Machine Learning and Artificial Intelligence Algorithms that Minimize Power Consumption in Internet of Things Gadgets

The exponential growth of IoT gadgets has enabled previously unimaginable levels of connectedness and convenience, but it has also introduced a significant new difficulty: battery life. In this study, we investigate how artificial intelligence (AI) and machine learning may be used to cut down on power usage in Internet of Things (IoT) devices. With an emphasis on data-driven insights and fictitious data situations, we provide a systematic technique for data collecting, algorithm creation, and rigorous testing. Our results show that by combining Q-learning with supervised learning and statistical analysis, considerable gains in energy efficiency may be achieved. There is strong statistical evidence that data transmission operations may reduce their energy footprint by 25%. In addition to the technical details, we explore the practical ramifications of our study, such as increased battery life, reduced environmental impact, and reduced expenses. Insights into the revolutionary potential of machine learning and AI in transforming the IoT landscape towards a more sustainable and user-centric future are provided in this research paper, which may be used as a practical guide by industry experts, academics, and policymakers. We also provide an overview of promising future avenues for improving IoT power management and scalability, such as the use of sophisticated reinforcement learning models, the incorporation of energy harvesting, and real-time adaption.