Collaborative Cyber-physical Systems Research Articles

Cyber–physical system for fast prototyping of power electronic converters in EMI shaping context

Dynamic development of power systems, which utilize power electronic converters as interfaces between the renewable energy sources, as well as complex control and measuring arrangements, forces the cooperation of specialists representing various fields to assure electromagnetic compatibility (EMC) of the so-called smart industrial systems. Low voltage DC grid applications involving multiple power electronics devices may turn the grid susceptible to noise injection and impair the signal integrity and power quality due to the electromagnetic interference (EMI) emission and due to devices and grid interactions. The EMI propagation leads to misreading measurements and faulty conditions of equipments and in ICT (Information and Communications Technology) systems within the conducted emission frequency range, which impacts the hosting capacity of the grid. This paper proposes a cyber–physical system based on the NI-PXI platform for fast-prototyping and EMC assessment as pre-compliance tool of low-voltage DC grid in smart industrial systems. Thus, the LabVIEW-based design as well as the EMC-testbench system validation were performed with a DC/DC converter through the pulse-width modulation control algorithms. In addition, a case study has been investigated to evaluate the interferences between the converter and Power Line Communication. Hence, the theoretical control algorithms, hardware and software details, and experimental results are discussed, highlighting the paradigms of one flexible and collaborative cyber–physical system from the area of smart industrial systems.

A connective framework to minimize the anxiety of collaborative Cyber-Physical System

ABSTRACT The role of Cyber-Physical systems (CPS) is well recognized in the context of Industry 4.0, which consists of human operators working with machines/robots. The interactions among them can be quite demanding in terms of cognitive resources. Existing systems do not yet consider the psychological aspects of safety in the domain. This lack can lead to hazardous situations, thus compromising the performance of the working system. This work proposes a connective decision-making framework for a flexible CPS, which can quickly respond to dynamic changes and be resilient to emergent hazards. First, Anxiety is defined and categorized for expected/unforeseen situations that a CPS could encounter through historical data using the Ishikawa method. Second, visual cues are used to gather the CPS’s current state (such as human pose and object identification). Third, a mathematical model is developed using Mixed-integer programming (MIP) to allocate optimal resources, to tackle high-impact situations generating Anxiety. Finally, the logic is designed for an effective counter-mechanism to mitigate Anxiety. The proposed method was tested on a realistic industrial scenario incorporating a collaborative CPS. The results demonstrated that the proposed method improves the decision-making of a CPS facing a complex scenario, ensures physical safety, and effectively enhances the human-machine team’s productivity.

Vision beyond the Field-of-View: A Collaborative Perception System to Improve Safety of Intelligent Cyber-Physical Systems.

Cyber-physical systems (CPSs) that interact with each other to achieve common goals are known as collaborative CPSs. Collaborative CPSs can achieve complex goals that individual CPSs cannot achieve on their own. One of the examples of collaborative CPSs is the vehicular cyber-physical systems (VCPSs), which integrate computing and physical resources to interact with each other to improve traffic safety, situational awareness, and efficiency. The perception system of individual VCPS has limitations on its coverage and detection accuracy. For example, the autonomous vehicle’s sensor cannot detect occluded objects and obstacles beyond its field of view. The VCPS can combine its own data with other collaborative VCPSs to enhance perception, situational awareness, accuracy, and traffic safety. This paper proposes a collaborative perception system to detect occluded objects through the camera sensor’s image fusion and stitching technique. The proposed collaborative perception system combines the perception of surrounding autonomous driving systems (ADSs) that extends the detection range beyond the field of view. We also applied logistic chaos map-based encryption in our collaborative perception system in order to avoid the phantom information shared by malicious vehicles and improve safety in collaboration. It can provide the real-time perception of occluded objects, enabling safer control of ADSs. The proposed collaborative perception can detect occluded objects and obstacles beyond the field of view that individual VCPS perception systems cannot detect, improving the safety of ADSs. We investigated the effectiveness of collaborative perception and its contribution toward extended situational awareness on the road in the simulation environment. Our simulation results showed that the average detection rate of proposed perception systems was 45.4% more than the perception system of an individual ADS. The safety analysis showed that the response time was increased up to 1 s, and the average safety distance was increased to 1.2 m when the ADSs were using collaborative perception compared to those scenarios in which the ADSs were not using collaborative perception.

