Fog Based Intelligent Transportation Big Data Analytics in The Internet of Vehicles Environment: Motivations, Architecture, Challenges, and Critical Issues
Fog Based Intelligent Transportation Big Data Analytics in The Internet of Vehicles Environment: Motivations, Architecture, Challenges, and Critical Issues
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DOI：
10.1109/access.2018.2815989
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发表时间：
2018-01-01
期刊：
影响因子：
3.9
通讯作者：
Abu Bakar, Kamalrulnizam
中科院分区：
文献类型：
--
作者：
Darwish, Tansneem S. J.;Abu Bakar, Kamalrulnizam
智能交通系统（ITS）的概念被引入，以提高道路安全，有效地管理交通，并保护我们的绿色环境。如今，ITS应用程序变得越来越数据密集，并且使用“大数据的5V”来描述它们的数据。因此，为了充分利用这些数据，需要应用大数据分析。车联网（IoV）将ITS设备连接到云计算中心，在那里进行数据处理。然而，从地理上分布的设备传输大量数据会产生网络开销和瓶颈，并且会消耗网络资源。此外，采用集中式方法处理ITS大数据会导致延迟敏感的ITS应用程序无法容忍的高延迟。雾计算被认为是一种有前途的实时大数据分析技术。基本上，雾技术补充了云计算的作用，并将数据处理分布在网络边缘，从而为ITS应用查询提供更快的响应并节省网络资源。然而，在车联网动态环境中，实现雾计算和lambda架构来进行实时大数据处理是一项挑战。在这方面，本文提出了一种新的架构，在车联网环境中的实时ITS大数据分析。该架构融合了三个维度，包括智能计算（即云和雾计算）维度，实时大数据分析维度和IoV维度。此外，本文还对车联网环境、ITS大数据特征、实时大数据分析的lambda架构、几种智能计算技术进行了全面描述。更重要的是，本文讨论了在车联网环境中实施雾计算和实时大数据分析所面临的机遇和挑战。最后，关键问题和未来的研究方向部分讨论了一些应该考虑的问题，以有效地实现所提出的架构。
The intelligent transportation system (ITS) concept was introduced to increase road safety, manage traffic efficiently, and preserve our green environment. Nowadays, ITS applications are becoming more data-intensive and their data are described using the "5Vs of Big Data''. Thus, to fully utilize such data, big data analytics need to be applied. The Internet of vehicles (IoV) connects the ITS devices to cloud computing centres, where data processing is performed. However, transferring huge amount of data from geographically distributed devices creates network overhead and bottlenecks, and it consumes the network resources. In addition, following the centralized approach to process the ITS big data results in high latency which cannot be tolerated by the delay-sensitive ITS applications. Fog computing is considered a promising technology for real-time big data analytics. Basically, the fog technology complements the role of cloud computing and distributes the data processing at the edge of the network, which provides faster responses to ITS application queries and saves the network resources. However, implementing fog computing and the lambda architecture for real-time big data processing is challenging in the IoV dynamic environment. In this regard, a novel architecture for real-time ITS big data analytics in the IoV environment is proposed in this paper. The proposed architecture merges three dimensions including intelligent computing (i.e. cloud and fog computing) dimension, real-time big data analytics dimension, and IoV dimension. Moreover, this paper gives a comprehensive description of the IoV environment, the ITS big data characteristics, the lambda architecture for real-time big data analytics, several intelligent computing technologies. More importantly, this paper discusses the opportunities and challenges that face the implementation of fog computing and real-time big data analytics in the IoV environment. Finally, the critical issues and future research directions section discusses some issues that should be considered in order to efficiently implement the proposed architecture.