The huge amount of streaming information generated by the new wave of edge devices that are used to monitor a plethora of everyday aspects, prompts the need for efficient techniques to handle, process, aggregate, and visualize this stream of data. One such field is the continuous transport infrastructure monitoring, where smartphones inside driving vehicles act as edge sensing and computing nodes to measure the quality of the road pavement among other things. Although the location accuracy of such devices is within the acceptable bounds, the accumulated error can lead to large deviations from the location of interest, reducing the measurement credibility. The data represent a geographically vast infrastructure network in need of real-time, bird-eye visualization. This paper describes the implementation of such a real-time platform and the challenges the task provides. By implementing relatively new open-source cross-platform environments, the platform is scalable and versatile, reducing the costs associated with cloud-based implementation systems.
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