Projektlogo An Open, Trusted Fog Computing Platform Facilitating the Deployment, Orchestration and Management of Scalable, Heterogeneous and Secure IoT Services and cross-Cloud Apps


An Open, Trusted Fog Computing Platform Facilitating the Deployment, Orchestration and Management of Scalable, Heterogeneous and Secure IoT Services and cross-Cloud Apps

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The vision of RAINBOW is to design and develop an open and trusted fog computing platform that facilitates

the deployment and management of scalable, heterogeneous and secure IoT services and cross-cloud applications (i.e, microservices). RAINBOW falls within the bigger vision of delivering a platform enabling users to remotely control the infrastructure that is running, potentially, on hundreds of edge devices (e.g., wearables),

thousands of fog nodes in a factory building or flying in the sky (e.g., drones), and millions of vehicles travelling in

a certain area or across Europe. RAINBOW aspires to enable fog computing to reach its true potential by providing the deployment, orchestration, network fabric and data management for scalable and secure edge

applications, addressing the need to timely process the ever-increasing amount of data continuously gathered

from heterogeneous IoT devices and appliances. Our solution will provide significant benefits for popular cloud

platforms, fog middleware, and distributed data management engines, and will extend the open-source ecosystem by pushing intelligence to the network edge while also ensuring security and privacy primitives across the

device-fog-cloud-application stack. To evaluate its wide applicability, RAINBOW will be demonstrated in

various real world and demanding scenarios, such as automated manufacturing (Industry 4.0), connected vehicles

and critical infrastructure surveillance with drones. These application areas are safety-critical and demanding;

requiring guaranteed extra-functional properties, including real-time responsiveness, availability, data freshness,

efficient data protection and management, energy-efficiency and industry-specific security standards.

Duration 01.01.2020 - 31.12.2022, Funded by H2020

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Projektlogo Gamifiziertes KI-Assistenzsystem zur Unterstützung des manuellen Montageprozesses
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Gamified AI Assistance System for Support of Manual Assembly Processes

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In this research project, a novel assistance system for manual assembly stations based on artificial intelligence will be developed. On the one hand, the system monitors the assembly process and verifies the quality of the completed product, and, on the other hand, it considers and individually supports the employee when working at the manual workstation. The system will analyse the sensory information collected at the assembly station using image processing and machine learning methods with regard to the ergonomic and production-related work situation of the employee. This enables the newly developed assistance system to adapt to the individual needs of the employee in order to improve his work situation through specific support as well as motivation and training strategies. Furthermore, by monitoring both progress and assembly components, the system will increase the efficiency and quality of the manual assembly process.

Duration 01.06.2019 - 30.11.2020, Funded by EFRE: Europäischer Fonds für regionale Entwicklung
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Complementary application of mathematical and discrete-event models to solve complex planning and control problems in offshore construction logistics

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Offshore construction logistics pose an exceptionally challenging problem in terms of planning and control. Generally, one can differentiate two approaches: event-discrete simulations as well as mathematical or stochastic optimizations. By themselves, both methods provide their own advantages and disadvantages in terms of computational time, level of detail und optimality.

This project aims to investigate new ways for the complementary utilization of both types of methods in the context of offshore construction logistics. Under the basic assumption that despite formal differences, both types of models describe the same elements of the real world system, this project aims to develop a method to convert in between or to generate each kind of model with its own level of aggregation/abstraction based on a more basic description of the real world system. Consequently, the advantage of both types of models can be used complementary within computer aided planning and control methods.

Duration 01.04.2019 - 30.09.2021, Funded by DFG
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LNG Armaturen Set

Development of a sensitive valve set for high-volume ship to ship LNG transfer

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The project aims at the development of a system which can be used on a large number of different ship types and thus leads to a significantly higher level of safety, installability and maintainability while at the same time reducing costs. The task of BIBA is to develop an Augmented Reality (AR) solution that can be used for maintenance and service purposes alongside the valve set.

By means of a combination of a commercial data goggle, a camera and an embedded PC, an easily configurable application solution is created. This solution should be able to identify the existing components, to read out the corresponding status information both visually and via radio, and to supply the users with maintenance information and checklists.

The AR solution will be developed to support technicians in operation, installation and maintenance of the sensitive LNG valve set. By means of image processing and object recognition techniques, the first step is to collect information on the condition of the valves. Subsequently, an AR-User Interface will be developed, which acts as an assistance system for the users.

Duration 01.03.2019 - 28.02.2021, Funded by BMWi
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LNG Safety

Safety process system for cryogenic fluid transfer

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The handling of cryogenic fluids (e.g. liquefied natural gas) bears major risks with regard to operational safety. If the liquid leaks during a transfer process (e.g. fueling of ships), large amounts of gas can quickly be produced which are highly flammable and explosive. Therefore, an appropriate safety system for process monitoring is necessary.

The aim of the project is to improve operational safety during the LNG transfer process by means of a redundant optical monitoring system. This system should be able to both detect fittings, ship superstructures, and people automatically and to perform an automated visual inspection of the correct coupling.

The multi-camera system consists of a wide-angle, a zoom and an infrared camera and can therefore react to a wide variety of environmental conditions (day, night, weather influences). It automatically monitors the LNG transfer process. By using Deep Machine Learning, the object recognition of fittings, ship superstructures and people is made possible, which is necessary for monitoring the danger zone.

Duration 01.03.2019 - 28.02.2021, Funded by BMWi
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Training "Technologies of Logistics"
January 29th, 2020, Bremen
Doctoral Workshop
February 10th - 11th, 2020, Bremen
LDIC 2020
February 12th - 14th, 2020, Bremen
Industry Meets Science
March 18th, 2020, Bremen
SysInt 2020
June 3rd - 5th, 2020, Bremen

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