Adaptive Time Synchronization for Wireless Sensor Networks with Self-Calibration

Time synchronization is important for wireless sensor networks because it facilitates cooperation among nodes and helps raise power efficiency. Time synchronization protocols like TPSN, RBS and FTSP have provided great schemes to fulfill fast synchronization with efficiency. In some applications, nodes might hope to sleep for a long time without timestamp exchanges with other nodes. In that case, accurate time drift prediction is quite necessary. For that purpose, firstly, we propose a time synchronization scheme, which fully utilizes the broadcast nature. The scheme achieves time synchronization with fewer timestamps compared with RBS and TPSN. Secondly, we introduce a method to find relative time drift rate on the fly. Thirdly, we introduce a scheme to predict time drift rates of the next few hours. We also analyze a few factors that deteriorate frequency drift or time drift rate. The diurnal periodical environment trend, instead of mathematical extrapolation, is used for time drift rates prediction of the next few hours.

[1]  Huazhong Yang,et al.  A precision adaptive average time synchronization protocol in wireless sensor networks , 2008, 2008 International Conference on Information and Automation.

[2]  Saurabh Ganeriwal,et al.  Timing-sync protocol for sensor networks , 2003, SenSys '03.

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

[4]  Theo Brandsma,et al.  Application of nearest‐neighbor resampling for homogenizing temperature records on a daily to sub‐daily level , 2006 .

[5]  Mani B. Srivastava,et al.  Estimating clock uncertainty for efficient duty-cycling in sensor networks , 2009, TNET.

[6]  Keith Ansel Marzullo,et al.  Maintaining the time in a distributed system: an example of a loosely-coupled distributed service (synchronization, fault-tolerance, debugging) , 1984 .

[7]  Ossama Younis,et al.  HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks , 2004, IEEE Transactions on Mobile Computing.

[8]  David L. Mills,et al.  Internet time synchronization: the network time protocol , 1991, IEEE Trans. Commun..

[9]  D. Hauden,et al.  Frequency instabilities in SAW resonators and oscillators , 1989, Proceedings., IEEE Ultrasonics Symposium,.

[10]  Lin Li,et al.  A Study of Temperature Compensated Crystal Oscillator Based on Stress Processing , 2007, 2007 IEEE International Frequency Control Symposium Joint with the 21st European Frequency and Time Forum.

[11]  Andreas Willig,et al.  Protocols and Architectures for Wireless Sensor Networks , 2005 .

[12]  Theo A. C. M. Claasen,et al.  An Industry Perspective on Current and Future State of the Art in System-on-Chip (SoC) Technology , 2006, Proceedings of the IEEE.

[13]  Amit Kumar Saha,et al.  Adaptive clock synchronization in sensor networks , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[14]  D. Macii,et al.  An Adaptive-Rate Time Synchronization Protocol for Wireless Sensor Networks , 2007, 2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007.

[15]  Cheng Li,et al.  Distributed Minimum-Cost Clustering Protocol for UnderWater Sensor Networks (UWSNs) , 2007, 2007 IEEE International Conference on Communications.

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