Federated Learning and Trajectory Compression for Enhanced AIS Coverage
This addresses enhanced maritime monitoring for safety and security applications, but it appears incremental as it combines existing techniques (federated learning and compression) for a specific domain.
The paper tackles the problem of limited maritime situational awareness due to insufficient AIS coverage by developing the VesselEdge system, which uses federated learning and trajectory compression to transform vessels into mobile sensors; preliminary results show it effectively improves AIS coverage and situational awareness.
This paper presents the VesselEdge system, which leverages federated learning and bandwidth-constrained trajectory compression to enhance maritime situational awareness by extending AIS coverage. VesselEdge transforms vessels into mobile sensors, enabling real-time anomaly detection and efficient data transmission over low-bandwidth connections. The system integrates the M3fed model for federated learning and the BWC-DR-A algorithm for trajectory compression, prioritizing anomalous data. Preliminary results demonstrate the effectiveness of VesselEdge in improving AIS coverage and situational awareness using historical data.