Using the Compute Continuum for Data Analysis: Edge-cloud Integration for Urban Mobility

Abstract

More and more in recent years, IT companies have adopted edge-cloud continuum solutions to efficiently perform analysis tasks on data generated by IoT devices. As an example, in the context of urban mobility, the use of edge solutions can be extremely effective in managing tasks that require real-time analysis and low response times, such as driver assistance, collision avoidance and traffic sign recognition. On the other hand, the integration with cloud systems can be convenient for tasks that require a lot of computing resources for accessing and analyzing big data collections, such as route calculations and targeted advertising. Designing and testing such hybrid edge-cloud architectures are still open issues due to their novelty, large scale, heterogeneity, and complexity. In this paper, we analyze how the compute continuum can be exploited for efficiently managing urban mobility tasks. In particular, we focus on a case study related to taxi fleets that need to find locations where they are more likely to find new passengers. Through a simulation-based approach, we demonstrate that these solutions turn out to be effective for this class of problems, especially as the number of connected vehicles increases.