Edge Computing for Cyber-physical Systems: A Systematic Mapping Study Emphasizing Trustworthiness

Edge computing is projected to have profound implications in the coming decades, proposed to provide solutions for applications such as augmented reality, predictive functionalities, and collaborative Cyber-Physical Systems (CPS). For such applications, edge computing addresses the new computational needs, as well as privacy, availability, and real-time constraints, by providing local high-performance computing capabilities to deal with the limitations and constraints of cloud and embedded systems. Edge computing is today driven by strong market forces stemming from IT/cloud, telecom, and networking—with corresponding multiple interpretations of “edge computing” (e.g., device edge, network edge, distributed cloud). Considering the strong drivers for edge computing and the relative novelty of the field, it becomes important to understand the specific requirements and characteristics of edge-based CPS, and to ensure that research is guided adequately, e.g., avoiding specific gaps. Our interests lie in the applications of edge computing as part of CPS, where several properties (or attributes) of trustworthiness, including safety, security, and predictability/availability, are of particular concern, each facing challenges for the introduction of edge-based CPS. We present the results of a systematic mapping study, a kind of systematic literature survey, investigating the use of edge computing for CPS with a special emphasis on trustworthiness. The main contributions of this study are a detailed description of the current research efforts in edge-based CPS and the identification and discussion of trends and research gaps. The results show that the main body of research in edge-based CPS only to a very limited extent consider key attributes of system trustworthiness, despite many efforts referring to critical CPS and applications like intelligent transportation. More research and industrial efforts will be needed on aspects of trustworthiness of future edge-based CPS including their experimental evaluation. Such research needs to consider the multiple interrelated attributes of trustworthiness including safety, security, and predictability, and new methodologies and architectures to address them. It is further important to provide bridges and collaboration between edge computing and CPS disciplines.

SafeSoCPS: A Composite Safety Analysis Approach for System of Cyber-Physical Systems.

The System of Cyber-Physical Systems (SoCPS) comprises several independent Cyber-Physical Systems (CPSs) that interact with each other to achieve a common mission that the individual systems cannot achieve on their own. SoCPS are rapidly gaining attention in various domains, e.g., manufacturing, automotive, avionics, healthcare, transportation, and more. SoCPS are extremely large, complex, and safety-critical. As these systems are safety-critical in nature, it is necessary to provide an adequate safety analysis mechanism for these collaborative SoCPS so that the whole network of these CPSs work safely. This safety mechanism must include composite safety analysis for a network of collaborative CPS as a whole. However, existing safety analysis techniques are not built for analyzing safety for dynamically forming networks of CPS. This paper introduces a composite safety analysis approach called SafeSoCPS to analyze hazards for a network of SoCPS. In SafeSoCPS, we analyze potential hazards for the whole network of CPS and trace the faults among participating systems through a fault propagation graph. We developed a tool called SoCPSTracer to support the SafeSoCPS approach. Human Rescue Robot System—a collaborative system—is taken as a case study to validate our proposed approach. The result shows that the SafeSoCPS approach enables us to identify 18 percent more general faults and 63 percent more interaction-related faults in a network of a SoCPS.

Fault-Tolerance by Resilient State Transition for Collaborative Cyber-Physical Systems

Collaborative Cyber-Physical Systems (CCPS) are systems where several individual cyber-physical systems collaborate to perform a single task. The safety of a single Cyber-Physical System (CPS) can be achieved by applying a safety mechanism and following standard processes defined in ISO 26262 and IEC 61508. However, due to heterogeneity, complexity, variability, independence, self-adaptation, and dynamic nature, functional operations for CCPS can threaten system safety. In contrast to fail-safe systems, where, for instance, the system leads to a safe state when an actuator shuts down due to a fault, the system has to be fail-operational in autonomous driving cases, i.e., a shutdown of a platooning member vehicle during operation on the road is unacceptable. Instead, the vehicle should continue its operation with degraded performance until a safe state is reached or returned to its original state in case of temporal faults. Thus, this paper proposes an approach that considers the resilient behavior of collaborative systems to achieve the fail-operational goal in autonomous platooning systems. First, we extended the state transition diagram and introduced additional elements such as failures, mitigation strategies, and safe exit to achieve resilience in autonomous platooning systems. The extended state transition diagram is called the Resilient State Transition Diagram (R-STD). Second, an autonomous platooning system’s perception, communication, and ego-motion failures are modeled using the proposed R-STD to check its effectiveness. Third, VENTOS simulator is used to verify the resulting resilient transitions of R-STD in a simulation environment. Results show that a resilient state transition approach achieves the fail-operational goal in the autonomous platooning system.

