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Sensors 2018, 18(3), 747; doi:10.3390/s18030747

LESS: Link Estimation with Sparse Sampling in Intertidal WSNs

College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Department of Computer Science, University of Texas at Austin, Austin, TX 78712, USA
Department of Computer Science, University of South Carolina Columbia, Columbia, SC 29208, USA
Author to whom correspondence should be addressed.
Received: 17 January 2018 / Revised: 25 February 2018 / Accepted: 26 February 2018 / Published: 1 March 2018
(This article belongs to the Special Issue Sensor Networks for Environmental Observations)
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Deploying wireless sensor networks (WSN) in the intertidal area is an effective approach for environmental monitoring. To sustain reliable data delivery in such a dynamic environment, a link quality estimation mechanism is crucial. However, our observations in two real WSN systems deployed in the intertidal areas reveal that link update in routing protocols often suffers from energy and bandwidth waste due to the frequent link quality measurement and updates. In this paper, we carefully investigate the network dynamics using real-world sensor network data and find it feasible to achieve accurate estimation of link quality using sparse sampling. We design and implement a compressive-sensing-based link quality estimation protocol, L E S S , which incorporates both spatial and temporal characteristics of the system to aid the link update in routing protocols. We evaluate L E S S in both real WSN systems and a large-scale simulation, and the results show that L E S S can reduce energy and bandwidth consumption by up to 50 % while still achieving more than 90 % link quality estimation accuracy. View Full-Text
Keywords: link quality reconstruction; topology construction; compressive sensing; wireless sensor networks; environmental monitoring link quality reconstruction; topology construction; compressive sensing; wireless sensor networks; environmental monitoring

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhou, X.; Ji, X.; Chen, Y.-C.; Li, X.; Xu, W. LESS: Link Estimation with Sparse Sampling in Intertidal WSNs. Sensors 2018, 18, 747.

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