Paper Accepted @ ScaleSys 2025 (IoT)

Title: Wi-Fi Enabled Edge Intelligence Framework for Smart City Traffic Monitoring using Low-Power IoT Cameras

Authors: Raphael Walcher, Kurt Horvath, Dragi Kimovski, Stojan Kitanov

Abstract: Real-time traffic monitoring in smart cities demands ultra-low latency processing to support time-critical decisions such as incident detection and congestion management. While cloud-based solutions offer robust computation, their inherent latency limits their applicability for such tasks. This work proposes a localized edge AI framework that connects low-power IoT camera sensors to a client, or applies offloading of inference to an NVIDIA Jetson Nano (GPU). Networking is achieved via Wi-Fi, enabling image classification without relying on wide-area infrastructure such as 5G, or wired networks. We evaluate two processing strategies: local inference on camera nodes and GPU-accelerated offloading to the Jetson Nano. We show that local processing is only feasible for lightweight models and low frame rates, whereas offloading enables near-real-time performance even for more complex models. These results demonstrate the viability of cost-effective, Wi-Fi-based edge AI deployments for latency-critical urban monitoring.

Overview – 1st Workshop on Intelligent and Scalable Systems across the Computing Continuum