  Timely message delivery is a key enabler for Internet of Things (IoT) and
cyber-physical systems to support wide range of context-dependent applications.
Conventional time-related metrics (e.g. delay and jitter) fails to characterize
the timeliness of the system update. Age of information (AoI) is a
time-evolving metric that accounts for the packet inter-arrival and waiting
times to assess the freshness of information. In the foreseen large-scale IoT
networks, mutual interference imposes a delicate relation between traffic
generation patterns and transmission delays. To this end, we provide a
spatiotemporal framework that captures the peak AoI (PAoI) for large scale IoT
uplink network under time-triggered (TT) and event triggered (ET) traffic.
Tools from stochastic geometry and queueing theory are utilized to account for
the macroscopic and microscopic network scales. Simulations are conducted to
validate the proposed mathematical framework and assess the effect of traffic
load on PAoI. The results unveil a counter-intuitive superiority of the ET
traffic over the TT in terms of PAoI, which is due to the involved temporal
interference correlations. Insights regarding the network stability frontiers
and the location-dependent performance are presented. Key design
recommendations regarding the traffic load and decoding thresholds are
highlighted.
