面向交通的智慧城市电动汽车集成的大数据分析平台

M. Hussain, M. Beg, M. S. Alam, S. Laskar
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引用次数: 1

摘要

电动汽车(ev)是由智能电网(SG)供电的交通导向智能城市(TOSC)的关键参与者,因为它们通过减少汽车排放和碳足迹,帮助这些城市变得更环保。在本文中,作者分析了不同的用例,以展示大数据分析(BDA)如何在成功的电动汽车(EV)与智能电网(SG)集成中发挥重要作用。随后,本文提出了一种边缘计算模型,并强调了采用这种分布式边缘范式来满足tosc中智能电动汽车应用的存储、计算和网络(SCN)需求的优势。本文还强调了边缘范式的显著特征,以支持EV中的BDA活动到tosc中的SG集成。最后,作者详细概述了这两种计算技术的机遇、趋势和挑战。本文特别讨论了边缘隐私和边缘取证方面的部署挑战和最先进的解决方案。
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Big Data Analytics Platforms for Electric Vehicle Integration in Transport Oriented Smart Cities
Electric vehicles (EVs) are key players for transport oriented smart cities (TOSC) powered by smart grids (SG) because they help those cities to become greener by reducing vehicle emissions and carbon footprint. In this article, the authors analyze different use-cases to show how big data analytics (BDA) can play vital role for successful electric vehicle (EV) to smart grid (SG) integration. Followed by this, this article presents an edge computing model and highlights the advantages of employing such distributed edge paradigms towards satisfying the store, compute and networking (SCN) requirements of smart EV applications in TOSCs. This article also highlights the distinguishing features of the edge paradigm, towards supporting BDA activities in EV to SG integration in TOSCs. Finally, the authors provide a detailed overview of opportunities, trends, and challenges of both these computing techniques. In particular, this article discusses the deployment challenges and state-of-the-art solutions in edge privacy and edge forensics.
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CiteScore
1.80
自引率
40.00%
发文量
20
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