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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Radio and Medium Access Contention Aware Routing for Lifetime Maximization in Multichannel Sensor Networks
TL;DR: This paper proposes energy and cross-layer aware routing schemes for multichannel access WSNs that account for radio, MAC contention, and network constraints, and guarantees to meet data rate requirements of end-to-end flows while maximizing the network lifetime.
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Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey
Vikas Hassija,Vinay Chamola,Adhar Agrawal,Adit Goyal,Nguyen Cong Luong,Dusit Niyato,F. Richard Yu,Mohsen Guizani +7 more
TL;DR: In this article, the authors present a detailed review of the security-critical drone applications, and security-related challenges in drone communication such as DoS attacks, Man-in-the-middle attacks, De-Authentication attacks, and so on.
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Energy-Efficient Caching for Mobile Edge Computing in 5G Networks
TL;DR: Simulation results show that the proposed solution can effectively determine the near-optimal caching placement to obtain better performance in terms of energy efficiency gains compared with conventional caching placement strategies and can significantly reduce the joint cost when backhaul capacity is low.
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Honeypot Identification in Softwarized Industrial Cyber–Physical Systems
TL;DR: Wang et al. as mentioned in this paper proposed a secure fuzzy testing approach for honeypot identification inspired by vulnerability mining, which utilizes error handling to distinguish honeypots and real devices, and adopts mutation rules and security rules to generate effective and secure probe packets.
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Big Data Mining of Users’ Energy Consumption Patterns in the Wireless Smart Grid
TL;DR: Three types of big data mining schemes for analyzing stolen data from wireless communication networks and several ongoing defense strategies in the era of the wireless smart grid are discussed.