Nandan Banerji | Internet of Things | Editorial Board Member

Editorial Board Member

Nandan Banerji, Birla Institute of Technology, India

Nandan Banerji
Affiliation Birla Institute of Technology
Country India
Scopus ID 57209101586
Documents 13
Citations 8
h-index 2
Subject Area Internet of Things
Event Technology Scientists Awards
ORCID 0000-0002-0698-0404

Nandan Banerji is an academic researcher associated with the Birla Institute of Technology, India, whose work focuses on Internet of Things (IoT), federated learning systems, real-time analytics, and distributed intelligent infrastructures. His scholarly contributions explore the intersection of machine learning methodologies and resilient IoT frameworks for emerging computational environments.[1] His research publications demonstrate applications in electricity generation analytics, fintech-oriented federated learning infrastructures, and adaptive decentralized learning systems.[2][3]

Abstract

This article presents an academic overview of Nandan Banerji and his contributions within the field of Internet of Things and intelligent distributed computing systems. The discussion highlights research activities related to machine learning-driven electricity analytics, federated learning architectures for IoT systems, and resilient infrastructures for decentralized computational environments.[1][2] The article also examines the scholarly significance of his publications and their relevance to modern computational challenges in fintech services, adaptive networking, and real-time data processing.[3]

Keywords

Internet of Things, Federated Learning, Distributed Computing, Machine Learning, Real-Time Analytics, Fintech Infrastructure, Adaptive IoT Systems, Decentralized Intelligence, Electricity Generation Analytics, Resilient Networks

Introduction

The evolution of Internet of Things technologies has significantly transformed the landscape of intelligent systems and distributed computational environments. Researchers working in this domain increasingly investigate adaptive infrastructures capable of supporting resilient communication, secure data aggregation, and decentralized machine learning operations.[2] Nandan Banerji has contributed to these developments through scholarly work centered on federated learning mechanisms and real-time analytical systems applicable to IoT-driven environments.[3]

His publications address contemporary issues associated with large-scale data processing, intelligent decision-making, and distributed learning infrastructures. Such work reflects ongoing academic interest in scalable and privacy-aware computational systems suitable for modern digital ecosystems.[1]

Research Profile

Nandan Banerji is affiliated with Birla Institute of Technology, India, where his research activities are associated with Internet of Things technologies and intelligent distributed infrastructures. His Scopus profile documents scholarly output related to machine learning applications, decentralized systems, and adaptive network architectures.[4]

  • Research specialization in Internet of Things and federated learning infrastructures.[2]
  • Experience in machine learning-based real-time data analysis systems.[1]
  • Academic contributions related to decentralized fintech and IoT service architectures.[3]
  • Participation in collaborative interdisciplinary computational research initiatives.[1]

Research Contributions

One of the significant areas of contribution by Nandan Banerji involves the integration of machine learning methodologies into real-time electricity generation analytics. The study focusing on Sikkim regional electricity generation explored predictive and analytical methods for understanding real-time energy data patterns within computational intelligence frameworks.[1]

Another notable contribution concerns adaptive federated learning infrastructures for ad hoc IoT environments. This work proposed resilient and scalable architectures designed to support decentralized learning operations while preserving distributed data privacy and communication efficiency.[2]

Additional scholarly work investigated threshold-based federated learning infrastructures for fintech services, highlighting the practical application of distributed intelligence systems within financial technology ecosystems. The research addressed challenges associated with trust management, learning synchronization, and distributed analytical processing.[3]

Publications

  1. Limboo, S., Katel, A., Koirala, T. K., Nag, A., & Banerji, N. (2023). Machine Learning-Based Analysis of Electricity Generation on Real-Time Data from Sikkim Regions. Springer.
    DOI: https://doi.org/10.1007/978-3-032-20253-6_35
  2. Bhattacharjee, S., Katel, A., Singh, Y., & Banerji, N. (2022). An Adaptive and Resilient Federated Learning Infrastructure for Adhoc IoT Scenario. TechRxiv.
    DOI: https://doi.org/10.36227/techrxiv.176404090.05996485/v1
  3. Banerji, N., & Sherpa, L. (2022). A Threshold-Based Federated Learning Infrastructure for Fintech Services. TechRxiv.
    DOI: https://doi.org/10.36227/techrxiv.176003148.82070541/v1

