Machine Learning-Driven Strategies for Improving Energy Efficiency in IoT Communication Protocols

In this paper, strategies for enhancing energy efficiency in IoT communication protocols are proposed, driven by machine learning. Fast expansion of the Internet of Things increases energy consumption in communication protocols and raises concerns about sustainability and operational costs. This work investigates the role of machine learning techniques, especially supervised learning, reinforcement learning, and deep learning, in enabling protocol parameter optimization, prediction of energy consumption patterns, and adaptation of the real-time communication strategy. The propose a much different approach that deals with runtime adjustments in communication frequencies, data transmission rates, and routing paths to minimize energy by integrating those techniques into IoT frameworks. In this work, it is shown that such integrations show a quite impressive improvement in energy efficiency regarding reductions in power consumption and extended device life. This paper offers insight into how machine learning could fundamentally change the way IoT devices communicate to achieve far more sustainable and efficient IoT systems.