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Modeling WiFi Traffic for White Space Prediction in Wireless Sensor Networks

机译：为无线传感器网络中的空白预测建模WiFi流量

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摘要

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 causing a significant degradation of the overall performance. We propose here 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 channeloccupancy 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.
机译：交叉技术干扰（CTI）是2.4 GHz非授权频谱中的一种普遍现象，导致数据包丢失和信道争用增加。尤其是，WiFi干扰对于低功率无线网络而言是一个严重的问题，会导致整体性能显着下降。我们在此提出一种基于WiFi干扰建模的主动方法，以准确预测低功率无线网络的传输机会。我们利用对现实世界中WiFi迹线的统计分析来了解到达时间（IAT）的汇总流量特征，一旦将其捕获到特定的二阶马尔可夫调制泊松过程（MMPP（2））模型中，就可以准确估算干涉。我们进一步使用隐马尔可夫模型（HMM）进行信道占用预测。我们评估了以下各项的性能：i）MMPP（2）流量模型w。河t。真实世界的痕迹和现有的Pareto模型，以准确地描述WiFi流量，并且，ii）将基于HMM的空白预测与随机信道访问进行了比较。我们报告了使用干扰建模进行空白预测的令人鼓舞的结果。

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