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-realtime 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.
Keywords: IoT Services, Computing Continuum, Edge AI, Smart City
How to Cite:
Walcher, R., Horvath, K., Kimovski, D. & Kitanov, S., (2025) “Wi-Fi Enabled Edge Intelligence Framework for Smart City Traffic Monitoring using Low-Power IoT Cameras”, IoT Workshop Proceedings 1(1), 43-49. doi: https://doi.org/10.34749/3061-1008.2025.7
Rights: Copyright © 2025 The author(s)
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