John Panneerselvam
-
- IoT and Edge/Fog Computing 19
- Caching and Content Delivery 13
- Energy Efficient Wireless Sensor Networks 5
- Peer-to-Peer Network Technologies 4
- Information Systems top 2%
- Cloud Computing and Resource Management 20
- Recommender Systems and Techniques 4
- Artificial Intelligence top 5%
-
- Complex Network Analysis Techniques 11
- Hardware and Architecture top 10%
-
- Traffic Prediction and Management Techniques 4
- Journals
- IEEE Access (5 papers)IEEE Transactions on Industrial Informatics (3 papers)Neurocomputing (3 papers)
- Partner nations
- United KingdomChinaAustralia
In The Last Decade
John Panneerselvam
65 papers receiving 856 citations
Peers
Comparison fields: 5 of 101
- Computer Networks and Communications 424
- Information Systems 319
- Artificial Intelligence 267
- Statistical and Nonlinear Physics 93
- Hardware and Architecture 44
Countries citing papers authored by John Panneerselvam
This map shows the geographic impact of John Panneerselvam'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 John Panneerselvam with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Panneerselvam more than expected).
Fields of papers citing papers by John Panneerselvam
This network shows the impact of papers produced by John Panneerselvam. 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 John Panneerselvam. The network helps show where John Panneerselvam may publish in the future.
Co-authorship network
The 25 scholars most cited alongside John Panneerselvam, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2024 | 5 | |
| 5 | 2024 | 26 | |
| 6 | 2024 | 2 | |
| 7 | 2024 | 0 | |
| 8 | 2023 | 1 | |
| 9 | 2023 | 18 | |
| 10 | 2023 | 6 | |
| 11 | 2023 | 1 | |
| 12 | 2022 | 0 | |
| 13 | 2021 | 9 | |
| 14 | 2021 | 18 | |
| 15 | 2021 | 5 | |
| 16 | 2021 | 1 | |
| 17 | 2021 | 0 | |
| 18 | 2019 | 29 | |
| 19 | 2019 | 13 | |
| 20 | 2019 | 28 |
About John Panneerselvam
John Panneerselvam is a scholar working on Computer Networks and Communications, Information Systems and Statistical and Nonlinear Physics, having authored 74 papers that have together received 892 indexed citations. Recurring topics across this work include Cloud Computing and Resource Management (20 papers), IoT and Edge/Fog Computing (19 papers), Caching and Content Delivery (13 papers), Complex Network Analysis Techniques (11 papers), Energy Efficient Wireless Sensor Networks (5 papers), Peer-to-Peer Network Technologies (4 papers), Traffic Prediction and Management Techniques (4 papers) and Recommender Systems and Techniques (4 papers). The work is most often cited by research in Computer Networks and Communications (424 citations), Information Systems (319 citations) and Artificial Intelligence (267 citations). John Panneerselvam has collaborated with scholars based in United Kingdom, China and Australia. Frequent co-authors include Lu Liu, Nick Antonopoulos, Yao Lu, Xiaojun Zhai, Haider Ali, Bo Yuan, Umair Ullah Tariq, Leilei Shi, Yan Wu and Liang Jiang. Their work appears in journals such as IEEE Access, IEEE Transactions on Industrial Informatics, Neurocomputing, IEEE Transactions on Computational Social Systems and Digital Communications and Networks.
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.