Hani Sami

870 total citations
24 papers, 583 citations indexed

About

Hani Sami is a scholar working on Computer Networks and Communications, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Hani Sami has authored 24 papers receiving a total of 583 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Computer Networks and Communications, 10 papers in Artificial Intelligence and 8 papers in Electrical and Electronic Engineering. Recurrent topics in Hani Sami's work include IoT and Edge/Fog Computing (13 papers), Privacy-Preserving Technologies in Data (6 papers) and Vehicular Ad Hoc Networks (VANETs) (5 papers). Hani Sami is often cited by papers focused on IoT and Edge/Fog Computing (13 papers), Privacy-Preserving Technologies in Data (6 papers) and Vehicular Ad Hoc Networks (VANETs) (5 papers). Hani Sami collaborates with scholars based in Lebanon, Canada and United Arab Emirates. Hani Sami's co-authors include Azzam Mourad, Hadi Otrok, Jamal Bentahar, Wassim El‐Hajj, Rabeb Mizouni, Ahmad Hammoud, Ernesto Damiani, Mohsen Guizani, Chamseddine Talhi and Safa Otoum and has published in prestigious journals such as IEEE Communications Surveys & Tutorials, Information Sciences and IEEE Internet of Things Journal.

In The Last Decade

Hani Sami

20 papers receiving 568 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Hani Sami Lebanon	12	375	208	205	157	55	24	583
Long Luo China	14	453 1.2×	330 1.6×	274 1.3×	156 1.0×	45 0.8×	44	759
Dianlei Xu China	10	324 0.9×	102 0.5×	186 0.9×	126 0.8×	25 0.5×	16	538
Lanlan Rui China	15	492 1.3×	187 0.9×	217 1.1×	129 0.8×	29 0.5×	107	707
Khaleel Mershad Lebanon	13	585 1.6×	204 1.0×	419 2.0×	93 0.6×	59 1.1×	54	792
Konglin Zhu China	16	444 1.2×	112 0.5×	298 1.5×	124 0.8×	111 2.0×	69	720
Le Thanh Tan United States	12	596 1.6×	136 0.7×	431 2.1×	111 0.7×	36 0.7×	28	739
Dongfeng Fang United States	12	350 0.9×	126 0.6×	318 1.6×	133 0.8×	44 0.8×	47	630
Tigang Jiang China	9	351 0.9×	212 1.0×	316 1.5×	83 0.5×	35 0.6×	27	531
Yiming Liu China	12	470 1.3×	340 1.6×	283 1.4×	163 1.0×	20 0.4×	47	787
Lewis Tseng United States	9	322 0.9×	251 1.2×	173 0.8×	217 1.4×	18 0.3×	54	580

Countries citing papers authored by Hani Sami

Since Specialization

Citations

This map shows the geographic impact of Hani Sami's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hani Sami with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hani Sami more than expected).

Fields of papers citing papers by Hani Sami

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hani Sami. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Hani Sami. The network helps show where Hani Sami may publish in the future.

Co-authorship network of co-authors of Hani Sami

This figure shows the co-authorship network connecting the top 25 collaborators of Hani Sami. A scholar is included among the top collaborators of Hani Sami based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Hani Sami. Hani Sami is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Elmekki, Hanae, Hani Sami, Ehsan Zakeri, et al.. (2025). CACTUS: An open dataset and framework for automated Cardiac Assessment and Classification of Ultrasound images using deep transfer learning. Computers in Biology and Medicine. 190. 110003–110003.

