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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| Veröffentlicht in: | IEEE access 2019, Vol.7, p.50669-50680 |
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| creator | Tayeh, Gaby Bou Makhoul, Abdallah Perera, Charith Demerjian, Jacques |
| 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. |
| doi_str_mv | 10.1109/ACCESS.2019.2910886 |
| format | Article |
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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. 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(IEEE) 2019</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c372t-4b6bdf08ef3836ad3b5e8a42ef25cee7c8f534bdb86afbd65babe26c21b30e1e3</citedby><cites>FETCH-LOGICAL-c372t-4b6bdf08ef3836ad3b5e8a42ef25cee7c8f534bdb86afbd65babe26c21b30e1e3</cites><orcidid>0000-0003-0485-097X ; 0000-0002-0190-3346</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8689010$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,864,885,2102,4024,27633,27923,27924,27925,54933</link.rule.ids><backlink>$$Uhttps://hal.science/hal-03020525$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Tayeh, Gaby Bou</creatorcontrib><creatorcontrib>Makhoul, Abdallah</creatorcontrib><creatorcontrib>Perera, Charith</creatorcontrib><creatorcontrib>Demerjian, Jacques</creatorcontrib><title>A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks</title><title>IEEE access</title><addtitle>Access</addtitle><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.</description><subject>Adaptation models</subject><subject>Adaptive sampling</subject><subject>Algorithms</subject><subject>Computer Science</subject><subject>Correlation</subject><subject>Cryptography and Security</subject><subject>Data collection</subject><subject>Data loss</subject><subject>data reconstruction</subject><subject>Data reduction</subject><subject>Datasets</subject><subject>Distributed, Parallel, and Cluster Computing</subject><subject>Emerging Technologies</subject><subject>Energy dissipation</subject><subject>Modeling and Simulation</subject><subject>Monitoring</subject><subject>Multiagent Systems</subject><subject>Optimization</subject><subject>Predictive models</subject><subject>Reconstruction algorithms</subject><subject>Sensors</subject><subject>Software Engineering</subject><subject>spatial-temporal correlation</subject><subject>Ubiquitous Computing</subject><subject>Wireless networks</subject><subject>Wireless sensor networks</subject><subject>Workstations</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpVkc1u1DAUhaMKpFalT9BNJFYsMvh_7GUIhVYagcSUVRfWtXPdZkjHwc6AeHs8TVWBN7aOz_mu7FNVl5SsKCXmfdt1V9vtihFqVsxQorU6qc4YVabhkqtX_5xPq4ucd6QsXSS5Pqvu2no7wTzA2Nzi4xQTjHUXU8KxiHFft9OUIviHOsRUf4QZ6m_YH_zT3bCvu_GQZ0zNB8jY11vc52L7gvPvmH7kN9XrAGPGi-f9vPr-6eq2u242Xz_fdO2m8XzN5kY45fpANAauuYKeO4kaBMPApEdcex0kF653WkFwvZIOHDLlGXWcIEV-Xt0s3D7Czk5peIT0x0YY7JMQ072FNA9-RCu8lk4aZcooIZnUQQtJDDBmpBSeF9a7hfUA43-o63ZjjxrhhJGS_EWL9-3iLV_084B5trt4SPvyVMuElIquqVDFxReXTzHnhOEFS4k9FmiXAu2xQPtcYEldLqkBEV8SWmlDKOF_ATs-lY4</recordid><startdate>2019</startdate><enddate>2019</enddate><creator>Tayeh, Gaby Bou</creator><creator>Makhoul, Abdallah</creator><creator>Perera, Charith</creator><creator>Demerjian, Jacques</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Adaptation models Adaptive sampling Algorithms Computer Science Correlation Cryptography and Security Data collection Data loss data reconstruction Data reduction Datasets Distributed, Parallel, and Cluster Computing Emerging Technologies Energy dissipation Modeling and Simulation Monitoring Multiagent Systems Optimization Predictive models Reconstruction algorithms Sensors Software Engineering spatial-temporal correlation Ubiquitous Computing Wireless networks Wireless sensor networks Workstations |
| title | A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks |
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