Cloud-Integrated Cyber-Physical Systems for Complex Industrial Applications

Shu, Zhaogang; Wan, Jiafu; Zhang, Daqiang; Li, Di

doi:10.1007/s11036-015-0664-6

Cited by 126 publications

(46 citation statements)

References 37 publications

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“…However, there comes a point at which additional expenditure on infrastructure is not feasible. This point may be reached quicker with the adoption of IIoT technologies that can rapidly overload existing network architectures with extra data transport, a significant part of which is directly incurred as a by-product of the additional authentication transactions for the additional physical objects, and the associated queries that become possible as a result [33,34,35]. This is also in addition to essential infrastructure services that invariably increase as more equipment is added.…”

Section: Bi Security Challenges and Controlsmentioning

confidence: 99%

Securing Manufacturing Intelligence for the Industrial Internet of Things

Al-Aqrabi

Hill

Lane

et al. 2020

Advances in Intelligent Systems and Computing

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Widespread interest in the emerging area of predictive analytics is driving industries such as manufacturing to explore new approaches to the collection and management of data provided from Industrial Internet of Things (IIoT) devices. Often, analytics processing for Business Intelligence (BI) is an intensive task, and it also presents both an opportunity for competitive advantage as well as a security vulnerability in terms of the potential for losing Intellectual Property (IP). This article explores two approaches to securing BI in the manufacturing domain. Simulation results indicate that a Unified Threat Management (UTM) model is simpler to maintain and has less potential vulnerabilities than a distributed security model. Conversely, a distributed model of security out-performs the UTM model and offers more scope for the use of existing hardware resources. In conclusion, a hybrid security model is proposed where security controls are segregated into a multi-cloud architecture.

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Section: Bi Security Challenges and Controlsmentioning

confidence: 99%

Securing Manufacturing Intelligence for the Industrial Internet of Things

Al-Aqrabi

Hill

Lane

et al. 2020

Advances in Intelligent Systems and Computing

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“…Decision (the requirements@run.time Approach) SCPS involves multi-level scheduling [47,62], the scheduling strategies at each level have different optimization goals and different constraints, and some of them may even be conflicting. In current centralized architectures, subsystems generally know nothing about the global goals and constraints, they just try their best to respond to scheduling commands.…”

Section: Guaranteeing the Dependability With Goal/contract Based On Dmentioning

confidence: 99%

“…As SCPS contains an abundance of subsystems (especially the cloud-based SCPS and MAS-based SCPS), subsystem level redundancy is a natural solution to improve system dependability. For cloud-based SCPS, VM-based resources isolation can significantly simplify the fault isolation approaches [45,47,49]. Ruleor knowledge-based fault diagnosis and reasoning show good results in fixed or rarely changed environments [59].…”

Section: Traditional Solutions To Improve the Dependability Of Infrasmentioning

confidence: 99%

A Comprehensive Technological Survey on the Dependable Self-Management CPS: From Self-Adaptive Architecture to Self-Management Strategies

Zhou¹,

Zuo²,

Hou³

et al. 2019

Preprint

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Cyber Physical Systems (CPS) has been a popular research area in the last decade. The dependability of CPS is still a critical issue, and rare survey has been published in this domain. CPS is a dynamic complex system, which involves various multidisciplinary technologies. To avoid human error and to simplify management, self-management CPS (SCPS) is a wise choice. And to achieve dependable self-management, systematic solution is necessary to verify the design and to guarantee the safety of self-adaptation decision, as well as to maintain the health of SCPS. This survey first recalls the concepts of dependability, and proposes a generic environment-in-loop processing flow of self-management CPS, and then analyzes the error sources and challenges of self-management through the formal feedback flow. Focus on reducing the complexity, we first survey the self-adaptive architecture approaches and applied dependability means; then we introduce a hybrid multi-role self-adaptive architecture, and discuss the supporting technologies for dependable self-management at the architecture level. Focus on dependable environment-centered adaption, we investigate the verification and validation (V&V) methods for making safe self-adaptation decision and the solutions for processing decision dependably. For system-centered adaption, the comprehensive self-healing methods are summarized. Finally, we analyze the missing pieces of the technology puzzle and the future directions. In this survey, the technical trends for dependable CPS design and maintenance are discussed, an all-in-one solution is proposed to integrate these technologies and build a dependable organic SCPS. To the best of our knowledge, this is the first comprehensive survey on dependable SCPS building and evaluation.

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“…With the advent of industry 4.0 [31,32], the introduction of cyber-physical systems [33,34,70] and cloud technologies makes it possible to significantly improve robotic performance. In recent years, the study of cloud robotics has achieved rapid development through the support of relevant cloud technologies.…”

Section: Typical Applications Of Cloud Roboticsmentioning

confidence: 99%

Cloud Robotics: Current Status and Open Issues

et al. 2016

Self Cite

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1  Abstract-With the development of cloud computing, big data and other emerging technologies, the integration of cloud technology and multi-robot systems makes it possible to design multi-robot systems with improved energy efficiency, high real-time performance and low cost. In order to address the potential of clouds in enhancing robotics for industrial systems, this paper describes the basic concepts and development process of cloud robotics and the overall architecture of these systems. Then, the major driving forces behind the development of cloud robotics are carefully analyzed from the point of view of cloud computing, big data, open source resources, robot cooperative learning, and network connectivity. Subsequently, the key issues and challenges in the current cloud robotic systems are proposed, and some possible solutions are also given. Finally, the potential value of cloud robotic systems in different practical applications is discussed.

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Cloud-Integrated Cyber-Physical Systems for Complex Industrial Applications

Cited by 126 publications

References 37 publications

Securing Manufacturing Intelligence for the Industrial Internet of Things

Securing Manufacturing Intelligence for the Industrial Internet of Things

A Comprehensive Technological Survey on the Dependable Self-Management CPS: From Self-Adaptive Architecture to Self-Management Strategies

Cloud Robotics: Current Status and Open Issues

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