Innovative Research Award
Pranay Obla Anandbabu
University of Southern California, United States
Pranay Obla Anandbabu is a researcher whose recent scholarly work spans Internet of Things (IoT), renewable-energy-related monitoring, machine learning, deep learning, and data-driven engineering applications. His publication record includes research on IoT-enabled aquaculture, solar irradiance monitoring, prediction of material tensile strength, and wind-speed forecasting. These studies demonstrate an interdisciplinary research profile connecting sensing technologies, computational intelligence, predictive analytics, and engineering systems. The available publication record provides a basis for considering his work within the context of the International Research Hypothesis Excellence Award, particularly in relation to innovative approaches to technology-enabled environmental and energy applications. [1]
Abstract
Pranay Obla Anandbabu has developed a multidisciplinary publication profile involving IoT systems, renewable-energy monitoring, machine learning, deep learning, and engineering prediction. His recent publications include a 2026 book chapter on a data-driven IoT module for pisciculture, a multi-site study of ground-based IoT solar irradiance monitoring, a machine-learning study of tensile strength in carbon-nanotube-reinforced AA2024 materials, and a deep-learning approach to wind-speed prediction. Together, these works illustrate the application of computational and sensing technologies to practical engineering and environmental problems. The publication portfolio also reflects collaboration with researchers across several technical disciplines. [2] [3] [4] [5]
Keywords
Renewable Energy Technologies; Internet of Things; Solar Irradiance Monitoring; Machine Learning; Deep Learning; Wind-Speed Prediction; Predictive Analytics; Smart Sensing; Data-Driven Engineering; Innovative Research.
Introduction
The integration of IoT, artificial intelligence, and predictive analytics has become an important research direction for monitoring and optimizing complex engineering and environmental systems. In this context, Pranay Obla Anandbabu’s research combines sensor-based data acquisition with computational methods for applications involving renewable-energy resources, environmental observation, industrial materials, and aquaculture. His recent studies indicate an interest in translating data-driven methods into practical systems capable of monitoring conditions and supporting prediction. [3] [5]
Research Profile
Pranay Obla Anandbabu’s documented research interests can be characterized by the intersection of renewable-energy technologies, IoT-enabled sensing, artificial intelligence, and predictive modeling. His listed research output includes four publications and a reported six citations, with an h-index of 2. These bibliometric indicators provide a quantitative snapshot of the available indexed record but should be interpreted alongside publication quality, research contribution, collaboration, and practical relevance. [1]
Research Contributions
One contribution of the research portfolio is the application of distributed IoT technologies to solar-resource monitoring. The 2026 Sensors article, “Regional Ground-Based IoT Solar Irradiance Monitoring: A Multi-Site Study Across Mountain, Rural, and Urban Environments,” examines monitoring across different environmental settings, illustrating the value of multi-site sensing for understanding solar irradiance variability. The article was published on 11 September 2026 and has DOI 10.3390/s26185781. [3]
Publications
- Data Driven IoT Module for Pisciculture. Lecture Notes in Networks and Systems, 2026, book chapter.
- Regional Ground-Based IoT Solar Irradiance Monitoring: A Multi-Site Study Across Mountain, Rural, and Urban Environments. Sensors, 2026-09-11.
- Machine learning approach for the prediction of tensile strength of carbon nanotubes reinforced AA2024 by friction stir welding and friction stir processing. Journal of Mechanical Science and Technology.
- Enhanced Miss Forest and Multivariate Time Series Prediction of Wind Speed Using Deep Learning. Journal of Circuits, Systems and Computers.
Research Impact
The potential impact of Pranay Obla Anandbabu’s research is reflected in its emphasis on deployable technologies and predictive methods. IoT-based monitoring can support the collection of geographically distributed environmental data, while machine-learning and deep-learning models can help transform such data into predictions and analytical insights. The solar irradiance study is particularly relevant to renewable-energy data acquisition, whereas the wind-speed study addresses forecasting of a variable associated with energy and environmental systems. [3] [5]
Award Suitability
The available evidence provides a reasonable basis for considering Pranay Obla Anandbabu for recognition under an Innovative Research Award framework. His work combines IoT, machine learning, deep learning, environmental monitoring, and engineering prediction across multiple application domains. The solar irradiance publication is directly aligned with renewable-energy technology and demonstrates the use of distributed sensing across different environmental contexts. [3]
Conclusion
Pranay Obla Anandbabu’s documented research profile represents an interdisciplinary combination of IoT, renewable-energy monitoring, artificial intelligence, predictive analytics, and engineering applications. His recent publications demonstrate the application of data-driven methodologies to solar irradiance observation, wind-speed prediction, advanced materials, and pisciculture. With four reported documents, six citations, and an h-index of 2, the current bibliometric record is developing, while the diversity of recent research topics provides evidence of a broad technical orientation. [1] [2] [3] [4] [5]
External Links
References
- Researcher profile and bibliometric information supplied for Pranay Obla Anandbabu, including four documents, six citations, h-index 2, subject area, affiliation, and researcher identifiers.
- Obla, Pranay Anandbabu, et al. (2026). Data Driven IoT Module for Pisciculture. Lecture Notes in Networks and Systems.
DOI: https://doi.org/10.1007/978-3-032-19179-3_21
- Vujičić, Dejan, Marković, Dušan, Obla Anandbabu, Pranay, et al. (2026). Regional Ground-Based IoT Solar Irradiance Monitoring: A Multi-Site Study Across Mountain, Rural, and Urban Environments. Sensors.
DOI: https://doi.org/10.3390/s26185781
- Sidhaarth, Tarran, Obla, Pranay Anandbabu, Ramasamy, Lokeshkumar, and Selvaraj, Senthil Kumaran. (2026). Machine learning approach for the prediction of tensile strength of carbon nanotubes reinforced AA2024 by friction stir welding and friction stir processing. Journal of Mechanical Science and Technology.
DOI: https://doi.org/10.1007/s12206-026-0119-y
- Sidhaarth, Tarran, Obla Anandbabu, Pranay, Patil, Nikhil Naganagouda, Stamenkovic, Zoran, and Raja, S. P. (2025). Enhanced Miss Forest and Multivariate Time Series Prediction of Wind Speed Using Deep Learning. Journal of Circuits, Systems and Computers.
DOI: https://doi.org/10.1142/S0218126625300065