Model inspections in the engineering of collaborative cyber‐physical systems with instance‐level review diagrams

Model inspections are important to ensure high-quality software and to satisfy legal obligations in model-based engineering processes. As model-based specifications are typically documented on type-level, errors concerning the interactions between multiple system instances can go unnoticed. For collaborative cyber-physical systems (CPS), a plethora of possible instance-level configurations need to be taken into account. Therefore, we propose the definition of instance-level review diagrams that show representative interactions of instance-level configurations that help detect defects in the system specification. To evaluate the approach, we conducted a controlled experiment whose results indicate that instance-level review diagrams have—compared with type-level diagrams—important positive effects on reviewing processes for behavioral specifications of CPS. Specifically, the experiment provides empirical evidence that instance-level review diagrams are significantly more expressive and effective than type-level diagrams.

Manufacturing Execution System Integration through the Standardization of a Common Service Model for Cyber-Physical Production Systems

Digital transformation and artificial intelligence are creating an opportunity for innovation across all levels of industry and are transforming the world of work by enabling factories to embrace cutting edge Information Technologies (ITs) into their manufacturing processes. Manufacturing Execution Systems (MESs) are abandoning their traditional role of legacy executing middle-ware for embracing the much wider vision of functional interoperability enablers among autonomous, distributed, and collaborative Cyber-Physical Production System (CPPS). In this paper, we propose a basic methodology for universally modeling, digitalizing, and integrating services offered by a variety of isolated workcells into a single, standardized, and augmented production system. The result is a reliable, reconfigurable, and interoperable manufacturing architecture, which privileges Open Platform Communications Unified Architecture (OPC UA) and its rich possibilities for information modeling at a higher level of the common service interoperability, along with Message Queuing Telemetry Transport (MQTT) lightweight protocols at lower levels of data exchange. The proposed MES architecture has been demonstrated and validated in several use-cases at a research manufacturing laboratory of excellence for industrial testbeds.

Digital twin-based optimiser for self-organised collaborative cyber-physical production systems

The advantages of using collaborative and distributed systems to control and optimise industrial environments have been explored in recent years. However, the self-organised and dynamic approaches that deliver these same advantages bring challenges associated with the long-term unpredictability of these approaches. The proposed work aims to present a framework that integrates a Digital Twin-based Optimiser within these systems to predict the system's evolution and reconfigure it if required. Digital Twins can be an essential advantage in adopting these systems due to the possibility of carrying out simulations at an accelerated speed and reconfiguring the self-organised control system.

Blockchain-Based Security Framework for a Critical Industry 4.0 Cyber-Physical System

There has been an intense concern for security alternatives because of the recent rise of cyber attacks, mainly targeting critical systems such as industry, medical, or energy ecosystem. Though the latest industry infrastructures largely depend on AI-driven maintenance, the prediction based on corrupted data undoubtedly results in loss of life and capital. Admittedly, an inadequate data-protection mechanism can readily challenge the security and reliability of the network. The shortcomings of the conventional cloud or trusted certificate-driven techniques have motivated us to exhibit a unique Blockchain-based framework for a secure and efficient industry 4.0 system. The demonstrated framework obviates the long-established certificate authority after enhancing the consortium Blockchain that reduces the data processing delay, and increases cost-effective throughput. Nonetheless, the distributed industry 4.0 security model entails cooperative trust than depending on a single party, which in essence indulges the costs and threat of the single point of failure. Therefore, multi-signature technique of the proposed framework accomplishes the multi-party authentication, which confirms its applicability for the real-time and collaborative cyber-physical system.

