Cyber-physical System Architecture Research Articles

Explainable Artificial Intelligence into Cyber-Physical System Architecture of Smart Cities: Technologies, Challenges, and Opportunities

The demand for smart cities stems from the increasing urbanization, limitations on resources, and the aspiration to establish metropolitan areas that are more habitable, adaptable, and interconnected. The integration of Cyber physical Systems (CPS) into smart cities has proven advantageous across diverse areas, such as healthcare, transportation, and manufacturing but as the intricacy of CPS Architecture escalates, the cognitive processes involved in decision-making get progressively convoluted, hence posing a challenge in comprehending and elucidating the system's conclusions in Smart Cities. To enhance the comprehensibility of judgements, it is imperative to combine the CPS architecture with XAI. To visualize the benefits of this integration, the authors carried out a comprehensive analysis utilizing the Scopus database. Diverse search queries were formulated and executed to acquire pertinent articles. Following the search, a total of 24 papers pertaining to the integration of Explainable Artificial Intelligence (XAI) and Cyber-Physical Systems (CPS) were chosen for analysis. A comprehensive evaluation of these publications and a meta-analysis of the results received from the Scopus database allowed for a deep grasp of the topic. The importance of XAI and CPS in Smart Cities rests in their ability to enhance decision-making, promote transparency, and increase efficiency. This study examines the factors that come before and the outcomes that result from combining XAI and CPS, encompassing the technologies involved, the difficulties faced, and the potential advantages. Furthermore, this study highlights the urgent necessity to incorporate XAI with CPS, as evidenced by the research findings published in the Scopus database.

A Risk Assessment Framework for Cyber-Physical Security in Distribution Grids with Grid-Edge DERs

Integration of inverter-based distributed energy resources (DERs) is reshaping the landscape of distribution grids to fulfill the socioeconomic, environmental, and sustainability goals. Addressing the technological challenges of DER grid integration requires an adaptive communication layer for efficient DER management and control. This transition has given rise to a cyberphysical system (CPS) architecture within the distribution system, causing new vulnerabilities for cyberphysical attacks. To better address potential threats, this paper presents a comprehensive risk assessment framework for cyberphysical security in distribution grids with grid-edge DERs. The framework incorporates a detailed CPS model accounting for dynamic DER characteristics within the distribution grid. It identifies vulnerabilities in DER communication systems, models attack scenarios, and addresses communication latency crucial for inverter control timescales. Subsequently, the quantification of attack impacts employs an attack probability model including both the vulnerability and criticality of cyber components. The proposed risk assessment framework was validated through testing on the modified IEEE 13-node and 123-node test feeders.

Path Planning for UAVs Under GPS Permanent Faults

Unmanned aerial vehicles (UAVs) have various applications in different settings, including e.g., surveillance, packet delivery, emergency response, data collection in the Internet of Things (IoT), and connectivity in cellular networks. However, this technology comes with many risks and challenges such as vulnerabilities to malicious cyber-physical attacks. This paper studies the problem of path planning for UAVs under GPS sensor permanent faults in a cyber-physical system (CPS) perspective. Based on studying and analyzing the CPS architecture of the UAV, the cyber “attacks and threats” are differentiated from attacks on sensors and communication components. An efficient way to address this problem is to introduce a novel approach for UAV’s path planning resilience to GPS permanent faults artificial potential field algorithm (RCA-APF). The proposed algorithm completes the three stages in a coordinated manner. In the first stage, the permanent faults on the GPS sensor of the UAV are detected, and the UAV starts to divert from its initial path planning. In the second stage, we estimated the location of the UAV under GPS permanent fault using Received Signal Strength (RSS) trilateration localization approach. In the final stage of the algorithm, we implemented the path planning of the UAV using an open-source UAV simulator. Experimental and simulation results demonstrate the performance of the algorithm and its effectiveness, resulting in efficient path planning for the UAV.

Control Strategy for Building Air Conditioning Cluster Loads Participating in Demand Response Based on Cyber-Physical System