Research Impact

The research activities associated with Nandan Banerji contribute to the broader advancement of intelligent IoT ecosystems and decentralized machine learning systems. His work on federated learning architectures aligns with ongoing global efforts toward privacy-preserving distributed intelligence and scalable computational frameworks.[2]

The application-oriented nature of his publications demonstrates practical relevance for emerging domains such as energy analytics, fintech infrastructures, and adaptive communication systems. Such contributions support the integration of machine learning technologies into real-world computational environments and industrial applications.[1][3]

Award Suitability

Nandan Banerji’s academic profile demonstrates alignment with the objectives of the Technology Scientists Awards, particularly within the subject area of Internet of Things. His scholarly contributions emphasize innovation in federated learning infrastructures, intelligent distributed systems, and real-time analytical methodologies applicable to emerging digital ecosystems.[2]

The interdisciplinary character of his work further supports recognition within academic and scientific award frameworks that emphasize technological innovation, computational intelligence, and scalable IoT-based architectures.[3]

Conclusion

Nandan Banerji represents an emerging scholarly contributor within the field of Internet of Things and intelligent distributed systems research. His academic publications illustrate engagement with contemporary computational challenges involving federated learning, resilient infrastructures, and machine learning-enabled analytical systems.[1][2] Through collaborative and application-oriented research, his work contributes to the ongoing advancement of adaptive and decentralized intelligent technologies.[3]

References

  1. Limboo, S., Katel, A., Koirala, T. K., Nag, A., & Banerji, N. (2023). Machine Learning-Based Analysis of Electricity Generation on Real-Time Data from Sikkim Regions. Springer.
    DOI: https://doi.org/10.1007/978-3-032-20253-6_35
  2. Bhattacharjee, S., Katel, A., Singh, Y., & Banerji, N. (2022). An Adaptive and Resilient Federated Learning Infrastructure for Adhoc IoT Scenario. TechRxiv.
    DOI: https://doi.org/10.36227/techrxiv.176404090.05996485/v1
  3. Banerji, N., & Sherpa, L. (2022). A Threshold-Based Federated Learning Infrastructure for Fintech Services. TechRxiv.
    DOI: https://doi.org/10.36227/techrxiv.176003148.82070541/v1
  4. Elsevier. (n.d.). Scopus author details: Nandan Banerji, Author ID 57209101586. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57209101586

Lei Tian | Embedded Systems | Best Paper Award

Assoc Prof. Dr. Lei Tian | Embedded Systems | Best Paper Award

Laboratory Director at Xi’an University of Posts and Telecommunications | China

Lei Tian is a laboratory director at Xi’an University of Posts & Telecommunications whose work spans embedded systems, new semiconductor materials, and optoelectronic interconnection. He has focused on the analysis, modeling, and design of photoelectric coupling systems, including conversion‑efficiency optimization and noise‑reduction modeling. He has led and completed provincial and municipal R&D projects, contributed to State Grid initiatives, and authored both a monograph and a ministry‑planned textbook. His publication record includes more than sixty papers across SCI, EI, and core journals, with recent articles in the International Journal of Hydrogen Energy, Diamond & Related Materials, Physica Status Solidi B, and on power‑management circuits. Tian’s recent research advances 2D/Janus heterostructures for water splitting and gas sensing, and investigates device‑level co‑design strategies where materials inform embedded hardware architectures. His work targets sustainable energy, intelligent sensing, and robust, low‑noise, high‑efficiency systems suitable for real‑world deployment.

