Abstract
Recently, a new metric, called Age of Information (AoI), has become popular to quantify the freshness of information collected at network edge. AoI research is still in its infancy and most prior efforts assume overly simplified models in their investigation. In this paper, we consider a more general model for AoI research that is closer to what happens in the real world. Specifically, we consider general and heterogeneous sampling behaviors among source nodes, varying sample size, and multiple data transmission units in each time slot. Under this much general setting, we develop new theoretical results (in terms of properties and performance bounds) and a new near-optimal low-complexity scheduling algorithm. Our results make a major advance of AoI research in terms of more realistic models.
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