Timely Monitoring of Events Over a Finite Time Horizon for Smart Agriculture

Data freshness is extremely important in sensing scenarios with sporadic reporting, as typical, for example, of smart agriculture or forestry monitoring, to enact proper network control. Several papers are proposing metrics akin to age of information to quantify it, but they generally assume that status updates can be generated frequently, possibly at will. In this paper, we investigate how to track freshly and accurately a phenomenon that is bound to happen within a certain time window, but whose precise timing is not known in advance. The resulting evaluations offer insights for planning and managing random sporadic events in smart monitoring for agricultural applications.