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A survey of secure fingerprinting localization in wireless local area networks

机译:无线局域网中安全指纹定位的调查

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Secure localization has gained considerable attention because position estimation is often required for location based applications. This problem becomes more important for wireless local area networks (WLANs), where the radio signals are susceptible to a variety of attacks due to the nature of the open medium. This paper proposes a survey of security positioning systems in WLANs. We classify secure positioning mechanisms into two categories: signal-based and model-based approaches. The former attempts to detect attacks and exclude them during positioning while the latter enhances localization schemes capable of tolerating attacks. This study examines three signal-based approaches, including median-based method, sensor selection, and RSM (Ration-based Signal strength Metric), and three model-based approaches, including cluster-based method, RWGH (Residual Weighting), and IDM (Inclusive Disjunction Model). We evaluate their robustness to attacks, positioning accuracy, and computational complexity. Simulation results show that the model-based techniques generally perform better robustness; However, the improvement is gained at the expense of degrading performance in an attack-free condition and higher computational overhead. This paper also investigates how the number of attacks impact these secure positioning algorithms.
机译:由于基于位置的应用程序经常需要位置估计,因此安全定位已经引起了相当大的关注。对于无线局域网(WLAN),此问题变得尤为重要,在无线局域网中,由于开放媒体的性质,无线电信号容易受到各种攻击。本文提出了对WLAN中的安全定位系统的调查。我们将安全定位机制分为两类:基于信号的方法和基于模型的方法。前者试图检测攻击并在定位过程中将其排除,而后者则增强了能够容忍攻击的定位方案。这项研究研究了三种基于信号的方法,包括基于中值的方法,传感器选择和RSM(基于比率的信号强度度量),以及三种基于模型的方法,包括基于聚类的方法,RWGH(残留权重)和IDM (包容性分离模型)。我们评估了它们对攻击的稳健性,定位精度和计算复杂性。仿真结果表明,基于模型的技术通常具有更好的鲁棒性。但是,以无攻击条件下的性能下降和较高的计算开销为代价获得了这种改进。本文还研究了攻击次数如何影响这些安全定位算法。

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