Approximating Optimal Estimation of Time Offset Synchronization With Temperature Variations

This paper addresses the problem of time offset synchronization in the presence of temperature variations, which lead to a non-Gaussian environment. In this context, regular Kalman filtering reveals to be suboptimal. A functional optimization approach is developed in order to approximate optimal estimation of the clock offset between master and slave. A numerical approximation is provided to this aim, based on regular neural network training. Other heuristics are provided as well, based on spline regression. An extensive performance evaluation highlights the benefits of the proposed techniques, which can be easily generalized to several clock synchronization protocols and operating environments.

[1]  Dragan Obradovic,et al.  Synchronization Performance of the Precision Time Protocol in Industrial Automation Networks , 2009, IEEE Transactions on Instrumentation and Measurement.

[2]  Gianluca Cena,et al.  The reference-broadcast infrastructure synchronization protocol , 2012, Proceedings of 2012 IEEE 17th International Conference on Emerging Technologies & Factory Automation (ETFA 2012).

[3]  G. Gaderer,et al.  Limits of synchronization accuracy using hardware support in IEEE 1588 , 2008, 2008 IEEE International Symposium on Precision Clock Synchronization for Measurement, Control and Communication.

[4]  J. Elson,et al.  Fine-grained network time synchronization using reference broadcasts , 2002, OSDI '02.

[5]  Daniele Fontanelli,et al.  Accurate time synchronization in PTP-based industrial networks with long linear paths , 2010, 2010 IEEE International Symposium on Precision Clock Synchronization for Measurement, Control and Communication.

[6]  Ruxandra Lupas Scheiterer,et al.  An optimal control approach to clock synchronization , 2010, 2010 IEEE International Symposium on Precision Clock Synchronization for Measurement, Control and Communication.

[7]  Aggelos Bletsas.  Evaluation of Kalman filtering for network time keeping , 2005 .

[8]  John R. Vig,et al.  Quartz Crystal Resonators and Oscillators for Frequency Control and Timing Applications , 1993 .

[9]  Gyula Simon,et al.  The flooding time synchronization protocol , 2004, SenSys '04.

[10]  A. Valenzano,et al.  Synchronize your watches: Part I: General-purpose solutions for distributed real-time control , 2013, IEEE Industrial Electronics Magazine.

[11]  H. Kushner,et al.  Stochastic Approximation and Recursive Algorithms and Applications , 2003 .

[12]  T. J. Lukaszek,et al.  Higher-Order Temperature Coefficients of the Elastic Stiffinesses and Compliances of Alpha-Quartz , 1962, Proceedings of the IRE.

[13]  Andrea Bondavalli,et al.  Experimental Characterization of Uncertainty Sources in a Software-Only Synchronization System , 2012, IEEE Transactions on Instrumentation and Measurement.

[14]  Costanzo Manes,et al.  Comparative Study of Unscented Kalman Filter and Extended Kalman Filter for Position/Attitude Estimation in Unmanned Aerial Vehicles , 2008 .

[15]  Duncan A. Campbell,et al.  Use of Precision Time Protocol to Synchronize Sampled-Value Process Buses , 2012, IEEE Transactions on Instrumentation and Measurement.

[16]  Mario Marchese,et al.  Neural approximation of open-loop feedback rate control in satellite networks , 2005, IEEE Transactions on Neural Networks.

[17]  A. Monti,et al.  A Software-Only PTP Synchronization for Power System State Estimation With PMUs , 2012, IEEE Transactions on Instrumentation and Measurement.

[18]  Thomas Parisini,et al.  Distributed-information neural control: the case of dynamic routing in traffic networks , 2001, IEEE Trans. Neural Networks.

[19]  George Lima,et al.  Evaluation of interrupt handling timeliness in real-time Linux operating systems , 2008, OPSR.

[20]  M. Sanguineti,et al.  Approximating Networks and Extended Ritz Method for the Solution of Functional Optimization Problems , 2002 .

[21]  Tarek R. Sheltami,et al.  RTSP: An Accurate and Energy-Efficient Protocol for Clock Synchronization in WSNs , 2013, IEEE Transactions on Instrumentation and Measurement.

[22]  Mario Marchese,et al.  Neural decision making for decentralized pricing-based call admission control , 2005, IEEE International Conference on Communications, 2005. ICC 2005. 2005.

[23]  Thilo Sauter,et al.  Achieving a Realistic Notion of Time in Discrete Event Simulation , 2011, Int. J. Distributed Sens. Networks.

[24]  A. Valenzano,et al.  Synchronize Your Watches: Part II: Special-Purpose Solutions for Distributed Real-Time Control , 2013, IEEE Industrial Electronics Magazine.

[25]  Mario Marchese,et al.  Non-linear coding and decoding strategies exploiting spatial correlation in wireless sensor networks , 2012, IET Commun..

[26]  Daniele Fontanelli,et al.  A clock state estimator for PTP time synchronization in harsh environmental conditions , 2011, 2011 IEEE International Symposium on Precision Clock Synchronization for Measurement, Control and Communication.

[27]  Niels Kjølstad Poulsen,et al.  A tool for kalman filter tuning , 2007 .