A HOLISTIC AI-DRIVEN ENERGY-EFFICIENT IOT FRAMEWORK FOR SMART AGRICULTURE USING MULTI-RESOURCE OPTIMIZATION

Gunawan Budi Sulistyo; Nani Purwati; Tri Wahyudi; Noor Hasan

doi:10.34288/jri.v8i3.525

Authors

Gunawan Budi Sulistyo Universitas Bina Sarana Informatika
Nani Purwati Universitas Bina Sarana Informatika
Tri Wahyudi
Noor Hasan

(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v8i3.525

Keywords:

Smart Agriculture, Internet of Things (IoT), Artificial Intelligence, Energy Efficiency

Abstract

The rapid adoption of Internet of Things (IoT) technologies has accelerated the development of smart agriculture systems. However, existing studies predominantly focus on single-resource optimization and lack integrated artificial intelligence (AI) approaches within distributed architectures, resulting in suboptimal system-wide performance. This study proposes an AI-driven energy-efficient IoT framework that integrates the Random Forest algorithm with an edge–fog–cloud computing architecture to enable holistic multi-resource optimization. A quantitative simulation-based approach was employed using soil moisture data from the NASA SMAP dataset, with a case study in Magelang, Indonesia. The system was evaluated using key performance metrics, including energy consumption, network latency, packet delivery ratio (PDR), and water usage efficiency. The results demonstrate significant improvements, including a 28.65% reduction in energy consumption, a 31.43% decrease in latency, an increase in PDR to 96.8%, and a 20.3% improvement in water usage efficiency. Statistical validation confirms that these improvements are significant (p < 0.05). The main contribution of this study lies in the development of a holistic AI-driven IoT framework that simultaneously optimizes energy, water, computation, and communication without trade-offs. The proposed approach offers a scalable, adaptive, and efficient solution for real-world smart agriculture systems.

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Author Biography

Gunawan Budi Sulistyo, Universitas Bina Sarana Informatika

Gunawan Budi Sulistyo

The author, born in Magelang on May 18, 1972, is a lecturer at the Faculty of Engineering and Informatics at Bina Informatika University. He is interested in Artificial Intelligence, Data Mining, Software Engineering, and Image Processing. The author has written books, "Creating a Website with the CodeIgniter Framework: A Case Study of Library Information Systems" (2013), and "Computer Networks" (2014). He has also won a national competition in the Student Creativity Program (PKM) scheme once. He has also published several of his research results in accredited national journals.

References

Abbassi, Y., & Benlahmer, H. (2021). The Internet of Things at the service of tomorrow’s agriculture. Procedia Computer Science, 191, 475–480. https://doi.org/10.1016/j.procs.2021.07.060 DOI: https://doi.org/10.1016/j.procs.2021.07.060

Alahmad, T., Neményi, M., & Nyéki, A. (2023). Applying IoT sensors and big data to improve precision crop production: A review. Agronomy, 13(10), 2603. DOI: https://doi.org/10.3390/agronomy13102603

Alharbi, H. A., & Aldossary, M. (2021). Energy-efficient edge-fog-cloud architecture for IoT-based smart agriculture environment. IEEE Access, 9, 110480–110492. DOI: https://doi.org/10.1109/ACCESS.2021.3101397

Al-Kadhim, M. A., & Al-Raweshidy, H. S. (2019). Energy-efficient data collection in wireless sensor networks. Cluster Computing, 22(1), 781–792.

Bouali, E.-T., Abid, M. R., Boufounas, E.-M., Hamed, T. A., & Benhaddou, D. (2021). Renewable energy integration into cloud & IoT-based smart agriculture. IEEE Access, 10, 1175–1191. DOI: https://doi.org/10.1109/ACCESS.2021.3138160

Briatore, F., Arena, S., Orrù, P., Melesse, T. Y., Alongi, P., Peer, M. S., Chessa, F., & Braggio, M. (2025). A conceptual framework for the implementation of Industry 4.0 systems in the agricultural sector. Procedia Computer Science.

Colliander, A., Reichle, R. H., Crow, W. T., Cosh, M. H., Chen, F., Chan, S., Das, N. N., Bindlish, R., Chaubell, J., & Kim, S. (2021). Validation of soil moisture data products from the NASA SMAP mission. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 364–392.

Hadj Abdelkader, O., Bouzebiba, H., Pena, D., & Aguiar, A. P. (2023). Energy-efficient IoT-based light control system in smart indoor agriculture. Sensors, 23(18), 7670. DOI: https://doi.org/10.3390/s23187670

Haseeb, K., Ud Din, I., Almogren, A., & Islam, N. (2020). An energy-efficient and secure IoT-based WSN framework: An application to smart agriculture. Sensors, 20(7), 2081. DOI: https://doi.org/10.3390/s20072081

Katal, A., Dahiya, S., & Choudhury, T. (2023). Energy efficiency in cloud computing data centers: A survey on software technologies. Cluster Computing, 26(3), 1845–1875. DOI: https://doi.org/10.1007/s10586-022-03713-0

Kaushik, S., & Singh, K. (2025). AI-driven smart irrigation and resource optimization for sustainable precision agriculture. Computers and Electronics in Agriculture.

