A distributed lightning fast maritime anomaly detection service
K.Chatzikokolakis, D.Zissis,M.Vodas, G.Spiliopoulos,I.Kontopoulos
OCEANS 2019 MTS/IEEE, 2019
Abstract
For applications such as navigation and others, timeliness is a top priority; making the right decision steering a vessel away from danger, is only useful if it is a decision made in due time. Effectiveness of such time critical computing systems is dependent not only on the accuracy of the result produced, but also on the time in which it was computed. In this paper, we present a distributed architecture capable of detecting possible collisions, groundings, unexpected communication gaps and travel pattern deviations of ships fitted with Automatic Identification System transponders. Our results demonstrate that the proposed system can achieve good accuracy and sub second latency in real world conditions.
This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 732310.
.webp)
.webp)
Previous
Next
Navigating through dense waters: a toolbox for creating maritime density maps
A Big Data framework for Modelling and Simulating high-resolution hydrodynamic models in sea harbours
Modelling and simulating vessel emissions in real time based on terrestrial AIS data
Real-time maritime anomaly detection: detecting intentional AIS switch-off
A comparison of supervised learning schemes for the detection of search and rescue (SAR) vessel patterns
BigDataOcean Project: Early Anomaly Detection from Big Maritime Vessel Traffic Data
Countering Real-Time Stream Poisoning: An Architecture for Detecting Vessel Spoofing in Streams of AIS Data
Determining the bilge water waste risk and management in the Gulf of Antalya by the Monte Carlo method
FleetMon Supports the Development of Environmental Impact Assessment on the Brazilian Coast
Scrapping Probabilities and Committed CO2 Emissions of the International Ship Fleet
Scalable In-Database Machine Learning for the Prediction of Port-to-Port Routes
How a real-time-based sea traffic forecast helps to organize and optimize the flow of maritime goods
Generating a node in an AIS-based routing graph for improved Estimated Time of Arrival. (Big) Data challenge: using AIS for generating a routing graph
Composition, spatial distribution and sources of macro-marine litter on the Gulf of Alicante seafloor (Spanish Mediterranean)
Climate change, non-indigenous species and shipping: assessing the risk of species introduction to a high-Arctic archipelago
The Big Picture: An Improved Method for Mapping Shipping Activities
GMSA: A Digital Twin Application for Maritime Route and Event Forecasting
Patterns of Life : Global Inventory for maritime mobility patterns
Require AIS data for your project?
Please reach out to us to discuss which Kpler offering will support your research project or academic study in the best possible and efficient way.
