The state of the oceans and marine life is an essential factor in the Earth’s climate and primary production, as well as seafood through fisheries and aquaculture. The oceans are huge and complex, with important phenomena ranging from micro- to macro-scales in space and time. Our ability to acquire data, analyze the data and build knowledge about ocean science is strongly limited by the technological tools available.

The approach taken is to develop methods to design, implement and operate a system-of-systems (SoS) consisting of small satellites, marine robots and drones, innovative bio-sensor systems, internet of things, AI/autonomy/algorithms, model-based systems engineering, and ocean modelling. Out hypothesis is that this will offer a powerful and novel technology platform that can be used to build a cyber-physical SoS for ocean monitoring with significantly enhanced resolution across space, time, and features when compared to what can be achieved with the individual technologies and assets.

The project will develop a general framework for real-time integrated ocean environmental monitoring, implement a proof-of-concept based on existing assets available to the partners, and demonstrate the usefulness in a challenging application. The selected case study is real-time monitoring of harmful algal blooms (HABs) in the context of aquaculture. The success of this highly ambitious project is disruptive and interdisciplinary, and it will enable digital transformation and sustainability in ocean sciences and aquaculture.

The expected long term impact is an approach to ocean monitoring for use in ocean science and industries that depend on the ocean. The coordinated use of innovative technologies such as small satellites, robots, drones and miniaturized sensor systems is expected to be a game-changer. The current ocean environmental monitoring strategies and standard methods used by government agencies, industry and research institutions are labor-intensive and expensive, as they are based on manual field sample collection and laboratory analysis. Current practice for HAB monitoring and warning is expected to be impacted by:
1. Improving accuracy of predictions in space and time.
2. Reducing monitoring costs by limiting the need for ship surveys and manual lab analyses.
3. Diminishing the time for identification of possible HABs events.
4. Provide fast decisions during a HAB for the industry (especially aquaculture, fisheries), wild-life, and food security (e.g., toxic blue mussels).

NTNU's industrial partners are Grieg Seafood and Moen Marin.

Key personnell

• Aria Alinejad, Dept. Engr. Cybernetics, PhD candidate
• Bjørn Kristiansen, Dept. Engr. Cybernetics, Postdoc
• Stephen Grant, Dept. Biology, Researcher
• Roger Birkeland, Dept. Electronic Systems, Researcher
• Joseph Garrett, Dept. Engr. Cybernetics, Researcher
• P. S. Vishnu, Dept. Engr. Cybernetics, Postdoc
• Prof. Tor Arne Johansen, Dept. Engr. Cybernetics, project leader
• Glaucia Fragoso, Dept. Biology, Researcher
• Prof. Geir Johnsen, Dept. Biology
• Assoc. Prof. Morten Alver, Dept. Engr. Cybernetics
• Assoc. Prof. Milica Orlandic, Dept. Electronic Systems