Performance metrics such as Age of Information are used to represent data freshness, which is a key element to track in sensing scenarios with sporadic reporting, as typical for ex-ample of cyberphysical platforms in industry, health monitoring, agriculture. However, when multiple sensors are employed, all tracking the same scenario, the presence of correlation in the sensed metrics results in the collection of redundant data, which implies interesting quantitative trends. This paper leverages on analytical derivations of age of information for queueing systems, to investigate how this metric behaves in a system of correlated sources, in particular for what concerns the number of sources, their correlation, and their offered traffic. The quantitative results that we obtain can offer interesting insights for planning and managing large scale systems where information is expected to be correlated, such as sensor networks for smart industrial and agricultural applications.
