L. Arockiam

917 citations
109 papers · 544 · h-index 13

Impact in

Papers in

    • Cloud Data Security Solutions 23
    • Cloud Computing and Resource Management 20
    • Blockchain Technology Applications and Security 10
    • Data Mining Algorithms and Applications 10
    • IoT and Edge/Fog Computing 21
    • Network Security and Intrusion Detection 8

L. Arockiam

92 papers receiving 470 citations

Peers

L. Arockiam
Comparison fields: 5 of 69
  • Information Systems 299
  • Computer Networks and Communications 248
  • Artificial Intelligence 213
  • Signal Processing 53
  • Computer Vision and Pattern Recognition 68
Replace Minqi Zhou with:
Minqi Zhou China
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Junho Shim South Korea
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Citations per year

Countries citing papers authored by L. Arockiam

Since Specialization
Citations

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

Fields of papers citing papers by L. Arockiam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 8 scholars most cited alongside L. Arockiam, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with L. Arockiam Line = papers co-authored together L. Arockiam links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 109 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201444
2 201819
3 201518
4 201716
5 201515
6 201715
7 202214
8 201214
9 201314
10 201314
11 201313
12 201513
13 201412
14
VeNADet: Version Number Attack Detection for RPL based Internet of Things
202111
15 201511
16 201811
17 201410
18
A Review on Big Data Integration
20159
19 20178
20 20198

About L. Arockiam

L. Arockiam is a scholar working on Information Systems, Computer Networks and Communications, Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing, having authored 109 papers that have together received 544 indexed citations. Recurring topics across this work include Cloud Data Security Solutions (23 papers), IoT and Edge/Fog Computing (21 papers), Cloud Computing and Resource Management (20 papers), Cryptography and Data Security (12 papers), Blockchain Technology Applications and Security (10 papers), Data Mining Algorithms and Applications (10 papers), Privacy-Preserving Technologies in Data (9 papers) and Network Security and Intrusion Detection (8 papers). The work is most often cited by research in Information Systems (299 citations), Computer Networks and Communications (248 citations), Artificial Intelligence (213 citations), Signal Processing (53 citations) and Computer Vision and Pattern Recognition (68 citations). L. Arockiam has collaborated with scholars based in United States, India and South Korea. Frequent co-authors include S. Baskar, N. Sasikaladevi, A. Aloysius, Stephen P. Charles, Sébastien Charles, A. Vijayalakshmi, Susan T. Charles and Tai-hoon Kim. Their work appears in journals such as Indian Journal of Science and Technology, Journal of Intelligent Manufacturing, Solid State Technology, SHILAP Revista de lepidopterología and International Journal of Machine Learning and Computing.

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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