Virtualization Technology for Dependable Embedded Systems: A Survey for Transportation and Industrial Domains

The effective use of simulation for the design and analysis of propulsion systems, and for operator training of these systems, requires a clear definition of the simulation's intended purpose and expected results. Issues such as model fidelity, computational performance and data requirements must be carefully balanced against the development effort and costs. The relative importance of each of these factors can only be determined once the purpose and expected outputs from the simulation are known.Experience with some recent marine propulsion simulation initiatives has highlighted the need for clear definitions of model requirements, especially with regard to simulation fidelity and the use of physics‐based models. Marine propulsion system simulations can be used for a variety of purposes ranging from machinery performance analysis through maneuvering and seakeeping analysis to equipment health monitoring studies. This wide range of applications, with differing requirements, provides an excellent forum for examining the influence of fidelity on simulation computational performance, data requirements, and development cost. Examples from various marine propulsion machinery simulations are used to illustrate the discussion.

The hyperloop transportation system has emerged as an innovative next-generation transportation system. In this system, a capsule-type vehicle inside a sealed near-vacuum tube moves at 1000 km/h or more. Not only must this transport tube span over long distances, but it must be clear of potential hazards to vehicles traveling at high speeds inside the tube. Therefore, an automated infrastructure anomaly detection system is essential. This study sought to confirm the applicability of advanced sensing technology such as Light Detection and Ranging (LiDAR) in the automatic anomaly detection of next-generation transportation infrastructure such as hyperloops. To this end, a prototype two-dimensional LiDAR sensor was constructed and used to generate three-dimensional (3D) point cloud models of a tube facility. A technique for detecting abnormal conditions or obstacles in the facility was used, which involved comparing the models and determining the changes. The design and development process of the 3D safety monitoring system using 3D point cloud models and the analytical results of experimental data using this system are presented. The tests on the developed system demonstrated that anomalies such as a 25 mm change in position were accurately detected. Thus, we confirm the applicability of the developed system in next-generation transportation infrastructure.

Industrial systems always prefer to reduce their operational expenses. To support such reductions, they need solutions that are capable of providing stability, fault tolerance, and flexibility. One such solution for industrial systems is cyber physical system (CPS) integration with the Internet of Things (IoT) utilizing cloud computing services. These CPSs can be considered as smart industrial systems, with their most prevalent applications in smart transportation, smart grids, smart medical and eHealthcare systems, and many more. These industrial CPSs mostly utilize supervisory control and data acquisition (SCADA) systems to control and monitor their critical infrastructure (CI). For example, WebSCADA is an application used for smart medical technologies, making improved patient monitoring and more timely decisions possible. The focus of the study presented in this paper is to highlight the security challenges that the industrial SCADA systems face in an IoT-cloud environment. Classical SCADA systems are already lacking in proper security measures; however, with the integration of complex new architectures for the future Internet based on the concepts of IoT, cloud computing, mobile wireless sensor networks, and so on, there are large issues at stakes in the security and deployment of these classical systems. Therefore, the integration of these future Internet concepts needs more research effort. This paper, along with highlighting the security challenges of these CI's, also provides the existing best practices and recommendations for improving and maintaining security. Finally, this paper briefly describes future research directions to secure these critical CPSs and help the research community in identifying the research gaps in this regard.

Detecting cyberattacks in industrial control systems using online learning algorithms

With the growing adoption of Industry 4.0 concepts in production systems, new challenges arise in engineering control software. Highly distributed control with tight real-time constraints and safety regulations results in increasingly complex software. Current research focuses on increasing the abstraction with new architectures and modularization of software. The presented PhD research addresses modeling of the interactions between control software components, and of the emergent behavior of these compositions. Such behavior models can support the initial implementation, and facilitate (semi-)automated testing and monitoring of control software. Finally, visualizing behavior in a model can enhance understandability of existing control software, when software developers need not access abstracted hierarchy levels to deduct their functionality. This work aims at optimizing the benefit of behavior models in developing control software: Modeling the expected behavior directly for new software will allow using them throughout the software life-cycle. For legacy software, the initial development effort of behavior models will be minimized by automatically capturing behavior models from the implementation. The approach is evaluated in case studies and user studies to integrate experiences from the industrial domain into this software engineering research.

