Prinkle Sharma

551 total citations
11 papers, 363 citations indexed

About

Prinkle Sharma is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Prinkle Sharma has authored 11 papers receiving a total of 363 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Electrical and Electronic Engineering, 8 papers in Artificial Intelligence and 6 papers in Computer Networks and Communications. Recurrent topics in Prinkle Sharma's work include Vehicular Ad Hoc Networks (VANETs) (10 papers), Autonomous Vehicle Technology and Safety (5 papers) and Network Security and Intrusion Detection (5 papers). Prinkle Sharma is often cited by papers focused on Vehicular Ad Hoc Networks (VANETs) (10 papers), Autonomous Vehicle Technology and Safety (5 papers) and Network Security and Intrusion Detection (5 papers). Prinkle Sharma collaborates with scholars based in United States and India. Prinkle Sharma's co-authors include Hong Liu, Jonathan Petit, David Austin, J. H. Gillanders, Hong Liu, Jyoti Grover and Meenakshi Tripathi and has published in prestigious journals such as Scientific Reports, IEEE Access and IEEE Internet of Things Journal.

In The Last Decade

Prinkle Sharma

10 papers receiving 349 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Prinkle Sharma United States	6	257	144	135	108	65	11	363
Sohan Gyawali United States	7	454 1.8×	155 1.1×	246 1.8×	115 1.1×	34 0.5×	19	584
Rens W. van der Heijden Germany	11	167 0.6×	84 0.6×	131 1.0×	83 0.8×	33 0.5×	20	295
Mhafuzul Islam United States	10	136 0.5×	58 0.4×	73 0.5×	89 0.8×	33 0.5×	18	258
Hamssa Hasrouny France	4	484 1.9×	143 1.0×	306 2.3×	141 1.3×	33 0.5×	4	529
Khaled Rabieh United States	11	262 1.0×	189 1.3×	164 1.2×	74 0.7×	14 0.2×	26	456
Arun Raghuramu United States	5	233 0.9×	58 0.4×	103 0.8×	131 1.2×	36 0.6×	7	338
Carole Bassil Lebanon	8	526 2.0×	166 1.2×	371 2.7×	150 1.4×	59 0.9×	17	628
Fátima Duarte-Figueiredo Brazil	8	321 1.2×	44 0.3×	193 1.4×	73 0.7×	27 0.4×	24	463
Mahdi Dibaei Australia	6	155 0.6×	72 0.5×	137 1.0×	37 0.3×	80 1.2×	10	320

Countries citing papers authored by Prinkle Sharma

Since Specialization

Citations

This map shows the geographic impact of Prinkle Sharma'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 Prinkle Sharma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Prinkle Sharma more than expected).

Fields of papers citing papers by Prinkle Sharma

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Prinkle Sharma. 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 Prinkle Sharma. The network helps show where Prinkle Sharma may publish in the future.

Co-authorship network of co-authors of Prinkle Sharma

This figure shows the co-authorship network connecting the top 25 collaborators of Prinkle Sharma. A scholar is included among the top collaborators of Prinkle Sharma 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 Prinkle Sharma. Prinkle Sharma is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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11 of 11 papers shown

1.

Grover, Jyoti, et al.. (2025). Securing the CAN bus using deep learning for intrusion detection in vehicles. Scientific Reports. 15(1). 13820–13820. 2 indexed citations

2.

Sharma, Prinkle, et al.. (2024). GenVRAM: Dataset Generator for Vehicle to Roadside Attacks and Misbehavior. IEEE Access. 12. 86176–86193.

3.

Grover, Jyoti, et al.. (2024). LA-DETECTS: Local and Adaptive Data-Centric Misbehavior Detection Framework for Vehicular Technology Security. IEEE Open Journal of Vehicular Technology. 1–25. 1 indexed citations

4.

Sharma, Prinkle & J. H. Gillanders. (2022). Cybersecurity and Forensics in Connected Autonomous Vehicles: A Review of the State-of-the-Art. IEEE Access. 10. 108979–108996. 25 indexed citations

5.

Sharma, Prinkle & Hong Liu. (2020). A Machine-Learning-Based Data-Centric Misbehavior Detection Model for Internet of Vehicles. IEEE Internet of Things Journal. 8(6). 4991–4999. 119 indexed citations

6.

Sharma, Prinkle, et al.. (2020). Towards an AI-Based After-Collision Forensic Analysis Protocol for Autonomous Vehicles. 240–243. 4 indexed citations

7.

Sharma, Prinkle, David Austin, & Hong Liu. (2019). Attacks on Machine Learning: Adversarial Examples in Connected and Autonomous Vehicles. 1–7. 41 indexed citations

8.

Sharma, Prinkle, et al.. (2018). Integrating Plausibility Checks and Machine Learning for Misbehavior Detection in VANET. IEEE Conference Proceedings. 2018. 564–571. 1 indexed citations

9.

Sharma, Prinkle, Jonathan Petit, & Hong Liu. (2018). Pearson Correlation Analysis to Detect Misbehavior in VANET. 1–5. 9 indexed citations

10.

Sharma, Prinkle, et al.. (2018). Integrating Plausibility Checks and Machine Learning for Misbehavior Detection in VANET. 564–571. 115 indexed citations

11.

Sharma, Prinkle, et al.. (2017). Securing wireless communications of connected vehicles with artificial intelligence. 1–7. 46 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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