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Data-centered and Secure Computing (DSComputing)

Focusing on data, we will obtain value and impact by computing while maintaining privacy and trustworthiness.

Publisert: Endret:
About us
Researchers

10

PhDs

8

Projects

10

Using Big Data in the Triangulum smart city project

The group focuses on real-world solutions using computing technologies (cloud/edge, AI, blockchain, high performance, etc) in domains such as smart cities, health and energy, while managing the cloud data hub infrastructure at two sites: UiS campus and Green Mountain. The group ensures security and privacy remain paramount for the trustworthiness of any services and data management. DSComputing is part of the university's initiative on artificial intelligence – Stavanger AI Lab.

project:

Smart Community Neighborhood – driven by energy informatics (NFR EnergiX)

This project focuses on the development of mechanisms which will allow prosumers to share information about their production and consumption behavior.

It creates a decentralized virtual neighborhood where each household operates autonomously and trades energy with other households and communities, in which case, the central authority or the main grid only act as a facilitator. Such mechanisms will be particularly applicable for fluctuating loads and solving congestion problems.

The project aims to achieve this with ICT based smart-solutions. Machine learning based methods are used to learn and predict household production and consumption behavior. Incentive systems for energy trading are facilitated through the use of blockchains.

Furthermore, edge and fog computing drive energy information solutions. Finally, all technical components are driven by data privacy and security elements.

project:

Partnership for joint Curriculum Development and Research in Energy Informatics (PACE)

PACE is a project under the Norwegian Research Council's INTPART programme.

The transition to a globally sustainable low-carbon emission society requires a significant increase in the use of renewable energy. We are facing increased decentralized energy production, and digitalization of the whole value chain.

Advanced use of ICT is crucial for realizing this energy shift. This is confirmed in the revised national strategy for Norway on energy research, Energy21 which recommends to give Digitalization and integrated energy systems top priority. In this context, the energy sector is facing two key challenges:

  1. The sector must be able to apply state-of-the-art ICT
  2. There is a lack of talents with the necessary expertise in the intersection between energy systems and ICT, which we refer to as energy informatics.

This project, which is a collaboration between research groups at University of Stavanger, University of Oslo, Technical University of Munich, and University of Lille, is one response to these challenges. The groups form the core of energy informatics educators and researchers at their respective institutions.

Energy informatics (EI) is an emerging interdisciplinary field that deals with the digitalization of the whole value chain of the energy sector. EI is concerned with how to exploit state-of-the-art ICT methods, tools and techniques to achieve sustainable energy generation and use. Research and education in EI requires a solid foundation in informatics. EI research and education can therefore naturally be envisioned as extensions of research and educational programs in informatics.

The goal of the project is to strengthen the research and educational activity on EI at the partner institutions.

PROJECT:

Kenya-Norway Mobility Programme for Computer Science Education

Sju hender møtes

KeNoMo is a SIU/NFR NORPART project.

The objective of the project is to facilitate mutual student mobility and academic cooperation between the partners, and will thus enhance the quality of computer science education in both Norway and Kenya.

PROJECT:

Cloud Artificial Intelligence for Pathology (CLARIFY)

Logo for the Clarify project

An EU ITN project, CLARIFY’s main goal is to develop a robust automated digital diagnostic environment based on artificial intelligence and cloud-oriented data algorithms that facilitates whole-slide-image (WSI) interpretation and diagnosis everywhere with the aim of maximising the benefits of digital pathology and aiding pathologists in their daily work.

Read more about the project on the BMDLab page.

PROJECT:

Future Energy Hub (NFR)

To forskere rygg i rygg med ulike energibærere i bakgrunnen, med grønt raster over

Future Energy Hub aims to create greener buildings and districts in collaboration with business and the public sector. The project serves as a connection point for various disciplines. By connecting cutting-edge expertise from several fields, the best possible basis is created for innovative innovation and development towards greener cities.

Read more on Future Energy Hub's page (in Norwegian)

PROJECT:

Digital Well Center (DigiWells)

Full name: Digital Well Center for Value Creation, Competitiveness and Minimum Environmental Footprint.

Illustrasjon av bore- og brønnteknologi

The objective of this NFR/SFI centre is to develop new knowledge, methodologies, and innovative solutions to improve the well delivery process enabled by digitalization, new sensors, high speed telemetry, automation, and autonomy.

