Data synchronization is crucial in ubiquitous computing systems, where heterogeneous sensor devices, modalities, and different communication capabilities and protocols are the norm. A common notion of time among devices is required to make sense of their sensing data. Traditional synchronization methods rely on wireless communication between devices to synchronize, potentially incurring computational and power costs. Furthermore, they are unsuitable for synchronizing data streams that have already been collected. We present CRONOS: a post-hoc, data-driven framework for sensor data synchronization for wearable and Internet-of-Things devices that takes advantage of independent, omni-present motion events in the data streams of two or more sensors. Experimental results on pairwise and multi-sensor synchronization show a drift improvement as high as 98% and a mean absolute synchronization error of approximately 6ms for multi-sensor synchronization with sensors sampling at 100Hz.