Dr. Anelia Kurteva

Circular Resource Planning for IT (RePlanIT)(2022-ongoing)

The aim of the project is to develop and test a new methodology, Circular Resource Planning for IT. It should enable suppliers and users of IT services to manage their hardware in a circular way. In practice this means: double the lifetime of the equipment, slowing down replacement cycles, while running the hardware on maximum energy efficiency. The result of the project is, firstly, a prototype of the RePlanIT system that is developed and validated in practical real-life environments in the municipality of Amsterdam and other big IT consumers in the business market. Secondly, with the research into preconditions for trust, behavior, and acceptance of circular IT, the project provides insight into how to include stakeholders so that they actually make circular choices.

Smart Dispatcher for Secure and Controlled Sharing of Distributed Personal and Industrial Data (smashHit)(2020-2022)

The objective of smashHit is to assure trusted and secure sharing of data streams from both personal and industrial platforms, needed to build sectorial and cross-sectorial services, by establishing a GDPR-compliant framework for processing of data owner consent and legal rules and effective contracting, as well as joint security and privacy preserving mechanisms.

CampaNeo (2019-2022)

In the CampaNeo project, an open platform will be developed on which private and public institutions can create campaigns and collect and analyze vehicle data in real time. The goal is to set up a prototype platform for secure campaign-based data collection in Hanover, Wolfsburg and in cross-regional scenarios, as well as the implementation of the first smart use cases based on the campaign data. The focus is in particular on the data ownerships of vehicle owners and the traceability of data processing. The analysis of vehicle sensor data (e.g. for the detection of driving behaviour, finding of driver efficiency scores, anomaly detection, etc.) requires the creation and use of models of machine learning (ML).