Mobility analytics using data generated from the Internet of Mobile Things(IoMT) is facing many challenges which range from the ingestion of data streamscoming from a vast number of fog nodes and IoMT devices to avoiding overflowingthe cloud with useless massive data streams that can trigger bottlenecks [1].Managing data flow is becoming an important part of the IoMT because it willdictate in which platform analytical tasks should run in the future. Data flowsare usually a sequence of out-of-order tuples with a high data input rate, andmobility analytics requires a real-time flow of data in both directions, fromthe edge to the cloud, and vice-versa. Before pulling the data streams to thecloud, edge data stream processing is needed for detecting missing, broken, andduplicated tuples in addition to recognize tuples whose arrival time is out oforder. Analytical tasks such as data filtering, data cleaning and low-leveldata contextualization can be executed at the edge of a network. In contrast,more complex analytical tasks such as graph processing can be deployed in thecloud, and the results of ad-hoc queries and streaming graph analytics can bepushed to the edge as needed by a user application. Graphs are efficientrepresentations used in mobility analytics because they unify knowledge aboutconnectivity, proximity and interaction among moving things. This posterdescribes the preliminary results from our experimental prototype developed forsupporting transit systems, in which edge and cloud computing are combined toprocess transit data streams forwarded from fog nodes into a cloud. Themotivation of this research is to understand how to perform meaningfulnessmobility analytics on transit feeds by combining cloud and fog computingarchitectures in order to improve fleet management, mass transit and remoteasset monitoring
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