  We consider an Internet of Things (IoT) system in which a sensor delivers
updates to a monitor with exponential service time and first-come-first-served
(FCFS) discipline. We investigate the freshness of the received updates and
propose a new metric termed as \textit{Age upon Decisions (AuD)}, which is
defined as the time elapsed from the generation of each update to the epoch it
is used to make decisions (e.g., estimations, inferences, controls). Within
this framework, we aim at improving the freshness of updates at decision epochs
by scheduling the update arrival process and the decision making process.
Theoretical results show that 1) when the decisions are made according to a
Poisson process, the average AuD is independent of decision rate and would be
minimized if the arrival process is periodic (i.e., deterministic); 2) when
both the decision process and the arrive process are periodic, the average AuD
is larger than, but decreases with decision rate to, the average AuD of the
corresponding system with Poisson decisions (i.e., random); 3) when both the
decision process and the arrive process are periodic, the average AuD can be
further decreased by optimally controlling the offset between the two
processes. For practical IoT systems, therefore, it is suggested to employ
periodic arrival processes and random decision processes. Nevertheless, making
periodical updates and decisions with properly controlled offset also is a
promising solution if the timing information of the two processes can be
accessed by the monitor.
