Adaptive Filter Updating for Energy-Efficient Top- Queries in Wireless Sensor Networks Using Gaussian Process Regression
Adopting filtering mechanism of dynamic filtering windows installed on sensor nodes to process top- k queries is an important research direction in wireless sensor networks. The mechanism can reduce transmissions of redundant data by utilizing filters. However, existing algorithms based on filters c...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-06-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/304198 |
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| _version_ | 1850161381303648256 |
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| author | Jiping Zheng Baoli Song Yongge Wang Haixiang Wang |
| author_facet | Jiping Zheng Baoli Song Yongge Wang Haixiang Wang |
| author_sort | Jiping Zheng |
| collection | DOAJ |
| description | Adopting filtering mechanism of dynamic filtering windows installed on sensor nodes to process top- k queries is an important research direction in wireless sensor networks. The mechanism can reduce transmissions of redundant data by utilizing filters. However, existing algorithms based on filters consume a vast amount of energy due to filter updating. In this paper, an energy-efficient top- k query technique based on adaptive filters is proposed. Due to updating filters consuming a large amount of energy, an algorithm named FUGPR based on Gaussian process regression to process top- k queries is provided for saving energy. When the filters change, the sensor readings are predicted to calculate the updating costs of filters; then FUGPR decides whether the filters need to be updated or not. Thus, the energy consumption for updating filters is decreased. Experimental results show that our approach can reduce energy consumption efficiently for updating filters on two distinct real datasets. |
| format | Article |
| id | doaj-art-ffe6bb6cb4be4625a7b335afcff1f35d |
| institution | OA Journals |
| issn | 1550-1477 |
| language | English |
| publishDate | 2015-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-ffe6bb6cb4be4625a7b335afcff1f35d2025-08-20T02:22:50ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-06-011110.1155/2015/304198304198Adaptive Filter Updating for Energy-Efficient Top- Queries in Wireless Sensor Networks Using Gaussian Process RegressionJiping Zheng0Baoli Song1Yongge Wang2Haixiang Wang3 State Key Laboratory for Novel Software Technology, Nanjing University, 163 Xianlin Avenue, Qixia District, Nanjing 210093, China College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Qinhuai District, Nanjing 210016, China College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Qinhuai District, Nanjing 210016, China College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Qinhuai District, Nanjing 210016, ChinaAdopting filtering mechanism of dynamic filtering windows installed on sensor nodes to process top- k queries is an important research direction in wireless sensor networks. The mechanism can reduce transmissions of redundant data by utilizing filters. However, existing algorithms based on filters consume a vast amount of energy due to filter updating. In this paper, an energy-efficient top- k query technique based on adaptive filters is proposed. Due to updating filters consuming a large amount of energy, an algorithm named FUGPR based on Gaussian process regression to process top- k queries is provided for saving energy. When the filters change, the sensor readings are predicted to calculate the updating costs of filters; then FUGPR decides whether the filters need to be updated or not. Thus, the energy consumption for updating filters is decreased. Experimental results show that our approach can reduce energy consumption efficiently for updating filters on two distinct real datasets.https://doi.org/10.1155/2015/304198 |
| spellingShingle | Jiping Zheng Baoli Song Yongge Wang Haixiang Wang Adaptive Filter Updating for Energy-Efficient Top- Queries in Wireless Sensor Networks Using Gaussian Process Regression International Journal of Distributed Sensor Networks |
| title | Adaptive Filter Updating for Energy-Efficient Top- Queries in Wireless Sensor Networks Using Gaussian Process Regression |
| title_full | Adaptive Filter Updating for Energy-Efficient Top- Queries in Wireless Sensor Networks Using Gaussian Process Regression |
| title_fullStr | Adaptive Filter Updating for Energy-Efficient Top- Queries in Wireless Sensor Networks Using Gaussian Process Regression |
| title_full_unstemmed | Adaptive Filter Updating for Energy-Efficient Top- Queries in Wireless Sensor Networks Using Gaussian Process Regression |
| title_short | Adaptive Filter Updating for Energy-Efficient Top- Queries in Wireless Sensor Networks Using Gaussian Process Regression |
| title_sort | adaptive filter updating for energy efficient top queries in wireless sensor networks using gaussian process regression |
| url | https://doi.org/10.1155/2015/304198 |
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