Cloud Controlled LoRaWAN Communication Smart Agriculture Gateway Load Regulation

Abstract

Agriculture remains the primary source of livelihood for a significant proportion of the population in developing countries, making the adoption of advanced technologies essential for achieving sustainable and productive farming practices. The integration of engineering innovations with Sustainable Development Goals (SDGs) supports precision agriculture through intelligent soil preparation, automated irrigation, crop nutrient monitoring, smart fertilizer application, and efficient multi-cropping strategies. This study proposes a low-power communication framework for smart agriculture by modelling distributed sensor nodes as a weighted network topology. Two routing approaches are evaluated: the conventional Flooding protocol and a LoRaWAN-based shortest-path routing mechanism implemented using Dijkstra’s algorithm. The comparative analysis focuses on key performance metrics, including communication latency, data accuracy, false alarm rate, and gateway load regulation. Experimental results demonstrate that the Flooding protocol suffers from excessive packet duplication, network congestion, increased hop count, and reduced communication efficiency. In contrast, the proposed LoRaWAN-based routing strategy identifies optimal transmission paths, minimizes communication distance, reduces latency, improves data delivery accuracy, and lowers the false alarm rate while maintaining balanced gateway utilization. The framework is implemented using Python, Flask for webbased interaction, NetworkX for network topology modelling, TinyDB for lightweight data storage, and Matplotlib for performance visualization. The findings confirm that intelligent shortest-path routing enhances communication reliability, scalability, and energy efficiency, providing a robust foundation for sustainable precision agriculture and next-generation smart farming applications.