Link Characterization and Edge-Centric Predictive Modeling in an Ocean Network

Abstract

One of the critical problems fishermen face in deep-sea fishing is the lack of low-cost communication mechanisms to the shore. The Offshore Communication Network (OCN) is a network of fishing vessels at sea whose goal is to provide wireless internet over the ocean. The impact of extreme weather conditions on wireless signals, the inability to deploy additional infrastructure, the movements induced by sea waves, the expanded mobility freedom at sea, and the misalignment of directional antenna links are all unique challenges that cause abrupt signal quality fluctuations in OCN. For this reason, it is necessary to integrate near real-time link quality assessment to improve the resilience of communication. This paper examines the characteristics of marine wireless links and the factors impacting communication using data collected through sea-trial experiments involving multiple fishing vessels. The paper proposes a Bayesian framework for forecasting signal strength by employing historical and real-time data. This hybrid learning integrates offline and online probabilistic learning methods to provide intelligence at edge devices. The evaluation of the learning scheme on real datasets and the comparison with baseline methods under different communication contexts show improved predictive accuracy in OCN links.