Reduction of power consumption in sensor network applications using machine learning techniques
Wireless sensor networking (WSN) and modern machine learning techniques have encouraged interest in the development of vehicle monitoring systems that ensure safe and secure operations of the rail vehicle. To make an energy efficient WSN application, power consumption due to raw data collection and...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2008
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/26969 |
| _version_ | 1848752134398410752 |
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| author | Shafiullah, G. Thompson, Adam Wolfs, Peter Ali, S. |
| author2 | M B Srinivas |
| author_facet | M B Srinivas Shafiullah, G. Thompson, Adam Wolfs, Peter Ali, S. |
| author_sort | Shafiullah, G. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Wireless sensor networking (WSN) and modern machine learning techniques have encouraged interest in the development of vehicle monitoring systems that ensure safe and secure operations of the rail vehicle. To make an energy efficient WSN application, power consumption due to raw data collection and pre-processing needs to be kept to a minimum level. In this paper, an energy-efficient data acquisition method has investigated for WSN applications using modern machine learning techniques. In an existing system, four sensor nodes were placed in each railway wagon to collect data to develop a monitoring system for railways. In this system, three sensor nodes were placed in each wagon to collect the same data using popular regression algorithms, which reduces power consumption of the system. This study was conducted using six different regression algorithms with five different datasets. Finally the best suitable algorithm have suggested based on the performance metrics of the algorithms that include: correlation coefficient, root mean square error (RMSE), mean absolute error (MAE), root relative squared error (RRSE), relative absolute error (RAE)and computation complexity. |
| first_indexed | 2025-11-14T08:03:47Z |
| format | Conference Paper |
| id | curtin-20.500.11937-26969 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:03:47Z |
| publishDate | 2008 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-269692017-10-02T02:27:48Z Reduction of power consumption in sensor network applications using machine learning techniques Shafiullah, G. Thompson, Adam Wolfs, Peter Ali, S. M B Srinivas Wireless sensor networking railway wagons machine - learning techniques regression analysis Wireless sensor networking (WSN) and modern machine learning techniques have encouraged interest in the development of vehicle monitoring systems that ensure safe and secure operations of the rail vehicle. To make an energy efficient WSN application, power consumption due to raw data collection and pre-processing needs to be kept to a minimum level. In this paper, an energy-efficient data acquisition method has investigated for WSN applications using modern machine learning techniques. In an existing system, four sensor nodes were placed in each railway wagon to collect data to develop a monitoring system for railways. In this system, three sensor nodes were placed in each wagon to collect the same data using popular regression algorithms, which reduces power consumption of the system. This study was conducted using six different regression algorithms with five different datasets. Finally the best suitable algorithm have suggested based on the performance metrics of the algorithms that include: correlation coefficient, root mean square error (RMSE), mean absolute error (MAE), root relative squared error (RRSE), relative absolute error (RAE)and computation complexity. 2008 Conference Paper http://hdl.handle.net/20.500.11937/26969 IEEE fulltext |
| spellingShingle | Wireless sensor networking railway wagons machine - learning techniques regression analysis Shafiullah, G. Thompson, Adam Wolfs, Peter Ali, S. Reduction of power consumption in sensor network applications using machine learning techniques |
| title | Reduction of power consumption in sensor network applications using machine learning techniques |
| title_full | Reduction of power consumption in sensor network applications using machine learning techniques |
| title_fullStr | Reduction of power consumption in sensor network applications using machine learning techniques |
| title_full_unstemmed | Reduction of power consumption in sensor network applications using machine learning techniques |
| title_short | Reduction of power consumption in sensor network applications using machine learning techniques |
| title_sort | reduction of power consumption in sensor network applications using machine learning techniques |
| topic | Wireless sensor networking railway wagons machine - learning techniques regression analysis |
| url | http://hdl.handle.net/20.500.11937/26969 |