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Taming the environmental impact of mobile networks through GREEN EDGE computing platforms

Project description

Training for sustainable edge computing technology

Many emerging applications, from extended reality to smart health, smart cities and autonomous driving, rely on the support of sophisticated systems that combine communication with computation. Multi-access edge computing (MEC) technology delivers the required computation functionalities closer to user devices, directly at mobile access points. However, the large-scale adoption of MEC technology will result in a surge of data and computation in mobile networks, increasing their energy consumption. The EU-funded GREENEDGE project aims to reduce the carbon footprint of MEC technology by designing energy-efficient communication and computing functionalities for the network edge and combining them with efficient management of ambient energy sources and new energy storage and supply models. The project will train 15 early-stage researchers in the areas of energy harvesting and storage, edge computing, machine learning and wireless communications.

Objective

Modern communication networks are rapidly evolving into sophisticated systems combining communication and computing capabilities. Computation at the network edge is key to supporting many emerging applications, from extended reality to smart health, smart cities, smart factories and autonomous driving. Multi-access edge computing (MEC) technology is being developed to deliver the required computation functionalities closer to user devices, directly at mobile access points. GREENEDGE is motivated by the fact that the large scale adoption of MEC technology, while benefiting human productivity and efficiency, will result in a surge of data and computation in mobile networks, which, in turn, will exacerbate their energy consumption. GREENEDGE is set out to tame the growing carbon footprint of MEC technology, devising highly energy efficient communication and computing functionalities for the network edge, combining them with ambient energy sources and with new energy storage and supply paradigms. As a result, GREENEDGE technology will allow mobile systems to offer the much anticipated communication and computing services in a sustainable manner. Fifteen early stage researchers (ESRs) will be trained by a consortium of world-class leaders across the fields of energy harvesting, storage, edge computing, optimization, machine learning and wireless communications. Ample inter-sectoral opportunities will be offered thanks to secondments among academy/research centers, two network operators, and other prominent industrial partners operating in the domains of Internet of Things, smart cities, critical infrastructure management and data analytics. A carefully planned and coordinated training and research program will ensure excellent employability prospects for the ESRs after the project completion.

Fields of science (EuroSciVoc)

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

MSCA-ITN - Marie Skłodowska-Curie Innovative Training Networks (ITN)

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Call for proposal

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(opens in new window) H2020-MSCA-ITN-2020

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Coordinator

UNIVERSITA DEGLI STUDI DI PADOVA
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 772 368,36
Address
VIA 8 FEBBRAIO 2
35122 PADOVA
Italy

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Region
Nord-Est Veneto Padova
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 772 368,36

Participants (7)

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