Knowledge Distillation-Based AIoT Framework for Efficient Wireless Gesture Sensing in B5G/6G Networks

The advancements in 5G and 6G networks have opened new possibilities for wireless sensing in smart environments. Gesture and activity recognition are crucial for interactive systems, particularly in Internet of Things (IoT) applications, where real-time performance and cost efficiency are critical. This article presents a novel Knowledge Distillation (KD)-based Artificial Intelligence of Things (AIoT) framework that enables efficient, lightweight models for gesture and activity recognition using Wireless Fidelity (Wi-Fi) Channel State Information (CSI). Unlike conventional camera-based or wearable sensor methods, Wi-Fi-based sensing offers a low-cost, privacy-preserving alternative suitable for security-sensitive applications in B5G/6G networks. Experimental results demonstrate that despite its compact structure, the proposed KD-based model achieves accuracy comparable to state-of-the-art methods while being optimized for resource-constrained edge devices. These findings underscore the potential of KD to develop robust, scalable, and energy-efficient AIoT solutions for next-generation wireless sensing.