Kumar, S., & Ch, N. (2019). IoT-based multi-sensor data acquisition system for smart agriculture. Proceedings of International Conference on Computational Intelligence. DOI: https://doi.org/10.1007/978-981-15-3666-3_27

Monsiváis-Huertero, A., Constantino-Recillas, D. E., Hernández-Sánchez, J. C., Huerta-Bátiz, H. E., Judge, J., López-Estrada, P. A., Jiménez-Escalona, J. C., Arizmendi-Vasconcelos, E., García-Bernal, M. A., & Zambrano-Gallardo, C. F. (2022). Assessment of NASA SMAP soil moisture products for agricultural regions in Central Mexico. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 3421–3443. DOI: https://doi.org/10.1109/JSTARS.2022.3165078

Moussaoui, A., Ghelfi, R., & Viaggi, D. (2025). Agritech policy landscape: Insights from stakeholders. Bio-Based and Applied Economics, 14(4), 121–133. https://doi.org/10.36253/bae-17356 DOI: https://doi.org/10.36253/bae-17356

Naser, M. Z., & Alavi, A. H. (2023). Error metrics and performance fitness indicators for artificial intelligence and machine learning. Architecture, Structures and Construction, 3(4), 499–517. DOI: https://doi.org/10.1007/s44150-021-00015-8

Nawaz, M., & Babar, M. I. K. (2025). IoT and AI for smart agriculture in resource-constrained environments. Discover Internet of Things, 5(1). https://doi.org/10.1007/s43926-025-00119-3 DOI: https://doi.org/10.1007/s43926-025-00119-3

Patil, B. D., Gupta, S., Sheikh, A. I., Lalitha, S., & Raj, K. (2023). IoT and big data integration for real-time agricultural monitoring. Journal of Advanced Zoology, 44(2), 3079–3089.

Patrizi, G., Bartolini, A., Ciani, L., Gallo, V., Sommella, P., & Carratù, M. (2022). A virtual soil moisture sensor for smart farming using deep learning. IEEE Transactions on Instrumentation and Measurement, 71, 1–11. DOI: https://doi.org/10.1109/TIM.2022.3196446

Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45–77. DOI: https://doi.org/10.2753/MIS0742-1222240302

Rahman, F., & Prakash, V. V. (2026). AI-enhanced embedded systems for smart agriculture. AIP Conference Proceedings. DOI: https://doi.org/10.1063/5.0298660

Shankar, P., Thakur, A., Ansari, H., Bilal, M., & Chaugule, A. (2024). IoT and machine learning in agriculture: A comparative review. Proceedings of ICDICI 2024. DOI: https://doi.org/10.1109/ICDICI62993.2024.10810798

Sohail, S., Kalaiselvi, K., Naidu, P. S. B., & Shashank. (2025). Intelligent IoT-based system for precision agriculture monitoring. Proceedings of ACT 2025.

Tariqul Islam, A. F. M., Saha, K. K., & Mahaboob, M. G. (2026). Computational intelligence in climate-adaptive agriculture. In IoT and AI in Agriculture (pp. 67–88). Springer. DOI: https://doi.org/10.1007/978-981-95-5218-4_5

Thakare, B. D., & Rojatkar, D. V. (2021). A review on smart agriculture using IoT. Proceedings of ICCES 2021. DOI: https://doi.org/10.1109/ICCES51350.2021.9489109

Titirmare, S., Margal, P. B., Gupta, S., & Kumar, D. (2024). AI-powered predictive analytics for crop yield optimization. In Agriculture 4.0 (pp. 89–110). CRC Press. DOI: https://doi.org/10.1201/9781003570219-5

Ullah, R., Abbas, A. W., Ullah, M., Khan, R. U., Khan, I. U., Aslam, N., & Aljameel, S. S. (2021). EEWMP: An IoT-based energy-efficient water management platform for smart irrigation. Scientific Programming, 2021, 5536884. DOI: https://doi.org/10.1155/2021/5536884

Verma, V., Aishwarya, & Kumar, A. (2025). Federated learning-based IoT in smart agriculture. Proceedings of ICCCA 2025. DOI: https://doi.org/10.1109/ICCCA66364.2025.11325265

Wieringa, R. (2014). Design science methodology for information systems and software engineering. Springer. DOI: https://doi.org/10.1007/978-3-662-43839-8