Vessels serve as distributed mobile sensors, hence, Machine Learning will happen in-situ (on-board) of the vessels (close to the data production). This will enable efficient data operations across the maritime data life cycle and will lead to better situational awareness, increased safety, and reduction of illegal activity.
Technologies used
MobiSpaces delivers an end-to-end mobility-aware and mobility optimised Data Governance Platform. It is surrounded by a Green & Environmental Dimensioning Workbench for the monitoring and advising of the processing behaviour, making suren guidelines and legislations are complied with towards minimising the environmental footprint. MobiSpaces envisions a set of toolboxes, suites, and tools that implement the MobiSpaces concept in its use case, see them showcased below.
Declarative Querying
Decentralized Data Management
Online Data Aggregator
Edge-driven Federated Learning
Visual Analytics
What will MobiSpaces improve?
Performance in re-scheduling the service to peak period will be improved in the following Key Performance Indicators:
successful deployment of a novel smart AIS system (target: deployments on 3 ships, baseline: current vessel AIS systems are only used for on-board situation awareness)
extended AIS coverage beyond the reach of coastal AIS antennas (target: extend AIS coverage on-demand by linking sensors on-board of moving vessels, baseline: current terrestrial AIS coverage at the use case location)
Who is involved?
