A Study on Real-Time Edge Computed Occupancy Estimation in an Indoor Environment

Das, Anirban; Gupta, Rohan; Chakraborty, Suchetana

doi:10.1109/comsnets48256.2020.9027463

Cited by 5 publications

(10 citation statements)

References 13 publications

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“…Alternatively, Das et al [ 26 ] have developed a framework to fuse data at the edge node. The data are temporarily stored in a data stream buffer.…”

Section: Resultsmentioning

confidence: 99%

“…The most used method is to combine parameters until an optimal combination is reached, which provides the highest accuracy. In addition, there are authors who have implemented more sophisticated methods, such as edge node fusion using Kalman Filter [ 26 ], Particle Filter [ 76 ], ANFIS [ 27 ], and BP-ANN [ 85 ]. All publications have shown that data fusion improves the accuracy of models to detect or estimate the occupancy, except for one study, which contradicts the benefits of data fusion [ 1 ].…”

Section: Discussionmentioning

confidence: 99%

“…People NO YES 1 min BMCMC Accuracy 43.5% [ 89 ] 20 D Num. People NO YES 15 min K-NN, GP, RF, BR, MLP MAE 0.21–1.84 [ 26 ] Detection, Levels NO YES 15 sec SVM, LR, CS Accuracy 96.9–97.95% [ 90 ] 120 D Num. People YES YES CA [ 66 ] 6 D Detection, Num.…”

Section: Table A1mentioning

confidence: 99%

“…Some models used are the Hidden Markov Model (HMM) and its variations [ 15 , 16 , 17 ], models based on Bayes’ theorem [ 7 , 18 ], supervised Machine Learning models, such as Support Vector Machine (SVM) [ 19 , 20 ], Random Forest (RF) [ 21 , 22 ], and the popular Artificial Neural Networks (ANN) as well as their variants [ 23 , 24 ]. Furthermore, some researchers have also proposed combining multiple environmental parameters in the models to obtain higher precision and accuracy [ 25 , 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Measuring Indoor Occupancy through Environmental Sensors: A Systematic Review on Sensor Deployment

Mena

Ceballos

Alvarado-Uribe

2022

Sensors

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The COVID-19 pandemic has changed our common habits and lifestyle. Occupancy information is valued more now due to the restrictions put in place to reduce the spread of the virus. Over the years, several authors have developed methods and algorithms to detect/estimate occupancy in enclosed spaces. Similarly, different types of sensors have been installed in the places to allow this measurement. However, new researchers and practitioners often find it difficult to estimate the number of sensors to collect the data, the time needed to sense, and technical information related to sensor deployment. Therefore, this systematic review provides an overview of the type of environmental sensors used to detect/estimate occupancy, the places that have been selected to carry out experiments, details about the placement of the sensors, characteristics of datasets, and models/algorithms developed. Furthermore, with the information extracted from three selected studies, a technique to calculate the number of environmental sensors to be deployed is proposed.

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“…Alternatively, Das et al [ 26 ] have developed a framework to fuse data at the edge node. The data are temporarily stored in a data stream buffer.…”

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Table A1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Measuring Indoor Occupancy through Environmental Sensors: A Systematic Review on Sensor Deployment

Mena

Ceballos

Alvarado-Uribe

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The most important of these factors is occupancy, which affects the heating and cooling loads of buildings.As people spend most of their time inside the rooms, occupants' behaviour plays an important role in the control system [3]. This indoor occupancy estimation is done by using temperature, humidity, motion, and gas sensors [4].…”

Section: Introductionmentioning

confidence: 99%

An Improved ANN Approach for Occupancy Detection of A Smart Building

RAY

Samal

Panigrahi

2023

IJEEI

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Building energy performance can be improved with a reduction in energy consumption. The heating and cooling loads of a building are important factors to consider in the field of energy conservation. It is possible to estimate energy consumption by predicting the presence of occupants in a room based on information provided by the HVAC (Heating, Ventilation, and Air Conditioning) system using standard information. Temperature, humidity, light, and CO2 levels from various sensors are taken as input parameters. In addition, the output of the network is programmed to be "0" when the building is not occupied and "1" when the building is occupied for the purpose of occupant detection. Pattern recognition using an Elman back propagation network is being proposed for occupancy detection. The data sets were used for training and testing (with the office door open and closed) the models during occupancy. The proposed ANN-based method is trained and tested and was found to be more effective, with an accuracy of 98.5% and 97.5% in cases of closed and opened doors, respectively.

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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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A Study on Real-Time Edge Computed Occupancy Estimation in an Indoor Environment

Cited by 5 publications

References 13 publications

Measuring Indoor Occupancy through Environmental Sensors: A Systematic Review on Sensor Deployment

Measuring Indoor Occupancy through Environmental Sensors: A Systematic Review on Sensor Deployment

An Improved ANN Approach for Occupancy Detection of A Smart Building

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

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