Increasing amounts of data are sensed at the edge of the Edge-to-Cloud (E2C) continuum, enabling the rapid development of data-driven applications based on, e.g., Machine Learning. This is especially true for Vehicular Cyber-Physical Systems (VCPSs), networks of connected vehicles equipped with high-bandwidth sensors, where Big Data originating on the vehicles is crucial for the advancement of autonomous drive, developing new cars, and more. Limited bandwidth and storage mean that moving this vehicular Big Data from the edge to central processing increasingly poses challenges. In this work, we present our research on how to alleviate these through efficiently localizing data on the edge, selecting relevant data in a data stream, and distributing the processing of data in a VCPS.
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Havers et al. (2024) studied this question.
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