رایانش مه در سطح صنعتی
Abstract
۱٫ Introduction
۲٫ Methodology
۳٫ Overview of key background concepts
۴٫ Conducted studies
۵٫ Discussion
۶٫ Conclusions and ongoing work
Declarations
Acknowledgements
References
Abstract
The industrial applications in the cloud do not meet the requirements of low latency and reliability since variables must be continuously monitored. For this reason, industrial internet of things (IIoT) is a challenge for the current infrastructure because it generates a large amount of data making cloud computing reach the edge and become fog computing (FC). FC can be considered as a new component of Industry 4.0, which aims to solve the problem of big data, reduce energy consumption in industrial sensor networks, improve the security, processing and storage real-time data. It is a promising growing paradigm that offers new opportunities and challenges, beside the ones inherited from cloud computing, which requires a new heterogeneous architecture to improve the network capacity for delivering edge services, that is, providing computing resources closer to the end user. The purpose of this research is to show a systematic review of the most recent studies about the architecture, security, latency, and energy consumption that FC presents at industrial level and thus provide an overview of the current characteristics and challenges of this new technology.
Introduction
The fast development of technology has contributed to the increase of devices connected to the cloud, which generates large amounts of data. The cloud is the biggest data unit where processing and storage is performed with the main goal of making use of services and resources sought by the customers [1]. In 2015, the number of connected devices was 15.41 billion, later in 2017 it was of 20.35 billion, and it is expected to be 30.73 billion in 2020 [2]. These services and applications are used in different scenarios in the world such as Smart Factory, Smart Farming and Smart Cities [3]. Due to this rapid increase of data, a large amount of storage is required, which has generated a greater bandwidth consumption and high latency in data processing. The development of portable computing processes, more intelligent measurements in homes/cities and vehicles, and wider wireless sensor networks has made everything interconnected through the IoT [4]. Due to the number of interconnected devices, technology is generating big quantity of data that are processed, filtered and analyzed in the cloud causing problems related with traffic congestion, delays, and privacy concerns [2, 5]. Many limitations also emerge since the cloud is unable to support some of the needful such as heterogeneous devices, low latency, mobility, and location recognition [6]. In addition, the current infrastructure is not designed for the quantity and speed of data generated by IoT [7], which has generated a high consumption of resources and a decrease in the quality of services for the end user.