26 citations · 28 across the 6 of their papers we have counts for
6 papers
How Reliable is Your Service at the Extreme Edge? Analytical Modeling of Computational Reliability
MHD Saria Allahham, Hossam S. Hassanein
Extreme Edge Computing (XEC) distributes streaming workloads across consumer-owned devices, exploiting their proximity to users and ubiquitous availability. Many such workloads are…
Zero-touch realization of Pervasive Artificial Intelligence-as-a-service in 6G networks
Emna Baccour, Mhd Saria Allahham, Aiman Erbad +3
The vision of the upcoming 6G technologies, characterized by ultra-dense network, low latency, and fast data rate is to support Pervasive AI (PAI) using zero-touch solutions enabli…
On the Modeling of Reliability in Extreme Edge Computing Systems
Mhd Saria Allahham, Amr Mohamed, Aiman Erbad +1
Extreme edge computing (EEC) refers to the endmost part of edge computing wherein computational tasks and edge services are deployed only on extreme edge devices (EEDs). EEDs are c…
Multi-Agent Reinforcement Learning for Network Selection and Resource Allocation in Heterogeneous multi-RAT Networks
Mhd Saria Allahham, Alaa Awad Abdellatif, Naram Mhaisen +3
The rapid production of mobile devices along with the wireless applications boom is continuing to evolve daily. This motivates the exploitation of wireless spectrum using multiple…
Energy-Efficient Multi-Orchestrator Mobile Edge Learning
Mhd Saria Allahham, Sameh Sorour, Amr Mohamed +2
Mobile Edge Learning (MEL) is a collaborative learning paradigm that features distributed training of Machine Learning (ML) models over edge devices (e.g., IoT devices). In MEL, po…
Motivating Learners in Multi-Orchestrator Mobile Edge Learning: A Stackelberg Game Approach
Mhd Saria Allahham, Sameh Sorour, Amr Mohamed +2
Mobile Edge Learning (MEL) is a learning paradigm that enables distributed training of Machine Learning models over heterogeneous edge devices (e.g., IoT devices). Multi-orchestrat…