Adaptive Filter Updating for Energy-Efficient Top-k Queries in Wireless Sensor Networks Using Gaussian Process Regression Article Swipe
Jiping Zheng
,
Baoli Song
,
Yongge Wang
,
Haixiang Wang
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1155/2015/304198
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1155/2015/304198
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.
Related Topics
Concepts
Computer science
Wireless sensor network
Energy consumption
Energy (signal processing)
Filter (signal processing)
Gaussian
Process (computing)
Gaussian filter
Gaussian process
Efficient energy use
Adaptive filter
Data mining
Real-time computing
Algorithm
Computer network
Artificial intelligence
Mathematics
Statistics
Quantum mechanics
Ecology
Physics
Electrical engineering
Operating system
Computer vision
Engineering
Image (mathematics)
Biology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2015/304198
- OA Status
- gold
- Cited By
- 4
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W1569254172
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W1569254172Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1155/2015/304198Digital Object Identifier
- Title
-
Adaptive Filter Updating for Energy-Efficient Top-k Queries in Wireless Sensor Networks Using Gaussian Process RegressionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2015Year of publication
- Publication date
-
2015-06-01Full publication date if available
- Authors
-
Jiping Zheng, Baoli Song, Yongge Wang, Haixiang WangList of authors in order
- Landing page
-
https://doi.org/10.1155/2015/304198Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1155/2015/304198Direct OA link when available
- Concepts
-
Computer science, Wireless sensor network, Energy consumption, Energy (signal processing), Filter (signal processing), Gaussian, Process (computing), Gaussian filter, Gaussian process, Efficient energy use, Adaptive filter, Data mining, Real-time computing, Algorithm, Computer network, Artificial intelligence, Mathematics, Statistics, Quantum mechanics, Ecology, Physics, Electrical engineering, Operating system, Computer vision, Engineering, Image (mathematics), BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2021: 3, 2018: 1Per-year citation counts (last 5 years)
- References (count)
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36Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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