Public lighting accounts for a considerable amount of the total electricity consumption. Solutions to reduce its energy (and cost) footprint do exists, and they often translate into costly lamp replacement projects, which cannot be afforded by the majority of municipalities. To solve this situation, we propose Aurora: a low-budget, easy-to-deploy IoT control system that exploits the ubiquity of cellular networks (2- 4G) and scalable Cloud computing architectures to allow Smart Cities to save on the public lighting electrical bill. We deploy Aurora in a mid-size Italian municipality and we evaluate its performance over 4 months to quantify both the power and the economic saving achieved by our solution. We estimate the impact of a city-level Aurora installation and further extend the benefit analysis to most populated EU countries.

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Recent advances in green and smart mobility aim to reduce congestion and foster greener, cheaper and with less delay transportation. The reduction of fuel consumption and CO2 emissions have worked on light-duty vehicles. However, the reduction of emissions and consumables without sacrificing on emission standards is an important challenge for heavy-duty vehicles. The paper introduces a big data system architecture that provides an on-demand route optimization service reducing NOx emissions of heavy-duty vehicles. The system utilizes the information provided by the navigation systems, big data analytics such as predictive traffic and weather conditions, road topography and road network and information about vehicle payload, vehicle configuration and transport mission to develop a strategy for the best route and the best velocity profile. The system was proven efficient during the performance evaluation phase, since the cumulative engine-out NOx has been decreased more than 10%.

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