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AI-Driven Smart Connectivity and Sustainable Energy Model for Rural Agricultural Communities
Subject area: Science,Engineering and Technology · Area of research: Internet of Things
Abstract
Rural agriculture, vital to global food security and livelihoods, continues to face persistent challenges of weak connectivity, unreliable energy access, and inefficient data management, all of which constrain productivity and economic growth in underserved regions. This study introduces an AI-driven smart connectivity and sustainable energy model that integrates solar-based energy management, AI-optimized signal amplification, and IoT-enabled precision farming into a unified off-grid framework. Field deployment in Agbanganam village, Nigeria, demonstrated that intelligent solar management sustained IoT and communication services for up to four days under limited sunlight, while adaptive amplification improved average reference signal received power by ?10 dB, raising levels from below ?110 dBm to above ?85 dBm, extending coverage from 0.3 km? to over 5 km?, and boosting throughput by 28%. On the agricultural front, smart farming validation using an LSTM model achieved an F1-score of 0.87 in predicting irrigation events, enabling more efficient water use and reducing irrigation by 15%. Statistical analysis further confirmed that AI-assisted plots yielded significantly higher cassava output than control plots (ANOVA, p < 0.01), with yield improvements of up to 22%. These outcomes demonstrate that the proposed AHOM framework not only enhances network accessibility and energy reliability but also delivers measurable gains in agricultural efficiency and productivity. Overall, the findings affirm that strategically combining AI, IoT, and renewable energy within a community-centered platform can sustainably bridge the digital divide, improve food security, and empower rural economies.
Keywords
Smart Agriculture, Sustainable Energy, Signal Boosting, Internet of Things (IoT) Artificial Intelligence (AI)
References
[1] Adewusi, A. O., Asuzu, O. F., Olorunsogo, T., Iwuanyanwu, C., Adaga, E., & Daraojimba, D. O. (2024). AI in precision agriculture: A review of technologies for sustainable farming practices. World Journal of Advanced Research and Reviews, 21(1), 2276-2285
[2] Bala, D. V. (2024). AI, IoT, and Smart Technologies for environmental resilience and sustainability–Comprehensive review. International Journal of Information Engineering and Electronic Business., 16, 75-84.
[3] Brown, R., Thomas, J., & Ahmed, N. (2020). A systematic review of IoT solutions for smart farming. Journal of Agricultural Informatics, 15(1), 45–61.
[4] Ayim, C., Kassahun, A., Tekinerdogan, B., & Addison, C. (2020). Adoption of ICT innovations in the agriculture sector in Africa: A Systematic Literature Review. arXiv. https://arxiv.org/abs/2006.13831
[5] Gupta, D., Gujre, N., Singha, S., & Mitra, S. (2022). Role of existing and emerging technologies in advancing climate-smart agriculture through modeling: A review. Ecological Informatics, 71, 101805.
[6] Mohamed Rafi, M. S., Behjati, M., & Rafsanjani, A. S. (2025). Reliable and cost-efficient IoT connectivity for Smart Agriculture: A comparative Study of LPWAN, 5G, and Hybrid Connectivity Models. arXiv. https://arxiv.org/abs/2503.11162
[7] Bakare, B. I., & Alalibo, T. J. (2018). Gigabit Fidelity (GIFI) as future wireless technology in Nigeria. International Journal of Engineering Science Invention, 7(12), 1-6.
[8] Orike, S., & Alalibo, T. O. J. (2019). Comparative analysis of computer network protocols in wireless communication technology. International Journal of Electronics Communication and Computer Engineering, 10(3), 76-85.
[9] Wejie-Okachi, U., Alalibo, T. O. J., & Obuah, E. C. (2023). Enhancing Industrial Occupational Safety through an IoT-based Monitoring and Access Management. Research and Developments in Engineering Research Vol. 9, 109-125. https://hal.science/hal-05214388
[10] Doe, J., Smith, T., & Iwueze, M. (2023). Smart technology domains with implications for smart rural communities. International Journal of Rural Innovation, 8(2), 112–130.
[11] Adat, V., & Gupta, B. B. (2018). Security in Internet of Things: Issues, challenges, taxonomy, and architecture. Telecommunication Systems, 67, 423-441.
[12] Jotawar, D., Karoli, K., Biradar, M., & Pyruth, N. (2020). IoT based smart security and home automation. International Research Journal of Engineering and Technology (IRJET), 7(8), 2846-2850.
[13] Gryshova, I., Balian, A., Antonik, I., Miniailo, V., Nehodenko, V., & Nyzhnychenko, Y. (2024). Artificial Intelligence in climate smart in agricultural: toward a sustainable farming future. Access Journal, 5(1), 125-140.
[14] Kumari, K., Nafchi, A. M., Mirzaee, S., & Abdalla, A. (2025). AI-driven future farming: Achieving climate-smart and sustainable agriculture. AgriEngineering, 7(3), 89.
[15] Muraina, S. A., Akinbamiwa, B. P., Abiola, D. S., & Charles, S. I. Artificial Intelligence-driven renewable energy solutions for rural electrification in Africa. International Journal of Engineering and Modern Technology (IJEMT), 11(3), 81-102
[16] Mack, E. A., Loveridge, S., Keene, T., & Mann, J. (2023). A review of the literature about broadband internet connections and rural development (1995-2022). International Regional Science Review, 47(3), 231–292.
