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On-board embedded software developed for spaceflight systems (space software) must adhere to stringent software quality assurance procedures. For example, verification and validation activities are typically performed and assessed by third party organizations. To further minimize the risk of human mistakes, space agencies, such as the European Space Agency (ESA), are looking for automated solutions for the assessment of software testing activities, which play a crucial role in this context. Over the years, mutation analysis has shown to be a promising solution for the automated assessment of test suites; it consists of measuring the quality of a test suite in terms of the percentage of injected faults leading to a test failure. A number of optimization techniques, addressing scalability and accuracy problems, have been proposed to facilitate the industrial adoption of mutation analysis. However, to date, two major problems prevent space agencies from enforcing mutation analysis in space software development. In this paper, we enhance mutation analysis optimization techniques to enable their applicability to embedded software and propose a pipeline that successfully integrates them to address scalability and accuracy issues in this context, as described above. Further, we report on the largest study involving embedded software systems in the mutation analysis literature. Our research is part of a research project funded by ESA ESTEC involving private companies (GomSpace Luxembourg and LuxSpace) in the space sector. These industry partners provided the case studies reported in this paper; they include an on-board software system managing a microsatellite currently on-orbit, a set of libraries used in deployed cubesats, and a mathematical library certified by ESA., Accepted for publication on IEEE TRANSACTIONS ON SOFTWARE ENGINEERING

This paper investigates the secure control problem for cyber-physical systems when the malicious data is injected into the cyber realm which is directly connecting to the actuators. Based on moving target defense and reinforcement learning, we propose a novel proactive and reactive defense control scheme. First, the system $(A,B)$ is modeled as a switching system consisting of several controllable pairs $(A,\mathcal{B}_{l})$ to facilitate the construction of the moving target defense control scheme. The controllable pairs $(A,\mathcal{B}_{l})$ can be altered to update system dynamics under certain unpredictable switching probabilities for each subsystem, which can prevent the adversaries from effective attacks. Second, both attack detection and isolation schemes are designed to accurately locate and exclude the compromised actuators from a switching sequence. Third, a reinforcement learning algorithm based on the zero-sum game theory is proposed to design the defense control scheme when there exist no controllable subsystems to switch. To demonstrate the effectiveness of the defense control scheme, a three-tank system under unknown cyber attacks is illustrated.

Industrial cyber-physical systems rely increasingly on data from IoT devices and other systems as continuously emerging use cases implement new intelligent features. Edge computing can be seen as an extension of the cloud in close physical proximity, in which some of the typical cloud computing loads are beneficial to run. This paper studies data analytics application development for integration of industrial IoT data and composition of application services executed on edge and cloud. A solution is designed to support heterogeneous hardware and run-time platforms, and focuses on the service layer that enables flexible orchestration of data flows and dynamic service compositions. The unified model and system architecture implemented, using the open Arrowhead Framework model, is verified through two representative industrial use cases. publishedVersion

Modern society is living in an age of paradigm changes. In part, these changes have been driven by new technologies, which provide high performance computing capabilities that enable the creation of complex Artificial Intelligence systems. Those developments are allowing the emergence of new Cyber Systems where the continuously generated data is utilized to build Artificial Intelligence models used to perform specialized tasks within the system. While, on one hand, the isolated application of the cyber systems is becoming widespread, on the other hand, their synchronical integration with other cyber systems to build a concise and cognitive structure that can interact deeply and autonomously with a physical system is still a completely open question, only addressed in some works from a philosophical point of view. From this standpoint, the AI can play an enabling role to allow the existence of these cognitive CPSs. This review provides a look at some of the aspects that will be crucial in the development of cyber-physical systems, focusing on the application of artificial intelligence to confer cognition to the system. Topics such as control and optimization architectures and digital twins are presented as components of the CPS. It also provides a conceptual overview of the impacts that the application of these technologies might have in the chemical industry, more specifically in the purification of methane.

With the advancing digitisation of society and industry we observe a progressing blending of computational, physical, and social processes. The trustworthiness and sustainability of these systems will be vital for our society. However, engineering modern computing systems is complex as they have to: i) operate in uncertain and continuously changing environments, ii) deal with huge amounts of data, and iii) require seamless interaction with human operators. To that end, we argue that both systems and the way we engineer them must become smarter. With smarter we mean that systems and engineering processes adapt and evolve themselves through a perpetual process that continuously improves their capabilities and utility to deal with the uncertainties and amounts of data they face. We highlight key engineering areas: cyber-physical systems, self-adaptation, data-driven technologies, and visual analytics, and outline key challenges in each of them. From this, we propose a research agenda for the years to come.

International audience; Research work presented in this paper considers the contemporary Enterprise Information Systems (EIS) functional architecture and carries out the literature review of AI applications integrated in Customer Relationship Management, Supply Chain Management, Inventory and logistics, Production Planning and Scheduling, Finance and accounting, Product Lifecycle Management and Human Resources, with special attention to the manufacturing enterprises. As a review outcome, the enhanced capabilities are identified in each of those processes and proposed as AI services. AI-enablement is related to the capabilities of the enterprise to implement improved decision making or automation by using (a) Machine Learning (ML) models or (b) logicbased systems. It is a process of the enterprise transformation leading to the convergence of the 4 major disruptive technologies, namely Industrial Internet of Things (or Cyber-Physical Systems), Agent-based Distributed Systems, Cloud Computing and Artificial Intelligence (non-symbolic AI like Machine Learning, Neural Networks; symbolic AI like Reasoning and Logic/Model-based AI).

