Research on Energy Efficiency Optimization of IoT Nodes Based on Event-Triggered Pulse Control

The advent of the Internet of Things (IoT) has brought with it a rapid proliferation of applications, which has in turn created a significant challenge for IoT nodes operating in resource-constrained environments in terms of energy efficiency. In this paper, we put forward a model for optimising the energy efficiency of IoT nodes based on event-triggered pulse control. Based on the existing pulse-width modulation (PWM) and eventtriggered control models, we propose a novel hybrid method that combines event-triggered control and adaptive pulse modulation to dynamically adjust the energy consumption of node activities. The model employs a multi-level control architecture that integrates real-time monitoring, threshold-based event detection, and machine learning algorithms for adaptive control parameter tuning, thereby optimising energy utilisation and computational load. The experimental results demonstrate that the proposed model exhibits a {3 0%} improvement in energy efficiency compared to existing energy efficiency optimisation models, while maintaining the same level of performance and reliability.