  This paper presents a novel architecture for data analytics targeting an
anticipatory learning process in the context of the Internet of Mobile Things.
The architecture is geo-distributed and composed by edge, fog, and cloud
resources that operate collectively to support such an anticipatory learning
process. We designed the architecture to manage large volumes of data streams
coming from the IoMT devices, analyze in successive phases climbing up in the
hierarchy of resources from edge, fog and cloud. We discuss the characteristics
of the analytical tasks at each layer. We notice that the amount of data being
transported in the network decreases going from the edge, to the fog and
finally to the cloud, while the complexity of the computation increases. Such
design allows to support different kind of analytical needs, from real-time to
historical according to the type of resource being utilized. We have
implemented the proposed architecture as a proof-of-concept using the transit
data feeds from the area of Greater Moncton, Canada.
