Padmalaya Nayak
- Computer Networks and Communications top 2%
- Electrical and Electronic Engineering top 10%
- Artificial Intelligence top 10%
- Water Science and Technology top 10%
- Oncology
- Co-authors
- Surbhi GuptaG. KarunaNeeraj MohanP. RavichandranNiranjan Kumar RayMohammad S. ObaidatKumar LaxmanK. Swaraja
- Topics
- Energy Efficient Wireless Sensor Networks (15 papers)Energy Harvesting in Wireless Networks (6 papers)IoT-based Smart Home Systems (6 papers)
- Cited by
- Computer Networks and CommunicationsElectrical and Electronic EngineeringWater Science and Technology
- Journals
- SHILAP Revista de lepidopterologíaIEEE Sensors JournalIEEE Communications Letters
- Partner nations
- IndiaRussiaSouth Korea
In The Last Decade
Padmalaya Nayak
30 papers receiving 788 citations
Hit Papers
Peers
Comparison fields: 5 of 69
- Computer Networks and Communications 642
- Electrical and Electronic Engineering 495
- Artificial Intelligence 116
- Water Science and Technology 85
- Oncology 70
Countries citing papers authored by Padmalaya Nayak
This map shows the geographic impact of Padmalaya Nayak'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 Padmalaya Nayak with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Padmalaya Nayak more than expected).
Fields of papers citing papers by Padmalaya Nayak
This network shows the impact of papers produced by Padmalaya Nayak. 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 Padmalaya Nayak. The network helps show where Padmalaya Nayak may publish in the future.
Co-authorship network of co-authors of Padmalaya Nayak
This figure shows the co-authorship network connecting the top 25 collaborators of Padmalaya Nayak. A scholar is included among the top collaborators of Padmalaya Nayak 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 Padmalaya Nayak. Padmalaya Nayak is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 12 | |
| 5 | 2 | |
| 6 | 31 | |
| 7 | 6 | |
| 8 | 90 | |
| 9 | 6 | |
| 10 | 13 | |
| 11 | 18 | |
| 12 | 2 | |
| 13 | 13 | |
| 14 | 130 | |
| 15 | 23 | |
| 16 | 3 | |
| 17 | 2 | |
| 18 | 12 | |
| 19 | Comparison of Routing Protocols in WSN using NetSim Simulator: LEACH Vs LEACH-C | 15 |
| 20 | 10 |
About Padmalaya Nayak
Padmalaya Nayak is a scholar working on Computer Networks and Communications, Health Information Management and Water Science and Technology, having authored 34 papers that have together received 862 indexed citations. Recurring topics across this work include Energy Efficient Wireless Sensor Networks (15 papers), Energy Harvesting in Wireless Networks (6 papers) and IoT-based Smart Home Systems (6 papers). The work is most often cited by research in Computer Networks and Communications (642 citations), Electrical and Electronic Engineering (495 citations) and Water Science and Technology (85 citations). Padmalaya Nayak has collaborated with scholars based in India, Russia and South Korea. Frequent co-authors include Surbhi Gupta, G. Karuna, Neeraj Mohan, P. Ravichandran, Niranjan Kumar Ray, Mohammad S. Obaidat, Kumar Laxman, K. Swaraja, Saurav Dixit and Garimella Rama Murthy. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Sensors Journal and IEEE Communications Letters.
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.