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Controlling and analyzing cyberphysical and robotics systems is increasinglybecoming a Big Data challenge. Pushing this data to, and processing in thecloud is more efficient than on-board processing. However, current cloud-basedsolutions are not suitable for the latency requirements of these applications.We present a new concept, Discretized Streams or D-Streams, that enablesmassively scalable computations on streaming data with latencies as short as asecond.  We experiment with an implementation of D-Streams on top of the Sparkcomputing framework. We demonstrate the usefulness of this concept with a novelalgorithm to estimate vehicular traffic in urban networks. Our online EMalgorithm can estimate traffic on a very large city network (the San FranciscoBay Area) by processing tens of thousands of observations per second, with alatency of a few seconds.

Large Scale Estimation in Cyberphysical Systems using Streaming Data: a Case Study with Smartphone Traces