A low complexity linear regression approach to time synchronization in underwater networks

Khandoker, Tarik-Ul Islam; Huang, Defeng; Sreeram, Victor

doi:10.1109/icics.2011.6173596

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…The comparison was made with other models such as Gaussian process regression (GPR) [46], linear regression (L.R.) [47], and nonlinear gaussian regression (NGR) [48] using…”

Section: Performance Evaluationmentioning

confidence: 99%

An improved clock synchronization model for typical IoT applications

Upadhyay,

Dubey

2024

Eng. Res. Express

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— The Internet of Things (IoT) has transformed the way people live their lives by enabling data exchange between pervasive devices in various applications. However, clock synchronization is essential to ensure seamless transmission and synchronization among IoT entities involved in processing and communication. In this paper, we propose a clock synchronization algorithm based on linear quadratic regression to address synchronization errors in IoT applications. The algorithm uses a linear model of skew and offset to estimate clock parameters, and performance is evaluated in terms of Root Mean Square Error (RMSE) and R-Square Error. Our proposed algorithm outperformed traditional algorithms with an RMSE of 0.379% and an R-Square Error of 0.71%. We also evaluated the stability of the proposed model using the correlation coefficient, which indicated a high correlation between the variables at 86%. These results demonstrate the effectiveness of our proposed algorithm in addressing clock synchronization errors for IoT applications.

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“…The comparison was made with other models such as Gaussian process regression (GPR) [46], linear regression (L.R.) [47], and nonlinear gaussian regression (NGR) [48] using…”

Section: Performance Evaluationmentioning

confidence: 99%

An improved clock synchronization model for typical IoT applications

Upadhyay,

Dubey

2024

Eng. Res. Express

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“…Like TSVP [10], RSUN employs two-way signal transmissions to obtain timestamps. Four timestamps are recorded for each beacon exchange: beacon sending (R i,1 ) and response receiving (R i,2 ) timestamps recorded at reference node (R) and beacon receiving (N i,1 ) and response sending (N i,2 ) timestamps recorded at neighbouring node (N ).…”

Section: Detail Of the Rsunmentioning

confidence: 99%

“…To analyze the impact of collision on synchronization accuracy, we study the performance of alternative approaches (TSVP [10] and MU-Sync [9]) in the literature under two different scenarios. First, we consider an ideal scenario with no collision and observe the performance.…”

Section: Impact Of Collisionsmentioning

confidence: 99%

“…To deal with some of the acoustic communication constraints such as large and time-varying propagation delay, several synchronization algorithms have been proposed e.g TSHL [6], Tri-Message [7], Reduce-Sync [8], MU-Sync [9] and TSVP [10]. Among these, TSHL, Tri-Message and Reduce-Sync are designed to handle a long but time-invariant propagation delay, while MU-Sync and TSVP are opted to combat with both long and time-varying propagation delay.…”

Section: Introductionmentioning

confidence: 99%

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A robust time synchronization approach for underwater networks with inconsistent timestamps

Khandoker

Fang

Huang

et al. 2012

2012 International Symposium on Communications and Information Technologies (ISCIT)

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To achieve time synchronization in underwater networks, a linear regression is often applied over a set of sendingreceiving timestamps, assuming the participating timestamps are consistent. In this paper, we show that collisions, which are not uncommon during signal exchange in underwater environment, would lead to inconsistent timestamps. These inconsistent timestamps are outliers. However, existing synchronization approaches ignore the presence of outliers. To obtain a reliable synchronization, we propose a robust algorithm that identifies and eliminates the outliers by employing Cook's distance before applying linear regression. We justify the proposed algorithm in a mobile underwater environment through extensive simulations.

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