activity
20132021
most citedEdge Computing For Smart Health: Context-aware Approaches, Opportunities, and Challenges

275 citations · 651 across the 14 of their papers we have counts for

collaborators

25 papers

cs.CV2021

VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results

Dawei Du, Longyin Wen, Pengfei Zhu +52

Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint.…

cs.LG2021

Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data

Alaa Awad Abdellatif, Naram Mhaisen, Amr Mohamed +4

Federated learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model, without the need to share their local data. I…

cs.NI202110 cited

An Intelligent Resource Reservation for Crowdsourced Live Video Streaming Applications in Geo-Distributed Cloud Environment

Emna Baccour, Fatima Haouari, Aiman Erbad +4

Crowdsourced live video streaming (livecast) services such as Facebook Live, YouNow, Douyu and Twitch are gaining more momentum recently. Allocating the limited resources in a cost…

cs.DC202158 cited

Distributed CNN Inference on Resource-Constrained UAVs for Surveillance Systems: Design and Optimization

Mohammed Jouhari, Abdulla Al-Ali, Emna Baccour +4

Unmanned Aerial Vehicles (UAVs) have attracted great interest in the last few years owing to their ability to cover large areas and access difficult and hazardous target zones, whi…

cs.CY2020

I-Health: Leveraging Edge Computing and Blockchain for Epidemic Management

Alaa Awad Abdellatif, Lutfi Samara, Amr Mohamed +5

Epidemic situations typically demand intensive data collection and management from different locations/entities within a strict time constraint. Such demand can be fulfilled by lev…

cs.LG2020156 cited

Analysis and Optimal Edge Assignment For Hierarchical Federated Learning on Non-IID Data

Naram Mhaisen, Alaa Awad, Amr Mohamed +2

Distributed learning algorithms aim to leverage distributed and diverse data stored at users' devices to learn a global phenomena by performing training amongst participating devic…