Artificial Intelligence and Machine Learning Approaches based on Power Reduction in Automated Internet of Things Devices

To guarantee extended battery life and sustainability, there is an increasing need to address power consumption concerns as IoT devices increase in various applications. AI and ML techniques have shown promise in reducing power usage in automated Internet of Things devices. To improve power utilization in IoTdevices, this study investigates the application of AI and ML approaches to various tactics, including energy-efficient scheduling, adaptive power management, and intelligent sensor data processing. IoT devices may adaptively modify their power consumption using AI and ML algorithms depending on contextual elements like user behavior, ambient circumstances, and workload needs. Predictive analytics powered by AI can also foresee future power needs and optimize energy use before they arise. The usefulness and possible drawbacks of AI and ML-based power-saving approaches for automated IoT devices are reviewed in this study, along with previous research on the subject. Therefore, intelligent power management that improves energy economy without sacrificing performance or user experience is made possible by incorporating AI and ML techniques into IoT device design. By offering insights into cutting-edge power reduction techniques made possible by DL and ML, this article adds to the continuing conversation on the growth of the Internet of Things in a sustainable manner.