Fully autonomous nano-scale unmanned aerial vehicles (UAVs), due to their tiny form factor and compact size, are particularly suitable for indoor applications that require safe navigation close to humans. These applications encompass surveillance, monitoring, ambient awareness, and interaction with smart environments. Recent research shows that deploying AI models in Nano-Drone systems with onboard computations offers several advantages, including reduced operational costs, minimized inference latency for real-time scenarios, and enhanced data security and privacy. However, these small devices face severe constraints in terms of memory and computational resources. Adaptive or Dynamic Neural Networks are an emerging research topic in deep learning. These Networks can adapt their structures or parameters based on the input during inference, thus efficiently allocating computations on demand, selectively activating model components (e.g., layers, channels, or sub-networks).