The field of Wi-Fi research is moving towards improving the performance and reliability of Wi-Fi networks in industrial environments. This is driven by the increasing demand for low-latency and continuous operation in such settings. Researchers are exploring the use of deep learning models to predict Wi-Fi performance metrics, such as the Frame Delivery Ratio, and to adapt to changing network conditions. Additionally, there is a focus on developing more precise clock synchronization protocols, such as Reference Broadcast Infrastructure Synchronization, to ensure determinism in industrial networks.

Noteworthy papers include: On the Prediction of Wi-Fi Performance through Deep Learning, which presents a comparison of two deep learning models for predicting the Frame Delivery Ratio. Widening the Coverage of Reference Broadcast Infrastructure Synchronization in Wi-Fi Networks, which proposes an evolution of the RBIS protocol to increase its coverage area. Wi-Fi Rate Adaptation for Moving Equipment in Industrial Environments, which evaluates the performance of the Minstrel rate adaptation algorithm in industrial scenarios. Performance Evaluation of Parallel Wi-Fi Redundancy with Deferral Techniques, which analyzes the use of parallel redundancy to improve the dependability of Wi-Fi networks. Machine Learning to Predict Slot Usage in TSCH Wireless Sensor Networks, which proposes the use of machine learning to improve the energy efficiency of wireless sensor networks.