Adaptive AI for Power-Efficient IoT in Smart Agriculture
摘要
In precision agriculture, efficient livestock monitoring is essential, yet energy constraints in IoT-based tracking devices limit their long-term deployment. This study presents an AI-driven adaptive system that dynamically regulates data collection and transmission based on power availability. Utilizing reinforcement learning, the device optimizes energy usage by adjusting its operational schedule in real-time, ensuring continuous monitoring while extending battery life. The model was trained in a simulated environment and deployed on a prototype, demonstrating a significant improvement in power efficiency and data retention. The findings highlight the potential of AI-powered automation in sustainable IoT solutions for remote monitoring applications.