Nandan Banerji | Internet of Things | Editorial Board Member

Published on by Technology Scientists

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

Sirmayanti | AI and Digital Communication | Women Researcher Award

Published on by Technology Scientists

Assoc. Prof. Dr. Sirmayanti | AI and Digital Communication | Women Researcher Award

Associate Professor at Politeknik Negeri Ujung Pandang, Indonesia.

Assoc. Prof. Dr. Sirmayanti, Ph.D., is a prominent Indonesian scholar in telecommunication engineering, currently serving at the State Polytechnic of Ujung Pandang (PNUP). With a career spanning over two decades, she combines academic excellence, cutting-edge research, and impactful community engagement. She earned her Ph.D. in Electrical and Electronic Telecommunication Engineering from Victoria University, Australia, with Cum Laude distinction. Her research explores a wide spectrum—from 5G and RF engineering to artificial intelligence, IoT, and green communication technologies. As Head of the Telecommunication Network Engineering Technology program and the Centre for Applied Telecommunications Technology Research at PNUP, she has mentored numerous scholars and led various national and international innovation projects. Her prolific output includes over 60 publications, 15 technical books, and numerous patented inventions. A passionate advocate for education and digital equity, she’s been widely recognized for her contributions, including multiple presidential awards and the Australia Awards Alumni honor.

🌐Author Profile

🎓 Education 

Sirmayanti’s educational journey reflects a trajectory of excellence across multiple continents. She earned her Ph.D. in Electrical and Electronic Telecommunication Engineering from Victoria University, Australia (2015), where she graduated cum laude for her innovative research in digital RF architectures. Her Master of Engineering from the same university (2008) was awarded with First-Class Honors, reinforcing her expertise in signal processing and software radio systems. Her Bachelor of Engineering in Telecommunications, from Hasanuddin University, Indonesia (2001), was completed with High Distinction. She began her technical training at SMK Telkom Sandhy Putra 2 (1997), where she laid the foundation for her career in communications technology. In 2024, she was formally recognized as an Insinyur Profesional Utama (IPU), ASEAN Engineer, and APEC Engineer, further validating her technical leadership at the global level. Her continuous professional development includes numerous international certifications in Open RAN, IoT, AI, and RF engineering, demonstrating her commitment to lifelong learning.

Strengths for the Award

  1. Academic Excellence & Global Exposure:

    • Holds a Ph.D. with Cum Laude honors in Electrical and Electronic Telecommunication Engineering from Victoria University, Australia.

    • Her academic track includes top honors at every level, showcasing consistent high performance.

  2. Extensive Multidisciplinary Research:

    • Research covers Wireless Communications, IoT, AI, RF Engineering, Satellite Communications, and 5G.

    • Led and contributed to over 35+ funded research projects from national and international agencies (e.g., BRIN, KEMENRISTEKDIKBUD, DIKTI, AFS, Australia Direct Aid).

  3. High Research Output:

    • Over 60 peer-reviewed publications, including Scopus-indexed journal articles (Q1–Q3) and IEEE Award proceedings.

    • Significant work on metaheuristic algorithms for diabetes prediction, 5G networks, and digital RF systems.

  4. Innovation and Patents:

    • Holds multiple patents and copyrights (2020–2025) on innovations such as:

      • Wireless Bridge Mi-Fi 4G

      • Techniques for RF Spectrum Image Cancellation

      • Tools for lithium battery drop testing

    • Demonstrates intellectual property development at a national level.

  5. Leadership and Capacity Building:

    • Head of the Telecommunication Network Engineering Program and Center for Applied Telecommunications Technology Research (PNUP).

    • National assessor, ISO lead auditor, and active in curriculum reform and national education initiatives.

💼 Professional Experience 

Since 2001, Sirmayanti has served as a Lecturer and Researcher at the State Polytechnic of Ujung Pandang (PNUP), where she currently leads both the Telecommunication Network Engineering Technology Program and the Centre for Applied Telecommunications Technology Research (CATTAR). Her experience spans academia, national policy, quality assurance, and technology innovation. She is a certified lead auditor for ISO 9001 and an assessor for national educational programs like BKD, BIP, and Praktisi Mengajar under Indonesia’s Ministry of Research and Education. Her leadership in internal quality systems and curriculum design has transformed vocational education frameworks. She has led over 40 funded research and community service projects across 5G, AI, IoT, and rural connectivity systems. Internationally, she collaborates with UiTM Malaysia and is a certified practitioner in Open RAN, AI, and RF planning. Her blend of administrative leadership, hands-on technical innovation, and commitment to community service uniquely positions her in advancing AI and digital communication technologies.

🏅 Awards and Honors

Assoc. Prof. Sirmayanti has been decorated with some of Indonesia’s highest honors for civil service, including the Satyalancana Karya Satya 20 Tahun and 10 Tahun awards from the President of the Republic of Indonesia in 2024 and 2018. She is also among the “70 Outstanding Alumni” of Australian institutions recognized by Australia Awards Indonesia. She received the PIN LEMHANNAS award in 2024 and the prestigious INSINAS Research Grant Award from BRIN for her research on distortion mitigation in 5G systems. Her academic journey was supported by multiple competitive scholarships, such as the Australian Development Scholarship (ADS) and DIKTI Fellowships for postgraduate research. Beyond institutional honors, her contributions to community service and STEM education have garnered attention from various national and international programs. Her consistent record of excellence in education, research, innovation, and social impact establishes her as a leading figure in digital communication and AI technology in Southeast Asia.

