Analysis of the collective perception using granular computing and virtual sensors

The increasing complexity of cyber-physical systems demands for always more sophisticated approaches to the environmental and structural monitoring of both internal and external environments. In such circumstances, the data gathered by physical sensors alone could be not sufficient to satisfy the information needs. Indeed, the perception of people that lives and acts in such environments can be useful to improve these monitoring capabilities. This perception can be quantitatively measured by analyzing the huge amount of user-generated contents on Social Web. In this work, we define an approach for monitoring the collective perception and for using it as a quantitative measure useful for supporting decision making in complex environments. In this approach, each user of a community is modeled as a virtual sensor that generates a stream of data containing the updated opinions of the user. A multi-level granulation technique, based on the rough set theory, allows the analysts to properly aggregate and analyze the data produced by the virtual sensors from multiple views. The approach, which aims at improving the monitoring of internal and external environments, has been applied to a real case study related to the perception of the safety in the football stadium of the city of Salerno, Italy.