The Maritime industry is the backbone of global trade, with millions of vessels traversing the world’s oceans each year. Ensuring the safety and security of these vessels is of significant importance. With the advent of big data and advanced analytics, we are now able to monitor and predict maritime events in real-time, enhancing the safety and efficiency of maritime operations. In this blog post, we will explore two cutting-edge technologies, developed by MT (now owned by Kpler) in the context of PERIVALLON, designed to achieve these goals: (a) Maritime Event Detection and (b) Route Prediction.
Maritime Event Detection: A Distributed Framework for Real-Time Monitoring
Overview
Maritime Event Detection is a sophisticated system built on top of the Akka framework. This distributed framework is designed to handle large streams of Automatic Identification System (AIS) data, which provides real-time information about the location, speed, and course of vessels. By leveraging the power of Akka, this framework can process vast amounts of data in real-time, detecting suspicious maritime events that could indicate potential security threats or operational anomalies.
Key Features
- Real-Time Data Processing: The use of the actor model, implemented using the Akka framework, ensures that AIS data streams are processed in real-time, allowing for immediate detection of maritime events.
- Distributed Architecture: The system’s distributed nature ensures scalability and resilience, capable of handling data from thousands of vessels simultaneously.
- Event Detection Algorithms: Advanced algorithms are used to detect various suspicious events, such as AIS-OFF (when a vessel’s AIS transponder is turned off) and route deviations (when a vessel deviates from its expected route).
- Alert Mechanism: When a suspicious event is detected, the system generates alerts, enabling swift response from maritime authorities or vessel operators.
Route Prediction: Navigating the Unknown
Overview
Route Prediction is a complementary technology that addresses the challenge of predicting the path of a vessel that has gone “dark” by switching off its AIS transponder. This capability is crucial for maintaining maritime situational awareness and ensuring the continued monitoring of vessels, even when they attempt to avoid detection.
Key Features
- Real-time route prediction: Every time a vessel switches off its AIS transponder, the system predicts the route that the vessel followed in real-time.
- Scalability: The system relies on a scalable architecture, allowing for the prediction of routes for tens of thousands of vessels simultaneously
- Hybrid nature, combining the best of two worlds: Using a combination of real-time kinematic features and historical AIS data and advanced AI algorithms, the system can predict the likely route of a vessel based on its last known position and behavior patterns.
Applications
● Security: Detecting AIS-OFF events and predicting the route of the vessels while they were untracked, can help identify vessels attempting to evade detection, a common tactic in illegal fishing, smuggling, or piracy.
● Safety: Monitoring route deviations can alert operators to potential navigational errors, helping prevent accidents.
● Operational Efficiency: Real-time monitoring allows for better management of maritime traffic and logistics.
Conclusion
The integration of Maritime Event Detection and Route Prediction technologies marks a significant advancement in maritime safety and security. By leveraging real-time data processing, distributed architectures, and predictive analytics, these systems provide unparalleled situational awareness and proactive monitoring capabilities. As the maritime industry continues to evolve, these technologies will play a crucial role in ensuring safe, secure, and efficient maritime operations.