Occupancy detection and estimation using smartphone
Currently, the focus on energy consumption reduction and efforts to reduce climate change has been spoken about widely. The recent Paris Agreement is a testimony to that. The need to reduce the consumption of energy is all places has become paramount. The technology that has been made use of to c...
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sg-ntu-dr.10356-719832023-07-07T16:19:04Z Occupancy detection and estimation using smartphone Kruthika Priyadharshini A. Soh Yeng Chai School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Currently, the focus on energy consumption reduction and efforts to reduce climate change has been spoken about widely. The recent Paris Agreement is a testimony to that. The need to reduce the consumption of energy is all places has become paramount. The technology that has been made use of to combat these problems has mostly been revolved around sensors such as motion and carbon dioxide. Wi-Fi which is a technology with devices set on the IEEE 802.11 standards for WLAN can be a viable method to achieve energy efficiency. Wi-Fi is a standard feature of smartphones across brands and is continuously improved fuelled by the demand in the smartphone industry. Hence the already provided structure of WLAN connections can be leveraged on for the purpose of occupancy detection. Moreover, the rise is the ubiquity of smartphones has allowed for the exploitation of the wireless connectivity capability that they are already equipped with. This project analyses the various methods that can be undertaken with regards to smartphone and Wi-Fi to detect occupancy is an indoor environment and concludes with lean algorithms and a test the accuracy so that they can be implemented in smartphone environments to retrieve location information of smartphones which can be used for occupancy estimation. Bachelor of Engineering 2017-05-23T06:40:35Z 2017-05-23T06:40:35Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/71983 en Nanyang Technological University 51 p. application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering Kruthika Priyadharshini A. Occupancy detection and estimation using smartphone |
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Currently, the focus on energy consumption reduction and efforts to reduce climate change
has been spoken about widely. The recent Paris Agreement is a testimony to that. The need
to reduce the consumption of energy is all places has become paramount. The technology
that has been made use of to combat these problems has mostly been revolved around
sensors such as motion and carbon dioxide.
Wi-Fi which is a technology with devices set on the IEEE 802.11 standards for WLAN can
be a viable method to achieve energy efficiency. Wi-Fi is a standard feature of smartphones
across brands and is continuously improved fuelled by the demand in the smartphone
industry. Hence the already provided structure of WLAN connections can be leveraged on
for the purpose of occupancy detection. Moreover, the rise is the ubiquity of smartphones
has allowed for the exploitation of the wireless connectivity capability that they are already
equipped with.
This project analyses the various methods that can be undertaken with regards to
smartphone and Wi-Fi to detect occupancy is an indoor environment and concludes with
lean algorithms and a test the accuracy so that they can be implemented in smartphone
environments to retrieve location information of smartphones which can be used for
occupancy estimation. |
author2 |
Soh Yeng Chai |
author_facet |
Soh Yeng Chai Kruthika Priyadharshini A. |
format |
Final Year Project |
author |
Kruthika Priyadharshini A. |
author_sort |
Kruthika Priyadharshini A. |
title |
Occupancy detection and estimation using smartphone |
title_short |
Occupancy detection and estimation using smartphone |
title_full |
Occupancy detection and estimation using smartphone |
title_fullStr |
Occupancy detection and estimation using smartphone |
title_full_unstemmed |
Occupancy detection and estimation using smartphone |
title_sort |
occupancy detection and estimation using smartphone |
publishDate |
2017 |
url |
http://hdl.handle.net/10356/71983 |
_version_ |
1772827044574396416 |