Maintaining the time in a distributed system: an example of a loosely-coupled distributed service (synchronization, fault-tolerance, debugging)
Timing-sync protocol for sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Adaptive clock synchronization in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
The flooding time synchronization protocol
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks
IEEE Transactions on Mobile Computing
Fine-grained network time synchronization using reference broadcasts
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Protocols and Architectures for Wireless Sensor Networks
Protocols and Architectures for Wireless Sensor Networks
Estimating clock uncertainty for efficient duty-cycling in sensor networks
Proceedings of the 3rd international conference on Embedded networked sensor systems
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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.