In recent years, with rising urbanization and ongoing adjustments in industrial structures, there has been a growing dependence on public buildings. The load of public buildings gradually becomes the main component of the peak load in summer, among which the load of air conditioning is particularly prominent. To clarify the key problems and solutions to these challenges, this study proposes a multi-objective optimization control strategy for building air conditioning cluster participation in demand response based on Cyber-Physical System (CPS) architecture. In a three-layer typical CPS architecture, the unit level of the CPS achieves dynamic information perception of air conditioning clusters through smart energy terminals. An air conditioning load model based on the multiple parameter types of air conditioning compressors is presented. Then, the system level of the CPS fuses multiple pieces of information through smart energy gateways, analyzing the potential for air conditioning clusters when they participate in demand response. The system of system level (SoS level) of the CPS deploys a multi-objective optimization control strategy which includes the uncertainty of the initial states of air conditioning clusters within the intelligent building energy management system. The optimal model takes into account the differences in the environmental conditions of each individual air conditioning unit within the cluster and sets different operating modes for each unit to achieve load reduction while maintaining temperatures within a comfortable range for the human body. The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm based on Pareto frontiers is applied to solve this optimization control strategy and to optimize the operational parameters of the air conditioning clusters. A comparative analysis is conducted with single-objective optimization results obtained using the traditional Particle Swarm Optimization (PSO) algorithm. The case study results indicate that the proposed multi-objective optimization control strategy can effectively improve the thermal comfort of the human body towards the controlled temperatures of air conditioning clusters while meeting the accuracy of demand response. In the solution phase, the highest temperature within the air conditioning clusters is 24 °C and the lowest temperature is 23 °C. Adopting the proposed multi-objective optimization control strategy, the highest temperature is 26 °C and the lowest temperature is 23.5 °C within the clusters and the accuracy of demand response is up to 92%. Compared to the traditional PSO algorithm, the MOPSO algorithm has advantages in convergence and optimization precision for solving the proposed multi-objective optimization control strategy.

Software and Architecture Orchestration for Process Control in Industry 4.0 Enabled by Cyber-Physical Systems Technologies

In the context of Industry 4.0, this paper explores the vital role of advanced technologies, including Cyber–Physical Systems (CPS), Big Data, Internet of Things (IoT), digital twins, and Artificial Intelligence (AI), in enhancing data valorization and management within industries. These technologies are integral to addressing the challenges of producing highly customized products in mass, necessitating the complete digitization and integration of information technology (IT) and operational technology (OT) for flexible and automated manufacturing processes. The paper emphasizes the importance of interoperability through Service-Oriented Architectures (SOA), Manufacturing-as-a-Service (MaaS), and Resource-as-a-Service (RaaS) to achieve seamless integration across systems, which is critical for the Industry 4.0 vision of a fully interconnected, autonomous industry. Furthermore, it discusses the evolution towards Supply Chain 4.0, highlighting the need for Transportation Management Systems (TMS) enhanced by GPS and real-time data for efficient logistics. A guideline for implementing CPS within Industry 4.0 environments is provided, focusing on a case study of real-time data acquisition from logistics vehicles using CPS devices. The study proposes a CPS architecture and a generic platform for asset tracking to address integration challenges efficiently and facilitate the easy incorporation of new components and applications. Preliminary tests indicate the platform’s real-time performance is satisfactory, with negligible delay under test conditions, showcasing its potential for logistics applications and beyond.

RA4Self-CPS: A Reference Architecture for Self-adaptive Cyber-Physical Systems

RA4Self-CPS: A Reference Architecture for Self-adaptive Cyber-Physical Systems

Status, challenges, and promises of data‐driven battery lifetime prediction under cyber‐physical system context

AbstractEnergy storage is playing an increasingly important role in the modern world as sustainability is becoming a critical issue. Within this domain, rechargeable battery is gaining significant popularity as it has been adopted to serve as the power supplier in a broad range of application scenarios, such as cyber‐physical system (CPS), due to multiple advantages. On the other hand, battery inspection and management solutions have been constructed based on the CPS architecture in order to guarantee the quality, reliability and safety of rechargeable batteries. In specific, lifetime prediction is extensively studied in recent research as it can help assess the quality and health status to facilitate the manufacturing and maintenance. Due to the aforementioned importance, the authors aim to conduct a comprehensive survey on the data‐driven techniques for battery lifetime prediction, including their current status, challenges and promises. In contrast to existing literature, the battery lifetime prediction methods are studied under CPS context in this survey. Hence, the authors focus on the algorithms for lifetime prediction as well as the engineering frameworks that enable the data acquisition and deployment of prediction models in CPS systems. Through this survey, the authors intend to investigate both academic and practical values in the domain of battery lifetime prediction to benefit both researchers and practitioners.

