The deep ocean, below 200 m water depth, and the open ocean environment above it, are largest habitats but least observed ones of our planet. These highly dynamic systems provide critical climate regulation and at the same time hold a wealth of energy, mineral, and biological resources. In the open ocean, biological, chemical, and physical processes are generally influenced by larger-scale physical and climatic forcing, like geostrophic currents and climate change, respectively, that require long-term observational strategies to determine trends and effects. Therefore, research of the ocean is of great interest and requires now a further expansion of the observing community - academia, industry, Non-governmental organizations (NGO), national governments, international governmental organizations, and the public at large - to unlock the critical knowledge contained in the ocean over upcoming decades, and to realize the mutual benefits of marine observation for all elements of a sustainable ocean.

NAUTILOS - New Approach to Underwater Technologies for Innovative, Low-cost Ocean observation - will fill-in marine observation and modeling gaps for chemical, biological and deep ocean physics variables through the development of a new generation of cost-effective sensors and samplers. Furthermore, the project will force the integration of the aforementioned technologies within observing platforms and their deployment in large-scale demonstrations in European seas. The fundamental aim of the project will be to complement and expand current European observation tools and services, to obtain a collective data investigation at a much higher spatial resolution, temporal regularity and observation length than currently available at the European scale. This enables a further democratization of the monitoring of the marine environment to both traditional and non-traditional data users.

NAUTILOS is an H2020 project funded under the Future of Seas and Oceans Flagship Initiative, coordinated by the National Research Council of Italy (CNR). It brings together a significant group of 21 entities from 11 European countries with multidisciplinary expertise, among which are ocean instrumentation development and integration, ocean sensing and sampling instrumentation, data processing, modeling and control, as well as operational oceanography, bio-geochemistry, water and climate change science.

The next step in the evolution of marine monitoring systems will be the widespread adoption of autonomous in situ sensing. There is a need to explore and test new technological solutions that will lower the costs of acquiring, deploying and maintaining of observation stations to fill the gaps of ocean in situ observation systems. To achieve this it is required to use samplers in an effective way (since they have only a limited storage for probes), monitoring sensors and self-supervise sensor measurements to reduce the necessity of manual inspection and maintenance of observation stations. This also requires an intelligent control of the observation itself to adopt the frequency of measurements and sampling based on relevant situation or detected events. The DFKI will contribute to the project by using its expertise of data analysis and machine learning to develop intelligent autonomous sampling and self-monitoring systems. DFKI will analyze and develop methods for real time data stream analysis that combine measures of different sensors and platforms to realize an event based online adaptation of sampling strategies.


  • The Nаtionаl Reseаrch Council (CNR) Coordinator
  • Hellenic Centre for Mаrine Reseаrch (HCMR)
  • Norwegian Institute for Nature Research (NIVA)
  • Finnish Environment Institute (SYKE)
  • French Research Institute for Exploitation of the Sea (Ifremer)
  • French National Centre for Scientific Research (CNRS)
  • Edgelаb s.r.l.
  • University of Аlgаrve (UALG)
  • NKE Instrumentаtion
  • SubCtech (SCT)
  • Centro de Engenhаriа e Desenvolvimento (Аssociаção) (CEIIA)
  • Hаute Ecole Spéciаlisée de Suisse Occidentаle (HES-SO)
  • Centre Suisse d'Electronique et de Microtechnique SА (CSEM)
  • University of Ljubljаnа, Fаculty of Electricаl Engineering (UL-FE)
  • Fundаção EurOceаn (EUROCEAN)
  • University of Cаlаbriа, Depаrtment of Environmentаl Engineering (DIAM)
  • Instituto do Mаr (IMAR)


EU - European Union

This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No. 101000825

EU - European Union

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German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz