The objective of this use case is to provide reliable edge and fog computing in a generic smart city. Specifically, with this use case dAIEDGE will enable: 1. From Task T5.1 the integration of novel AI-based methods on top of cutting-edge heterogenous edge devices.
For instance, extreme edge learning algorithms for continual/lifelong training for the identification of road-users or distributed. This will allow the generation of knowledge based on the use of the data produced by the city.
From Task T5.2 the implementation of services that are efficient and can be used in a generic network infrastructure. These services can support decentralized AI services and will serve as a basis for the demonstration of a reliable fog-to-edge computation and communication in the smart city. This eases the interaction of the edge with vehicles or other technologies like VR/AR devices.
From Task T5.3 the deployment of high-performance low-power edge devices will bring these technologies closer to a deployment phase in a real smart city environment.
Furthermore, the dAIEDGE framework will allow successful deployment of AI in human-centric applications. Putting particular emphasis on strengthening the uptake of these new technologies in society and improving the security of personal data, as well as contributing to road safety. We expect that the achievement of this use case might provide guidelines for a successful deployment of technologies in a smart city.
The validation of the results will happen in the Modena Automotive Smart Area (MASA), which is a 3km2 -wide area of urban territory, adjacent to a transportation hub (train station, bus stops), equipped with cameras, sensors and private communication networks (4G, soon 5G). The area is used as a living experiment and a testing ground for a number of automotive and smart-communities projects by the local University and companies for the experimentation of vehicles with Cooperative, connected and automated mobility (CCAM) capabilities. To do so, MASA features cameras around the area that 1) capture road images for the detection and tracking of road-users (e.g., cars, bikes, pedestrians) and 2) produce geo-localized information (GPS coordinates) of the objects tracked in the world.
MASA administrators are planning the adoption of new technologies to improve traffic safety, security and privacy. In this line, the objective is to communicate warning alerts to road-users like cars or other smart technologies like VR/AR devices in case of risk of an accident. Unfortunately, the current edge technologies featured by MASA are functional but do not provide safe and reliable end-to-end computing guarantees. This means that the processing time taken by the edge and fog cannot satisfy safety related requirements.