  This paper develops a novel spatiotemporal model for large-scale IoT networks
with asynchronous periodic traffic and hard-packet deadlines. A static marked
Poisson bipolar point process is utilized to model the spatial locations of the
IoT devices, where the marks mimic the relative time-offsets of traffic duty
cycles at different devices. At each device, an absorbing Markov chain is
utilized to capture the temporal evolution of packets from generation until
either successful delivery or deadline expiry. The temporal evolution of
packets is defined in terms of the Aloha transmission/backoff states. From the
network perspective, the meta distribution of the transmission success
probability is used to characterize the mutual interference among of the
coexisting devices. To this end, the network performance is characterized in
terms of the probabilities of meeting/missing the delivery deadlines and
transmission latency. The results unveil counter-intuitive superior performance
of strict packet deadlines in terms of transmission success and latency.
