AI-Driven Self-Optimizing Networks for Integrated LoRaWAN and 5G in Next-Generation IoT Systems

The rapid growth of the Internet of Things (IoT) has been fueled by the widespread adoption of smart devices. While 5G networks offer high-speed, low-latency connectivity, their high power consumption and limited range pose challenges for energy-efficient, long-distance IoT applications. This study explores integrating 5G with low-power wide area network (LPWAN) technologies to improve IoT performance. We examine the challenges of merging these architectures, focusing on network efficiency, scalability, and reliability. Specifically, we compare standalone LPWAN and integrated LPWAN-5G networks using existing solutions and network simulations, assess operational complexity, and evaluate AI-driven optimization techniques. Our findings aim to demonstrate how AI-based network designs can enhance transmission efficiency, reduce failures, and support next-generation IoT applications in various environments.