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Open AccessArticle

Energy-Efficient Channel Coding Strategy for Underwater Acoustic Networks

Postgraduate Program in Computer Science (PPGIa), Pontifical Catholic University of Parana, Paraná 80215-901, Curitiba, Brazil
Graduate Program in Electrical and Computer Engineering (CPGEI), Federal University of Technology, Paraná 80230-901, Curitiba, Brazil
Department of Electrical Engineering, Federal University of Santa Catarina, Santa Catarina 88040-900, Florianópolis, Brazil
Author to whom correspondence should be addressed.
Academic Editor: Jaime Lloret Mauri
Sensors 2017, 17(4), 728;
Received: 20 December 2016 / Revised: 22 March 2017 / Accepted: 24 March 2017 / Published: 31 March 2017
(This article belongs to the Special Issue Advances and Challenges in Underwater Sensor Networks)
Underwater acoustic networks (UAN) allow for efficiently exploiting and monitoring the sub-aquatic environment. These networks are characterized by long propagation delays, error-prone channels and half-duplex communication. In this paper, we address the problem of energy-efficient communication through the use of optimized channel coding parameters. We consider a two-layer encoding scheme employing forward error correction (FEC) codes and fountain codes (FC) for UAN scenarios without feedback channels. We model and evaluate the energy consumption of different channel coding schemes for a K-distributed multipath channel. The parameters of the FEC encoding layer are optimized by selecting the optimal error correction capability and the code block size. The results show the best parameter choice as a function of the link distance and received signal-to-noise ratio. View Full-Text
Keywords: underwater acoustic networks; forward error correction codes; fountain codes; energy efficiency underwater acoustic networks; forward error correction codes; fountain codes; energy efficiency
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Barreto, G.; Simão, D.H.; Pellenz, M.E.; Souza, R.D.; Jamhour, E.; Penna, M.C.; Brante, G.; Chang, B.S. Energy-Efficient Channel Coding Strategy for Underwater Acoustic Networks. Sensors 2017, 17, 728.

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