Implementation of a Large-Scale Platform for Cyber-Physical System Real-Time Monitoring

The emergence of Industry 4.0 and the Internet of Things (IoT) has meant that the manufacturing industry has evolved from embedded systems to cyber-physical systems (CPSs). This transformation has provided manufacturers with the ability to measure the performance of industrial equipment by means of data gathered from on-board sensors. This allows the status of industrial systems to be monitored and can detect anomalies. However, the increased amount of measured data has prompted many companies to investigate innovative ways to manage these volumes of data. In recent years, cloud computing and big data technologies have emerged among the scientific communities as key enabling technologies to address the current needs of CPSs. This paper presents a large-scale platform for CPS real-time monitoring based on big data technologies, which aims to perform real-time analysis that targets the monitoring of industrial machines in a real work environment. This paper is validated by implementing the proposed solution on a real industrial use case that includes several industrial press machines. The formal experiments in a real scenario are conducted to demonstrate the effectiveness of this solution and also its adequacy and scalability for future demand requirements. As a result of the implantation of this solution, the overall equipment effectiveness has been improved.The authors are grateful to Goizper and Fagor Arrasate for providing the industrial case study, and specifically Jon Rodriguez and David Chico (Fagor Arrasate) for their help and support. Any opinions, findings and conclusions expressed in this article are those of the authors and do not necessarily reflect the views of the funding agencies

Latency, energy, and schedulability of real-time embedded systems

Systems are called real-time systems, if the correctness of the system does not only depend on the correctness of the system output but also on whether the output is delivered on time. Some examples of real-time systems are medical systems, automotive, aircrafts, etc. With the advent of Internet of Things (IoTs) and Cyber-Physical Systems (CPS), real-time systems and the systems that desire to apply real-time discipline are becoming ubiquitous. The increasing complexity of real-time software and the emerging new hardware inspire us to revisit the ``old-wise'' in the embedded system community and the real-time community and to propose novel solutions dealing with the drastic changes in real-time systems. Therefore, in this dissertation, we propose the new techniques and algorithms to improve the performance of real-time systems in terms of latency, energy, and schedulability.Computer Systems, Imagery and Medi

Compositional Analysis of Real-Time Embedded Systems

This tutorial is concerned with various aspects of component-based design and compositional analysis of real-time embedded systems. It will first give an overview of component-based frameworks and their underlying principles. It will then go in-depth into abstraction methods for real-time components and techniques for computing their optimal interfaces, for both systems implemented on uniprocessor and multiprocessor platforms, as well as extensions to multi-mode systems. Besides theoretical aspects, the tutorial will also present an implementation of the compositional analysis framework on Xen virtualization and a demonstration of the CARTS toolset with several examples seeing the techniques in action. It will also include two case studies highlighting the utility of the framework, including the ARINC-653 avionics software and a smart-phone application. We will conclude the tutorial with a number of open challenges and research opportunities in this domain

Multi-Agent-Based Cloud Architecture of Smart Grid

AbstractPower system is a huge hierarchical controlled network. Large volumes of data are within the system and the requirement of real-time analysis and processing is high. With the smart grid construction, these requirements will be further improved. The emergence of cloud computing provides an effective way to solve these problems low-costly, high efficiently and reliably. This paper analyzes the feasibility of cloud computing for the construction of smart grid, extends cloud computing to cloud-client computing. Through “Energy Hub”, Microgrid is separated into a network of three storeys that match with the conception of cloud-client computing. This paper introduces multi-agent technology to control each node in the system. On these bases, cloud architecture of smart grid is proposed. Finally, an example is given to explain the application of cloud computing in power grid CPS structure

Smart manufacturing for industry 4.0 using Radio Frequency Identification (RFID) technology

Industry 4.0 (I4.0) presents a unique challenge of efficiently transforming traditional manufacturing to smart and autonomous systems.Integrating manufacturing systems, materials, machinery, operators, products and consumers, improve interconnectivity and traceability across the entire product life cycle in order to ensure the horizontal and vertical integration of networked Smart Manufacturing (SM) systems. Manufacturing functions of Material Handling (MH)-control, storage, protection and transport of raw materials, work in process (WIP) and finished products- throughout a manufacturing and distribution process will need a revamp in ways they are currently being carried in order to transition them into the SM era. Radio Frequency Identification (RFID), an Automated Identification Data Capture (AIDC) technology increasingly being used to enhance MH functions in the (SM) industry, due to opportunities it presents for item tracking, out of sight data capturing, navigation and space mapping abilities. The technology readiness level of RFID has presented many implementation challenges as progress is being made to fully integrate the technology into the preexisting MH functions. Recently, many researchers in academia and industry have described various methods of using RFID for improving and efficiently carrying out MH functions as a gradual transition is being made into I4.0 era. This paper reviews and categorize research finding regarding RFID application developments according to various MH functions in SM, tabulates how various I4.0 enablers are needed to transform various traditional manufacturing functions into SM. It aims to let more experts know the current research status of RFID technology and provide some guidance for future research

Data-driven Adaptive Safety Monitoring using Virtual Subjects in Medical Cyber-Physical Systems: A Glucose Control Case Study

Medical cyber-physical systems (MCPS) integrate sensors, actuators, and software to improve patient safety and quality of healthcare. These systems introduce major challenges to safety analysis because the patient’s physiology is complex, nonlinear, unobservable, and uncertain. To cope with the challenge that unidentified physiological parameters may exhibit short-term variances in certain clinical scenarios, we propose a novel run-time predictive safety monitoring technique that leverages a maximal model coupled with online training of a computational virtual subject (CVS) set. The proposed monitor predicts safety-critical events at run-time using only clinically available measurements. We apply the technique to a surgical glucose control case study. Evaluation on retrospective real clinical data shows that the algorithm achieves 96% sensitivity with a low average false alarm rate of 0.5 false alarm per surgery