Cloud based passive building occupancy characterization for attack and disaster response

Nyarko, Kofi; Wright-Brown, Cecelia

doi:10.1109/ths.2013.6699097

Cited by 10 publications

(6 citation statements)

References 10 publications

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“…The model space is a seminar room with a total volume of 159 m 3 . The room is located on the third floor of the engineering building at the Kookmin University.…”

Section: Model Developmentmentioning

confidence: 99%

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Estimation of Occupancy in a Naturally Ventilated Room using Bayesian Method Based on CO2 Concentration

Rahman¹,

Han²

2017

Int J Mech Syst Eng

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We introduce a Bayesian Markov chain Monte Carlo approach for occupancy estimation in a room with an immeasurable ventilation rate, with the objective of investigating the effects of ventilation estimation and uncertainty in CO 2 data on the occupancy estimation. Measured CO 2 concentrations are used as inputs for the Bayesian estimation and ventilation calculation. The ventilation rate is obtained quantitatively by the concentration decay and the sum-up methods. The ventilation rate is determined by the decay rates at night with no occupancy and by sum up the average concentration level during a day with known occupancy. The Bayesian calculation uses a mathematical model based on the dynamic CO 2 mass-balance equation in space. The result shows that the accuracy of occupancy estimation depends upon the estimate of ventilation rate, as well as the uncertainty in CO 2 measurements.

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“…The model space is a seminar room with a total volume of 159 m 3 . The room is located on the third floor of the engineering building at the Kookmin University.…”

Section: Model Developmentmentioning

confidence: 99%

“…Occupancy information is useful for building management to operate lighting and ventilation systems with a view to reduce operational cost [1,2]. Furthermore, real-time occupancy monitoring can assume paramount importance during emergency evacuation [3].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Occupancy in a Naturally Ventilated Room using Bayesian Method Based on CO2 Concentration

Rahman¹,

Han²

2017

Int J Mech Syst Eng

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“…Combined with reduced government funding, this leads universities to alternative financing models. Newell and Manaf (2017) observe a clear tendency amongst five Australian universities to use different funding models for their investments such as leasing, debt funding, donations and private development. In the UK, universities have already invested significantly using models such as private bond issuing, commercial bank lending, and loans from the European Investment Bank (McCann et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Towards a smart campus: supporting campus decisions with Internet of Things applications

Valks

Arkesteijn

Koutamanis

et al. 2020

Building Research & Information

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At universities worldwide, creating a 'smart campus' is gaining significance. This is a response to the increasingly dynamic use of the campus and the pressure on resources: energy, financial and human resources. The university community has become more mobile, student numbers more unpredictable and funding more uncertain. Consequently, campus strategies focus on resource efficiency and sharing space, requiring investment in management information to improve decision making. The Internet of Things (IoT) can help to provide big data about use patterns: by collecting real-time data on space utilization, users can make better use of current spaces and real estate managers can make better decisions about long-term demand. Furthermore, space utilisation data can be complemented with user feedback data and environmental variables, e.g. noise levels, luminance. In this research we identify the capabilities of existing IoT applications through a literature study. Literature also suggests that information from IoT applications is not utilized in organisational decision-making processes. Through four case studies we analyse these decision-making processes and identify the process-level requirements to make strategic decisions in campus management. Then we show how information from the IoT can be directly connected to these processes, thus providing a valuable addition of real-time data as input.

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“…In addition to energy management, the fire system can benefit from such information. Survivors can be located and rescued in case of emergency situations [7]. Finally, occupancy information can be used to enhance the building security and surveillance [8].…”

Section: Introductionmentioning

confidence: 99%

An Edge Computing Approach to Explore Indoor Environmental Sensor Data for Occupancy Measurement in Office Spaces

Zemouri

Magoni

Zemouri

et al. 2018

2018 IEEE International Smart Cities Conference (ISC2)

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Human occupancy measurement has become a topic of increasing interest in the past few years, due to the important role it plays in controlling a number of demand-driven applications like smart lighting and smart heating, as well as improving the energy efficiency of these applications in a broader sense. Office occupancy monitoring in commercial buildings can yield huge savings and improvements in terms of thermal, visual, and air quality. However, this is often impeded due to the lack of fine-grained occupancy information. This paper explores the use of low-priced environmental (temperature and humidity) sensor data for measuring occupancy in an office space. The idea behind this work is to leverage the variation divergence between humidity and temperature caused by human presence. We used a Raspberry Pi with a daughterboard called Sense Hat, which is equipped with the environmental sensors used in this study. The results are compared with occupancy data obtained from camera feeds in order to assess the effectiveness and the accuracy of the combined occupancy measurements, and show up to 87% accuracy.

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Cloud based passive building occupancy characterization for attack and disaster response

Cited by 10 publications

References 10 publications

Estimation of Occupancy in a Naturally Ventilated Room using Bayesian Method Based on CO2 Concentration

Estimation of Occupancy in a Naturally Ventilated Room using Bayesian Method Based on CO2 Concentration

Towards a smart campus: supporting campus decisions with Internet of Things applications

An Edge Computing Approach to Explore Indoor Environmental Sensor Data for Occupancy Measurement in Office Spaces

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