Modeling WiFi Traffic for White Space Prediction in Wireless Sensor Networks

TL;DR

This paper introduces a statistical modeling approach using Markov models to predict WiFi interference and white space availability, improving low-power wireless network performance in congested 2.4 GHz spectrum.

Contribution

It presents a novel combination of MMPP(2) and HMM models for accurate WiFi interference and channel occupancy prediction based on real-world data.

Findings

MMPP(2) accurately models WiFi traffic patterns.

HMM-based prediction outperforms random channel access.

Encouraging results for interference-aware white space prediction.

Abstract

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.