Low-Cost, Intelligent Drifter Fleet for Large-Scale, Distributed Ocean Observation

• Published in: OCEANS 2022, Hampton Roads pp. 1 - 8
• Main Authors: Cocker, Eric, Bert, Julie A., Torres, Francisco, Shreve, Matthew, Kalb, Jamie, Lee, Joseph, Poimboeuf, Michael, Fautley, Paloma, Adams, Samuel, Lee, Joanne, Lu, Jengping, Chua, Chris, Chang, Norine, Neltner, Steven, Gray, Michael
• Format: Conference Proceeding
• Language: English
• Published: IEEE 17.10.2022
• Subjects: anomaly detection; Cloud computing; cloud-edge algorithms; data reduction; Distributed databases; edge processing; environmental data; lagrangian drifter; maritime domain awareness; ocean circulation; Oceans; Roads; Satellites; Sensors; Turning; vessel tracking
• DOI: 10.1109/OCEANS47191.2022.9977209

We have developed Persistent Environmental Awareness Reporting and Location (PEARL) ocean drifters. PEARL drifters are small, rugged, low-cost, autonomous, environmentally friendly ocean drifters that represent a significant opportunity for high-impact applications in both national security and environmental ecosystem monitoring. Drifters record and report data which is processed by advanced edge analytics before being compressed for satellite transmission to populate a large data repository with sensor data that is combined and analyzed to discover signals of interest in the ocean environment with the goal of increasing distributed maritime awareness. Each drifter is entirely self-contained, powered by solar panels and backup batteries, with a large array of sensors, compute module for onboard data processing, and satellite modem for data reporting. The drifter architecture is flexible and can be customized for a specific purpose. The complete data record is stored locally and processed by the onboard compute module, which runs anomaly detection algorithms that detect nearby activity. Anomalous events, as well as baseline environmental data, are reported to a cloud database using satellite short burst data transmission. Though each independent drifter is a powerful sensing tool, the low unit cost permits large scale deployment. To date thousands of drifters have been deployed over vast areas of the ocean and are reporting data to a remote database where cloud-based analytics algorithms develop global situational awareness and update local edge algorithms on the drifters based on learnings across the full network.