M
Mohsen Guizani
Researcher at Qatar University
Publications - 1337
Citations - 48275
Mohsen Guizani is an academic researcher from Qatar University. The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 79, co-authored 1110 publications receiving 31282 citations. Previous affiliations of Mohsen Guizani include Jaypee Institute of Information Technology & University College for Women.
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Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey, and Future Directions
Reza Shakeri,Mohammed Ali Al-Garadi,Ahmed Badawy,Amr Mohamed,Tamer Khattab,Abdulla Al-Ali,Khaled A. Harras,Mohsen Guizani +7 more
TL;DR: In this paper, the most fundamental and important design challenges of multi-UAV systems for cyber-physical systems (CPSs) applications are identified and compared with the current state-of-the-art algorithms.
Proceedings ArticleDOI
Light-Weight Solution to Defend Implantable Medical Devices against Man-In-The-Middle Attack
TL;DR: The proposed signature protocol is dynamic, which means that the signature output depends on a key and the same message can have different signatures if this key is different, and this dynamic part will be introduced using chaotic generators.
Proceedings ArticleDOI
Rebalance Modern Bike Sharing System: Spatio-Temporal Data Prediction and Path Planning for Multiple Carriers
TL;DR: This work designs the Spatial-Temporal Bike Flow Prediction (ST-BFP) model, which is a convolutional network based on residual framework with history external factors to predict the bike flows, and proposes an Improved Local Search Algorithm (ILSA) for path planning with multiple carriers based on forecast result.
Journal ArticleDOI
Optimal Configuration of Network Coding in Ad Hoc Networks
TL;DR: The theoretical results demonstrate that NC does not bring about order gain on delay/goodput tradeoff for each network model and scheme, except for the flooding scheme in a random i.i.d. mobility model, but the goodput improvement is exhibited for all the proposed schemes in mobile networks.
Journal ArticleDOI
Cybertwin-Driven Federated Learning Based Personalized Service Provision for 6G-V2X
Sahaya Beni Prathiba,Gunasekaran Raja,Sudha Anbalagan,Sugeerthi Gurumoorthy,Neeraj Kumar,Mohsen Guizani +5 more
TL;DR: A Federated Learning and edge Cache-assisted Cybertwin (FLCC) framework for personalized service provision in 6G-V2X and results reveal that the proposed system outperforms the baseline learning approaches by 17.6%.