Machine Learning Innovations in LoRaWAN: A Comprehensive Survey of Technology, Trends, and Applications

ABSTRACT The integration of Machine Learning with Low Power Wide Area Network (LPWAN) technologies, particularly LoRaWAN (Long Range Wide Area Network), is transforming the Internet of Things (IoT) landscape by enhancing network performance, scalability, and intelligence. LoRaWAN, an open LPWAN standard developed by the LoRa Alliance, enables long‐range communication with minimal power consumption. Although LoRaWAN provides low‐power and long‐range communication capabilities, it still faces several challenges, including scalability limitations, energy efficiency concerns, resource allocation issues, limited coverage, and susceptibility to interference. Recent advancements in machine learning, deep learning, and federated learning have introduced innovative solutions to address these challenges, fostering the development of intelligent, adaptive, and efficient LoRaWAN networks. This survey also presents a comparative analysis of various LPWAN technologies by examining key parameters such as bandwidth, data rate, coverage, and other critical factors. Furthermore, it explores the performance metrics and practical applications of LoRaWAN across various domains, emphasizing the impact of ML‐based approaches. By synthesizing recent research findings and real‐world implementations, this survey provides a comprehensive understanding of how ML can significantly enhance the performance and capabilities of LoRaWAN networks.