20 citations · 44 across the 16 of their papers we have counts for
17 papers
Opinion Dynamics-based Coalition Formation for Federated Learning in Heterogeneous IoT Systems
Mohammed El Hanjri, Anas Abouaomar, Hamidou Tembine +1
Federated learning (FL) enables privacy-preserving, on-device training across heterogeneous Internet-of-Things (IoT) deployments such as smart-city water-metering networks, where e…
Hierarchical Federated Learning for Crop Yield Prediction in Smart Agricultural Production Systems
Anas Abouaomar, Mohammed El hanjri, Abdellatif Kobbane +2
In this paper, we presents a novel hierarchical federated learning architecture specifically designed for smart agricultural production systems and crop yield prediction. Our appro…
Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systems
Mohammed El Hanjri, Hamza Reguieg, Adil Attiaoui +3
In the era of the Internet of Things (IoT), decentralized paradigms for machine learning are gaining prominence. In this paper, we introduce a federated learning model that capital…
Applications of machine learning and IoT for Outdoor Air Pollution Monitoring and Prediction: A Systematic Literature Review
Ihsane Gryech, Chaimae Assad, Mounir Ghogho +1
According to the World Health Organization (WHO), air pollution kills seven million people every year. Outdoor air pollution is a major environmental health problem affecting low,…
A Comparative Evaluation of FedAvg and Per-FedAvg Algorithms for Dirichlet Distributed Heterogeneous Data
Hamza Reguieg, Mohammed El Hanjri, Mohamed El Kamili +1
In this paper, we investigate Federated Learning (FL), a paradigm of machine learning that allows for decentralized model training on devices without sharing raw data, there by pre…
Vehicles Control: Collision Avoidance using Federated Deep Reinforcement Learning
Badr Ben Elallid, Amine Abouaomar, Nabil Benamar +1
In the face of growing urban populations and the escalating number of vehicles on the roads, managing transportation efficiently and ensuring safety have become critical challenges…
Federated Learning for Water Consumption Forecasting in Smart Cities
Mohammed El Hanjri, Hibatallah Kabbaj, Abdellatif Kobbane +1
Water consumption remains a major concern among the world's future challenges. For applications like load monitoring and demand response, deep learning models are trained using eno…
Communication and Computation O-RAN Resource Slicing for URLLC Services Using Deep Reinforcement Learning
Abderrahime Filali, Boubakr Nour, Soumaya Cherkaoui +1
The evolution of the future beyond-5G/6G networks towards a service-aware network is based on network slicing technology. With network slicing, communication service providers seek…
Dynamic SDN-based Radio Access Network Slicing with Deep Reinforcement Learning for URLLC and eMBB Services
Abderrahime Filali, Zoubeir Mlika, Soumaya Cherkaoui +1
Radio access network (RAN) slicing is a key technology that enables 5G network to support heterogeneous requirements of generic services, namely ultra-reliable low-latency communic…
Experimental Investigation of Variational Mode Decomposition and Deep Learning for Short-Term Multi-horizon Residential Electric Load Forecasting
Mohamed Aymane Ahajjam, Daniel Bonilla Licea, Mounir Ghogho +1
With the booming growth of advanced digital technologies, it has become possible for users as well as distributors of energy to obtain detailed and timely information about the ele…
Resource Provisioning in Edge Computing for Latency Sensitive Applications
Amine Abouaomar, Soumaya Cherkaoui, Zoubeir Mlika +1
Low-Latency IoT applications such as autonomous vehicles, augmented/virtual reality devices and security applications require high computation resources to make decisions on the fl…
A Deep Reinforcement Learning Approach for Service Migration in MEC-enabled Vehicular Networks
Amine Abouaomar, Zoubeir Mlika, Abderrahime Filali +2
Multi-access edge computing (MEC) is a key enabler to reduce the latency of vehicular network. Due to the vehicles mobility, their requested services (e.g., infotainment services)…
Mean-Field Game and Reinforcement Learning MEC Resource Provisioning for SFC
Amine Abouaomar, Soumaya Cherkaoui, Zoubeir Mlika +1
In this paper, we address the resource provisioning problem for service function chaining (SFC) in terms of the placement and chaining of virtual network functions (VNFs) within a…
A Resources Representation For Resource Allocation In Fog Computing Networks
Amine Abouaomar, Soumaya Cherkaoui, Abdellatif Kobbane +1
Fog computing is emerging as a new paradigm to deal with latency-sensitive applications, by making data processing and analysis close to their source. Due to the heterogeneity of d…
Matching-Game for User-Fog Assignment
Amine Abouaomar, Abdellatif Kobbane, Soumaya Cherkaoui
Fog computing has emerged as a new paradigm in mobile network communications, aiming to equip the edge of the network with the computing and storing capabilities to deal with the h…
Service Function Chaining in MEC: A Mean-Field Game and Reinforcement Learning Approach
Amine Abouaomar, Soumaya Cherkaoui, Zoubeir Mlika +1
Multi-access edge computing (MEC) and network virtualization technologies are important enablers for fifth-generation (5G) networks to deliver diverse applications and services. Se…
QGRP: A Novel QoS-Geographic Routing Protocol for Multimedia Wireless Sensor Networks
Mohammed-Amine Koulali, Mohammed El Koutbi, Abdellatif Kobbane +1
Thanks to the potential they hold and the variety of their application domains, Multimedia Wireless Sensor Networks (MWSN) are forecast to become highly integrated into our daily a…