Machine learning based underwater optical-acoustic communications channel switching for throughput improvement.

Stewart, Craig; Muhammad, Aminu; Fung, Wai-Keung; Fough, Nazila; Prabhu, Radhakrishna

Abstract

Underwater Wireless Optical Communication (UWOC) is at the cutting edge of subsea networking, offering high capacity, low-latency, energy-efficient connectivity that offers many advantages over the acoustic standard which has been embedded in submersible systems for a century. One aspect in which it fails currently, however, is transmission range and reliability, only achieving 10s of metres in range and requiring Line of Sight (LOS) to operate, meaning that changes of turbidity and ambient noise originating from the Sun or ROV light sources can actively interfere with transmission success. An investigation into machine learning algorithms has been carried out that aimed to enable a modem to utilise environmental sensors to interpret the UWOC channel and make accurate predictions on whether it should transmit, potentially store in memory for later transmission, at the cost of latency, when the channel is clearer, or use another mechanism such as acoustics or radio frequency to transmit promptly, with minimal latency. It was found using a synthesized dataset compiled using simulation and a regarded photon-counting model, that common ML algorithms such as Support Vector Machines (SVM), Random Forest (RF) and Narrow-Neural Networks (N-NN) can successfully use parameters such as distance, transmission power and extinction coefficient to determine the nature of the channel, thus, whether to transmit or not, with classification accuracies greater than 98.5% providing a reliable method to switch between acoustic and optical signalling in response to channel conditions in the latter, maximising data throughput, reliability whilst managing energy consumption and latency.

Citation

STEWART, C., MUHAMMAD, A., FUNG, W.-K., FOUGH, N. and PRABHU, R. 2024. Machine learning based underwater optical-acoustic communications channel switching for throughput improvement. In Proceedings of the 2024 IEEE (Institute of Electrical and Electronics Engineers) International workshop on Metrology for the sea; learning to measure sea health parameters (IEEE MetroSea 2024), 14-16 October 2024, Portorose, Slovenia. Piscataway: IEEE [online], pages 46-51. Available from: https://doi.org/10.1109/MetroSea62823.2024.10765751

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2024 IEEE (Institute of Electrical and Electronics Engineers) International workshop on Metrology for the sea; learning to measure sea health parameters (IEEE MetroSea 2024)
• Start Date: Oct 14, 2024
• End Date: Oct 16, 2024
• Acceptance Date: Jul 13, 2024
• Online Publication Date: Oct 14, 2024
• Publication Date: Dec 31, 2024
• Deposit Date: Dec 6, 2024
• Publicly Available Date: Dec 20, 2024
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Pages: 46-51
• ISBN: 9798350379006
• DOI: https://doi.org/10.1109/metrosea62823.2024.10765751
• Keywords: Support vector machines; Wireless communication; Wireless sensor networks; Accuracy; Optical switches; Optical variables measurement; Throughput; Acoustics; Reliability; Optical sensors
• Public URL: https://rgu-repository.worktribe.com/output/2613801