Autonomous Optimization and Configuration of Communication Systems for IoT: A Comparative Study

The rapid expansion of the Internet of Things(IoT) has underscored the critical need for efficient and autonomous communication systems to sustain the massive, interconnected network of smart devices. Central to this challenge is optimizing communication protocols to ensure energy efficiency, reliability, and self-configurability across diverse IoT applications. This paper reviews the essence of leveraging artificial intelligence, specifically deep reinforcement learning, distributed AI services, swarm intelligence, metaheuristic optimization, and cross-layer approaches, for autonomous optimization and configuration in IoT and IoE (Internet of Everything) environments within the context of emerging 6 G technologies. It also implies a focus on comparing various methodologies and approaches to achieve efficient communication systems for IoT. The key criteria used in this comparison study are energy efficiency, transmission power, protocols, scalability, and Quality of service (QoS). By synthesizing these findings, our study highlights the strengths, limitations, and potential synergies between different approaches, offering insights into the future direction of IoT communication optimization.