Dilip Senapati

500 total citations
26 papers, 252 citations indexed

About

Dilip Senapati is a scholar working on Computer Networks and Communications, Electrical and Electronic Engineering and Statistical and Nonlinear Physics. According to data from OpenAlex, Dilip Senapati has authored 26 papers receiving a total of 252 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computer Networks and Communications, 8 papers in Electrical and Electronic Engineering and 6 papers in Statistical and Nonlinear Physics. Recurrent topics in Dilip Senapati's work include IoT and Edge/Fog Computing (8 papers), Statistical Mechanics and Entropy (4 papers) and Complex Systems and Time Series Analysis (4 papers). Dilip Senapati is often cited by papers focused on IoT and Edge/Fog Computing (8 papers), Statistical Mechanics and Entropy (4 papers) and Complex Systems and Time Series Analysis (4 papers). Dilip Senapati collaborates with scholars based in India, Denmark and Italy. Dilip Senapati's co-authors include Sujit Bebortta, Bibudhendu Pati, Amit Kumar Singh, Chhabi Rani Panigrahi, Karmeshu, Prayag Tiwari, Jia Qian, Amit Kumar Jaiswal, Hari Mohan Pandey and Nikhil Kumar Rajput and has published in prestigious journals such as IEEE Internet of Things Journal, Chaos Solitons & Fractals and Neural Computing and Applications.

In The Last Decade

Dilip Senapati

24 papers receiving 247 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dilip Senapati India 9 120 81 46 34 31 26 252
Gang Chuai China 9 137 1.1× 187 2.3× 28 0.6× 9 0.3× 23 0.7× 58 308
Andrés Suárez-González Spain 8 122 1.0× 103 1.3× 14 0.3× 11 0.3× 18 0.6× 36 249
Tiantian Yang China 8 67 0.6× 38 0.5× 24 0.5× 32 0.9× 36 1.2× 19 281
Tung‐Kuang Wu Taiwan 9 72 0.6× 64 0.8× 11 0.2× 16 0.5× 80 2.6× 25 277
Heung Seok Jeon South Korea 9 37 0.3× 18 0.2× 49 1.1× 84 2.5× 47 1.5× 37 268
Chen Luo China 11 99 0.8× 47 0.6× 26 0.6× 55 1.6× 33 1.1× 32 290
Chenming Zhu China 7 35 0.3× 62 0.8× 65 1.4× 24 0.7× 48 1.5× 18 230
João Roberto Bertini Brazil 10 35 0.3× 14 0.2× 31 0.7× 25 0.7× 175 5.6× 36 268
Ying‐Hsin Liang Taiwan 11 206 1.7× 236 2.9× 16 0.3× 24 0.7× 21 0.7× 49 351

Countries citing papers authored by Dilip Senapati

Since Specialization
Citations

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

Fields of papers citing papers by Dilip Senapati

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dilip Senapati

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

All Works

20 of 20 papers shown
1.
Senapati, Dilip, et al.. (2025). Stochastic FitzHugh–Nagumo neuron model with Gamma distributed delay kernel. Chaos Solitons & Fractals. 196. 116378–116378. 1 indexed citations
2.
3.
Bebortta, Sujit, et al.. (2024). Mist–fog-assisted real-time emotion recognition using deep transfer learning framework for smart city 4.0. Internet of Things. 27. 101237–101237. 2 indexed citations
4.
Bebortta, Sujit, Bibudhendu Pati, Chhabi Rani Panigrahi, & Dilip Senapati. (2024). Dynamic performance modeling framework for QoS-aware 5G-based IoT-edge systems. International Journal of Information Technology. 16(4). 2149–2160. 8 indexed citations
5.
Tripathy, Subhranshu Sekhar, Sujit Bebortta, Chinmay Chakraborty, et al.. (2024). Leveraging Resource-Aware Deep Collaborative Learning Toward Secure B5G-Driven IoT–Fog-Based Consumer Electronic Systems. IEEE Transactions on Consumer Electronics. 71(2). 4443–4450. 2 indexed citations
6.
7.
Bebortta, Sujit & Dilip Senapati. (2023). Toward Cost-Aware Computation Offloading in IoT-Based MEC Systems. National Academy Science Letters. 46(6). 531–534. 3 indexed citations
8.
Bebortta, Sujit, et al.. (2022). An intelligent spatial stream processing framework for digital forensics amid the COVID-19 outbreak. Smart Health. 26. 100308–100308. 5 indexed citations
9.
Senapati, Dilip, et al.. (2022). An adaptive q-Lognormal model towards the computation of average channel capacity in slow fading channels. Telecommunication Systems. 79(3). 341–355. 6 indexed citations
10.
Senapati, Dilip, et al.. (2022). A Tight Approximation Towards the SEP Computation Over Nakagami-m Fading Channels. National Academy Science Letters. 45(5). 423–426. 4 indexed citations
11.
Bebortta, Sujit, Dilip Senapati, Chhabi Rani Panigrahi, & Bibudhendu Pati. (2021). Adaptive Performance Modeling Framework for QoS-Aware Offloading in MEC-Based IIoT Systems. IEEE Internet of Things Journal. 9(12). 10162–10171. 52 indexed citations
12.
Singh, Amit Kumar, et al.. (2021). A new tight approximation towards the computation of option price. International Journal of Information Technology. 14(3). 1295–1303. 5 indexed citations
13.
Senapati, Dilip, et al.. (2021). Evaluation of symbol error probability using a new tight Gaussian <i>Q</i> approximation. International Journal of Systems Control and Communications. 12(1). 60–60. 1 indexed citations
14.
Bebortta, Sujit, Dilip Senapati, Chhabi Rani Panigrahi, & Bibudhendu Pati. (2021). An Adaptive Modeling and Performance Evaluation Framework for Edge-Enabled Green IoT Systems. IEEE Transactions on Green Communications and Networking. 6(2). 836–844. 19 indexed citations
15.
Senapati, Dilip, et al.. (2021). Evaluation of symbol error probability using a new tight Gaussian <i>Q</i> approximation. International Journal of Systems Control and Communications. 12(1). 60–60. 8 indexed citations
16.
Senapati, Dilip, et al.. (2020). Option pricing model based on sentiment using the Gram-Charlier expansion. 16(1). 669–677. 1 indexed citations
17.
Singh, Amit Kumar, et al.. (2020). Computational Modeling of Non-Gaussian Option Price Using Non-extensive Tsallis’ Entropy Framework. Computational Economics. 57(4). 1353–1371. 8 indexed citations
18.
Senapati, Dilip, et al.. (2020). Integrated bacteria-algal bioreactor for removal of toxic metals in acid mine drainage from iron ore mines. Bioresource Technology Reports. 11. 100422–100422. 21 indexed citations
19.
Singh, Amit Kumar, et al.. (2019). Performance Evaluation of Wireless Communication Systems over Weibull/q-Lognormal Shadowed Fading Using Tsallis’ Entropy Framework. Wireless Personal Communications. 106(2). 789–803. 18 indexed citations
20.
Senapati, Dilip & Karmeshu. (2015). Generation of cubic power-law for high frequency intra-day returns: Maximum Tsallis entropy framework. Digital Signal Processing. 48. 276–284. 18 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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