Insight into marine ecosystem dynamics and animal movements is critical to assess climate change impacts on biodiversity and managing ocean resources. Traditional oceanographic data collection faces accessibility, connectivity, and in situ analysis challenges. As a solution, proposed is FRANCIS, a novel real-time edge computing platform for autonomous marine data collection and visualization. FRANCIS seamlessly integrates satellite-based communications, specialized marine telemetry systems, and cloud infrastructure, to provide robust, global, and real-time oceanographic data communication and management capabilities. Initially validated, FRANCIS successfully processes live multi-sensor data streams from autonomous surface vehicles and reduces latency and system downtime for time-sensitive marine monitoring. The platform’s intuitive dashboard enables immediate visualization and informed decision-making across geo-referenced data like alkalinity, temperature, salinity, depth, and other essential oceanographic variables. FRANCIS is a robust foundation to incorporate machine learning to analyze data, predict marine mammal migration patterns, and understand underlying marine phenomena. FRANCIS offers a scalable, adaptable solution to enhance effectiveness of ocean monitoring initiatives like the Global Ocean Observing System and the Animal-Borne Ocean Sensors Network to advance state-of-the-art comprehensive marine telemetry and ecosystem efforts. Work sponsored by the OFI Transforming Climate Action Research Program.
Patel et al. (Wed,) studied this question.