CPS data streams analytics based on machine learning for Cloud and Fog Computing: A survey

Fei, Xiang; Shah, Nazaraf; Verba, Nandor; Chao, Kuo-Ming; Sánchez-Anguix, Víctor; Lewandowski, Jerzy; James, Anne; Usman, Zahid

doi:10.1016/j.future.2018.06.042

Cited by 95 publications

(65 citation statements)

References 133 publications

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“…Fei et al [49] presented physical Cyber Systems and reviewed machine learning techniques that can be developed in a cloud-fog-Edge Computing architecture. However, the article also focuses on the limitations of Edge Computing, especially in terms of its storage and processing capacity.…”

Section: Resultsmentioning

confidence: 99%

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A Review on Edge Computing in Smart Energy by means of a Systematic Mapping Study

et al. 2019

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Context: Smart Energy is a disruptive concept that has led to the emergence of new energy policies, technology projects, and business models. The development of those models is driven by world capitals, companies, and universities. Their purpose is to make the electric power system more efficient through distributed energy generation/storage, smart meter installation, or reduction of consumption/implementation costs. This work approaches Smart Energy as a paradigm that is concerned with systemic strategies involving the implementation of innovative technological developments in energy systems. However, many of the challenges encountered under this paradigm are yet to be overcome, such as the effective integration of solutions within Smart Energy systems. Edge Computing is included in this new technology group. Objective: To investigate developments that involve the use of Edge Computing and that provide solutions to Smart Energy problems. The research work will be developed using the methodology of systematic mapping of literature, following the guidelines established by Kitchenham and Petersen that facilitate the identification of studies published on the subject. Results: Inclusion and exclusion criteria have been applied to identify the relevant articles. We selected 80 papers that were classified according to the type of publication (journal, conferences, or book chapter), type of research (conceptual, experience, or validation), type of activity (implement, validate, analyze) and asset (architecture, framework, method, or models). Conclusion: A complete review has been conducted of the 80 articles that were closely related to the questions posed in this research. To reach the goal of building Edge Computing architectures for Smart Energy environments, several lines of research have been defined. In the future, such architectures will overcome current problems, becoming highly energy-efficient, cost-effective, and capacitated to process and respond in real-time.Electronics 2020, 9, 48 2 of 33 Ledger Technologies (such as blockchain) [15]; or distributed sensing and actuation technologies such as the Internet of Things (IoT) [3,16].Regarding the Internet of Things (IoT), its recognition as a paradigm dates back to 2011-2012, when renowned companies and institutions such as Gartner, Forbes, or Wired started using this term to refer to the emerging technology [17]. The Internet of Things implies the connection of different heterogeneous objects, including buildings, machinery, vehicles, and electronic devices, such as sensors and actuators interconnected by means of communication protocols and forming wireless or wired networks [18] to collect information and extract knowledge [19]. Since then, the scope of IoT has spread throughout a great variety of environments and disciplines, including solutions for development of Smart Cities [20,21], Industry 4.0 [22][23][24], transportation and logistics [25,26], smart homes and hotels [13,27,28] or, more relevant to this research, energy efficiency [8,29,30]. IoT provid...

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Section: Resultsmentioning

confidence: 99%

“…Another relevant state-of-the-art research is that of Fei et al [49]. In this case, the authors have performed a systematic study of Machine Learning techniques for cloud and fog computing in IoT and Cyber-Physical Systems (CPS).…”

Section: Related Workmentioning

confidence: 99%

A Review on Edge Computing in Smart Energy by means of a Systematic Mapping Study

et al. 2019

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“…In industrial environments, AI has been widely applied for both, business and operational tasks [20,17,7]. Examples of AI used at business levels include several kinds of online and offline batch, exploratory and descriptive data analysis tasks from reporting and forecasting to product design, supply chain and customer relationship management.…”

Section: Intelligence In Industrial Cpsmentioning

confidence: 99%

“…In the same sense, operational tasks, like planning, optimization and process fault diagnosis are also suitable to be performed at Cloud. Given the broad scope and resource availability of the Cloud, the kinds of ML algorithms are diverse, from simple linear regression to robust classification and complex Deep Neural Networks [7].…”