Software and systems modeling 20(3), 611-639 (2021). doi:10.1007/s10270-021-00876-z, Published by Springer, Berlin; Heidelberg; New York, NY

Software systems are complicated, and the scientific and engineering methodologies for software development are relatively young. Cyber-physical systems are now in every corner of our lives, and we need robust methods for handling the ever-increasing complexity of their software systems. Model-Driven Development is a promising approach to tackle the complexity of systems through the concept of abstraction, enabling analysis at earlier phases of development. In this paper, we propose a model-driven approach with a focus on guaranteeing safety using formal verification. Cyber-physical systems are distributed, concurrent, asynchronous and event-based reactive systems with timing constraints. The actor-based textual modeling language, Rebeca, with model checking support is used for formal verification. Starting from structured requirements and system architecture design the behavioral models, including Rebeca models, are built. Properties of interest are also derived from the structured requirements, and then model checking is used to formally verify the properties. This process can be performed in iterations until satisfaction of desired properties are ensured, and possible ambiguities and inconsistencies in requirements are resolved. The formally verified models can then be used to develop the executable code. The Rebeca models include the details of the signals and messages that are passed at the network level including the timing, and this facilitates the generation of executable code. The natural mappings among the models for requirements, the formal models, and the executable code improve the effectiveness and efficiency of the approach.

Development of Embedded Real-Time Systems is prone to error, and developing bug-free applications is expensive and no guarantees can be provided. We introduce the concept of Digital Quadruplet which includes: a 3D virtual representation of the physical world (a Digital Twin), a Discrete-Event formal model of the system of interest (called the “Digital Triplet”), which can be used for formal analysis as well as simulation studies, and a physical model of the real system under study for experimentation (called the “Digital Quadruplet”). We focus on the definition of the idea of a Digital Quadruplet and how to make these four apparati consistent and reusable. To do so, we use the Discrete-Event formal model as a center for both simulation and execution of the real-time embedded components with timing constraints, as well as a common mechanism for interfacing with the digital counterparts, providing model continuity throughout the process. Here we focus on a principal part of the Digital Quadruplet idea: the provision of an environment to allow models to be used for simulation (in virtual time), visualization, or execution in real-time. A Discrete-EVent Systems specifications (DEVS) kernel runs on bare-metal hardware platforms, avoiding the use of an Operating RTOS in the platform, and the combination with discrete-event modeling engineering.

Robust encryption techniques require heavy computational capability and consume large amount of memory which are unaffordable for resource constrained IoT devices and Cyber-Physical Systems with an inclusion of general-purpose data manipulation tasks. Many encryption techniques have been introduced to address the inability of such devices, lacking in robust security provision at low cost. This article presents an encryption technique, implemented on a resource constrained IoT device (AVR ATmega2560) through utilizing fast execution and less memory consumption properties of curve25519 in a novel and efficient lightweight hash function. The hash function utilizes GMP library for multi-precision arithmetic calculations and pre-calculated curve points to devise a good cipher block using ECDH based key exchange protocols and large random prime number generator function.

As a cyber-physical system (CPS), the security of microgrids (MGs) is threatened by unknown faults and cyberattacks. Most existing distributed control methods for MGs are proposed based on the assumption that secondary controllers of distributed generation units operate in normal conditions. However, the faults and attacks of the distributed control system could lead to a significant impact and consequently influence the security and stability of MGs. In this article, a distributed resilient control strategy for multiple energy storage systems (ESSs) in islanded MGs is proposed to deal with these hidden but lethal issues. By introducing an adaptive technique, a distributed resilient control method is proposed for frequency/voltage restoration, fair real power sharing, and state-of-charge balancing in MGs with multiple ESSs in abnormal condition. The stability of the proposed method is rigorously proved by Lyapunov methods. The proposed method is validated on test systems developed in OPAL-RT simulator under various cases. Ministry of Education (MOE) This work was supported by the Ministry of Education of Singapore Grant MOE2017-T2-1-050 and in part by the Joint Funds of the National Natural Science Foundation of China under Grant U1966202. Paper no. TII-20-0599.

The improvement of homophonic encryption using the well-known Z408 cipher as an example has been examined in this article. Their problems to further correct the main shortcomings of this type of encryption have been analyzed. In the practical part, an encryption system similar to Z408 has been created and the complexity of the language analysis for this cipher has been increased. The correlation data of the number of alternative characters has been used to improve the transmission of encrypted data depending on the load of the device. The more symbols and the higher the load, the fewer the number of alternative symbols will be used.