[17] Bature, B., Aliyu, S., & Shehu, M. (2021). Interoperability challenges in IoT devices and frameworks. Journal of Engineering Research and Reports, 20(1), 27-33.
[18] World Bank Group. (2025, June 25). Energy access has improved, yet international financial support still needed to boost progress and address disparities. World Bank. Retrieved September, 9, 2025 from https://www.worldbank.org/en/news/press-release/2025/06/25/energy-access-has-improved-yet-international-financial-support-still-needed-to-boost-progress-and-address-disparities
[19] Kyriakarakos, G., Balafoutis, A. T., & Bochtis, D. (2020). Proposing a Paradigm Shift in Rural Electrification Investments in Sub-Saharan Africa through Agriculture. Sustainability, 12(8), 3096. https://doi.org/10.3390/su12083096
[20] Seuyong, F. T., Silwal, A. R., Begazo, T., Newhouse, D., & N'Ghauran, A. (2023). The size and distribution of digital connectivity gaps in sub-Saharan Africa. International Bank for Reconstruction and Development/The World Bank.
[21] The Global Goals. (2024a). Goal 2: Zero Hunger. The Global Goals. Retrieved September 9, 2025, from https://globalgoals.org/goals/2-zero-hunger/
[22] The Global Goals. (2024b). Goal 7: Affordable and clean energy. The Global Goals. Retrieved September 10, 2025, from https://globalgoals.org/goals/7-affordable-and-clean-energy/
[23] The Global Goals. (2024c). Goal 9: Industry, innovation and infrastructure. The Global Goals. Retrieved September 12, 2025, from https://globalgoals.org/goals/9-industry-innovation-and-infrastructure/
[24] Ayaz, M., Ammad-Uddin, M., Sharif, Z., Mansour, A., & Aggoune, E. M. (2019). Internet-of-Things (IoT)-based smart agriculture: toward making the fields talk. IEEE Access, 7, 129551–129583. https://doi.org/10.1109/ACCESS.2019.2938660
[25] Jayadatta, S. (2024). A Study on AI-Driven agricultural innovations for rural and industrial development in Indian context. Journal of Rural and Industrial Development, 12(1), 01-16.
[26] Daraz, U., Bojnec, Š., & Khan, Y. (2025). Energy-efficient smart irrigation technologies: A pathway to water and energy sustainability in agriculture. Agriculture, 15(5), 554.
[27] Agupugo, C. P., Barrie, I., Makai, C. C., & Alaka, E. (2024). AI learning-driven optimization of microgrid systems for rural electrification and economic empowerment. Engineering Science & Technology Journal, 5(9), 2835-2851.
[28] Fawait, A. B., Aprilani, P., Sugiarto, S., & Sok, V. (2024). Applications of Artificial Intelligence in Weather Prediction and Agricultural Risk Management in India. Techno Agriculturae Studium of Research, 1(3), 163-174.
[29] Roy, S., Hoque, A., Saikia, P., & Padhiary, M. (2025). Climate-Smart Agriculture: AI-Based solutions for enhancing crop resilience and reducing environmental impact. Asian Research Journal of Agriculture, 18(1), 291–310.
[30] Smith, R., & Jones, T. (2022). AI-optimized low-cost cellular network boosters for rural connectivity. IEEE Transactions on Smart Technology, 15(2), 99–115.
[31] Carvalho, T. P., Soares, F. A., Vita, R., Francisco, R. D. P., Basto, J. P., & Alcalá, S. G. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024.
[32] Hashemian, H. M., & Bean, W. C. (2011). State-of-the-art predictive maintenance techniques. IEEE Transactions on Instrumentation and Measurement, 60(10), 3480-3492.
How to cite this paper
@article{1710715,
author = {Terry Eda Okwari, Matthew Ehikhamenle},
title = {AI-Driven Smart Connectivity and Sustainable Energy Model for Rural Agricultural Communities},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {3},
pages = {898-911},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1710715.pdf},
abstract = {Rural agriculture, vital to global food security and livelihoods, continues to face persistent challenges of weak connectivity, unreliable energy access, and inefficient data management, all of which constrain productivity and economic growth in underserved regions. This study introduces an AI-driven smart connectivity and sustainable energy model that integrates solar-based energy management, AI-optimized signal amplification, and IoT-enabled precision farming into a unified off-grid framework. Field deployment in Agbanganam village, Nigeria, demonstrated that intelligent solar management sustained IoT and communication services for up to four days under limited sunlight, while adaptive amplification improved average reference signal received power by ?10 dB, raising levels from below ?110 dBm to above ?85 dBm, extending coverage from 0.3 km? to over 5 km?, and boosting throughput by 28%. On the agricultural front, smart farming validation using an LSTM model achieved an F1-score of 0.87 in predicting irrigation events, enabling more efficient water use and reducing irrigation by 15%. Statistical analysis further confirmed that AI-assisted plots yielded significantly higher cassava output than control plots (ANOVA, p < 0.01), with yield improvements of up to 22%. These outcomes demonstrate that the proposed AHOM framework not only enhances network accessibility and energy reliability but also delivers measurable gains in agricultural efficiency and productivity. Overall, the findings affirm that strategically combining AI, IoT, and renewable energy within a community-centered platform can sustainably bridge the digital divide, improve food security, and empower rural economies.},
keywords = {Smart Agriculture, Sustainable Energy, Signal Boosting, Internet of Things (IoT) Artificial Intelligence (AI)},
month = {September},
}