Distributed Inference for Energy-efficient Monitoring at the Network Edge

Informations projet

DIME

N° de convention de subvention: 101062011

DOI

10.3030/101062011

Projet clôturé

Date de signature de la CE 19 Mai 2022

Date de début 1 Juin 2022

Date de fin 31 Mai 2024

Financé au titre de

• Marie Skłodowska-Curie Actions (MSCA)

Coût total

Aucune donnée

Contribution de l’UE

€ 181 152,96

Coordonné par

FUNDACION IMDEA NETWORKS
Spain

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Livrables

Data Management Plan (1)

Data Management Plan

The Data Management Plan describes the data management life cycle for all data sets that will be collected processed or generated by the action It is a document describing what data will be collected processed or generated and following what methodology and standards whether and how this data will be shared andor made open and how it will be curated and preserved

Documents, reports (1)

Communication, Dissemination & Outreach Plan

The plan describes the planned measures to maximize the impact of the project, including the dissemination and exploitation measures that are planned, and the target group(s) addressed. Regarding communication measures and public engagement strategy, the aim is to inform and reach out to society and show the activities performed, and the use and the benefits the project will have for citizens.

Publications

Conference proceedings (3)

Improved Decision Module Selection for Hierarchical Inference in Resource-Constrained Edge Devices

Auteurs: Adarsh Prasad Behera, Roberto Morabito, Joerg Widmer, and Jaya Prakash Champati
Publié dans: ISBN 9781450399906
Éditeur: ACM
DOI: 10.1145/3570361.3615732

An Offloading Algorithm for Maximizing Inference Accuracy on Edge Device in an Edge Intelligence System

Auteurs: Andrea Fresa and Jaya Prakash Champati
Publié dans: MSWIM '22, Numéro 25TH, 2022, ISBN 9781450394826
Éditeur: Association for Computing Machinery (ACM)
DOI: 10.1145/3551659.3559044

The Case for Hierarchical Deep Learning Inference at the Network Edge

Auteurs: Ghina Al-Atat, Andrea Fresa, Adarsh Prasad Behera, Vishnu Narayanan Moothedath, James Gross, and Jaya Prakash Champati
Publié dans: ISBN 9798400702129
Éditeur: ACM
DOI: 10.1145/3597062.3597278

Peer reviewed articles (2)

Getting the Best Out of Both Worlds: Algorithms for Hierarchical Inference at the Edge

Auteurs: Vishnu N. Moothedath, Jaya Prakash Champati, James Gross
Publié dans: IEEE Transactions on Machine Learning in Communications and Networking, ISSN 2831-316X
Éditeur: IEEE
DOI: 10.1109/TMLCN.2024.3366501

Offloading Algorithms for Maximizing Inference Accuracy on Edge Device in an Edge Intelligence System

Auteurs: A. Fresa and J. P. Champati
Publié dans: IEEE Transactions on Parallel and Distributed Systems, ISSN 1045-9219
Éditeur: IEEE
DOI: 10.1109/TPDS.2023.3267458