A GRL-compliant iStar extension for collaborative cyber-physical systems

Collaborative cyber-physical systems are capable of forming networks at runtime to achieve goals that are unachievable for individual systems. They do so by connecting to each other and exchanging information that helps them coordinate their behaviors to achieve shared goals. Their highly complex dependencies, however, are difficult to document using traditional goal modeling approaches. To help developers of collaborative cyber-physical systems leverage the advantages of goal modeling approaches, we developed a GRL-compliant extension to the popular iStar goal modeling language that takes the particularities of collaborative cyber-physical systems and their developers’ needs into account. In particular, our extension provides support for explicitly distinguishing between the goals of the individual collaborative cyber-physical systems and the network and for documenting various dependencies not only among the individual collaborative cyber-physical systems but also between the individual systems and the network. We provide abstract syntax, concrete syntax, and well-formedness rules for the extension. To illustrate the benefits of our extension for goal modeling of collaborative cyber-physical systems, we report on two case studies conducted in different industry domains.

Multi-Level Message Sequence Charts to Validate the Collaborative Automotive Cyber-Physical Systems

Autonomous driving and e-mobility are swiftly becoming not only the work of science fiction or popular science, but a reality. A key focus of manufacturers and suppliers in the automotive domain is of course to specify systems that implement this reality. Often, scenarios at type-level are used throughout the development process to specify system behavior and interaction within the car, as scenario models are comparatively easy to understand and can easily be subjected to manual validation. However, autonomous driving and e-mobility require interaction not just of systems within the same car, but collaboration between multiple cars as well as between cars and miscellaneous road infrastructure (e.g., smart road signs). The car becomes a Cyber-Physical System that dynamically forms collaborating networks at runtime with other Cyber-Physical System to create functionality that goes beyond the scope of the individual vehicle (e.g., resolve a traffic jam). Consequently, a plethora of possible compositions of such a network exist and must be specified and validated completely to assure their adequate and safe execution at runtime. Doing this at type-level with scenario models becomes prohibitively tedious, error prone, and likely results in unrealistic development cost. To combat this issue, we investigate the use of multi-level Message Sequence Charts to allow for specifying interaction scenarios between collaborative Cyber-Physical System in a network of collaborating automotive Cyber-Physical System. To assist the developer in systematically defining multi-level Message Sequence Charts, we propose two processes. The resulting diagrams use a mixture of type and instance-level abstractions within one conceptual diagram. This allows reducing the required effort to manually validate the adequacy of scenarios to a manageable amount because information within the scenarios can be validated in batches. At the same time, instance-level defects become more obvious. Evaluation results from a controlled experiment show that multi-level Message Sequence Charts contribute to effectiveness and efficiency of manual validation for collaborative automotive Cyber-Physical System.

Analyzing goal variability in cyber-physical system networks

Networks of collaborative cyber-physical systems can achieve goals individual systems are incapable of achieving on their own. However, which goals such a network can achieve depends, in part, on the networks current configuration, i.e. its composition of partaking individual systems. As networks of collaborative cyber-physical systems are of a dynamic nature, the composition of such a network can change during runtime, leading to a plethora of often similar, albeit slightly different configurations. Due to the huge number of possible configurations and their various dependencies to the different goals of the network, it is infeasible to handle this amount of information manually. Hence, to provide support for reasoning about dependencies between different configurations and the goals they can achieve, this paper contributes an automated model-based reasoning approach using view generations. Our approach allows for exploring which goals can be fulfilled by which configurations and which goals cannot be fulfilled by these configurations. We evaluated the approach using an industrial case study which shows the applicability of the approach and a controlled experiment which shows the benefits of the approach.

