Intelligent, distributed, human-centered and trustworthy IoT environments

Livrables

This deliverable describe the initial design and implementation of the core security privacy and trust components providing endtoend protection and assurance tool to guarantee their proper operation

HyperMAS for (semi-)autonomous IoT applications (final version)

D3.5 will contain enhancements of these components after receiving feedback from the use cases and evaluations in WP5 and 1st Open Call.

Human-in-the-loop in intelligent IoT environments (final version)

D3.7 will report on the final developed solutions to facilitate input from users and to streamline the human intervention in the system.

Final project report

Report on project achievements and execution aspects during the report interval of year 3, and cost statements of the project, and allocation of funds according to contracted schedule. This will also summarize project results with references to deliverables created in technical WPs.

High level architecture (final version)

D2.6 will provide the final version of the architecture reflecting experiences gained from implementation/integration in WP3, WP4 and WP5 as well as feedback from the 1st Open Call.

Use case specification & Open Call definition (first version)

D21 will contain a detailed specification of the use cases and a definition of the scope of the 1st Open Call

Trust mechanisms (final version)

This deliverable describe the final version of design and implementation of the core security, privacy, and trust components providing end-to-end protection and assurance tool to guarantee their proper operation.

Validation & evaluation (final version)

This deliverable will validate and evaluate the IntellIoT approach through final defined set of KPIs.

Integrated IntellIoT framework & use case implementations (first version)

D51 will provide the first version of the integrated IntellIoT framework of the first release of technology components from WP3 and WP4 as well as the first implementation of use case applications and services

Interim report on exploitation & impact creation (M35)

This deliverables will describe intermediate exploitation plans and business models at M35.

Dynamic network management (final version)

D4.7 summarizes the complete solution for a dynamic network management of IoT environments.

5G network functionalities (final version)

D4.6 updates D4.2 based on received feedback from different use cases.

HyperMAS for (semi-)autonomous IoT applications (first version)

D31 will describe the design and implementation of open source software components for HyperMAS

Standardization (first version)

This deliverable will describe intermediate standardization plan and contributions

Distributed AI algorithms & methods (first version)

This deliverable will provide initial findings about decentralized and algorithmic AI solutions

Dissemination & ecosystem building (final version)

This deliverable will describe final status of dissemination plan and activities.

Standardization (final version)

This deliverable will describe final status of standardization plan and contributions.

5G network functionalities (first)

D42 provides and describes the 5G network functionalities and test sites used for technology components WP3WP4 and use cases WP5

Technology analysis & requirements specification (first version)

D22 will include the stateoftheart technology analysis and the initial requirements specification for the 1st version of the IntellIoT framework and the use cases

Interim report on exploitation & impact creation (M18)

This deliverables will describe intermediate exploitation plans and business models at M18

Final report on exploitation & impact creation

This deliverable will describe final status of exploitation activities and business models.

Validation & evaluation (first version)

D5.3 will validate and evaluate the IntellIoT approach through first defined set of KPIs.

Period 1 project report and project plan updates

This deliverable will include project management report and financial report for Period 1. It will report on achievements and actions during the reporting period, as well as cost statements of the project, and allocation of funds according to contracted schedule, and plans for the next period. The deliverable will also include necessary updates of QMP, IMP and DMP.

Open Call coordination & results (first call)

This deliverable will report details on coordination and results of the first Open Call.

Human-in-the-loop in intelligent IoT environments (first version)

First version of this deliverable will report on initial solutions to facilitate input from users and to streamline the human intervention in the system.

Integrated IntellIoT framework & use case implementations (final version)

D5.4 will provide the final version of the integrated framework and implemented use cases.

Decentralized trust via secure interaction and contracts (first version)

D34 will provide the problem formulation initial concepts and technical details of DLT and Smart Contract solutions for IoTedge devices with computation communication constraints

Technology analysis & requirements specification (final version)

D2.5 will refine the requirements based on evaluations (Task 5.3) and Open Call feedback and will be input for final version of the IntellIoT framework.

IoT/edge infrastructure management (final version)

D4.5 updates D4.1 and provides final version.

Decentralized trust via secure interaction and contracts (final version)

D3.8 will summarize the final findings of the project on technical details of DLT and Smart Contract solutions for IoT/edge devices with computation & communication constraints.

Use case specification & Open Call definition (final version)

The final version, D2.4, update the use case specifications and will define the scope of the 2nd Open Call.

Interim report on exploitation & impact creation (M24)

This deliverables will describe intermediate exploitation plans and business models at M24.

Dynamic network management (first version)

D43 includes initial solutions for management of the network resources

Open Call coordination & results (second call)

This deliverable will report details on coordination and results of the second Open Call.

High level architecture (first version)

D23 will specify the 1st version of the architecture of the IntellIoT framework the components constituting it and the key functionalities and interfaces of these components

Distributed AI algorithms & methods (final version)

D3.6 will provide finalised AI solutions, addressing updated requirements and lessons learned.

