Poster: Optimizing Radio Tomography with Edge Computing: A Low-Latency Approach for Human Detection

This paper presents the first edge computing framework for RTI systems combining intermediate sensor fusion with model quantization. Traditional RTI approaches [1] rely on centralized architectures (450 ms latency), while our key innovations enable 73% faster edge processing (120 ms) through hybrid ResNet architecture compressed via 8-bit quantization and intermediate fusion of RTI attenuation maps and RGB features. Our work introduces an on-device processing pipeline implemented on low-power IoT devices---including Jetson Nano, Raspberry Pi, and ESP32---that enables real-time inference without cloud-based computation.