Abstract
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 long-term 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 time-synchronization algorithm that can adapt to changing clock drift and environmental conditions while achieving application-specified 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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