Section: Intelligence In Industrial Cpsmentioning

confidence: 99%

An Agent-Based Industrial Cyber-Physical System Deployed in an Automobile Multi-stage Production System

Queiroz

Leitão

Barbosa

et al. 2019

Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future

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Industrial Cyber-Physical Systems (CPS) are promoting the development of smart machines and products, leading to the next generation of intelligent production systems. In this context, Artificial Intelligence (AI) is posed as a key enabler for the realization of CPS requirements, supporting the data analysis and the system dynamic adaptation. However, the centralized Cloud-based AI approaches are not suitable to handle many industrial scenarios, constrained by responsiveness and data sensitive. Edge Computing can address the new challenges, enabling the decentralization of data analysis along the cyber-physical components. In this context, distributed AI approaches, such those based on Multi-agent Systems (MAS), are essential to handle the distribution and interaction of the components. Based on that, this work uses a MAS approach to design cyber-physical agents that can embed different data analysis capabilities, supporting the decentralization of intelligence. These concepts were applied to an industrial automobile multi-stage production system, where different kinds of data analysis were performed in autonomous and cooperative agents disposed along Edge, Fog and Cloud computing layers.Industrial Cyber-Physical Systems (CPS) are enabling the next generation of intelligent production systems, mainly based on the concepts of smart machines and products. Driven by the needs to attend the ever-changing market trends, such digital transformation is mainly based on the use of Internet of Things (IoT), Cloud Computing and Artificial Intelligence (AI) technologies [12]. While the first enables the interconnection of equipment and consequently the digitization of the industrial environment [22], the second provides on demand high processing and storage resources [15]. On the other hand, AI provides advanced data analysis algorithms, such those based on Machine-Learning (ML), that can take advantage of the huge amounts of IoT data and the power of Cloud Computing, in order to provide actionable information and support data-driven decision-making [20,8].Although Cloud manufacturing [15] has been seen as a new paradigm in the realization of the 4th industrial revolution (4IR) [12], the traditional Cloud-based approaches, where IoT devices send all the data to be processed by centralized applications, present some drawbacks. Indeed, besides information security and privacy concerns [21], this approach is not suitable for many real-time, data-sensitive and constrained network applications [2]. In this context, Fog Computing emerged to cover the Cloud limitations, promoting the deployment of data processing capabilities closer to the data sources [4]. It defines an intermediate computing layer between Cloud applications and IoT devices that besides providing a more direct, reliable, secure and fast link between them, also promotes the decentralization of data analysis, decision-making and control, increasing local components autonomy.Besides Fog, which considers equipment at the local network, CPS also considers processing ca...

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“…The core to realize integration is the cyber‐physical system (CPS), which can connect the real physical world and the virtual cyber world . Through effective utilization of computation, communication, and control (3 C) technology, CPS can realize information processing, real‐time communication, and autonomous control in a highly integrated and interactive network environment . It focuses on the reasonable and effective allocation of system resources, as well as the optimization of system performance and efficiency, and provides personalized and humanized service for users …”

Section: Introductionmentioning

confidence: 99%

System Structure and Network Computing Architecture of Petrochemical Cyber‐Physical System: Overview and Perspective

Suo

Liu

2019

Can J Chem Eng

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The cyber-physical system (CPS) has become the key infrastructure to support the development of smart manufacturing. On the basis of analyzing the CPS theory and industrial characteristics of the petrochemical industry, this paper defines the system structure of the CPS in petrochemical called petrochemical cyber-physical system (PCPS), and divides PCPS into three system structure levels, proceeding from the analysis on the overall development of the petrochemical industry and the production situation of Sinopec Group. On the basis of this system structure, the unit pattern structure, unit level structure, network architecture, computing architecture, and working model of PCPS are studied and analyzed. Finally, this paper introduces the application scenario of PCPS in the construction of a Sinopec smart factory.

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CPS data streams analytics based on machine learning for Cloud and Fog Computing: A survey

Cited by 95 publications

References 133 publications

A Review on Edge Computing in Smart Energy by means of a Systematic Mapping Study

A Review on Edge Computing in Smart Energy by means of a Systematic Mapping Study

An Agent-Based Industrial Cyber-Physical System Deployed in an Automobile Multi-stage Production System

System Structure and Network Computing Architecture of Petrochemical Cyber‐Physical System: Overview and Perspective

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