A Structure Fidelity Approach for Big Data Collection in Wireless Sensor Networks
One of the most widespread and important applications in wireless sensor networks (WSNs) is the continuous data collection, such as monitoring the variety of ambient temperature and humidity. Due to the sensor nodes with a limited energy supply, the reduction of energy consumed in the continuous obs...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-12-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/15/1/248 |
| _version_ | 1798006099582910464 |
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| author | Mou Wu Liansheng Tan Naixue Xiong |
| author_facet | Mou Wu Liansheng Tan Naixue Xiong |
| author_sort | Mou Wu |
| collection | DOAJ |
| description | One of the most widespread and important applications in wireless sensor networks (WSNs) is the continuous data collection, such as monitoring the variety of ambient temperature and humidity. Due to the sensor nodes with a limited energy supply, the reduction of energy consumed in the continuous observation of physical phenomenon plays a significant role in extending the lifetime of WSNs. However, the high redundancy of sensing data leads to great waste of energy as a result of over-deployed sensor nodes. In this paper, we develop a structure fidelity data collection (SFDC) framework leveraging the spatial correlations between nodes to reduce the number of the active sensor nodes while maintaining the low structural distortion of the collected data. A structural distortion based on the image quality assessment approach is used to perform the nodes work/sleep scheduling, such that the number of the working nodes is reduced while the remainder of nodes can be put into the low-power sleep mode during the sampling period. The main contribution of SFDC is to provide a unique perspective on how to maintain the data fidelity in term of structural similarity in the continuous sensing applications for WSNs. The simulation results based on synthetic and real world datasets verify the effectiveness of SFDC framework both on energy saving and data fidelity. |
| first_indexed | 2024-04-11T12:49:39Z |
| format | Article |
| id | doaj.art-495617ddec214b3795360c19db6c3244 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:49:39Z |
| publishDate | 2014-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-495617ddec214b3795360c19db6c32442022-12-22T04:23:14ZengMDPI AGSensors1424-82202014-12-0115124827310.3390/s150100248s150100248A Structure Fidelity Approach for Big Data Collection in Wireless Sensor NetworksMou Wu0Liansheng Tan1Naixue Xiong2Department of Computer Science, Central China Normal University, Wuhan 430079, ChinaDepartment of Computer Science, Central China Normal University, Wuhan 430079, ChinaSchool of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, ChinaOne of the most widespread and important applications in wireless sensor networks (WSNs) is the continuous data collection, such as monitoring the variety of ambient temperature and humidity. Due to the sensor nodes with a limited energy supply, the reduction of energy consumed in the continuous observation of physical phenomenon plays a significant role in extending the lifetime of WSNs. However, the high redundancy of sensing data leads to great waste of energy as a result of over-deployed sensor nodes. In this paper, we develop a structure fidelity data collection (SFDC) framework leveraging the spatial correlations between nodes to reduce the number of the active sensor nodes while maintaining the low structural distortion of the collected data. A structural distortion based on the image quality assessment approach is used to perform the nodes work/sleep scheduling, such that the number of the working nodes is reduced while the remainder of nodes can be put into the low-power sleep mode during the sampling period. The main contribution of SFDC is to provide a unique perspective on how to maintain the data fidelity in term of structural similarity in the continuous sensing applications for WSNs. The simulation results based on synthetic and real world datasets verify the effectiveness of SFDC framework both on energy saving and data fidelity.http://www.mdpi.com/1424-8220/15/1/248data collectionspatial correlationwireless sensor networkstructure fidelity |
| spellingShingle | Mou Wu Liansheng Tan Naixue Xiong A Structure Fidelity Approach for Big Data Collection in Wireless Sensor Networks Sensors data collection spatial correlation wireless sensor network structure fidelity |
| title | A Structure Fidelity Approach for Big Data Collection in Wireless Sensor Networks |
| title_full | A Structure Fidelity Approach for Big Data Collection in Wireless Sensor Networks |
| title_fullStr | A Structure Fidelity Approach for Big Data Collection in Wireless Sensor Networks |
| title_full_unstemmed | A Structure Fidelity Approach for Big Data Collection in Wireless Sensor Networks |
| title_short | A Structure Fidelity Approach for Big Data Collection in Wireless Sensor Networks |
| title_sort | structure fidelity approach for big data collection in wireless sensor networks |
| topic | data collection spatial correlation wireless sensor network structure fidelity |
| url | http://www.mdpi.com/1424-8220/15/1/248 |
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