Professional Profile

Scopus

Education 

Lei Tian earned a Ph.D. in Circuits and Systems from Xidian University, emphasizing the intersection of signal integrity, noise modeling, and device‑level architectures for mixed‑signal and optoelectronic systems. Postdoctoral training at the Institute of Modern Physics, Northwest University, strengthened his first‑principles and multi‑physics modeling toolkit, including density‑functional workflows that bridge material properties to circuit‑level specifications. This background shaped a research style that connects quantum‑scale material parameters with embedded‑system requirements such as power budgets, spectral response, and noise floors. Coursework and mentoring activities have centered on semiconductor devices, optoelectronic interfaces, embedded firmware for instrumentation, and algorithm‑hardware co‑optimization. Tian’s graduate and postdoctoral path fostered collaborations across materials science, device physics, and systems engineering, informing a translational approach from theory to prototypes. The resulting expertise supports end‑to‑end pipelines—from ab initio predictions and sensor stack design to embedded control, calibration routines, and system‑level validation for power, reliability, and real‑time performance.

Experience 

As Laboratory Director at Xi’an University of Posts & Telecommunications, Lei Tian leads a group focused on optoelectronic interconnection and embedded hardware–software co‑design. The team develops modeling frameworks for photoelectric conversion efficiency, designs low‑noise coupling schemes, and validates concepts through simulations and targeted prototypes. He has steered key provincial R&D programs and municipal science projects, as well as multiple State Grid engagements, delivering deployable insights for power and sensing infrastructure. Tian’s portfolio extends from novel 2D/Janus heterostructures and graphene‑based stacks to practical power‑management ICs such as high‑voltage, low‑quiescent‑current LDOs with stability‑oriented impedance buffers. He regularly collaborates with materials scientists and circuit designers to translate computed properties into embedded constraints, addressing latency, energy, thermal limits, and field robustness. Alongside publications and books, his experience includes curriculum and lab development, fostering hands‑on training that connects material innovation with firmware, drivers, diagnostics, and system bring‑up.

Research Focus

Tian’s research targets the convergence of embedded systems with novel semiconductor and 2D materials. The thrusts include first‑principles discovery of van der Waals and Janus heterojunctions optimized for hydrogen evolution and gas sensing  photoelectric conversion analysis and noise‑reduction modeling for optoelectronic coupling embedded co‑design, where device physics informs circuit topologies, firmware routines, and on‑board diagnostics; and power‑management solutions such as high‑voltage LDOs with ultra‑low quiescent current for edge instrumentation. A defining feature is the “materials‑to‑metrics” pipeline—mapping band alignments, excitonic effects, and defect physics to embedded KPIs like SNR, dynamic range, and power efficiency. This enables predictive selection of sensor stacks and control algorithms prior to fabrication, accelerating time‑to‑prototype. Recent studies on MoSSe‑based heterostructures for water splitting exemplify this approach, linking catalytic descriptors to embedded monitoring strategies and stability management for scalable, field‑ready hydrogen‑generation systems.

Publication Top Notes

Title: Z-scheme WSTe/MoSSe van der Waals heterojunction as a hydrogen evolution photocatalyst: First-principles predictions
Year: 2025

Title: First-principles exploration of hydrogen evolution ability in MoS₂/hBNC/MoSSe vdW trilayer heterojunction for water splitting
Year: 2025

Title: Research of Power Inspection Based on Intelligent Algorithm
Year: 2025.

Conclusion

Lei Tian’s research exhibits high originality, technical depth, and relevance to global energy challenges, making the candidate a strong contender for the Best Paper Award. The contributions to hydrogen evolution photocatalysts using novel van der Waals heterojunctions represent valuable advancements in computational materials science. With further emphasis on experimental validation and broader impact demonstration, the works could achieve even greater recognition. Overall, the candidate’s publications align well with the award’s objectives, and the research output shows significant promise for long-term influence in sustainable energy technologies.