A
Abbas Bradai
Researcher at University of Poitiers
Publications - 59
Citations - 1231
Abbas Bradai is an academic researcher from University of Poitiers. The author has contributed to research in topics: Quality of service & Video quality. The author has an hindex of 15, co-authored 55 publications receiving 760 citations. Previous affiliations of Abbas Bradai include University of Bordeaux & Centre national de la recherche scientifique.
Papers
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Scheduling Wireless Virtual Networks Functions
TL;DR: This work formalizes the wireless V NF placement problem in the radio access network as an integer linear programming problem and proposes a VNF placement heuristic, named wireless network embedding (WiNE), to solve the problem.
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A Survey of Localization Systems in Internet of Things
TL;DR: This survey provides a general overview of the localization in Wireless Sensor Networks (WSN) and surveys technical details related to approaches and algorithms of various important localization techniques using different technologies.
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MobQoS: Mobility-Aware and QoS-Driven SDN Framework for Autonomous Vehicles
TL;DR: This work presents a composite framework with a distributed SDN-DMM approach in ITS ecosystems that handles both the mobility and QoS challenges of the underlying vehicular networks and outperforms the existing state of the art in terms of overall communication latency and bandwidth utilization.
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Cellular software defined networking: a framework
TL;DR: The proposed architecture, which the authors call CSDN (Cellular SDN), leverages software defined networking (SDN) and network functions virtualization (NFV), and argues that dynamic resource orchestration and optimal control need real-time context data analyses to make intelligent decisions.
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Deep Federated Q-Learning-Based Network Slicing for Industrial IoT
Seifeddine Messaoud,Abbas Bradai,Olfa Ben Ahmed,Pham Tran Anh Quang,Mohamed Atri,M. Shamim Hossain +5 more
TL;DR: A novel deep RL scheme to provide a federated and dynamic network management and resource allocation for differentiated QoS services in future IIoT networks, and a multiagent deep Q-learning-based dynamic slices TP and SF adjustment process that aims at maximizing self-QoS requirements in term of throughput and delay.