Building wireless sensor network: prototype datalogging enhancement & development as a real-time modelling device
This paper delves into an extensive exploration of fire sensing methodologies, placing a specific emphasis on the development of an enhanced prototype model designed for efficient dataset collection and backtesting within the framework of a "No-Fire detector" system. Subsequently, a compre...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/177568 |
| _version_ | 1824454384901160960 |
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| author | Tay, Owen Jia Hao |
| author2 | Li King Ho, Holden |
| author_facet | Li King Ho, Holden Tay, Owen Jia Hao |
| author_sort | Tay, Owen Jia Hao |
| collection | NTU |
| description | This paper delves into an extensive exploration of fire sensing methodologies, placing a specific emphasis on the development of an enhanced prototype model designed for efficient dataset collection and backtesting within the framework of a "No-Fire detector" system. Subsequently, a comprehensive analysis is conducted, comparing the performance of Ordinary Least Squares (OLS), Polynomial Regression (PR) & K-Nearest Neighbors (KNN) models using a 22-day dataset of pressure data. Key performance metrics, including R2, Cross Validation Score Mean (CVSM), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), are employed to evaluate accuracy and stability over time. The findings
underscore the consistent and superior performance of the KNN model in contrast to
OLS & PR. Notably, the KNN algorithm demonstrates computational efficiency,
achieving a 95% accuracy within 9 minutes. This study extends to diverse locations,
providing insights into the KNN model's behavior under varying environmental
conditions. Overall, this research significantly contributes to advancing the
development of an efficient and reliable "No-Fire detector" system. |
| first_indexed | 2025-02-19T03:21:28Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/177568 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:21:28Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1775682024-06-01T16:52:06Z Building wireless sensor network: prototype datalogging enhancement & development as a real-time modelling device Tay, Owen Jia Hao Li King Ho, Holden School of Mechanical and Aerospace Engineering HoldenLi@ntu.edu.sg Engineering Wireless sensor network Machine learning This paper delves into an extensive exploration of fire sensing methodologies, placing a specific emphasis on the development of an enhanced prototype model designed for efficient dataset collection and backtesting within the framework of a "No-Fire detector" system. Subsequently, a comprehensive analysis is conducted, comparing the performance of Ordinary Least Squares (OLS), Polynomial Regression (PR) & K-Nearest Neighbors (KNN) models using a 22-day dataset of pressure data. Key performance metrics, including R2, Cross Validation Score Mean (CVSM), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), are employed to evaluate accuracy and stability over time. The findings underscore the consistent and superior performance of the KNN model in contrast to OLS & PR. Notably, the KNN algorithm demonstrates computational efficiency, achieving a 95% accuracy within 9 minutes. This study extends to diverse locations, providing insights into the KNN model's behavior under varying environmental conditions. Overall, this research significantly contributes to advancing the development of an efficient and reliable "No-Fire detector" system. Bachelor's degree 2024-05-30T00:54:49Z 2024-05-30T00:54:49Z 2024 Final Year Project (FYP) Tay, O. J. H. (2024). Building wireless sensor network: prototype datalogging enhancement & development as a real-time modelling device. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/177568 https://hdl.handle.net/10356/177568 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering Wireless sensor network Machine learning Tay, Owen Jia Hao Building wireless sensor network: prototype datalogging enhancement & development as a real-time modelling device |
| title | Building wireless sensor network: prototype datalogging enhancement & development as a real-time modelling device |
| title_full | Building wireless sensor network: prototype datalogging enhancement & development as a real-time modelling device |
| title_fullStr | Building wireless sensor network: prototype datalogging enhancement & development as a real-time modelling device |
| title_full_unstemmed | Building wireless sensor network: prototype datalogging enhancement & development as a real-time modelling device |
| title_short | Building wireless sensor network: prototype datalogging enhancement & development as a real-time modelling device |
| title_sort | building wireless sensor network prototype datalogging enhancement development as a real time modelling device |
| topic | Engineering Wireless sensor network Machine learning |
| url | https://hdl.handle.net/10356/177568 |
| work_keys_str_mv | AT tayowenjiahao buildingwirelesssensornetworkprototypedataloggingenhancementdevelopmentasarealtimemodellingdevice |