Industry 4.0 is the German vision for the future of manufacturing, where smart factories use information and communication technologies to digitise their processes to achieve improved quality, lower costs, and increased efficiency. It is likely to bring a massive change to the way control systems function today. Future distributed control systems are expected to have an increased connectivity to the Internet, in order to capitalize on new offers and research findings related to digitalization, such as cloud, big data, and machine learning. A key technology in the realization of distributed control systems is middleware, which is usually described as a reusable software layer between operating system and distributed applications. Various middleware technologies have been proposed to facilitate communication in industrial control systems and hide the heterogeneity amongst the subsystems, such as OPC UA, DDS, and RT-CORBA. These technologies can significantly simplify the system design and integration of devices despite their heterogeneity. However, each of these technologies has its own characteristics that may work better for particular applications. Selection of the best middleware for a specific application is a critical issue for system designers. In this paper, we conduct a survey on available standard middleware technologies, including OPC UA, DDS, and RT-CORBA, and show new trends for different industrial domains.

Both power consumption and SLA guarantees are important concerns for cloud gaming. Recently, various approaches have been developed to effectively reduce GPU power consumption by making GPU run at low frequencies. However, virtual machines (VMs) running on the same physical GPU with virtualization technology are correlated, because the change of GPU frequencies will affect the SLA performance of all the VMs. In fact, both reducing power consumption and guaranteeing SLA should work together under the considerations of their correlations. This paper proposes a novel two-layer control architecture called energy-efficient SLA guarantees for virtualized GPU (EvGPU) based on well-established feedback control techniques. The first control loop adopts a proportional-integral (PI) controller to ensure SLA guarantees, which in particular are measured at a predefined level of the frames per second (FPS) for each online game. The secondary power control loop then adjusts GPU frequency through dynamic voltage/frequency scaling (DVFS) to reduce power consumption based on the current FPS achieved by the first loop. Empirical results demonstrate that the proposed solution can effectively reduce GPU power consumption, while achieving the required SLA performance in virtualized GPU for cloud gaming.

Anomaly detection plays a critical role in large-scale industrial systems, where real-time streaming data is generated at high volumes. This research journal presents an in-depth study on developing an efficient anomaly detection algorithm specifically designed for such industrial systems. The goal is to identify abnormal patterns and deviations from normal behavior, enabling proactive maintenance, improved operational efficiency, and reduced downtime. The proposed algorithm leverages machine learning and stream processing techniques to handle the challenges associated with real- time streaming data analysis. The research covers algorithm design, implementation, evaluation, and performance analysis using large-scale datasets from industrial domains. Keywords: anomaly detection, real-time streaming data, large-scale industrial systems, machine learning, stream processing

Scheduling activities are carried out in the domains of industry (production scheduling), personnel (nurse scheduling) and transportation (train scheduling). Nevertheless, there is little available knowledge on how operators schedule. In general, laboratory studies have been found to be not entirely representative of real situations (i.e. there are problems of ecological validity). Furthermore, it is difficult to make generalizations because field studies are usually conducted with just one scheduler. To overcome these two issues, this paper suggests bringing laboratory and field studies closer together using a cognitive typology. First, typologies that do not explicitly refer to a cognitive point of view are discussed. Second, the properties of a cognitive typology are detailed. A cognitive typology specific to scheduling situations is presented. This typology associates seven dimensions with their related human strategies: complexity; uncertainty; time pressure; cycle synchronicity; process steadiness; process continuity; multiple and contradictory objectives. For each dimension, the theoretical, methodological and practical implications are detailed.

Cloud computing is a new computation technology that provides services to consumers and businesses. The main idea of Cloud computing is to present software and hardware services through the Internet to the users and organizations at all levels. In Cloud computing, the users pay for the services, which means a usage-based payment system is used in this technology. Using virtualization technology in computation resources enables the appropriate utilization of resources in cloud computing. One of the most significant challenging issues in virtualization technology is the placement of optimal virtual machines on physical machines in cloud data centers. The placement of virtual machines comprises a process wherein virtual machines are mapped onto physical machines in cloud data centers. Optimal deployment leads to the reduction in power consumption, optimal use of resources, traffic reduction in data centers, costs reduction, and efficiency enhancement of the data center in the cloud. The present article proposed a new approach using a combination of the Sine–Cosine Algorithm and Salp Swarm Algorithm as discrete multi-objective and chaotic functions for optimal virtual machine placement. The first goal of the proposed algorithm was to reduce the power consumption in cloud data centers by condensing the number of active physical machines. The second goal was to reduce the waste of resources and manage it by optimally virtual machine placement on physical machines in cloud data centers. The third objective was to minimize and reduce Service Level Agreement among the active physical machines in cloud data centers. The proposed method prevent the increase in the migration of virtual machines onto physical machines. Ultimately, the results obtained from the proposed algorithm were compared with those of previous akin algorithms in the literature, including First Fit, Virtual Machine Placement Ant Colony System, and Modified Best Fit Decreasing. The proposed scheme is tested using Amazon EC2 Instances and the result indicated that the proposed algorithm performs better than the existing algorithms for various performance metrics.

