Optimizing Edge Computing Systems for Real-Time Data Processing and Analytics In Smart Cities

Abstract

The increasing development of urban centers brings serious challenges for traffic management. In this
paper, we introduce a smart visual sensor, developed for a pilot project taking place in the Australian city of
Liverpool (NSW). The project’s aim was to design and evaluate an edge-computing device using computer vision
and deep neural networks to track in real-time multi-modal transportation while ensuring citizens’ privacy. The
performance of the sensor was evaluated on a town center dataset. We also introduce the interoperable Agnosticity
framework designed to collect, store and access data from multiple sensors, with results from two real-world
experiments.