Optimizing IoT Analytics: Energy-Efficient Approaches with Automated Machine Learning in Dynamic Contexts

Abstract

The proliferation of the Internet of Things (IoT) has led to the generation of massive amounts of data, necessitating efficient analytics for effective decision making. This paper presents innovative energy-efficient approaches for optimizing IoT analytics, leveraging Automated Machine Learning (AutoML) within dynamic contexts. The proposed methodologies focus on minimizing energy consumption while maintaining high performance and accuracy in IoT data processing. By integrating adaptive algorithms that respond to varying conditions and data streams, our approach ensures real-time analytics with reduced computational overhead. Extensive experiments demonstrate the effectiveness of our solutions in diverse IoT environments, highlighting significant improvements in energy efficiency without compromi- sing analytical precision. This work provides a robust framework for sustainable IoT deployments, promoting intelligent, context aware data analytics that align with the growing demand for energy conservation in IoT ecosystems.