Nano-Enabled Edge Computing Frameworks for Advanced Smart Learning Environments
| Authors | Abhishek Kumar1, Yashaswi Singh1, Swati Singh2, Kiran Deep Singh3, Prabh Deep Singh4 |
| Affiliations | 11Department of Biotechnology, Graphic Era (Deemed to be University), Dehradun, 28002, Uttarakhand, India2Department of Environmental Science, Graphic Era (Deemed to be University), Dehradun, 28002, Uttarakhand, India3Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, 140401, Punjab India 2Department of Computer Science and Engineering, Graphic Era (Deemed to be University), Dehradun, 28002, Uttarakhand, India |
| Е-mail | ssingh.prabhdeep@gmail.com |
| Issue | Volume 17, Year 2025, Number 6 |
| Dates | Received 14 August 2025; revised manuscript received December 2025; published online December 2025 |
| Citation | Abhishek Kumar, Yashaswi Singh, Swati Singh, и др., J. Nano- Electron. Phys. 17 No 6, 06013 (2025) |
| DOI | https://doi.org/10.21272/jnep.17(6).06013 |
| PACS Number(s) | 81.07.Bc, 81.70.Cv, 66.30.Xj |
| Keywords | Nano-materials, Edge computing, Smart Education, Latency reduction, Energy efficiency, (3190) . |
| Annotation | The incorporation of technologies in educational systems is now changing the education sector and smart education is a new feature. That is why one of the effective development trends for smart education technologies is the creation of low-latency energy-efficient IT systems that integrate real-time data processing and individual approaches to learning. To this end, this paper presents a nano-enabled edge computing framework to deal with such challenges through integrating nano-materials in edge devices. Advancements in nano-materials like graphene, carbon nanotubes, and quantum dots are used for their electrical, thermal, and mechanical characteristics to improve edge devices of smart education. The proposed framework just described helps to further decrease latencies and improve energy efficiency while allowing real-time and adaptive learning scenarios. These experimental results depict that the nano-physicality of edge units can make them 20-30 per cent faster and 40-50 per cent less power-intensive than conventional devices. Due to these enhancements, the proposed framework is well-suited to large-scale, effective, and adaptive smart education solutions. Specifying the role of nano-materials in the development of Europe’s edge computing for education, this paper elucidates the capacity of this technology to enhance performance, scalability, and energy efficiency. Subsequent studies will examine integration issues and attempt to test the variability of the framework in various learning contexts. |
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List of References English version of article |
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