, buildings are being fitted with sensors for the needs of different sectors, such as education, industry and business. Using Internet of Things (IoT) devices combined with analysis of data being generated by these devices, it is possible to infer a number of metrics, e.g. building occupancy and activities of occupants. The information thus gathered can be used to develop software applications to support energy management, occupant comfort, and space utilization. This survey explores the use of sensors in smart building environments, identifying different approaches to employ sensors in buildings. The most commonly used data-driven approaches for activity recognition in such buildings is also investigated, concluding by highlighting current research challenges and future research directions in this area.

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Section: Iot With Ai In Smart Buildingsmentioning

confidence: 99%

Sensing within Smart Buildings: A Survey

2023

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“…Using the PIR by itself only gives an accuracy of around 27% for estimating room occupation over time. However, this can easily be improved to 90% when combined with other door sensors (Gaonkar et al, 2019). One major drawback of PIR is that it only works in areas with clear doors.…”

Section: Passive Infrared Sensorsmentioning

confidence: 99%

Where to Go? Smart Guidance Based on Iot Sensor-Data

Effing¹,

Brouwer²,

Iliev³

et al. 2020

Proceedings of the International Conferences on ICT, Society and Human Beings (ICT 2020), Connected Smart Cities (CSC 2020) An

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There is an increasing interest in indoor occupation and guidance information for business and societal purposes. Scientific literature has paid attention to various ways of detecting occupation using different sensors as data source including various algorithms for estimating occupation rates from this data. Gaining meaningful insights from the data still faces challenges because the potential benefits are not well understood. This study presents a proof-of-concept of an indoor occupation information system, following the design science methodology. We review various types of sensor data that are typically available or easy-to-install in buildings such as offices, classrooms and meeting rooms. This study contributes to current research by incorporating business requirements taken from expert interviews and tackling one of the main barriers for business by designing an affordable system on a common existing infrastructure. We believe that occupation information systems call for further research, in particular also in the context of social distancing because of covid19.

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“…Door sensors consist of two components, one mounted on the door frame and the other on the movable door. When the door opens the electric circuit breaks, logical high is recorded as programmed, and when the door is closed logical low is recorded [124]. These sensors are low-cost, low-power, and easy to install [45].…”

Section: Electro-mechanical Sensorsmentioning

confidence: 99%

Comprehensive Simulation-based Workflow to Assess the Performance of Occupancy-based Controls and Operations in Office Buildings

Azimi¹

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Although many people have been forced to work remotely due to the pandemic, occupancy patterns in office buildings were already rapidly changing due to technological advances, enabling workers to work remotely or "telework". While having more workers telework could potentially lower an organization's need for real estate and associated costs, this style of work is not implemented in the most efficient way since many buildings still consume significant amounts of energy even if occupancy is low. Additionally, spaces are acquired assuming maximum occupancy, and cleaning and maintenance schedules do not change with respect to occupancy level, therefore wasting resources. Most services provided -from ventilation to cleaning and space -are (or should be) a function of occupancy. Yet occupancy is seldom measured in a comprehensive way such that the data can be widely used to improve building operations. There is a wide variety of applications I wish to thank members of the Building Performance Research Centre for projecting an essence of comradery and community through regular virtual seminars, discussions, and events; I am thankful to be part of such a tight-knit, diverse, and knowledgeable group.

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Occupancy Estimation in Semi-Public Spaces using Sensor Fusion and Context Awareness

Cited by 4 publications

References 4 publications

Sensing within Smart Buildings: A Survey

Sensing within Smart Buildings: A Survey

Where to Go? Smart Guidance Based on Iot Sensor-Data

Comprehensive Simulation-based Workflow to Assess the Performance of Occupancy-based Controls and Operations in Office Buildings

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