  Spectrum scarcity has surfaced as a prominent concern in wireless radio
communications with the emergence of new technologies over the past few years.
As a result, there is growing need for better understanding of the spectrum
occupancy with newly emerging access technologies supporting the Internet of
Things. In this paper, we present a framework to capture and model the traffic
behavior of short-time spectrum occupancy for IoT applications in the shared
bands to determine the existing interference. The proposed capturing method
utilizes a software defined radio to monitor the short bursts of IoT
transmissions by capturing the time series data which is converted to power
spectral density to extract the observed occupancy. Furthermore, we propose the
use of an unsupervised machine learning technique to enhance conventionally
implemented energy detection methods. Our experimental results show that the
temporal and frequency behavior of the spectrum can be well-captured using the
combination of two models, namely, semi-Markov chains and a
Poisson-distribution arrival rate. We conduct an extensive measurement campaign
in different urban environments and incorporate the spatial effect on the IoT
shared spectrum.
