A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks
In a resource-constrained Wireless Sensor Networks (WSNs), the optimization of the sampling and the transmission rates of each individual node is a crucial issue. A high volume of redundant data transmitted through the network will result in collisions, data loss, and energy dissipation. This paper...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8689010/ |
| _version_ | 1819179348251377664 |
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| author | Gaby Bou Tayeh Abdallah Makhoul Charith Perera Jacques Demerjian |
| author_facet | Gaby Bou Tayeh Abdallah Makhoul Charith Perera Jacques Demerjian |
| author_sort | Gaby Bou Tayeh |
| collection | DOAJ |
| description | In a resource-constrained Wireless Sensor Networks (WSNs), the optimization of the sampling and the transmission rates of each individual node is a crucial issue. A high volume of redundant data transmitted through the network will result in collisions, data loss, and energy dissipation. This paper proposes a novel data reduction scheme that exploits the spatial-temporal correlation among sensor data in order to determine the optimal sampling strategy for the deployed sensor nodes. This strategy reduces the overall sampling/transmission rates while preserving the quality of the data. Moreover, a back-end reconstruction algorithm is deployed on the workstation (Sink). This algorithm can reproduce the data that have not been sampled by finding the spatial and temporal correlation among the reported data set, and filling the “non-sampled” parts with predictions. We have used real sensor data of a network that was deployed at the Grand-St-Bernard pass located between Switzerland and Italy. We tested our approach using the previously mentioned data-set and compared it to a recent adaptive sampling based data reduction approach. The obtained results show that our proposed method consumes up to 60% less energy and can handle non-stationary data more effectively. |
| first_indexed | 2024-12-22T21:57:01Z |
| format | Article |
| id | doaj.art-4c85b596938345258f84509a229554c3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T21:57:01Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4c85b596938345258f84509a229554c32022-12-21T18:11:12ZengIEEEIEEE Access2169-35362019-01-017506695068010.1109/ACCESS.2019.29108868689010A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor NetworksGaby Bou Tayeh0Abdallah Makhoul1https://orcid.org/0000-0003-0485-097XCharith Perera2https://orcid.org/0000-0002-0190-3346Jacques Demerjian3Femto-St Institute, UMR 6174 CNRS, Université de Bourgogne Franche-Comté, Besançon, FranceFemto-St Institute, UMR 6174 CNRS, Université de Bourgogne Franche-Comté, Besançon, FranceCardiff University, Cardiff, U.K.LARIFA-EDST, Faculty of Sciences, Lebanese University, Fanar, LebanonIn a resource-constrained Wireless Sensor Networks (WSNs), the optimization of the sampling and the transmission rates of each individual node is a crucial issue. A high volume of redundant data transmitted through the network will result in collisions, data loss, and energy dissipation. This paper proposes a novel data reduction scheme that exploits the spatial-temporal correlation among sensor data in order to determine the optimal sampling strategy for the deployed sensor nodes. This strategy reduces the overall sampling/transmission rates while preserving the quality of the data. Moreover, a back-end reconstruction algorithm is deployed on the workstation (Sink). This algorithm can reproduce the data that have not been sampled by finding the spatial and temporal correlation among the reported data set, and filling the “non-sampled” parts with predictions. We have used real sensor data of a network that was deployed at the Grand-St-Bernard pass located between Switzerland and Italy. We tested our approach using the previously mentioned data-set and compared it to a recent adaptive sampling based data reduction approach. The obtained results show that our proposed method consumes up to 60% less energy and can handle non-stationary data more effectively.https://ieeexplore.ieee.org/document/8689010/Wireless sensor networksdata reconstructionspatial-temporal correlationdata reduction |
| spellingShingle | Gaby Bou Tayeh Abdallah Makhoul Charith Perera Jacques Demerjian A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks IEEE Access Wireless sensor networks data reconstruction spatial-temporal correlation data reduction |
| title | A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks |
| title_full | A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks |
| title_fullStr | A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks |
| title_full_unstemmed | A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks |
| title_short | A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks |
| title_sort | spatial temporal correlation approach for data reduction in cluster based sensor networks |
| topic | Wireless sensor networks data reconstruction spatial-temporal correlation data reduction |
| url | https://ieeexplore.ieee.org/document/8689010/ |
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