Link to more information at NORCE (in Norwegian)

PROJECT:

Distributed fibre optic sensing for production optimization (DIFI-PRO)

This NFR project will perform experiments and modelling to increase our understanding of distributed fibre optic measurement systems as a cost-efficient tool for production optimization and increased field recovery.

More information on the NORCE website

PROJECT:

5G Management and Orchestration for Data and Network Integration

5G-MODaNeI focuses on allocating together the network resources of 5G and the data resources of Multi-access Edge Computing (MEC).

5G-MODaNeI logo

The risks and the vulnerabilities for attacks and failures in 5G MEC will be identified. The impact of joint data and network resource allocation on security and dependability in 5G MEC will be determined. Next, innovative and intelligent solutions will be created. These solutions will allocate the network and data resources to maximize the security and dependability in 5G MEC.

More info on the 5G-MODaNeI page

PROJECT:

Next Generation 3D Machine Vision with Embedded Visual Computing

The overall objective of this research is to give up the currently manual and time-consuming operational processes with well-composed digitalization using real-time 3D vision and learning processes.

This research aims to initiate an automated process deploying high-precision 3D sensing and online visual learning engines that operate in natural environments and are able to adapt in a holistic lifetime manner to changes during operations.

It targets establishing a groundbreaking technological concept for 3D machine vision as an interdisciplinary initiative linking cost effective and reliable depth sensing with advanced computer vision and embedded artificial intelligence to tackle challenges posed by time-varying visual scene perception in natural environments.

This research has a potential to a radical change in many parts of the value chain in the industry and society and selected scenarios will be designed for impact evaluation and opening up integration opportunities.

UiS will lead one of the work packages in the 3DSmartCam project: What are the capabilities and performance of the new 3D machine vision for digitalization of services and processes?

PROJECT:

Automated Well Monitoring and Control

Well surveillance and control remain key issues for industries like oil and gas (O&G), geothermal, geological carbon storage (CCS) and compressed air energy storage (CAES).

Significant progress with real-time well measurements and automated data gathering has recently been achieved, while interpretation and well control optimization remain labour and time-consuming work mainly done manually.

The project aims at development and testing of a new methodology and tool (prototype software) for automated injection well monitoring and control based on real-time pressure, temperature and rate data. The basis is timelapse Pressure Transient Analysis (PTA) providing capabilities to monitor well performance and interference, containment of injected fluids and safety of abandoned wells.

Advanced interpretation and automation will be achieved via combining model- and data-driven approaches. Real field data will guide the project tasks and priorities and serve as a testing and application environment for the research and development. Automated well monitoring and warning system will be integrated with injection well control. The control includes rate changes designed to send signals with real-time interpretation of the response (on-the-fly well testing) to optimize well performance and interwell communication and prevent injection safety issues. The automated solution developed in the project will reduce costs, improve efficiency and minimize environmental footprint in O&G and facilitate CCS, CAES and geothermal industries.

UiS will lead one of the work packages in the Autowell project on prototype tool development.

Our researchers

51832013
Faculty of Science and Technology



Department of Electrical Engineering and Computer Science
Professor
51832061
Faculty of Science and Technology



Department of Electrical Engineering and Computer Science
professor
51832832
Faculty of Science and Technology



Department of Electrical Engineering and Computer Science
Førsteamanuensis
51831051
Faculty of Science and Technology



Department of Electrical Engineering and Computer Science
Professor
Trondheim
Faculty of Science and Technology



Department of Electrical Engineering and Computer Science
Professor II
Faculty of Science and Technology



Department of Electrical Engineering and Computer Science
Førsteamanuensis II
Faculty of Science and Technology



Department of Energy Resources
ekstern u/lønn
51831560
EOJ-218
UiS Business School



Department of Innovation, Management and Marketing
Professor

Our PhD students

51832839
Faculty of Science and Technology



Department of Electrical Engineering and Computer Science
Stipendiat
Faculty of Science and Technology



Department of Electrical Engineering and Computer Science
Forsker
Faculty of Science and Technology



Department of Electrical Engineering and Computer Science
Stipendiat
Faculty of Science and Technology



Department of Electrical Engineering and Computer Science
Stipendiat
Faculty of Science and Technology



Department of Electrical Engineering and Computer Science
Stipendiat
Faculty of Science and Technology



Department of Electrical Engineering and Computer Science
Stipendiat

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