Developing cyber-physical system and digital twin for smart manufacturing: Methodology and case study of continuous clarification

Biopharmaceutical production has recently begun transitioning toward the construction of highly advanced, digitalised production facilities, as per Industry Revolution 4.0 or smart manufacturing. Clarification is the bridging step in biotherapeutics continuous production, and this study proposes a Digital twin (DT) and a cyber-physical system (CPS) for the Cadence™ Acoustic Wave Separator (CAS), that uses the novel acoustic wave separation (AWS) technology for clarification of Chinese hamster ovary (CHO) cells. While the CPS - DT serves as the core, a real-time model based on the empirical and mechanistic relationship has been implemented in tandem to present a highly reliable hybrid model so as to improve the adaptability of the model. The framework employs a distributed control system (DCS), the heart of the CPS architecture, that updates the parameters of the physical system in real-time, thereby simplifying process control. To evaluate the hybrid-model predictive control strategy in real time, three case studies were conducted, which involved introducing abrupt turbidity pulses of high, low, and multi values. The sudden deviation in the turbidity of the feed sample was controlled by adjusting the acoustic power of the chamber (the control variable). The use of the proposed controller resulted in a 5 % mean deviation from the setpoint in the first chamber, while a > 33 % mean deviation resulted in the absence of the controller. The study's outcome demonstrates the high effectiveness of hybrid model-based CPS - DT control in consistently achieving high cell separation efficiency (> 90 %) and integrating the unit operation in a continuous processing train to establish a smart manufacturing prototype for the biopharmaceutical industry.

Digital twin architecture for assembly line performance monitoring

The forthcoming industrial requirements to ensure increasingly flexible and agile production processes will require an ever-increasing ability to recognise, predict and optimise the state of processes with respect to the planned state. Advanced tools such as Digital Twins (DT) and Cyber Physical Systems (CPS) can help manage and address these problems. This paper presents some results of the authors’ research on the adoption of a basic methodology for the design and implementation of Digital Twins aimed at manufacturing applications and subsequent integration into a Cyber Physical Production Systems (CPPS). Reference publications were selected based on the level of detail of DT and CPS architectures. The aim is also to combine methodological development with industrial tools and requirements. The architecture is potentially applicable to different production scenarios. Finally, the first results of application on a scale production line are shown.

How CPS and Autonomous Robots are Integrated to other I4.0 Technologies: a systematic literature review

ABSTRACT Industry 4.0 (I4.0) brings a set of challenges that need to be settled to enable the expected industrial revolution. Cyber-Physical Systems (CPS) and Autonomous Robots are two of mainstream I4.0 technologies and can be combined with other Enabling Technologies to overcome main I4.0 challenges. This Systematic Literature Review (SLR) aimed to understand how these Enabling Technologies were combined with CPS and Autonomous Robots to achieve fully featured I4.0 solutions. The 5C is a CPS architecture which was used as a measurement to CPS compliance. After filtering and processing 15,144 papers from six different academic search engines, it was found that full 5C compliant solutions are rare to find; that not all enabling technologies were integrated; and most of existing research focused in implementing practical solutions, but comparison to conventional approaches is scarce. We conclude that there is a need for further research to accomplish full 5C I4.0.

Social navigation framework for autonomous vehicle with hierarchical cyber-physical system architecture

An autonomous vehicle operating alongside humans should ideally have a high social capability, such as being able to communicate with humans, negotiate space, predict reactions, etc. This can be achieved by a prediction feature trained with diverse data on human behavior. Hierarchical cyber-physical system (CPS) architecture design, which sends the prediction feature to an external server while observing a limited operational design domain (ODD) and acquiring data continuously, has great potential to refine the training process as well as improve the performance. However, this architecture design requires the planning and prediction modules to be explicitly decoupled, which takes away from the recent success on social navigation. In this paper, we propose a novel autonomous navigation framework enabling social behavior while decoupling the planning and prediction modules to take advantage of the hierarchical CPS architecture. In the proposed framework, pedestrian trajectories are predicted as reactions to pre-generated candidates for an ego vehicle trajectory, and the ego vehicle trajectory is then selected to maximize mutual benefit to both the ego vehicle and surrounding pedestrians. We evaluated the proposed framework with simulations using the social force model and found that it was able to achieve the social behavior.

An Adaptive Multihop Branch Ensemble-Based Graph Adaptation Framework With Edge-Cloud Orchestration for Condition Monitoring

Condition monitoring plays a crucial role in securing smooth production, which has been facilitated into a cyber-physical system (CPS) integration paradigm with the new information and communication technologies and data-driven intelligence. However, traditional methods limit its successful deployment from the different distribution of training data and testing data, the missing relationships between the input signals, and the insufficient data size. To overcome these limitations, a novel graph-based adaptation framework with edge-cloud orchestration is proposed. A three-stage edge-cloud orchestration mechanism is encapsulated with CPS architecture. The proposed graph-based approach mainly consists of an adaptive multi-hop branch ensemble module to intelligently aggregate the node information, a distance metric learner to autonomously align the data distribution, and a classifier module to automatically generate the pseudo-labeled data to guide the edge-cloud orchestration and output results. Finally, a real-life case study and extensive experiments are conducted to prove the effectiveness of the proposed approach.

