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We consider an Internet of Things (IoT) system in which a sensor deliversupdates to a monitor with exponential service time and first-come-first-served(FCFS) discipline. We investigate the freshness of the received updates andpropose a new metric termed as \textit{Age upon Decisions (AuD)}, which isdefined as the time elapsed from the generation of each update to the epoch itis used to make decisions (e.g., estimations, inferences, controls). Withinthis framework, we aim at improving the freshness of updates at decision epochsby scheduling the update arrival process and the decision making process.Theoretical results show that 1) when the decisions are made according to aPoisson process, the average AuD is independent of decision rate and would beminimized if the arrival process is periodic (i.e., deterministic); 2) whenboth the decision process and the arrive process are periodic, the average AuDis larger than, but decreases with decision rate to, the average AuD of thecorresponding system with Poisson decisions (i.e., random); 3) when both thedecision process and the arrive process are periodic, the average AuD can befurther decreased by optimally controlling the offset between the twoprocesses. For practical IoT systems, therefore, it is suggested to employperiodic arrival processes and random decision processes. Nevertheless, makingperiodical updates and decisions with properly controlled offset also is apromising solution if the timing information of the two processes can beaccessed by the monitor.

Age-upon-Decisions Minimizing Scheduling in Internet of Things: To be Random or to be Deterministic?