Data de-identification procedures and tools

This deliverable reports on the procedures established for data deidentification on the deidentification tooling that has been implemented and on the assessment of the outcomes of the deidentification including reidentification risk assessment

Dissemination & ecosystem building (first version)

This deliverable will describe intermediate dissemination plan and activities

IoT/edge infrastructure management (first version)

D41 describes design and implementation of first version of optimized allocation for IoT application functions Learning from usage of first version in WP5 and 1st Open Call

Deployed & tested use case demonstrators (final version)

This deliverable will report on the final version of deployed and tested demonstrators.

Deployed & tested use case demonstrators (first version)

This deliverables will report on the first version of deployed and tested demonstrators

Project website

After initial delivery the website will be continuously updated to represent the project with information about eg project aims consortium deliverables news and events It will also integrate a central promotion and information hub for the Open Calls

Publications

Predictive Ultra-Reliable Communication: A Survival Analysis Perspective”

Auteurs: S. Samarakoon, M. Bennis, W. Saad, M. Debbah,
Publié dans: IEEE Communications Letters, 2020
Éditeur: IEEE Communications Letters

Communication efficient distributed learning with censored, quantized, and generalized group ADMM.

Auteurs: Issaid CB, Elgabli A, Park J, Bennis M, Debbah M.
Publié dans: WCNC 2021, 2021
Éditeur: WCNC 2021

Vehicular Cooperative Perception Through Action Branching and Federated Reinforcement Learning

Auteurs: Mohamed K. Abdel-Aziz; Cristina Perfecto; Sumudu Samarakoon; Mehdi Bennis; Walid Saad
Publié dans: IEEE Transactions on Communications (Volume: 70, Numéro: 2, Feb. 2022), Numéro 2, 2021, Page(s) 891903
Éditeur: IEEE
DOI: 10.1109/tcomm.2021.3126650

Peak Age of Information Distribution for Edge Computing With Wireless Links

Auteurs: Federico Chiariotti; Olga Vikhrova; Beatriz Soret; Petar Popovski
Publié dans: Chiariotti , F , Vikhorova , O , Soret , B & Popovski , P 2021 , ' Peak Age of Information Distribution for Edge Computing with Wireless Links ' , IEEE Transactions on Communications , vol. 69 , no. 5 , 9328793 , pp. 3176- 3191 . https://doi.org/10.1109/TCOMM.2021.3053038, Numéro 3, 2021, Page(s) 3176- 3191
Éditeur: IEEE
DOI: 10.1109/tcomm.2021.3053038

"""Communication Efficient Decentralized Learning Over Bipartite Graphs."""

Auteurs: C. B. Issaid, A. ElGabli, J. Park, M. Bennis and M. Debbah
Publié dans: IEEE Transactions on Wireless Communications, 2021
Éditeur: IEEE

”Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning”

Auteurs: A. Elgabli, J. Park, C. B. Issaid, M. Bennis
Publié dans: IEEE Transactions on Communications, 2021
Éditeur: IEEE Transactions on Communications

Open Call from IntellIoT boosts European deeptech startups and SMEs with up to €150K

Auteurs: IntellIoT / Thomas Ohr (editor EU Startups blog)
Publié dans: EU Startups blog, 2021
Éditeur: EU Startups

The Doctor Will See You Now: Standard of Care for Patients

Auteurs: IntellIoT
Publié dans: IoTfor All blog, 2021
Éditeur: IoTforAll

Open Call from IntellIoT boosts European Deep Tech Startups and SMEs

Auteurs: IntellIoT
Publié dans: "Press Release Open Call #1", 2021
Éditeur: IntellIoT

Communication-efficient and distributed learning over wireless networks:principles and applications

Auteurs: Park, J. (Jihong); Samarakoon, S. (Sumudu); Elgabli, A. (Anis); Kim, J. (Joongheon); Bennis, M. (Mehdi); Kim, S.-L. (Seong-Lyun); Debbah, M. (Mérouane)
Publié dans: Proceedings of the IEEE, Numéro 1, 2021
Éditeur: IEEE

Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs

Auteurs: Beatriz Soret; Lam D. Nguyen; Jan Seeger; Arne Broring; Chaouki Ben Issaid; Sumudu Samarakoon; Anis El Gabli; Vivek Kulkarni; Mehdi Bennis; Petar Popovski
Publié dans: Soret , B , Nguyen , L D , Seeger , J , Bröring , A , Ben Issaid , C , Samarakoon , S , El Gabli , A , Kulkarni , V , Bennis , M & Popovski , P 2022 , ' Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs ' , IEEE Transactions on Green Communications and Networking . https://doi.org/10.1109/TGCN.2021.3138792, Numéro 2, 2022, ISSN 2473-2400
Éditeur: IEEE
DOI: 10.1109/tgcn.2021.3138792

