A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks
| Autor: | Charith Perera, Gaby Bou Tayeh, Abdallah Makhoul, Jacques Demerjian |
|---|---|
| Přispěvatelé: | Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174) (FEMTO-ST), Université de Technologie de Belfort-Montbeliard (UTBM)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre National de la Recherche Scientifique (CNRS), Cardiff University (Cardiff University) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Adaptive sampling General Computer Science Computer science spatial-temporal correlation Real-time computing 02 engineering and technology Data loss [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE] data reconstruction QA76 Computer Science - Networking and Internet Architecture [INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR] FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering General Materials Science Electrical Engineering and Systems Science - Signal Processing Networking and Internet Architecture (cs.NI) General Engineering Sampling (statistics) 020206 networking & telecommunications Reconstruction algorithm Dissipation [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation Wireless sensor networks [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA] data reduction 020201 artificial intelligence & image processing [INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET] lcsh:Electrical engineering. Electronics. Nuclear engineering [INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC] lcsh:TK1-9971 Wireless sensor network Data reduction |
| Zdroj: | IEEE Access IEEE Access, IEEE, 2019, 7, pp.50669-50680. ⟨10.1109/ACCESS.2019.2910886⟩ IEEE Access, Vol 7, Pp 50669-50680 (2019) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/ACCESS.2019.2910886⟩ |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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