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Deep Learning for Reliable Mobile Edge Analytics in Intelligent Transportation Systems. (arXiv:1712.04135v1 [cs.IT])
来源于:arXiv
Intelligent transportation systems (ITSs) will be a major component of
tomorrow's smart cities. However, realizing the true potential of ITSs requires
ultra-low latency and reliable data analytics solutions that can combine, in
real-time, a heterogeneous mix of data stemming from the ITS network and its
environment. Such data analytics capabilities cannot be provided by
conventional cloud-centric data processing techniques whose communication and
computing latency can be high. Instead, edge-centric solutions that are
tailored to the unique ITS environment must be developed. In this paper, an
edge analytics architecture for ITSs is introduced in which data is processed
at the vehicle or roadside smart sensor level in order to overcome the ITS
latency and reliability challenges. With a higher capability of passengers'
mobile devices and intra-vehicle processors, such a distributed edge computing
architecture can leverage deep learning techniques for reliable mobile sensing
in ITSs. In th 查看全文>>