Hani Sami

Orcid: 0000-0002-6925-1006

According to our database1, Hani Sami authored at least 18 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Online presence:

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Bibliography

2024
LearnChain: Transparent and cooperative reinforcement learning on Blockchain.
Future Gener. Comput. Syst., January, 2024

2023
Reward shaping using convolutional neural network.
Inf. Sci., November, 2023

Opportunistic UAV Deployment for Intelligent On-Demand IoV Service Management.
IEEE Trans. Netw. Serv. Manag., September, 2023

On-Demand-FL: A Dynamic and Efficient Multicriteria Federated Learning Client Deployment Scheme.
IEEE Internet Things J., September, 2023

Reinforcement Learning Framework for Server Placement and Workload Allocation in Multiaccess Edge Computing.
IEEE Internet Things J., January, 2023

The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions.
CoRR, 2023

Towards On-Demand Deployment of Multiple Clients and Heterogeneous Models in Federated Learning.
Proceedings of the International Wireless Communications and Mobile Computing, 2023

2022
Demand-Driven Deep Reinforcement Learning for Scalable Fog and Service Placement.
IEEE Trans. Serv. Comput., 2022

Graph convolutional recurrent networks for reward shaping in reinforcement learning.
Inf. Sci., 2022

ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme.
CoRR, 2022

Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing.
CoRR, 2022

2021
AI-Based Resource Provisioning of IoE Services in 6G: A Deep Reinforcement Learning Approach.
IEEE Trans. Netw. Serv. Manag., 2021

2020
Vehicular-OBUs-As-On-Demand-Fogs: Resource and Context Aware Deployment of Containerized Micro-Services.
IEEE/ACM Trans. Netw., 2020

Dynamic On-Demand Fog Formation Offering On-the-Fly IoT Service Deployment.
IEEE Trans. Netw. Serv. Manag., 2020

Reinforcement R-learning model for time scheduling of on-demand fog placement.
J. Supercomput., 2020

AI, Blockchain, and Vehicular Edge Computing for Smart and Secure IoV: Challenges and Directions.
IEEE Internet Things Mag., 2020

FScaler: Automatic Resource Scaling of Containers in Fog Clusters Using Reinforcement Learning.
Proceedings of the 16th International Wireless Communications and Mobile Computing Conference, 2020

2019
On The Use of Software Defined Wireless Network in Vehicular Fog Computing Environments.
Proceedings of the 15th International Wireless Communications & Mobile Computing Conference, 2019


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