Intelligent Cyber-Physical Systems for Industry 4.0

Cogliati, Dario; Falchetto, Mirko; Pau, Danilo; Roveri, Manuel; Viscardi, Gabriele

doi:10.1109/ai4i.2018.8665681

Cited by 27 publications

(9 citation statements)

References 8 publications

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“…To overcome this, the idea of on device learning at the edge helps since it scales, enable better personalization, privacy and set the base for federated learning (FL) [9]. There are several approaches such as [10] introduces intelligent Cyber-Physical Systems (CPSs) and it is able to predict faults with autonomous behavior and self-adaptation on the CPS itself. This method helps in energy efficiency, reducing the bandwidth, autonomy can be achieved on CPSs.…”

Section: On Device Learning On Mcusmentioning

confidence: 99%

A quantitative review of automated neural search and on-device learning for tiny devices

Pau¹,

Ambrose²

2022

Preprint

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Section: On Device Learning On Mcusmentioning

confidence: 99%

A quantitative review of automated neural search and on-device learning for tiny devices

Pau¹,

Ambrose²

2022

Preprint

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“…This method mainly achieved system balance by controlling the performance of each loop, and ultimately optimized the overall performance of ICPSs. In addition, references [34] and [35] also studied the application of ICPSs in IoT.…”

Section: Related Workmentioning

confidence: 99%

Resource Management and Security Scheme of ICPSs and IoT Based on VNE Algorithm

Zhang¹,

Wang²,

Jiang³

et al. 2022

Preprint

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The development of Intelligent Cyber-Physical Systems (ICPSs) in virtual network environment is facing severe challenges. On the one hand, the Internet of things (IoT) based on ICPSs construction needs a large amount of reasonable network resources support. On the other hand, ICPSs are facing severe network security problems. The integration of ICPSs and network virtualization (NV) can provide more efficient network resource support and security guarantees for IoT users. Based on the above two problems faced by ICPSs, we propose a virtual network embedded (VNE) algorithm with computing, storage resources and security constraints to ensure the rationality and security of resource allocation in ICPSs. In particular, we use reinforcement learning (RL) method as a means to improve algorithm performance. We extract the important attribute characteristics of underlying network as the training environment of RL agent. Agent can derive the optimal node embedding strategy through training, so as to meet the requirements of ICPSs for resource management and security. The embedding of virtual links is based on the breadth first search (BFS) strategy. Therefore, this is a comprehensive two-stage RL-VNE algorithm considering the constraints of computing, storage and security three-dimensional resources. Finally, we design a large number of simulation experiments from the perspective of typical indicators of VNE algorithms. The experimental results effectively illustrate the effectiveness of the algorithm in the application of ICPSs.

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“…It also evaluated the CPSs in various characteristics, such as flexibility and mobility. The work by Cogliati et al 18 further developed a CPS architecture to consider intelligent mechanisms and introduce the notion of iCPS. In this architecture, intelligent embedded sensors are connected in the manufacturing in order to enhance the decision making in production.…”

Section: Related Workmentioning

confidence: 99%

Investigating alternatives for system architectures to enhance discrete manufacturing

Afshar

Koroorian

Ashjaei

et al. 2019

International Journal of Distributed Sensor Networks

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This article investigates the possibility of advancing discrete manufacturing using system architectures that are developed for Collaborative Process Automation Systems. Collaborative Process Automation System is a technology that has the potential to achieve production excellence for process industry. However, not much attention has been paid on using the architectures based on Collaborative Process Automation Systems for discrete manufacturing domains. In this article, we propose a base architecture consisting of three layers, and we discuss various alternatives to make the communications among the layers. We consider legacy components in the proposal, in contrast to most of the related works. In order to show the practicality of the proposed alternatives, we present an example that has been implemented in an ongoing project at ABB Robotics in Sweden.

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Intelligent Cyber-Physical Systems for Industry 4.0

Cited by 27 publications

References 8 publications

A quantitative review of automated neural search and on-device learning for tiny devices

A quantitative review of automated neural search and on-device learning for tiny devices

Resource Management and Security Scheme of ICPSs and IoT Based on VNE Algorithm

Investigating alternatives for system architectures to enhance discrete manufacturing

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