A Secure and Energy-Efficient IoT Architecture for Vegetable Cultivation in Tropical Environments
DOI:
https://doi.org/10.63561/jca.v3i1.1196Keywords:
IoT Architecture, Smart Agriculture, Energy Efficiency, Tropical Farming, ESP32Abstract
Vegetable cultivation in tropical environments is affected by inefficient water management, climate variability, and limited real-time farm monitoring. This paper presents a secure and energy-efficient Internet of Things (IoT) architecture for tropical vegetable farming. The system integrates ESP32-based sensor nodes, solar-powered energy management, and cloud-based monitoring to collect and process soil moisture, temperature, and humidity data. Security mechanisms, including device authentication and encrypted communication, are embedded to protect system integrity. The architecture was implemented and evaluated through field deployment, focusing on system reliability, water-use efficiency, and energy performance. Experimental results show improved irrigation control, reduced water usage, and stable system operation under tropical conditions. The proposed architecture demonstrates the feasibility of deploying secure, low-power IoT systems for sustainable vegetable cultivation in resource-constrained tropical environments.
References
Ayaz, M., Ammad-Uddin, M., Sharif, Z., Mansour, A., & Aggoune, E. H. 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.2019.2932609
FAO. (2021). The state of food and agriculture 2021: Making agrifood systems more resilient to shocks and stresses. Food and Agriculture Organization of the United Nations. https://www.fao.org
Ferrag, M. A., Maglaras, L., Moschoyiannis, S., & Janicke, H. (2020). Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study. Journal of Information Security and Applications, 50, 102419. https://doi.org/10.1016/j.jisa.2019.102419
Jawad, H. M., Nordin, R., Gharghan, S. K., Jawad, A. M., & Ismail, M. (2017). Energy-efficient wireless sensor networks for precision agriculture: A review. Sensors, 17(8), 1781. https://doi.org/10.3390/s17081781
Kamel Boulos, M. N., Peng, G., & VoPham, T. (2021). An overview of GeoAI applications in health and healthcare. International Journal of Health Geographics, 20(1), 7. https://doi.org/10.1186/s12942-021-00266-1
Li, L., Zhang, Q., & Huang, D. (2020). A review of imaging techniques for plant phenotyping. Sensors, 20(3), 787. https://doi.org/10.3390/s20030787
Mekonnen, Y., Namuduri, S., Burton, L., Sarwat, A., & Bhansali, S. (2022). Review Machine learning techniques in wireless sensor network based precision agriculture. Journal of the Electrochemical Society, 167(3), 037522. https://doi.org/10.1149/2.0222003JES
Nwankwo,W. and Ukhurebor, K.E.(2021).Big Data Analytics: A Single Window IoT-enabled Climate Variability System for all-year-round Vegetable Cultivation. In 2021 IOP Conference Series: Earth and Environmental Science, 655 012030. doi:10.1088/1755-1315/655/1/012030
Olayinka A.S., Adetunji C.O., Nwankwo W., Olugbemi O.T., Olayinka T.C. (2022) A Study on the Application of Bayesian Learning and Decision Trees IoT-Enabled System in Postharvest Storage. In: Pal S., De D., Buyya R. (eds) Artificial Intelligence-based Internet of Things Systems. Internet of Things (Technology, Communications and Computing). Springer, Cham. https://doi.org/10.1007/978-3-030-87059-1_18
Onwodi, G., Nwankwo,W., Ojosu, O.A., Oyenusi,F., Awodele,O.,Ebem, D., Ukaoha, K.C.(2024). Development of Intelligent Anti-Cannibalistic Prototyping for Sustainable Catfish Farming. 2024 IEEE 5th International Conference on Electro-Computing Technologies for Humanity (NIGERCON), Ado Ekiti, Nigeria, 2024, pp. 1-5, doi: 10.1109/NIGERCON62786.2024.10927292.
Sicari, S., Rizzardi, A., Grieco, L. A., & Coen-Porisini, A. (2015). Security, privacy and trust in Internet of Things: The road ahead. Computer Networks, 76, 146–164. https://doi.org/10.1016/j.comnet.2014.11.008