A Reference Architecture of Human Cyber-Physical Systems – PART I: Fundamental Concepts

We propose a reference architecture of safety-critical or industry-critical human cyber-physical systems (CPSs) capable of expressing essential classes of system-level interactions between CPS and humans relevant for the societal acceptance of such systems. To reach this quality gate, the expressivity of the model must go beyond classical viewpoints such as operational, functional, and architectural views and views used for safety and security analysis. The model does so by incorporating elements of such systems for mutual introspections in situational awareness, capabilities, and intentions in order to enable a synergetic, trusted relation in the interaction of humans and CPSs, which we see as a prerequisite for their societal acceptance. The reference architecture is represented as a metamodel incorporating conceptual and behavioral semantic aspects. We illustrate the key concepts of the metamodel with examples from cooperative autonomous driving, the operating room of the future, cockpit-tower interaction, and crisis management.

Toward a conceptual framework for designing sustainable cyber-physical system architectures: A systematic mapping study

Cyber-physical systems (CPS) represent devices whose components enable interaction between machines and processes. One of the biggest challenges of these systems today is the ability to adjust to changes at the time of execution as they are implemented in environments with a multidimensional complexity, this challenge is currently addressed from the design of the systems themselves by integrating sustainability. With this problem in mind, the present document describes a systematic mapping study of the literature with the goal of demonstrating the current panorama of the frameworks, designs, and/or models used at the time of initiating the development of a cyber-physical system. As a result, it has been concluded that there is a lack of guidelines to construct sustainable, and evolvable cyber-physical systems. To address these issues, a framework for designing sustainable CPS architectures is outlined.

Assessing Machine Learning Techniques for Intrusion Detection in Cyber-Physical Systems

Cyber-physical systems (CPS) are vital to key infrastructures such as Smart Grids and water treatment, and are increasingly vulnerable to a broad spectrum of evolving attacks. Whereas traditional security mechanisms, such as encryption and firewalls, are often inadequate for CPS architectures, the implementation of Intrusion Detection Systems (IDS) tailored for CPS has become an essential strategy for securing them. In this context, it is worth noting the difference between traditional offline Machine Learning (ML) techniques and understanding how they perform under different IDS applications. To answer these questions, this article presents a novel comparison of five offline and three online ML algorithms for intrusion detection using seven CPS-specific datasets, revealing that offline ML is superior when attack signatures are present without time constraints, while online techniques offer a quicker response to new attacks. The findings provide a pathway for enhancing CPS security through a balanced and effective combination of ML techniques.

Enhancing attack resilience of cyber-physical systems through state dependency graph models

AbstractThis paper presents a method that utilizes graph theory and state modelling algorithms to perform automatic complexity analysis of the architecture of cyber-physical systems (CPS). It describes cyber physical systems risk assessment (CPSRA), a tool to provide automatic decision support for enhancing the overall resilience of CPS architectures often used in critical infrastructures. CPRSA is built to enhance industrial risk assessment and improve the resilience of CPS architecture against malicious attacks on the cyber domain that can affect industrial processes, which is critical in a distributed cyber environment. Such attacks often compromise execution states on physical components and lead to hazards or even disasters through plant malfunction. CPSRA is tested against a real-world testbed model of a large SCADA system that is infused with real-world CVE vulnerabilities in some of its components. The tool creates an isomorphic graph of the CPS process model and uses graph algorithms and network analytics on the model to test cyber-attacks and evaluate attack resilience aspects. The tool’s output is then used to pinpoint high-complexity components in terms of influence on the overall CPS architecture and suggest mitigation points for security measure implementation while considering every potential subattack path and subliminal path on the model’s attack graph. The paper complements standardized assessment reports and contributes to automatic architecture assessment for critical infrastructure environments and can be used as the basis to model dependencies and threat propagation in larger digital twins, a need outlined in major NIST publications concerning the security of industrial systems that was previously done manually, without automatic insight into state and vulnerability influences.

