Simulation Analysis of Energy Consumption Optimization in an IoT-Based Smart Agriculture System for Monitoring and Automated Irrigation
Keywords:
Smart Agriculture, Internet of Things, Optimasi Energi, Irigasi Otomatis, Simulasi Konsumsi EnergiAbstract
The implementation of Internet of Things (IoT)-based smart agriculture enables automated environmental monitoring and irrigation control. However, sensor reading, data communication, and pump operation can increase energy consumption; thus, optimization strategies are required to maintain system efficiency without compromising the primary irrigation function. This study aims to analyze the potential for energy savings in an IoT-based smart agriculture system by adjusting intervals for sensor readings, data transmission, and pump activity, while also evaluating the impact on soil moisture. A quantitative, simulation-based approach was employed, comparing two scenarios: one without optimization and one with optimization. The non-optimized scenario utilized 5-second intervals for both sensor readings and data transmission, whereas the optimized scenario used 30-second intervals for sensor readings and 60-second intervals for data transmission. Analyzed parameters included voltage, current, power, total energy, pump activation frequency, pump operation duration, and soil moisture. Simulation results showed energy consumption of 3.16 Wh for the non-optimized scenario and 2.34 Wh for the optimized scenario, representing a potential energy saving of 25.95%. Average current decreased from 315 mA to 234 mA, while average power dropped from 1.58 W to 1.17 W. Final soil moisture levels were recorded at 70% for the non-optimized scenario and 68% for the optimized scenario. These results indicate that the optimization strategy has the potential to reduce energy consumption without causing significant changes to final soil moisture levels. However, as these findings are based on simulations, prototype implementation and experimental testing are necessary to validate system performance under real-world conditions.
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