Sami, Hani, Rabeb Mizouni, Jamal Bentahar, et al.. (2025). Reward shaping in DRL: A novel framework for adaptive resource management in dynamic environments. Information Sciences. 715. 122238–122238. 2 indexed citations

Boateng, Gordon Owusu, Hani Sami, Hanae Elmekki, et al.. (2025). A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions. IEEE Communications Surveys & Tutorials. 28. 527–566. 12 indexed citations

Elmekki, Hanae, Saidul Islam, Hani Sami, et al.. (2025). Comprehensive review of reinforcement learning for medical ultrasound imaging. Artificial Intelligence Review. 58(9). 284–284. 2 indexed citations

Sami, Hani, et al.. (2025). Efficient privacy-preserving ML for IoT: Cluster-based split federated learning scheme for non-IID data. Journal of Network and Computer Applications. 236. 104105–104105. 4 indexed citations

Sami, Hani, et al.. (2025). On-Demand Model and Client Deployment in Federated Learning With Deep Reinforcement Learning. IEEE Internet of Things Journal. 12(14). 26685–26698.

Sami, Hani, et al.. (2025). Privacy-Preserving Continuous User Authentication Using Federated Learning. Espace ÉTS (ETS). 1404–1409.

Sami, Hani, Ahmad Hammoud, Sarhad Arisdakessian, et al.. (2024). The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions. IEEE Communications Surveys & Tutorials. 26(4). 2914–2960. 22 indexed citations

Sami, Hani, et al.. (2023). Opportunistic UAV Deployment for Intelligent On-Demand IoV Service Management. IEEE Transactions on Network and Service Management. 20(3). 3428–3442. 19 indexed citations

10.

Sami, Hani, Hadi Otrok, Jamal Bentahar, Azzam Mourad, & Ernesto Damiani. (2023). Reward shaping using convolutional neural network. Information Sciences. 648. 119481–119481. 2 indexed citations

11.

Sami, Hani, et al.. (2023). Towards On-Demand Deployment of Multiple Clients and Heterogeneous Models in Federated Learning. 1556–1561.

12.

Sami, Hani, Rabeb Mizouni, Hadi Otrok, et al.. (2023). LearnChain: Transparent and cooperative reinforcement learning on Blockchain. Future Generation Computer Systems. 150. 255–271. 3 indexed citations

13.

Sami, Hani, Azzam Mourad, Safa Otoum, et al.. (2023). On-Demand-FL: A Dynamic and Efficient Multicriteria Federated Learning Client Deployment Scheme. IEEE Internet of Things Journal. 10(18). 15822–15834. 28 indexed citations

14.

Sami, Hani, Jamal Bentahar, Azzam Mourad, Hadi Otrok, & Ernesto Damiani. (2022). Graph convolutional recurrent networks for reward shaping in reinforcement learning. Information Sciences. 608. 63–80. 24 indexed citations

15.

Sami, Hani, Azzam Mourad, Hadi Otrok, & Jamal Bentahar. (2021). Demand-Driven Deep Reinforcement Learning for Scalable Fog and Service Placement. IEEE Transactions on Services Computing. 15(5). 2671–2684. 73 indexed citations

16.

Sami, Hani, Azzam Mourad, Hadi Otrok, & Jamal Bentahar. (2020). FScaler: Automatic Resource Scaling of Containers in Fog Clusters Using Reinforcement Learning. 1824–1829. 22 indexed citations

17.

Sami, Hani, Azzam Mourad, & Wassim El‐Hajj. (2020). Vehicular-OBUs-As-On-Demand-Fogs: Resource and Context Aware Deployment of Containerized Micro-Services. IEEE/ACM Transactions on Networking. 28(2). 778–790. 59 indexed citations

18.

Khoury, Joseph El, Hani Sami, Haı̈dar Safa, & Wassim El‐Hajj. (2019). On The Use of Software Defined Wireless Network in Vehicular Fog Computing Environments. 1198–1203. 8 indexed citations

19.

Sami, Hani, et al.. (2019). Reinforcement R-learning model for time scheduling of on-demand fog placement. The Journal of Supercomputing. 76(1). 388–410. 30 indexed citations

20.

Sami, Hani & Azzam Mourad. (2018). Towards Dynamic On-Demand Fog Computing Formation Based On Containerization Technology. 2014. 960–965. 11 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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