ABSTRACT The industrial Internet of Things (IIoT) depends on wireless sensor networks (WSNs) to enable low‐power, low‐data‐rate communication in resource‐limited settings. While the IEEE 802.15.4 standard provides the communication foundation, its medium access control (MAC) protocols face challenges including energy consumption, latency, scalability, and adaptability. Traditional MAC protocols cannot keep up with the demands of IIoT networks as the number of connected devices continues to increase. Therefore, edge artificial intelligence (Edge AI) and tiny machine learning (TinyML) represent emerging approaches that show potential for improving the performance of traditional MAC protocols directly on IIoT devices. Edge AI and TinyML allow intelligent decision‐making at the edge, which enables efficient data processing and adaptability to the environment without the need for cloud infrastructure, which may reduce latency and energy consumption. This paper systematically examines the emerging paradigm of combining Edge AI and TinyML to improve MAC protocols for WSNs in IIoT networks. We explore advanced machine learning (ML) methods applicable to resource‐limited devices, and we investigate how these methods can improve key performance metrics for MAC protocols, including energy efficiency, throughput, and network lifetime. We also discuss the challenges and limitations of applying AI solutions in WSNs, including computational constraints, data scarcity, and model scalability. Finally, we propose potential future research directions to improve the application of AI and ML techniques to develop more efficient, adaptive, and intelligent MAC protocols for future IIoT networks.
Zila et al. (Sun,) studied this question.