A Timing-Based Framework for Designing Resilient Cyber-Physical Systems under Safety Constraint

Cyber-physical systems (CPS) are required to satisfy safety constraints in various application domains such as robotics, industrial manufacturing systems, and power systems. Faults and cyber attacks have been shown to cause safety violations, which can damage the system and endanger human lives. Resilient architectures have been proposed to ensure safety of CPS under such faults and attacks via methodologies including redundancy and restarting from safe operating conditions. The existing resilient architectures for CPS utilize different mechanisms to guarantee safety, and currently, there is no common framework to compare them. Moreover, the analysis and design undertaken for CPS employing one architecture is not readily extendable to another. In this article, we propose a timing-based framework for CPS employing various resilient architectures and develop a common methodology for safety analysis and computation of control policies and design parameters. Using the insight that the cyber subsystem operates in one out of a finite number of statuses, we first develop a hybrid system model that captures CPS adopting any of these architectures. Based on the hybrid system, we formulate the problem of joint computation of control policies and associated timing parameters for CPS to satisfy a given safety constraint and derive sufficient conditions for the solution. Utilizing the derived conditions, we provide an algorithm to compute control policies and timing parameters relevant to the employed architecture. We also note that our solution can be applied to a wide class of CPS with polynomial dynamics and also allows incorporation of new architectures. We verify our proposed framework by performing a case study on adaptive cruise control of vehicles.

Unified modelling for continuous–discrete hybrid adaptive machining CPS of large thin-walled parts

ABSTRACT Traditional machining is transforming to digital and intelligent machining, in which adaptive machining cyber-physical system (CPS) provides a useful approach to control the machining quality of large thin-walled parts. And the running of adaptive machining CPS is a complex multi-processes execution flow, which can be regarded as a continuous–discrete hybrid system. To realise adaptive controlling of machining quality and adaptive managing of process flow, a unified model for continuous–discrete hybrid adaptive machining CPS is constructed. Firstly, an architecture of adaptive machining CPS is proposed. Next, the cutting process in adaptive machining CPS is modelled as a continuous-variable system (CVS), while the process flow in adaptive machining CPS is modelled as a discrete-events system (DES). Then, the finite state machine is adopted to integrate the CVS and DES to form the unified model of adaptive machining CPS. Finally, an adaptive machining CPS is developed based on the unified model, and the machining results demonstrate that machining quality is efficiently controlled, as well as the process flow is orderly managed. The built unified model has four features, respectively universality, integrability, scalability, and reconfigurability, which can be reconstructed to form a new instancing model according to the different machining requirements.

A guideline to implement a CPS architecture in an SME

ABSTRACT In Industry 4.0 context, data valorisation allows industries to develop new capabilities, create competitive advantages and achieve manufacturing sustainability, but technological infrastructures are needed to support system interoperability and to manage datas. These infrastructures are not enought mature in many industrial environments, especially in small and medium enterprises (SMEs). Technology integration is challenging due to system and information heterogeneity , and even more so in SMEs that have constraint environment and which lack specific research study. . Although several approaches have been proposed, the literature lacks empirical evidence of the adoption of new technologies in SMEs. This paper presents a guideline for implementing a Cyber-Physical system (CPS) architecture in an SME and its application in an organic flour mill in Montreal. The case study provides evidence of the possibility to implement a CPS architecture in SMEs and can serve as an inspiration for SME to develop an Industry 4.0 strategy.

РОЗРОБКА МОБІЛЬНОЇ КІБЕРФІЗИЧНОЇ СИСТЕМИ ІНТЕЛЕКТУАЛЬНОГО МОНІТОРИНГУ КЛІМАТИЧНИХ ПАРАМЕТРІВ

The development of Mobile Cyber-Physical Systems (MCPS) is a promising research and development direction for many industries, such as manufacturing, healthcare, home automation, and many others. That is why a prototype MCPS based on a smartphone has been developed to collect, process, and transmit data from various devices and sensors in real-time, anywhere. The architecture of a mobile cyber-physical system for monitoring climatic parameters with telegram notifications has been designed. An algorithm for intelligent analysis and optimization of data transmission processes has been proposed for the developed MCPS prototype. The advantage of the developed system is the ability to determine the priority of monitoring parameters, which allows for a quick response to critical temperature changes at the object where the measurements are taken. Additionally, a unique method for measuring end-to-end data transmission delay using a timestamp in the packet header metadata has been implemented. This method enables the determination of the processing time of each component of the MCPS and, in case of exceeding the norms, automatically notifies about the necessary control decisions. Supporting such a method in MCPS is a particularly effective solution for monitoring the quality of real-time service delivery in critical infrastructure objects. Based on the conducted research, it has been established that the proposed algorithm for intelligent data analysis and optimization has reduced the number of messages by 3 times and the amount of transmitted information by 2.3 times. In the future, the developed system, in combination with artificial intelligence, will ensure reliable and high-quality data transmission even in unpredictable situations, making it a promising solution for improving the quality of life and the efficiency of smart infrastructures in various fields.