🔬 Research Focus on AI and Digital Communication

Sirmayanti’s research sits at the intersection of artificial intelligence, wireless communication, and digital signal processing. Her key focus areas include 5G/6G networks, RF engineering, IoT-based infrastructure, AI-powered healthcare diagnostics, and green communication technologies. She applies metaheuristic algorithms and machine learning models to enhance wireless performance, such as through feature optimization for disease prediction or spectral efficiency in OFDM systems. Notably, she has developed low-power RF transmitters, digital baseband tuning systems, and hardware innovations like dual-band filters for 5G. She also works on rural connectivity solutions using mini BTS and community Wi-Fi networks. Her projects are not only technically advanced but socially driven—such as her work on digital inclusion in remote Indonesian islands and health monitoring systems. Her patents and intellectual property showcase strong emphasis on applied research, while her academic publications reflect rigorous peer-reviewed contributions to the fields of AI, digital modulation, and next-generation communication protocols.

📚 Publication Top Notes

1. E-Government dan Digitalisasi Layanan Publik

Authors: M. Mirfan, M. Mariana, S. Abdullah, A. Nurfadly, S. Suhada, S. Sirmayanti
Publisher: Yayasan Kita Menulis (2025)
Type: Book/Handbook
Summary:
This publication explores the digital transformation of public services through e-government frameworks. It addresses how Indonesian government institutions adopt ICT-based platforms for public administration, service efficiency, and transparency. Sirmayanti’s contribution lies in her analysis of telecommunication infrastructure enabling smart governance and public service digitization. The book combines theoretical frameworks with empirical insights and practical models for policymakers and technocrats.

2. A Systematic Literature Review of Diabetes Prediction Using Metaheuristic Algorithm-Based Feature Selection: Algorithms and Challenges Method

Authors: FR Sirmayanti, Pulung Hendro Prastyo, Mahyati
Journal: Applied Computer Science, 21(1), pp. 126–142, 2025 (Scopus Q3)
DOI: Applied Computer Science
Summary:
This systematic review focuses on AI-based diabetes prediction models utilizing metaheuristic feature selection algorithms. The authors classify and analyze recent algorithms (GWO, PSO, ACO, etc.), evaluating their effectiveness in reducing feature dimensionality while maintaining high diagnostic accuracy. Challenges such as computational cost, algorithm convergence, and overfitting are critically discussed. The work contributes a taxonomy of algorithmic strategies and highlights future research gaps for clinical decision support systems in e-health.

3. An Enhanced Grey Wolf Optimizer with Opposition, Mutation, and Local Search Strategy for Feature Selection

Authors: M. Sirmayanti, Pulung Hendro Prastyo
Conference: 2024 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), pp. 96–101
Summary:
This Award paper proposes an improved version of the Grey Wolf Optimizer (GWO) for feature selection by incorporating opposition-based learning, mutation, and local search mechanisms. The hybridized GWO algorithm demonstrated enhanced exploration-exploitation balance and was tested on medical and telecom datasets. Results showed better performance compared to standard GWO and other algorithms, with significant implications for AI-driven feature selection in classification systems.

4. Design of Dual-Band Bandpass Filter (DBPF) for 5G Applications

Authors: M. Mimsyad, A. Bazergan, S. Sirmayanti
Journal: AIP Conference Proceedings, Vol. 3140, 040018 (2024)
DOI: 10.1063/5.0222871
Summary:
This study presents the design and simulation of a dual-band bandpass filter (DBPF) aimed at 5G wireless applications. Utilizing advanced substrate materials and electromagnetic modeling, the design supports frequency bands relevant for mid-band 5G use cases. Sirmayanti contributed to the filter miniaturization and optimization phase. The paper outlines how the DBPF improves signal selectivity and reduces interference, with potential application in small-cell architectures and 5G-enabled devices.

5. Rekayasa Mitigasi Kebocoran Gas LPG dengan Sistem Monitoring Telegram Bot Berbasis Internet of Things (IoT)

Authors: S. Sirmayanti, E. Dwi Melda
Conference: Seminar Nasional Teknik Elektro dan Informatika (SNTEI), Vol. 9(1), pp. 223–228, 2023
Summary:
This paper discusses the development of an IoT-based real-time monitoring system for LPG gas leak detection, integrated with a Telegram bot alert system. The authors designed and implemented an Arduino-based sensor framework to monitor gas concentration in households and notify users immediately through messaging. Sirmayanti led the system integration and sensor calibration. The project highlights practical IoT use cases in smart safety systems for domestic environments.

✅ Conclusion

Assoc. Prof. Sirmayanti is highly suitable for the Women Researcher Award. Her extensive academic, research, innovation, and community engagement portfolio, combined with her leadership in engineering education and commitment to women’s empowerment in STEM, exemplify the spirit of the award. Her work not only advances telecommunications and AI research but also bridges technology with sustainable development and inclusive education—hallmarks of a transformative woman researcher.