Posts by Collection

3MT Finalist at IEEE ICC 2023, Rome, Italy

Finalist in the Three Minute Thesis (3MT) competition at the IEEE ICC 2023 conference in Rome, Italy… Read more

AI Training Webinar Series

Hosted an AI training webinar series with 100+ participants, covering AI awareness and hands-on training. Read more

Panelist at APTOM Event

Panelist at APTOM event discussing AI in the professional world: opportunities and challenges. Read more

In-Person ML Class for UM6P’s International Student Community

Conducted a machine learning class for the international student community at UM6P. Read more

Doctoral Days at UM6P, Benguerir, Morocco

Best Oral Presenter at UM6P Doctoral Days… Read more

MOMI Event at Inria, Université Côte d’Azur, France

Awarded A Best Poster Presentation at MOMI Event organized at Inria… Read more

Scientific Day at ISEP, Paris, France

A talk on Indoor Localization in IoT Networks presented at ISEP… Read more

Semi-Marathon Participation

Participated in two 21km semi-marathons, highlighting endurance and discipline. Read more

Panelist at THE ARC NEXUS Forum

Discussing the link between research and development in Africa as a panelist at THE ARC NEXUS Forum… Read more

Reviewer and TPC Member for IEEE Conferences and Journals

Reviewer and TPC Member for many IEEE conferences and journals. Read more

Presenting Author at 2023 IEEE VTC, Florence

Presented research on Deep Reinforcement Learning for LoRa Localization at the 2023 IEEE VTC in Florence, Italy. Read more

A distributed and collaborative localization algorithm for internet of things environments

Published in 18th International Conference on Advances in Mobile Computing & Multimedia, 2020

This paper proposes a distributed localization algorithm based on a convex relaxation of the Huber loss function. It addresses the challenge of accurate localization in IoT environments, particularly in the presence of Non-Line of Sight links, by using a distributed collaborative approach. Read more

Recommended citation: Etiabi, Yaya and Amhoud, El Mehdi and Sabir, Essaid, "A distributed and collaborative localization algorithm for internet of things environments," 18th International Conference on Advances in Mobile Computing & Multimedia, pp. 114 – 118, 2020.

Huber Estimator and Statistical Bootstrap Based Light-Weight Localization for IoT Systems

Published in UNet 2021 - International Conference on Ubiquitous Networking, 2021

This paper studies distributed cooperative localization algorithms for WSN and proposes a novel approach that improves the accuracy of the localization in IoT systems. It focuses on indoor localization in IoT infrastructures using a two-step position refinement method involving the Huber estimator and statistical bootstrapping. Read more

Recommended citation: Etiabi, Yaya and Amhoud, El Mehdi and Sabir, Essaid, "Huber Estimator and Statistical Bootstrap Based Light-Weight Localization for IoT Systems," UNet 2021 - International Conference on Ubiquitous Networking, vol. 12845 LNCS, pp. 79 – 92, 2021.

FedCTI: Federated Learning and Cyber Threat Intelligence on the Edge for secure IoT Networks

Published in Proceedings of the 13th International Conference on the Internet of Things, 2023

This paper explores leveraging Federated Learning (FL) techniques to facilitate secure and privacy-preserving CTI knowledge sharing in IoT networks. It addresses the cybersecurity challenges in IoT by proposing FedCTI to enable secure CTI knowledge sharing while preserving data privacy. Read more

Recommended citation: El Jaouhari, Saad and Etiabi, Yaya, "FedCTI: Federated Learning and Cyber Threat Intelligence on the Edge for secure IoT Networks," Proceedings of the 13th International Conference on the Internet of Things, pp. 98 – 104, 2023.

Federated Learning based Hierarchical 3D Indoor Localization

Published in IEEE Wireless Communications and Networking Conference, WCNC, 2023

This paper presents a federated learning (FL) framework for hierarchical 3D indoor localization using a deep neural network. It addresses the challenges of privacy issues and resource constraints in indoor positioning by exploiting the hierarchy between floors and buildings. Read more

Recommended citation: Etiabi, Yaya and Njima, Wafa and Amhoud, El Mehdi, "Federated Learning based Hierarchical 3D Indoor Localization," IEEE Wireless Communications and Networking Conference, WCNC, vol. 2023-March, 2023.

