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.

Thesis Overview

The project titled "Optimizing Energy Efficiency in Internet of Things (IoT) Devices using Machine Learning Techniques" aims to address the growing concern of energy consumption in IoT devices. As the number of connected devices continues to rise, there is a pressing need to develop strategies to improve energy efficiency in order to minimize environmental impact and reduce operational costs. This research project focuses on leveraging machine learning techniques to optimize energy usage in IoT devices. The Internet of Things (IoT) has revolutionized the way we interact with technology, enabling seamless communication and automation across various sectors such as healthcare, transportation, and smart homes. However, the proliferation of IoT devices has led to a surge in energy consumption, which can strain resources and contribute to greenhouse gas emissions. By optimizing energy efficiency in IoT devices, this project seeks to mitigate these challenges and create a more sustainable IoT ecosystem. Machine learning techniques offer a powerful toolset for analyzing data and making intelligent decisions to optimize energy consumption. By collecting and analyzing data from IoT devices, machine learning algorithms can identify patterns and trends that can help in predicting energy usage and optimizing device performance. This research project will explore the application of machine learning algorithms such as neural networks, decision trees, and clustering techniques to develop energy-efficient models for IoT devices. The research methodology will involve collecting data from a variety of IoT devices, including sensors, actuators, and communication modules. This data will be used to train machine learning models that can predict energy consumption patterns and identify opportunities for optimization. The project will also investigate different optimization strategies, such as dynamic power management, task scheduling, and load balancing, to maximize energy efficiency in IoT devices. The findings of this research project are expected to contribute to the development of sustainable IoT solutions that can operate efficiently while minimizing energy consumption. By optimizing energy efficiency in IoT devices using machine learning techniques, this project aims to pave the way for a greener and more environmentally friendly IoT ecosystem.