The future mass deployment of pervasive and dense sensor network infrastructures calls for proper mechanisms to enable extracting general-purpose data from them at limited energy costs and in a compact way. The approach presented in this paper relieson a simple algorithm to let a sensor network self-organize a virtual partitioning in correspondence of spatial regions characterized by similar sensing patterns, and to let distributed aggregation of sensorial data take place on a per-region basis.The result of this process is that a sensor network can be modeled as made up of virtual macro sensors, each associated to a well-characterized region of the physical environment. Within each region, each physical sensor has the local availability of aggregated data related to its region and can act as an access point to such data. This feature promises to be very suitable for a number of emerging usage scenarios. Our approach is described and analyzed, evaluated both in a simulation environment andon a real test bed, and quantitatively compared with related works in the area. The current limitations of our approach and the areas for future research are also discussed.