Process execution in Cyber-Physical Systems using cloud and Cyber-Physical Internet services

Bordel, Borja; Rivera, Diego

doi:10.1007/s11227-018-2416-4

Cited by 36 publications

(17 citation statements)

References 53 publications

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“…Vertical architectures are, probably, the genuine approach for Industry 4.0 applications. In this approach, computational processes are transformed and decomposed (Ivanov et al, 2016a;Bagheri et al, 2015), so executable units may be transferred and delegated to remote production infrastructures or, even, cloud services (Bordel et al, 2018c). On the contrary, horizontal architectures are traditional embedded control paradigms, which have been adapted to Industry 4.0 (Lalwani et al, 2006).…”

Section: State Of the Artmentioning

confidence: 99%

“…Connections among tasks and their temporal organization are strict and cannot be modified. Moreover, sometimes valid ranges for task outputs may also be defined as common industrial systems and robots are very predictable and precise (Bordel et al, 2018c). This top-down approach employs technologies such as YAWL (Van Der Aalst and Ter Hofstede, 2005) or BPMN (Geiger et al, 2018) to create and validate processes and assumes that every low-level infrastructure is following a request-actuation design paradigm.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Controlling Supervised Industry 4.0 Processes through Logic Rules and Tensor Deformation Functions

Bordel¹,

Alcarria²,

Robles³

2021

Informatica

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Section: State Of the Artmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Controlling Supervised Industry 4.0 Processes through Logic Rules and Tensor Deformation Functions

Bordel¹,

Alcarria²,

Robles³

2021

Informatica

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“… ≥   ≥  (13) Once obtained the STFT, the frequency information may be easily obtained detecting the discrete frequency so the STFT is maximum (14).…”

Section: B Signal Processing: Frequency and Amplitude Information Exmentioning

confidence: 99%

“…In this scheme, information about physical and biological processes is usually captured by geographically sparse mobile nodes (typically low-cost physical devices), interconnected through the Internet of Medical Things (IoMT). These data and biological signals are, then, injected into computational processes and employed by control mechanisms to make decisions and actuate (once more) over the patients and the physical world [14]. In past, critical infrastructures, and other similar application scenarios (such as manufacturing systems), were totally revolutionized by this new approach [15].…”

Section: Introductionmentioning

confidence: 99%

A Predictor-Corrector Algorithm Based on Laurent Series for Biological Signals in the Internet of Medical Things

Bordel

You

2020

IEEE Access

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In future engineered systems for medical applications, a tight real-time integration between physical and computational processes will be required. That integration is achieved using feedback control loops which need high quality input data streams. However, hardware platforms can barely provide such high-quality data sequences (especially if mobile nodes are considered), and mechanisms to improve and polish physical and biological signals are then necessary. This paper proposes a predictor-corrector algorithm to improve the quality and precision of data (biological) signals in Internet of Medical Things deployments, especially if composed of mobile nodes. The proposed algorithm employs an Artificial Intelligence approach and statistical learning techniques to predict future data samples and correct errors in received information. Employed mathematical models follow a prediction-correction scheme and are based on complex functions, Laurent series and the idea of complex envelope. Simulation techniques are used to evaluate the performance of the proposed solution, showing that it improves the precision of traditional linear interpolation techniques up to 85%, and cubic splines up to 20%. Processing delay during operation is, for the referred precision, around 200ms.

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“…All of them, nevertheless, share some common characteristics. First, all of them are distributed solutions, where many different physical agents support the execution of high-level services [4]. These agents may be very heterogenous, including resource-constrained controllers, legacy systems, traditional hosts, and even people.…”

Section: Introductionmentioning

confidence: 99%

People-as-a-Service Dilemma: Humanizing Computing Solutions in High-Efficiency Applications

Bordel¹,

Hernández²

2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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Next-generation computing solutions, such as cyber-physical systems or Industry 4.0, are focused on increasing efficiency in process execution as much as possible. Removing unproductive delays or keeping infrastructures operating at their total capacity are typical objectives in these future systems. Decoupling infrastructure providers and service providers using Anything-as-a-Service (XaaS) paradigms is one of the most common approaches to address this challenge. However, many real scenarios not only include machines or controllers but also people and workers. In this case, deploying process execution algorithms and XaaS solutions degenerates in a People-as-a-Service scenario, which poses a critical dilemma: Can highly efficient production scenarios guarantee people’s wellbeing? In this paper, we address this problem and propose a new process execution algorithm based on a novel understanding of efficiency. In this case, a humanized efficiency definition combining traditional efficiency ratios and wellbeing indicators is used to allocate tasks and assign them to different existing workers. In order to evaluate the proposed solution, a simulation scenario including social and physical elements was built. Using this scenario, a first experimental validation was carried out.

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Process execution in Cyber-Physical Systems using cloud and Cyber-Physical Internet services

Cited by 36 publications

References 53 publications

Controlling Supervised Industry 4.0 Processes through Logic Rules and Tensor Deformation Functions

Controlling Supervised Industry 4.0 Processes through Logic Rules and Tensor Deformation Functions

A Predictor-Corrector Algorithm Based on Laurent Series for Biological Signals in the Internet of Medical Things

People-as-a-Service Dilemma: Humanizing Computing Solutions in High-Efficiency Applications

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