ETAUS: An Edge and Trustworthy AI UAV System with Self-Adaptivity for Air Quality Monitoring

Abstract

This work presents the ETAUS, an Edge and Trustworthy AI UAV System, as a mobile sensing platform for air quality monitoring. ETAUS employs an FPGA device as the main hardware computing architecture rather than relying solely on a microprocessor or integrating with GPUs to meet real-time processing demands and achieve adaptivity and scalability. ETAUS contains a neural engine that can execute our customized AI model for air quality index (AQI) level classification and a pre-trained model for detecting objects containing private information. ETAUS also incorporates a de-identification process, cryptographic functions, and protection matrices to safeguard information and individuals' privacy. Furthermore, cryptographic functions and protection matrices are implemented as reconfigurable modules, which can accelerate processing, protect data privacy, and be reconfigured as needed. Experiments have demonstrated ETAUS can achieve a speedup of 3.15x to 72.46x for AQI level classification compared to microprocessor-based and GPU-based designs. ETAUS can also enhance energy efficiency by 5.02x compared to embedded GPU solutions such as NVIDIA Jetson Nano. To support all the cryptographic functions and protection matrices, system adaptivity in ETAUS can significantly increase resource utilization while decreasing power consumption by up to 2.79%.