TinyAirNet: TinyML Model Transmission for Energy-efficient Wireless IoT Image Retrieval

This letter advocates the use of a Tiny Machine Learning (TinyML) model for energy-efficient semantic data retrieval from the Internet of Things (IoT) devices. In our framework, the edge server (ES) transmits task-related TinyML model before starting data collection so that IoT devices can send only semantically relevant data. However, receiving the ML model and its processing at the IoT devices consumes additional energy. We consider the specific instance of image retrieval and investigate the gain brought by the proposed scheme in terms of energy efficiency, considering both the energy cost of introducing the ML model as well as that of wireless communication. Numerical evaluation shows that, compared to a baseline scheme, the proposed scheme can realize both high retrieval accuracy and high energy efficiency, which reaches up to 70% energy reduction when the number of stored images is sufficiently large. Although focused on image retrieval, our analysis is indicative of a broader set of communication scenarios in which the preemptive transmission of an ML model can increase communication efficiency.