Integrating Renewable Energy with Internet of Things (IoT): Pathways to a Smart Green Planet

The global use of IoT-enabled devices powered by renewable energy can create a smart, efficient, and eco-friendly network. By leveraging data analytics and machine learning, these systems can predict energy consumption, optimize resources, and maintain renewable energy assets proactively. IoT device adoption is rapidly increasing, with a projected 13% growth in 2024, bringing the total to 18.8 billion devices globally. However, there is a critical need for advanced applications of IoT technology, improved sensors implementation, and up-to-date software development to control IoT devices which is essential to fully harness renewable energy and combat global warming, ultimately achieving the vision of a smart green planet. This review examines the operational and technical needs of IoT devices, their integration with various renewable energy sources using Raspberry Pi or Arduino platforms equipped with sensors (such as motion, vibration, flow, and gyroscope), and the application of machine learning in IoT devices to showcase the potential of these technologies in fostering a smart, green world. To further this investigation, the study thoroughly examined the pros and cons of integrating various renewable energy sources to develop smart systems, such as smart grids, smart cities, smart transportation, and smart waste collection. Additionally, this study also examines the challenges faced by smart IoT systems, providing critical analysis and identifying promising directions for future advancements.

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Institutions

Publication Details

Journal
Kufa Journal of Engineering
Published
2025-02-04
DOI
https://doi.org/10.30572/2018/kje/160121
Citations
2
Primary Topic
IoT and Edge/Fog Computing
Type
article
Field-Weighted Citation Impact
2.97
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article

Integrating Renewable Energy with Internet of Things (IoT): Pathways to a Smart Green Planet

Ibrahim Haleem Khan, Mehedi Hasan Roni, Anjuman Ara Khatun, Khadijatul Cobra et al.
2 citations
Kufa Journal of Engineering
IoT and Edge/Fog Computing
2.97
article

Integrating Renewable Energy with Internet of Things (IoT): Pathways to a Smart Green Planet

Ibrahim Haleem Khan, Mehedi Hasan Roni, Anjuman Ara Khatun, Khadijatul Cobra, M. Humayun Kabir, Firoz Ahmed, Shahriar Mahmud, Shojib Mia
article en
2 citations

Abstract

The global use of IoT-enabled devices powered by renewable energy can create a smart, efficient, and eco-friendly network. By leveraging data analytics and machine learning, these systems can predict energy consumption, optimize resources, and maintain renewable energy assets proactively. IoT device adoption is rapidly increasing, with a projected 13% growth in 2024, bringing the total to 18.8 billion devices globally. However, there is a critical need for advanced applications of IoT technology, improved sensors implementation, and up-to-date software development to control IoT devices which is essential to fully harness renewable energy and combat global warming, ultimately achieving the vision of a smart green planet. This review examines the operational and technical needs of IoT devices, their integration with various renewable energy sources using Raspberry Pi or Arduino platforms equipped with sensors (such as motion, vibration, flow, and gyroscope), and the application of machine learning in IoT devices to showcase the potential of these technologies in fostering a smart, green world. To further this investigation, the study thoroughly examined the pros and cons of integrating various renewable energy sources to develop smart systems, such as smart grids, smart cities, smart transportation, and smart waste collection. Additionally, this study also examines the challenges faced by smart IoT systems, providing critical analysis and identifying promising directions for future advancements.

Kufa Journal of EngineeringVol. 16(1)
Louisiana State University (US), Gopalganj Science and Technology University (BD), University of Turku (FI), Wayne State University (US), University of Yamanashi (JP), University of Rajshahi (BD)
Openalex Percentile: Top 10%
IoT and Edge/Fog Computing
2.97
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