Modeling and Analysis of Data Trading on Blockchain-Based Market in IoT Networks

Auteurs: Lam Duc Nguyen; Israel Leyva-Mayorga; Amari N. Lewis; Petar Popovski
Publié dans: Nguyen , L D , Leyva-Mayorga , I , Lewis , A & Popovski , P 2021 , ' Modeling and Analysis of Data Trading on Blockchain-based Market in IoT Networks ' , IEEE Internet of Things Journal , vol. 8 , no. 8 , 9324804 , pp. 6487-6497 . https://doi.org/10.1109/JIOT.2021.3051923, Numéro 5, 2021
Éditeur: IEEE
DOI: 10.1109/jiot.2021.3051923

European Commission launches EUR 8 million Next-Generation IoT environments project amid Covid-19 pandemic

Auteurs: IntellioT
Publié dans: Press Release Project Start, Numéro 1, 2020
Éditeur: IntellIoT

Robust Reconfigurable Intelligent Surfaces via Invariant Risk and Causal Representations.

Auteurs: Samarakoon, S., Park, J. and Bennis, M.,
Publié dans: IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2021, Page(s) 301-305
Éditeur: IEEE
DOI: 10.1109/spawc51858.2021.9593252

"Age-optimal power allocation in industrial IoT: A risk-sensitive federated learning approach"""

Auteurs: Hsu, Y.L., Liu, C.F., Samarakoon, S., Wei, H.Y. and Bennis, M.
Publié dans: IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2021, Page(s) 1323-1328
Éditeur: IEEE
DOI: 10.1109/pimrc50174.2021.9569536

"""Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels"""

Auteurs: M. Krouka, A. Elgabli, C. B. Issaid and M. Bennis,
Publié dans: 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2021, Page(s) 1173-1178
Éditeur: 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)
DOI: 10.1109/pimrc50174.2021.9569707

"""Communication-Efficient Split Learning Based on Analog Communication and Over the Air Aggregation,"""

Auteurs: M. Krouka, A. Elgabli, C. b. Issaid and M. Bennis,
Publié dans: 2021 IEEE Global Communications Conference (GLOBECOM), 2021, Page(s) 1-6
Éditeur: 2021 IEEE Global Communications Conference (GLOBECOM)
DOI: 10.1109/globecom46510.2021.9685045

"""Federated Distributionally Robust Optimization for Phase Configuration of RISs,"""

Auteurs: C. B. Issaid, S. Samarakoon, M. Bennis and H. V. Poor
Publié dans: 2021 IEEE Global Communications Conference (GLOBECOM), 2021, Page(s) 1-6
Éditeur: 2021 IEEE Global Communications Conference (GLOBECOM)
DOI: 10.1109/globecom46510.2021.9685599

"""Joint Client Scheduling and Resource Allocation under Channel Uncertainty in Federated Learning."""

Auteurs: M. M. Wadu, S. Samarakoon, and M. Bennis.
Publié dans: IEEE Transactions on Communications, 2021
Éditeur: IEEE

BayGo:joint Bayesian learning and information-aware graph optimization

Auteurs: AlShammari, T. (Tamara); Samarakoon, S. (Sumudu); Elgabli, A. (Anis); Bennis, M. (Mehdi)
Publié dans: IEEE International Conference on Communications, Numéro 5, 2021, Page(s) 1-6
Éditeur: IEEE

V2V Cooperative Sensing using Reinforcement Learning with Action Branching

Auteurs: Mohamed K. Abdel-Aziz; Cristina Perlecto; Sumudu Samarakoon; Mehdi Bennis
Publié dans: ICC 2021 - IEEE International Conference on Communications, Numéro 4, 2021
Éditeur: ICC 2021 - IEEE International Conference on Communications
DOI: 10.1109/icc42927.2021.9500832

"""Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent"""

Auteurs: A. Elgabli, C. B. Issaid, A. S. Bedi, M. Bennis and V. Aggarwal,
Publié dans: 2021 IEEE Global Communications Conference (GLOBECOM), 2021, Page(s) 1-6
Éditeur: IEEE
DOI: 10.1109/globecom46510.2021.9685127

"""Communication-Efficient and Federated Multi-Agent Reinforcement Learning"""

Auteurs: M. Krouka, A. Elgabli, C. B. Issaid and M. Bennis
Publié dans: IEEE Transactions on Cognitive Communications and Networking, 2021
Éditeur: IEEE
DOI: 10.1109/tccn.2021.3130993

B-ETS: A Trusted Blockchain-based Emissions Trading System for Vehicle-to-Vehicle Networks

Auteurs: Duc Lam Nguyen; Amari N. Lewis; Israel Leyva-Mayorga; Amelia C. Regan; Petar Popovski
Publié dans: VEHITS, Numéro 7, 2021, Page(s) 171-179
Éditeur: VEHITS
DOI: 10.5220/0010460501710179

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Livrables

Publications

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