Adaptive Sampling Approach Exploiting Spatio-Temporal Correlation and Residual Energy in Periodic Wireless Sensor Networks
Energy limitation is a major issue in wireless sensor networks where a high volume of redundant data is collected periodically and transmitted through the network. Therefore, efficient energy consumption is the key solution to maximize the network lifetime. This paper proposes an adaptive sampling a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10017289/ |
| _version_ | 1797902226130206720 |
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| author | Marwa Fattoum Zakia Jellali Leila Najjar Atallah |
| author_facet | Marwa Fattoum Zakia Jellali Leila Najjar Atallah |
| author_sort | Marwa Fattoum |
| collection | DOAJ |
| description | Energy limitation is a major issue in wireless sensor networks where a high volume of redundant data is collected periodically and transmitted through the network. Therefore, efficient energy consumption is the key solution to maximize the network lifetime. This paper proposes an adaptive sampling approach based on spatio-temporal correlation of collected data and on nodes residual energy. This approach aims to optimize sampling rates of sensor nodes while ensuring a high quality of the collected data. In addition, a data reconstruction method based on linear regression is adopted in the sink to reconstruct the missing samples due to the sampling rate reduction and adaptation compared to the case of a constant maximal sampling rate. We compared our approach with recently proposed adaptive sampling benchmark methods in different scenarios of data temporal correlation. Simulation results demonstrate the effectiveness of our proposed method in optimizing energy consumption by reducing the sampling rate while maintaining data quality. Our contribution can be applied to several fields, particularly, the field of water resources management. |
| first_indexed | 2024-04-10T09:14:21Z |
| format | Article |
| id | doaj.art-791c95663ac84b02b3a3068dcbf4f8ec |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T09:14:21Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-791c95663ac84b02b3a3068dcbf4f8ec2023-02-21T00:01:13ZengIEEEIEEE Access2169-35362023-01-01117670768110.1109/ACCESS.2023.323702410017289Adaptive Sampling Approach Exploiting Spatio-Temporal Correlation and Residual Energy in Periodic Wireless Sensor NetworksMarwa Fattoum0https://orcid.org/0000-0001-7977-6079Zakia Jellali1https://orcid.org/0000-0002-2350-6640Leila Najjar Atallah2COSIM, Sup’Com, Carthage University, Tunis, TunisiaCOSIM, Sup’Com, Carthage University, Tunis, TunisiaCOSIM, Sup’Com, Carthage University, Tunis, TunisiaEnergy limitation is a major issue in wireless sensor networks where a high volume of redundant data is collected periodically and transmitted through the network. Therefore, efficient energy consumption is the key solution to maximize the network lifetime. This paper proposes an adaptive sampling approach based on spatio-temporal correlation of collected data and on nodes residual energy. This approach aims to optimize sampling rates of sensor nodes while ensuring a high quality of the collected data. In addition, a data reconstruction method based on linear regression is adopted in the sink to reconstruct the missing samples due to the sampling rate reduction and adaptation compared to the case of a constant maximal sampling rate. We compared our approach with recently proposed adaptive sampling benchmark methods in different scenarios of data temporal correlation. Simulation results demonstrate the effectiveness of our proposed method in optimizing energy consumption by reducing the sampling rate while maintaining data quality. Our contribution can be applied to several fields, particularly, the field of water resources management.https://ieeexplore.ieee.org/document/10017289/Wireless sensor networkadaptive samplingspatio-temporal correlationresidual energydata reconstruction |
| spellingShingle | Marwa Fattoum Zakia Jellali Leila Najjar Atallah Adaptive Sampling Approach Exploiting Spatio-Temporal Correlation and Residual Energy in Periodic Wireless Sensor Networks IEEE Access Wireless sensor network adaptive sampling spatio-temporal correlation residual energy data reconstruction |
| title | Adaptive Sampling Approach Exploiting Spatio-Temporal Correlation and Residual Energy in Periodic Wireless Sensor Networks |
| title_full | Adaptive Sampling Approach Exploiting Spatio-Temporal Correlation and Residual Energy in Periodic Wireless Sensor Networks |
| title_fullStr | Adaptive Sampling Approach Exploiting Spatio-Temporal Correlation and Residual Energy in Periodic Wireless Sensor Networks |
| title_full_unstemmed | Adaptive Sampling Approach Exploiting Spatio-Temporal Correlation and Residual Energy in Periodic Wireless Sensor Networks |
| title_short | Adaptive Sampling Approach Exploiting Spatio-Temporal Correlation and Residual Energy in Periodic Wireless Sensor Networks |
| title_sort | adaptive sampling approach exploiting spatio temporal correlation and residual energy in periodic wireless sensor networks |
| topic | Wireless sensor network adaptive sampling spatio-temporal correlation residual energy data reconstruction |
| url | https://ieeexplore.ieee.org/document/10017289/ |
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