Model-Driven Mobile CrowdSensing for Smart Cities

Abstract

Making cities smarter can help improve city services, optimize resource and infrastructure utilization and increase quality of life. Smart Cities connect citizens in novel ways by leveraging the latest advances in information and communication technologies (ICT). The integration of rich sensing capabilities in today's mobile devices allows their users to actively participate in sensing the environment. In Mobile CrowdSensing (MCS) citizens of a Smart City collect, share and jointly use services based on sensed data. The main challenges for smart cities regarding MCS is the heterogeneity of devices and the dynamism of the environment. To overcome these challenges, this paper presents an architecture based on models at runtime (M@rt) to support dynamic MCS queries in Smart Cities. The architecture is proposed as an extension of the InterSCity platform, leveraging on its existing services and on its capability to integrate city infrastructure resources.