物联网技术在大数据管理系统中的作用:综述与智能电网案例研究

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
A.R. Al-Ali , Ragini Gupta , Imran Zualkernan , Sajal K. Das
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引用次数: 0

摘要

在物联网(IoT)和云计算平台的推动下,智慧城市的概念正在从概念模型向开发和实施阶段过渡。智能电网和智能电表等多种智能城市计划和服务的出现,导致了海量能源大数据的积累。大数据通常具有五个显著特征,即数量、速度、种类、真实性和价值。要深入了解大数据并将其货币化,必须对数据进行收集、存储、处理、分析、挖掘和可视化。本文确定了大数据架构的主要层次,以及可用于从大数据中获得有意义的见解和情报的最先进的通信、存储和处理技术。此外,本文还为有意利用最新的大数据特定处理和可视化工具探索可用于利用大数据价值的各种技术和工艺的研发人员提供了深入的概述。最后,本文提出了一个利用上述技术的智能电网用例模型,以展示能源大数据从产生到货币化的路线图。我们的主要发现强调了为大数据架构的每一层选择合适的大数据工具和技术的重要性,并详细介绍了它们的优缺点。我们指出了现有工作的关键不足之处,尤其是缺乏一个统一的框架来有效整合这些层级,以实现智慧城市应用。这一差距既是未来研究的挑战,也是机遇,表明在大数据管理和利用方面需要更全面和可互操作的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Role of IoT technologies in big data management systems: A review and Smart Grid case study

Empowered by Internet of Things (IoT) and cloud computing platforms, the concept of smart cities is making a transition from conceptual models to development and implementation phases. Multiple smart city initiatives and services such as Smart Grid and Smart Meters have emerged that have led to the accumulation of massive amounts of energy big data. Big data is typically characterized by five distinct features namely, volume, velocity, variety, veracity, and value. To gain insights and to monetize big data, data has to be collected, stored, processed, analyzed, mined, and visualized. This paper identifies the primary layers of a big data architecture with start-of-the-art communication, storage, and processing technologies that can be utilized to gain meaningful insights and intelligence from big data. In addition, this paper gives an in-depth overview for research and development who intend to explore the various techniques and technologies that can be implemented for harnessing big data value utilizing the recent big data specific processing and visualization tools. Finally, a use case model utilizing the above mentioned technologies for Smart Grid is presented to demonstrate the energy big data road map from generation to monetization. Our key findings highlight the significance of selecting the appropriate big data tools and technologies for each layer of big data architecture, detailing their advantages and disadvantages. We pinpoint the critical shortcomings of existing works, particularly the lack of a unified framework that effectively integrates these layers for smart city applications. This gap presents both a challenge and an opportunity for future research, suggesting a need for more holistic and interoperable solutions in big data management and utilization.

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来源期刊
Pervasive and Mobile Computing
Pervasive and Mobile Computing COMPUTER SCIENCE, INFORMATION SYSTEMS-TELECOMMUNICATIONS
CiteScore
7.70
自引率
2.30%
发文量
80
审稿时长
68 days
期刊介绍: As envisioned by Mark Weiser as early as 1991, pervasive computing systems and services have truly become integral parts of our daily lives. Tremendous developments in a multitude of technologies ranging from personalized and embedded smart devices (e.g., smartphones, sensors, wearables, IoTs, etc.) to ubiquitous connectivity, via a variety of wireless mobile communications and cognitive networking infrastructures, to advanced computing techniques (including edge, fog and cloud) and user-friendly middleware services and platforms have significantly contributed to the unprecedented advances in pervasive and mobile computing. Cutting-edge applications and paradigms have evolved, such as cyber-physical systems and smart environments (e.g., smart city, smart energy, smart transportation, smart healthcare, etc.) that also involve human in the loop through social interactions and participatory and/or mobile crowd sensing, for example. The goal of pervasive computing systems is to improve human experience and quality of life, without explicit awareness of the underlying communications and computing technologies. The Pervasive and Mobile Computing Journal (PMC) is a high-impact, peer-reviewed technical journal that publishes high-quality scientific articles spanning theory and practice, and covering all aspects of pervasive and mobile computing and systems.
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