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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Energy-Efficient Resource Allocation and Provisioning Framework for Cloud Data Centers
TL;DR: In this article, an integrated energy-aware resource provisioning framework for cloud data centers is proposed, which predicts the number of virtual machine (VM) requests, along with the amount of CPU and memory resources associated with each of these requests, and reduces energy consumption by putting to sleep unneeded PMs.
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Blockchain-Based Mobile Edge Computing Framework for Secure Therapy Applications
Md. Abdur Rahman,M. Shamim Hossain,George Loukas,Elham Hassanain,Syed Sadiqur Rahman,Mohammed F. Alhamid,Mohsen Guizani +6 more
TL;DR: An in-home therapy management framework, which leverages the IoT nodes and the blockchain-based decentralized MEC paradigm to support low-latency, secure, anonymous, and always-available spatiotemporal multimedia therapeutic data communication within an on-demand data-sharing scenario.
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Machine learning in the Internet of Things: Designed techniques for smart cities
TL;DR: Different IoT-based machine learning mechanisms that are used in the mentioned fields among others are studied and the lessons learned are reported and the assessments are explored viewing the basic aim machine learning techniques are expected to play in IoT networks.
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DTRAB: combating against attacks on encrypted protocols through traffic-feature analysis
TL;DR: This work proposes an anomaly-based detection system by using strategically distributed monitoring stubs (MSs) to combat against attacks on encrypted protocols, which focuses on both Detection and TRAceBack in the MS level.
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Evaluating Reputation Management Schemes of Internet of Vehicles Based on Evolutionary Game Theory
TL;DR: The results indicate that the evaluation method is able to depict the evolving process of the dynamic attacking strategies in a vehicular network, and the final state of the simulation could be used to quantify the protection effectiveness of the reputation management scheme.