arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1709.08950cs.NI
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无线传感器网络中用于白空间预测的WiFi流量建模

Modeling WiFi Traffic for White Space Prediction in Wireless Sensor Networks

Indika S. A. Dhanapala, Ramona Marfievici, Sameera Palipana, Piyush Agrawal, Dirk Pesch

摘要:

交叉技术干扰(CTI)是2.4GHz非授权频谱中的普遍现象,导致分组丢失和增加的信道争用。特别是,WiFi干扰是低功率无线网络的严重问题,因为它的存在会导致整体性能的显著下降。在本文中,我们提出了一种基于WiFi干扰建模的主动方法,用于准确预测低功耗无线网络的传输机会。我们利用对真实世界WiFi跟踪的统计分析来了解到达间隔时间(IAT)方面的聚合流量特征,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,就可以准确估计干扰。我们进一步使用隐马尔可夫模型(HMM)的信道占用预测。我们评估了i)MMPP(2)流量模型的性能,真实世界的痕迹和现有的帕累托模型,用于准确地表征WiFi流量,ii)比较基于HMM的空白预测随机信道接入。我们报告令人鼓舞的结果,使用干扰建模的白色空间预测。

英文摘要:

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 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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