Reinforcement Learning in IoT: Enhancing Smart Device Autonomy through AI

Abstract

Reinforcement Learning (RL) has emerged as a powerful approach to enhancing the autonomy and intelligence of smart devices in the Internet of Things (IoT) ecosystem. As IoT networks grow in complexity, enabling smart devices to make independent, real-time decisions becomes crucial for optimizing performance, energy efficiency, and overall system resilience. This paper explores the application of RL techniques to empower IoT devices with adaptive decisionmaking capabilities, allowing them to autonomously learn from their environments and improve their operational efficiency. By leveraging AI-driven RL models, smart devices can dynamically adjust to changing network conditions, optimize resource allocation, and manage energy consumption more effectively, without the need for constant human intervention. The study also highlights how RL algorithms can address security challenges by enabling devices to proactively detect anomalies and respond to cyber threats in real time. The potential of integrating RL with edge computing frameworks to reduce latency and enhance scalability is further discussed, presenting a new paradigm in the IoT domain. The findings underscore RL's role in driving the next generation of smart device autonomy, facilitating intelligent decision-making and improving the overall robustness of IoT systems.