Integrating energy harvesting within the IoT ecosystem for sustainable wireless communication

Abstract

This dissertation addresses the challenges posed by the energy demands of IoT devices, highlighting the limitations of conventional batteries, which lead to high maintenance costs and environmental concerns. It proposes integrating Energy Harvesting (EH) technologies to extend device lifespan and reduce environmental impact. The research focuses on optimizing the Medium Access Control (MAC) layer to manage energy consumption in Wi-Fi-based IoT systems, particularly in e healthcare environments. A comprehensive framework is developed for assessing energy consumption across various wireless technologies. The study utilizes simulations in a densely deployed solar-powered Wi-Fi network, introducing an optimization algorithm for Access Point coordination and Reinforcement Learning (RL) methods to adapt to network dynamics. The findings demonstrate that fine-tuning MAC layer parameters and implementing a sleep/wake-up strategy significantly reduce energy consumption while maintaining Quality of Service (QoS). The work provides valuable insights for enhancing energy efficiency in IoT systems through EH technologies.