Hough Transform-Based Clock Skew Measurement Over Network

Abstract

The accurate clock skew measurement of remote devices over network connections is crucial to device fingerprinting and other related applications. Current approaches use the lower bound of offsets between the target device and the measurer to estimate clock skew; however, the accuracy of estimation is severely affected when even a few offsets appear below the crowd of offsets. This paper adopted the Hough transform to develop a new method, which searches for the densest part of the whole distribution. This method is effective in filtering out the upper and lower outliers such that the skew values derived from the remaining offsets are stable, even when lower outliers occur, or when the measuring time is not long enough for current approaches to achieve stable results. The experimental evaluation of the proposed method has been conducted in order to compare its performance with that of linear programming algorithm (LPA) and two other approaches. During the five consecutive measurements of 1000 offsets each, skews of the proposed method varied within the range of 0.59 ppm, whereas LPA resulted in the range of 0.89 ppm. Both ranges increased to 1.34 and 63.93 ppm, respectively, when the lower bounds encountered interference from lower outliers.