Networked and Embedded Systems Laboratory

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Time synchronization is critical to sensor networks at many layers
of its design and enables better duty-cycling of the radio, accurate
localization, beamforming and other collaborative signal
processing. While there has been significant work in sensor
network synchronization, measurement based studies have been
restricted to very short-term (few minutes) datasets and have
focused on obtaining accurate instantaneous synchronization.
Long-term synchronization has typically been handled by periodic
re-synchronization schemes with beacon intervals of a few
minutes based on the assumption that long-term drift is too hard
to model and predict. Thus, none of this work exploits the
temporally correlated behavior of the clock drift. Yet, there are
incredible energy gains to be achieved from better modeling and
prediction of long-term drift that can provide bounds on longterm
synchronization error across a sensor network. Better
synchronization can lead to significantly lower duty-cycles of the
radio, simplify signal processing and can enable an order of
magnitude greater lifetime than current techniques.
We measure, evaluate and analyze in-depth the long-term
behavior of synchronization skew and drift on typical Mica sensor
nodes and develop an efficient long-term time synchronization
protocol. We use four real time data sets gathered over periods of
12-30 hours in different environmental conditions to study the
interplay between three key parameters that influence long-term
synchronization – synchronization rate, history of past
synchronization beacons and the estimation scheme. We use this
measurement-based study to design an online adaptive timesynchronization
algorithm that can adapt to changing clock drift
and environmental conditions while achieving applicationspecified
precision with very high probability. We find that our
algorithm achieves between one and two orders of magnitude
improvement in energy efficiency over currently available time synchronization approaches.

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