“…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…”
— 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.
“…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…”
— 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.
“…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.…”
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.