Dr. Yaya Etiabi

Dr. Yaya Etiabi

He/Him/His

Ph.D., Eng. | Computer & Communication Sciences and Engineering | Researcher: ISCC & Edge AI, IoT| AI Literacy Advocate

My Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

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.

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.

Conference Papers

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.

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.

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.

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.

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.

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.