Exploiting manufacturing variations for compensating environment-induced clock drift in time synchronization

Authors:
Thomas Schmid;Zainul Charbiwala;Jonathan Friedman;Young H. Cho;Mani B. Srivastava
Affiliations:
University of California, Los Angeles, Los Angeles, CA, USA;University of California, Los Angeles, Los Angeles, CA, USA;University of California, Los Angeles, Los Angeles, CA, USA;University of California, Los Angeles, Los Angeles, CA, USA;University of California, Los Angeles, los Angeles, CA, USA
Venue:
SIGMETRICS '08 Proceedings of the 2008 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Year:
2008

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Abstract

Time synchronization is an essential service in distributed computing and control systems. It is used to enable tasks such as synchronized data sampling and accurate time-of-flight estimation, which can be used to locate nodes. The deviation in nodes' knowledge of time and inter-node resynchronization rate are affected by three sources of time stamping errors: network wireless communication delays, platform hardware and software delays, and environment-dependent frequency drift characteristics of the clock source. The focus of this work is on the last source of error, the clock source, which becomes a bottleneck when either required time accuracy or available energy budget and bandwidth (and thus feasible resynchronization rate) are too stringent. Traditionally, this has required the use of expensive clock sources (such as temperature compensation using precise sensors and calibration models) that are not cost-effective in low-end wireless sensor nodes. Since the frequency of a crystal is a product of manufacturing and environmental parameters, we describe an approach that exploits the subtle manufacturing variation between a pair of inexpensive oscillators placed in close proximity to algorithmically compensate for the drift produced by the environment. The algorithm effectively uses the oscillators themselves as a sensor that can detect changes in frequency caused by a variety of environmental factors. We analyze the performance of our approach using behavioral models of crystal oscillators in our algorithm simulation. Then we apply the algorithm to an actual temperature dataset collected at the James Wildlife Reserve in Riverside County, California, and test the algorithms on a waveform generator based testbed. The result of our experiments show that the technique can effectively improve the frequency stability of an inexpensive uncompensated crystal 5 times with the potential for even higher gains in future implementations.