Federated Learning and Trajectory Compression for Enhanced AIS Coverage

TL;DR

VesselEdge combines federated learning and trajectory compression to improve maritime AIS coverage, enabling real-time anomaly detection and efficient data sharing over low-bandwidth networks.

Contribution

The paper introduces VesselEdge, a novel system integrating federated learning and trajectory compression for enhanced maritime situational awareness.

Findings

Improved AIS coverage with VesselEdge

Effective anomaly detection in real-time

Efficient data transmission over bandwidth-limited channels

Abstract

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.