The design and implementation of a self-calibrating distributed acoustic sensing platform
Proceedings of the 4th international conference on Embedded networked sensor systems
An empirical study of collaborative acoustic source localization
Proceedings of the 6th international conference on Information processing in sensor networks
Fidelity and yield in a volcano monitoring sensor network
OSDI '06 Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation - Volume 7
Automatic event-based synchronization of multimodal data streams from wearable and ambient sensors
EuroSSC'09 Proceedings of the 4th European conference on Smart sensing and context
Phoenix: an epidemic approach to time reconstruction
EWSN'10 Proceedings of the 7th European conference on Wireless Sensor Networks
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Data Driven Time Synchronization (DDTS) provides synchronization across sensors by using underlying characteristics of data collected by an embedded sensing system. We apply the concept of Data Driven Time Synchronization through a seismic deployment consisting of 100 seismic sensors to repair data that was not time synchronized correctly. This deployment used GPS for time synchronization, but due to system faults common to environmental sensing systems, data was collected with large time offsets. In seismic deployments, offset data is often never used, but we show that Data Driven Time Synchronization can recover the synchronization and make the data usable. To implement Data Driven Time Synchronization to repair the time offsets we use microseisms as the underlying characteristics. Microseisms are waves that travel through the earth's crust and are independent of the seismic events used for the study of the earth's structure. We have developed a model of microseism propagation through a linear seismic array and use the model to obtain time correction shifts. By simulating time offsets in real data which does not have offsets, we determined that this method is able to repair the offset to between 0.05 and 0.2 seconds. Our ongoing work will attempt to refine the model to correct the offsets to less than 0.05 seconds and evaluate how errors in the correction affect seismic results such as event location. Data Driven Time Synchronization may be applicable to other high data rate embedded sensing applications such as acoustic source localization.