Spreading factor assisted LoRa localization with deep reinforcement learning

Published in IEEE 97th Vehicular Technology Conference (VTC2023-Spring), 2023

This paper investigates LoRa localization and proposes a solution using deep reinforcement learning. It addresses the challenges of LoRa localization by leveraging deep reinforcement learning to optimize spreading factor assignments for improved accuracy. Read more

Recommended citation: Y. Etiabi, M. Jouhari, A. Burg, and E. M. Amhoud, "Spreading factor assisted LoRa localization with deep reinforcement learning," in IEEE 97th Vehicular Technology Conference (VTC2023-Spring), IEEE, 2023, pp. 1-5.

FeMLoc: Federated Meta-Learning for Adaptive Wireless Indoor Localization Tasks in IoT Networks

Published in IEEE Internet of Things Journal, 2024

This paper proposes FeMLoc, a federated meta-learning (MTL) framework for localization. It addresses the challenges of existing indoor localization solutions in dynamic and harsh conditions by using a two-stage approach: collaborative meta-training and rapid adaptation for new environments. Read more

Recommended citation: Y. Etiabi, W. Njima, and E. M. Amhoud, "FeMLoc: Federated Meta-Learning for Adaptive Wireless Indoor Localization Tasks in IoT Networks," IEEE Internet of Things Journal, vol. 11, no. 22, pp. 36991 – 37007, 2024.

Federated distillation for communication-efficient collaborative indoor localization

Published in IEEE Sensors Journal, 2024

This paper explores federated distillation (FD) for communication-efficient collaborative indoor localization. It proposes an FD framework to reduce communication overhead in collaborative indoor localization, offering improved energy efficiency and scalability compared to federated learning (FL). Read more

Recommended citation: Y. Etiabi, W. Njima, and E. M. Amhoud, "Federated distillation for communication-efficient collaborative indoor localization," IEEE Sensors Journal, pp. 11678 – 11692, 2024.

A Unified Deep Transfer Learning Model for Accurate IoT Localization in Diverse Environments

Published in IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, 2024

This paper presents a unified indoor-outdoor localization solution that leverages transfer learning (TL) schemes to build a single deep learning model. It addresses the challenges of developing separate localization models for different environments by proposing a unified deep transfer learning model. Read more

Recommended citation: Ahmed, Abdullahi Isa and Etiabi, Yaya and Azim, Ali Waqar and Amhoud, El Mehdi, "A Unified Deep Transfer Learning Model for Accurate IoT Localization in Diverse Environments," IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, 2024.

Distributed and Collaborative Localization for IoT

Published: November 30, 2020

In this talk, I presented a distributed localization algorithm designed for IoT environments. The algorithm is based on a convex relaxation of the Huber loss function. I discussed how this approach addresses the challenges of achieving accurate localization in IoT networks, particularly in the presence of Non-Line-of-Sight links. Read more

Huber Estimator and Statistical Bootstrap for IoT Localization

Published: May 22, 2021

This presentation details a novel approach to improve the accuracy of localization in IoT systems. I presented a light-weight localization method using the Huber estimator and statistical bootstrapping. The method refines position estimates in a two-step process to enhance localization accuracy in IoT environments. Read more

Federated Learning for Hierarchical 3D Indoor Localization

Published: March 29, 2023

In this presentation, I introduced a federated learning (FL) framework for hierarchical 3D indoor localization using a deep neural network. This work tackles the challenges of privacy issues and resource constraints in indoor positioning. I presented an approach that exploits the hierarchy between floors and buildings to train an accurate localization model while preserving user privacy. Read more

Deep Reinforcement Learning for LoRa Localization

Published: June 23, 2023

This talk presents a deep reinforcement learning (DRL) solution for LoRa localization. It addresses the challenges of accurate localization with LoRa technology. Specifically, I presented a novel approach that uses DRL to optimize the assignment of spreading factors (SFs) in LoRa networks to improve localization accuracy. The results of the research demonstrate that the DRL-based approach achieves superior localization accuracy compared to benchmark methods. Read more