Anomaly detection based on random matrix theory for industrial power systems

The study focused on "Deciphering the role of molecular interactions on the interfacial structure of aqueous and non-aqueous interfaces" holds immense significance in diverse scientific and industrial domains. Our primary objective is to unravel the intricate web of molecular interactions occurring at the boundaries between aqueous and non-aqueous phases. The research presented here spans a wide scope, encompassing applications in microelectromechanical systems in the semiconductor industry, energy-efficient membranes for electrochemical separations, and renewable energy storage systems. Molecular dynamics simulations were conducted in collaboration with experimental studies, specifically atomic force microscopy, to achieve insights at the molecular level.The first project centered around comprehending the heterogeneous assembly of water vapor at the solid/vapor aqueous interface in the presence of self-assembled silane molecules. The self-assembly of nano-sized molecules on surfaces at ambient temperature has a technological interest in the design of microelectromechanical systems (MEMSs). Through atomic force microscopy (AFM) and reactive molecular dynamics simulations, a comprehensive understanding of interfacial chemistry was achieved, encompassing the mechanism and the parameters governing the water-driven assembly of trifunctional octadecyltrichlorosilane (OTS) at the solid/vapor aqueous interface. The second project presents the electrode/electrolyte simulations of sodium/glyme-based batteries. Sodium-ion batteries are considered the next generation of large-scale energy storage devices for sustainable development and still need improvements for commercialization. The electrode/electrolyte interface highly affects the battery performance. Sodium ion solvation structures at the interface between electrodes and electrolytes are studied using polarizable carbon electrodes and different glymes with NaTFSI as the electrolyte. The third project presents molecular dynamics simulation studies on ion-conducting polymer electrolytes, which show promising applications in electrochemical separation processes in collaboration with experimental research. Molecular dynamics simulations on polymer electrolytes with different side chain chemistries, ranging from hydrophobic alkyl side chains, to hydrophilic alkoxy and even zwitterionic side chains, were performed to gain insight into the ionic conduction mechanisms of the counterion in these systems. By comprehending the role of molecular interactions at these aqueous and non-aqueous interfaces, we are poised to develop innovative solutions, optimize processes, and drive forward technological advancements that rely on a fundamental understanding of interfacial phenomena.

A dominant trend in information technology is the convergence of different software functionalities. Even after the dot-com bust or, perhaps because of it, the convergence has gathered steam. Different tools and techniques get concentrated in one solution or software in a short duration. Distinct techniques and technologies, such as accounting software, Web-based businesses, supply chain management, data warehouses and artificial intelligence, are converging as the organizations move from ERP to ERP II, integration of internal functions to integration across supply chain, and the Internet to Internet 2. Today’s ERP software packs all applications into one monster package and offers tremendous functionalities.

The important innovations in wireless and digital electronics will support many applications in the areas of safety, environmental and emissions control, driving assistance, diagnostics, and maintenance in the transport domain. The last few years have seen the emergence of many new technologies that can potentially have major impacts on transportation systems. One of these technologies is Wireless Sensor Networks. A wireless sensor device is typically composed of a processing unit, memory, and a radio chip which allows it to communicate wirelessly with other devices within range. The Embedded Middleware in Mobility Applications (EMMA) project delivers a middleware that aims to facilitate the interaction between sensing technologies in transportation systems. This paper outlines our experience in the EMMA project and provides an illustration of the important role that wireless sensor technology can play in future transportation system. The paper discusses our experience of using heterogeneous sensors to develop transportation system applications in the EMMA project and focuses on how cooperation between vehicle and infrastructure can be addressed. It also presents encouraging results obtained from the experiments in investigating the feasibility of utilising wireless sensor in vehicle and vehicle-to-infrastructure communication in real transportation applications.

In this work, an interface ASIC for quartz vibrating gyroscope is presented. Its area is 5×4.4mm2. Compared with traditional interface constructed by separate devices, such interface implemented with integrated circuits is advantageous in size and power consumption. This satisfies the requirements of miniature and low power consumption in space industry and military domain. The test results show that the entire system presents high performance in short term bias stability, nonlinearity, output noise, bias variation over temperature, and power consumption.