Optimizing Energy Efficiency in Internet of Things (IoT) Devices using Machine Learning Techniques

Thesis Abstract

Abstract
The rapid proliferation of Internet of Things (IoT) devices in various domains has led to a significant increase in energy consumption, posing a challenge to sustainability and environmental conservation efforts. This research focuses on optimizing energy efficiency in IoT devices through the application of machine learning techniques. The primary objective is to develop and implement energy-efficient algorithms that can adapt to dynamic IoT environments, thereby reducing energy consumption without compromising the quality of service. The study begins with a comprehensive review of the literature on IoT devices, energy efficiency optimization, and machine learning algorithms. This review provides a theoretical foundation for understanding the current state of the art in energy optimization techniques for IoT devices and identifies gaps in existing research that this study aims to address. The research methodology involves the design and implementation of energy-efficient algorithms based on machine learning models such as reinforcement learning, deep learning, and ensemble learning. These algorithms will be trained and evaluated using real-world IoT datasets to assess their performance in optimizing energy consumption while maintaining the required level of service quality. The findings of this study are expected to demonstrate the effectiveness of machine learning techniques in optimizing energy efficiency in IoT devices. By leveraging the capabilities of machine learning models to adapt to changing environmental conditions and user requirements, IoT devices can achieve significant energy savings without sacrificing functionality or performance. The discussion of the findings will highlight the implications of the research results for the design and deployment of energy-efficient IoT systems. It will also address the limitations of the study and provide recommendations for future research directions in this field. In conclusion, this research contributes to the ongoing efforts to enhance the sustainability of IoT ecosystems by proposing and implementing novel energy optimization strategies based on machine learning techniques. The study underscores the importance of proactive energy management in IoT devices and provides valuable insights for industry practitioners, researchers, and policymakers seeking to promote energy efficiency in IoT deployments.