Maritime Linked Data for Situational Awareness Heterogeneous Sensor Networks

Abstract

Maritime heterogeneous sensor networks (HSN), which are widely used in support of marine monitoring, maritime security and safety applications, combine diversified sensing systems and platforms observing the state of environmental features of interest or the status of anthropogenic objects to achieve awareness on the situation. To model the information characteristics of HSN observations for maritime situational awareness (MSA), we developed the MSA-HSN ontology, an integrated semantic model for maritime linked data, specifically observations suiting the requirements of maritime information fusion systems. MSA-HSN integrates established ontologies and models for sensors, observations, measurements, quantities, and occurrents, tailored to MSA applications and requirements. To support the interoperability with existing maritime data models, MSA-HSN is aligned with the relevant aspects of the EUCISE/e-CISE (Common Information Sharing Environment) data models. As a validation use case, in this paper the MSA-HSN ontology is applied to model the information elements of the maritime surveillance system developed within the Interactive Extreme-Scale Analytics and Forecasting (INFORE) project, where different situational views offered by a variegate suite of sensors and platforms are fused using big data analytics to achieve maritime situational awareness for maritime security. The paper describes the design of the MSA-HSN ontology, illustrating its application through examples taken from the INFORE use case and from relevant datasets developed by recent European projects involving MSA use cases.