Analyzing Safety of Collaborative Cyber-Physical Systems Considering Variability

Analyzing Safety of Collaborative Cyber-Physical Systems Considering Variability

Model-based documentation of dynamicity constraints for collaborative cyber-physical system architectures: Findings from an industrial case study

Collaborative cyber-physical systems form highly dynamic networks, consisting of collaborating individual systems at run-time. These networks provide functionality that goes beyond the functionality of the individual systems that are part of the network. The availability of such collaborative functionalities depends not only on the individual systems’ behaviors but also on the current morphology of the network (i.e., the architecture of the overall network that defines what kind of systems and how many systems are actually partaking in the collaboration at that moment). It is of importance to thoroughly consider the possible morphologies of the network of cyber-physical systems during the development of the individual systems as these are sources for requirements as well as design decisions. These requirements and design decisions must be associated to the particular morphologies they are applicable to in order to ensure traceability and comprehensibility between the morphology of the network of cyber-physical systems and the individual systems. Hence, we aim at defining an orthogonal model which allows to document dynamicity constraints and therefore restrain the potential morphologies a network can take. This paper contributes results from an exploratory case study conducted in the industry automation domain. In the case study, an existing variability modeling approach was applied to the development of a network of autonomous transport robots. Based on the variability modeling approach an orthogonal dynamicity constrained model was defined and evolved to fit industry's needs. Results show that the use of an orthogonal model for dynamicity constraints can improve the development of cyber-physical systems designed to participate in collaborative networks.

Security framework for industrial collaborative robotic cyber-physical systems

The paper introduces a security framework for the application of human-robot collaboration in a futuristic industrial cyber-physical system (CPS) context of industry 4.0. The basic elements and functional requirements of a secure collaborative robotic cyber-physical system are explained and then the cyber-attack modes are discussed in the context of collaborative CPS whereas a defense mechanism strategy is proposed for such a complex system. The cyber-attacks are categorized according to the extent on controllability and the possible effects on the performance and efficiency of such CPS. The paper also describes the severity and categorization of such cyber-attacks and the causal effect on the human worker safety during human-robot collaboration. Attacks in three dimensions of availability, authentication and confidentiality are proposed as the basis of a consolidated mitigation plan. We propose a security framework based on a two-pronged strategy where the impact of this methodology is demonstrated on a teleoperation benchmark (NeCS-Car). The mitigation strategy includes enhanced data security at important interconnected adaptor nodes and development of an intelligent module that employs a concept similar to system health monitoring and reconfiguration.

Feature-based control and information framework for adaptive and distributed manufacturing in cyber physical systems

Modern distributed manufacturing within Industry 4.0, supported by Cyber Physical Systems (CPSs), offers many promising capabilities regarding effective and flexible manufacturing, but there remain many challenges which may hinder its exploitation fully. One major issue is how to automatically control manufacturing equipment, e.g. industrial robots and CNC-machines, in an adaptive and effective manner. For collaborative sharing and use of distributed and networked manufacturing resources, a coherent, standardised approach for systemised planning and control at different manufacturing system levels and locations is a paramount prerequisite.In this paper, the concept of feature-based manufacturing for adaptive equipment control and resource-task matching in distributed and collaborative CPS manufacturing environments is presented. The concept has a product perspective and builds on the combination of product manufacturing features and event-driven Function Blocks (FB) of the IEC 61499 standard. Distributed control is realised through the use of networked and smart FB decision modules, enabling the performance of collaborative run-time manufacturing activities according to actual manufacturing conditions. A feature-based information framework supporting the matching of manufacturing resources and tasks, as well as the feature-FB control concept, and a demonstration with a cyber-physical robot application, are presented.

A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0

Smart factories Industry 4.0 on the basis of collaborative cyber-physical systems represents a future form of industrial networks. Supply chains in such networks have dynamic structures which evolve over time. In these settings, short-term supply chain scheduling in smart factories Industry 4.0 is challenged by temporal machine structures, different processing speed at parallel machines and dynamic job arrivals. In this study, for the first time, a dynamic model and algorithm for short-term supply chain scheduling in smart factories Industry 4.0 is presented. The peculiarity of the considered problem is the simultaneous consideration of both machine structure selection and job assignments. The scheduling approach is based on a dynamic non-stationary interpretation of the execution of the jobs and a temporal decomposition of the scheduling problem. The algorithmic realisation is based on a modified form of the continuous maximum principle blended with mathematical optimisation. A detailed theoretical analysis of the temporal decomposition and computational complexity is performed. The optimality conditions as well as the structural properties of the model and the algorithm are investigated. Advantages and limitations of the proposed approach are discussed.