A Case for Distributed Multilevel Storage Infrastructure for Visual Surveillance in Intelligent Transportation Networks

Traffic surveillance devices such as cameras and sensors that are integrated into intelligent transportation systems (ITSs) generate large volumes of data. To deal with the extreme volume and the massively geographically distributed sources of data, we advocate a tiered storage and processing architecture, using edge nodes to augment a centralized backbone, facilitated by network coding (NetCod) and control techniques and emerging fog-computing solutions. To that end, we first survey several technologies and concepts, and then elaborate on the core components needed in a distributed multilevel storage infrastructure (DMSI) for secure and reliable acquisition, storage, and analysis of increasing volumes of intelligent surveillance system data.

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