  Cross Technology Interference (CTI) is a prevalent phenomenon in the 2.4 GHz
unlicensed spectrum causing packet losses and increased channel contention. In
particular, WiFi interference is a severe problem for low-power wireless
networks as its presence causes a significant degradation of the overall
performance. In this paper, we propose a proactive approach based on WiFi
interference modeling for accurately predicting transmission opportunities for
low-power wireless networks. We leverage statistical analysis of real-world
WiFi traces to learn aggregated traffic characteristics in terms of
Inter-Arrival Time (IAT) that, once captured into a specific 2nd order Markov
Modulated Poisson Process (MMPP(2)) model, enable accurate estimation of
interference. We further use a hidden Markov model (HMM) for channel occupancy
prediction. We evaluated the performance of i) the MMPP(2) traffic model w.r.t.
real-world traces and an existing Pareto model for accurately characterizing
the WiFi traffic and, ii) compared the HMM based white space prediction to
random channel access. We report encouraging results for using interference
modeling for white space prediction.
