在時脈偏移裝置辨識技術的領域，Oka等人最近提出了基於霍氏轉換的時脈偏移測量方法。此方法在短時間或是有高延遲抖動的情況下，時脈偏移測量值的穩定度皆優於蒐集最小偏移量或是線性規劃等既有方法。本論文進一步提出一個改良的方法，在指定寬度與角度下利用滑動視窗來求得散布圖上偏移量集合最密集的部分，因此可以有機會比原方法更快找到具代表性的子集合，且求得解必為最佳解。而因為測量結果更接近理想上的時脈偏移，也因此不再如原方法般需要再透過線性迴歸以求得更穩定的數值。
透過本論文的方法與原方法進行比較，在網路延遲較穩定的環境下，測量結果的誤差範圍由1.1ppm降低至0.4ppm。而在網路高延遲抖動的環境下，誤差範圍由1.8ppm降低至0.5ppm。這些結果顯示本研究提出的改良方法所量測的時脈偏移更為穩定，僅利用1000個封包進行量測便能得到更準確的結果，而且計算時間由8.3秒降低至1秒內，也顯示本研究的方法在實際應用上更有效率。

Precise measurement is one of the critical requirement in the field of clock skew based device identification. Oka et al. recently developed a Hough transform-based clock skew measurement method. This method is able to reach a ppm level precision estimation of clock skew with only few minutes of measurement, and it is robust in communications with lower outliers, which happens when the delay jitters are large. The existing approaches like piecewise minimum algorithm and linear programming algorithm, on the other hand, are severely affected by lower outliers. This research modifies Oka’s method further to pursue more stable estimation and possibly faster measurement. In this research, we use sliding window to find the densest quadrilateral region in the scatter diagram of offset set, replacing the offset voting function of Oka’s method. Since the results of the new method are guaranteed to be optimal, we may derive the clock skew directly. In contrast, Oka’s method needs a linear regression post-processing to stabilize the estimation values.
We compared our method with the origin one. Under the classical delay network connections, the experiment results show that the maximum difference reduces from 1.1ppm to 0.4ppm. Under high delay jitter connections, the maximum difference reduces from 1.8ppm to 0.5ppm. The proposed method also provides stable estimation for only 1000 packets, and reduces computation time from 8.3 second to less than 1 second.

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