Optimizing energy efficiency in underwater acoustic networks through machine learning classifiers.

Stewart, Craig; Fough, Faranak; Fough, Nazila; Prabhu, Radhakrishna

Abstract

Among many challenges in establishing an Underwater Wireless Sensor Network, is the challenge of resource constraints, battery and bandwidth being limited which renders acoustic networks limited in life and application. One identified application of TinyML is the potential of cutting the demand for network resources on the Internet of Things. Based on this hypothesis, this paper attempts to quantify the potential in using machine learning algorithms at the edge of the underwater network to reduce the burden on the battery powered acoustic node through an example automated of pipeline corrosion detection by transmitting only extracted conclusions from data.

Citation

STEWART, C., FOUGH, F., FOUGH, N. and PRABHU, R. 2024. Optimizing energy efficiency in underwater acoustic networks through machine learning classifiers. In Proceedings of the 31st IEEE (Institute of Electrical and Electronics Engineers) International conference on electronics, circuits, and systems (IEEE ICECS 2024), 18-20 November 2024, Nancy, France. Piscataway: IEEE [online], 10848718. Available from: https://doi.org/10.1109/ICECS61496.2024.10848718

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 31st IEEE (Institute of Electrical and Electronics Engineers) International conference on electronics circuits and systems 2024 (IEEE ICECS2024)
• Start Date: Nov 18, 2024
• End Date: Nov 20, 2024
• Acceptance Date: Jul 29, 2024
• Online Publication Date: Jan 28, 2025
• Publication Date: Dec 31, 2024
• Deposit Date: Feb 20, 2025
• Publicly Available Date: Feb 20, 2025
• Print ISSN: 2994-5755
• Electronic ISSN: 2995-0589
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Article Number: 10848718
• DOI: https://doi.org/10.1109/ICECS61496.2024.10848718
• Keywords: ML; Pipeline monitoring; Underwater acoustics; Underwater internet of things; Underwater wireless sensor networks
• Public URL: https://rgu-repository.worktribe.com/output/2709308