  Burst of transmissions stemming from event-driven traffic in machine type
communication (MTC) can lead to congestion of random access resources, packet
collisions, and long delays. In this paper, a directed information (DI)
learning framework is proposed to predict the source traffic in event-driven
MTC. By capturing the history of transmissions during past events by a sequence
of binary random variables, the DI between different machine type devices
(MTDs) is calculated and used for predicting the set of possible MTDs that are
likely to report an event. Analytical and simulation results show that the
proposed DI learning method can reveal the correlation between transmission
from different MTDs that report the same event, and the order in which they
transmit their data. The proposed algorithm and the presented results show that
the DI can be used to implement effective predictive resource allocation for
event-driven MTC.
