  We consider an Internet of Things (IoT) system in which a sensor observes a
phenomena of interest with exponentially distributed intervals and delivers the
updates to a monitor with the First-come-First-served (FCFS) policy. At the
monitor, the received updates are used to make decisions with deterministic or
random intervals. For this system, we investigate the freshness of the updates
at these decision epochs using the age upon decisions (AuD) metric. Theoretical
results show that 1) when the decisions are made with exponentially distributed
intervals, the average AuD of the system is smaller if the service time (e.g.,
transmission time) is uniformly distributed than when it is exponentially
distributed, and would be the smallest if it is deterministic; 2)when the
decisions are made periodically, the average AuD of the system is larger than,
and decreases with decision rate to, the average AuD of the corresponding
system with Poisson decision intervals; 3)the probability of missing to use a
received update for any decisions is decreasing with the decision rate, and is
the smallest if the service time is deterministic. For IoT monitoring systems,
therefore, it is suggested to use deterministic monitoring schemes,
deterministic transmitting schemes, and Poisson decision schemes, so that the
received updates are as fresh as possible at the time they are used to make
decisions.
