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We propose a methodology to identify nodes in fully anonymized wireless networks using collections of very simple sensors. Based on time series of counts of anonymous packets provided by the sensors, we estimate the number of nodes using principal component analysis. We then proceed to separate the collected packet data into traffic flows that, with help of the spatial diversity in the available sensors, can be used to estimate the location of the wireless nodes. Our simulation experiments indicate that the estimators show high accuracy and high confidence for anonymized TCP traffic. Additional experiments indicate that the estimators perform very well in anonymous wireless networks that use traffic padding.