A spatial-temporal correlation approach for data reduction in cluster-based sensor networks

Tayeh, Gaby, Makhoul, Abdallah, Perera, Charith ORCID: https://orcid.org/0000-0002-0190-3346 and Demerjian, Jacques 2019. A spatial-temporal correlation approach for data reduction in cluster-based sensor networks. IEEE Access 7 , pp. 50669-50680. 10.1109/ACCESS.2019.2910886

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Abstract

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 “nonsampled” 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 nonstationary data more effectively.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
ISSN:	2169-3536
Funders:	This work is partially funded by the EIPHI Graduate School (contract "ANR-17-EURE-0002"), the France-Suisse Interreg RESponSE project, Lebanese University Re-search Program (Number: 4/6132), and EPSRC PETRAS 2(EP/S035362/1).
Date of First Compliant Deposit:	12 April 2019
Date of Acceptance:	6 April 2019
Last Modified:	05 May 2023 16:38
URI:	https://orca.cardiff.ac.uk/id/eprint/121674

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