A malicious attack may endanger human life or pollute environment on a cyber-physical system (CPS). However, successfully attacking a CPS needs not only the knowledge of information technology (IT) but also the domain knowledge of the system’s operation technology (OT). Therefore, it is critical to identify the vulnerabilities of a CPS. This paper proposes a systematic method for the security verification of a CPS, focusing on OT by using model checking with UPPAAL, so as to enhance cyber security. In our security analysis, we considered unsafe situations to be the result of a potentially effective security attack. Thus, we suggested a systematic method to generate security constraints based on the safety constraints (or safety checks) of the CPS and then enhance these security constraints by security verification using model checking with UPPAAL. UPPAAL’s simulation tool can perform a detailed search for each state in various possible model combinations and can explore human-computer interactions more deeply. The contributions of our method are as follows: First, a systematic method is proposed to generate security constraints based on the overall safety requirements at the OT level. Second, the security constraints thus generated can be used for run-time monitoring to identify the possible security attacks when they are violated. Third, this paper proposes to augment normal system modeling with a suggested Attack Module to simulate the potential OT attacks. Finally, the verification results may be used in the following twofold directions: to identify the vulnerabilities for possible design improvements and to suggest the further additions of security constraints.

As urban population increases steadily, current mobility and transportation services become inefficient, needing an urgent improvement since they represent a major source of discomfort and economic losses. In this context, the concept of intelligent transportation systems takes relevance as a way of optimizing those services with the use of technology. This work proposes a cyber-physical systems approach to collaborative urban traffic control. Specifically, a three-layer architecture is proposed to address the intersection management problem, making use of modular cyber-physical object abstractions and timed Petri nets. Preliminary implementations in a pseudo-real environment show that the proposed system is capable of handling the communication and processing loads, while improving traffic performance with respect to classical solutions for traffic signals, outperforming timed, Webster and coordinated control methods in pilot-scale tests.

Industry 4.0 is a revolution in manufacturing by introducing disruptive technologies such as Internet of Things (IoT) and cloud-computing into the heart of the factory. The resulting increased automation and the improved production synergy between stocks, supply chains and customer demands, come along with the threats and attacks from the Internet. Despite extensive literature on the cybersecurity topic, many actors in manufacturing factories are just realizing the impact of cybersecurity in the preservation of their business. This paper introduces step-by-step the concepts and practical aspects of an Industry 4.0 manufacturing factory that are related to cybersecurity. Based on a subdivision of a typical factory into several generic perimeters, we present the vulnerabilities and threats regarding the network and devices usually found in each perimeter. Therefore, it is more efficient to present the recent proposals of the literature regarding cybersecurity guidelines and solutions in Industry 4.0. Instead of spreading a lot of references regarding every aspect of cybersecurity, we focused on a limited number of papers among the recent references. However, for each paper, we provide the details about the purpose of the proposal, the methodology adopted, the technical solution developed and its evaluation by the authors. These solutions range from classical cybersecurity countermeasures to innovative ones, such as those based on honeypots and digital twins. In order to deliver a review also useful to non scientists, we present our guidelines along with those of some organizations involved in cybersecurity harmonization and standardization in the world.

In this article, we present an incentive mechanism for Vehicular Crowdsensing in the context of autonomous vehicles (AVs). In particular, we propose a solution to the problem of sensing coverage of regions located out of the AVs’ planned trajectories. We tackle this problem by dynamically modifying the AVs’ trajectories and collecting sensing samples from regions otherwise unreachable by originally planned routes. We model this problem as a non-cooperative game in which a set of AVs equipped with sensors are the players and their trajectories are the strategies. Thus, our solution corresponds to a model in which expected individual utility drives the mobility decision of participants. Using open-street maps, SUMO vehicular traffic simulator, and extensive simulations, we show our algorithm significantly outperforms traditional approaches for trajectory generation. In particular, our performance evaluation shows a significant lift in crowdsourcer coverage, road utilization, and average participant utility.

Cyber-Physical Systems (CPS) embed computation and communication capability into its core to regulate physical processes and seamlessly mediate between the cyber and the physical world for various control and monitoring tasks. Health CPS, a variant of CPS in the healthcare sector, acts as a health monitoring system to dynamically capture, process, and analyze health sensor data through integrated internet of things (IoT)-enabled cyber-physical processes. These systems can suitably support patients suffering from non-communicable diseases (NCDs) or who are at risk of suffering from those. Identifying the risk of NCDs, such as heart disease and diabetes, requires artificial intelligence (AI) techniques into the core of health CPS. Recently, there has been growing interest to incorporate machine learning into CPS, which can facilitate the disease classification, detection, monitoring, and prediction of several NCDs. However, there is a shortage of visible work that focus on early-stage risk prediction of these diseases. In this work, we propose a novel machine learning based health CPS framework that addresses the challenge of effectively processing the wearable IoT sensor data for early risk prediction of diabetes as an example of NCDs. In the experiment, a verified diabetic dataset has been used for training, while the testing has been performed on an artificially generated data collection from sensors. The experiment with several machine learning algorithms shows the effectiveness of the proposed approach in achieving the maximum precision from the Random Tree algorithm, which requires a minimum time of 0.01s to construct a model and obtains 94% accuracy to predict the probability of diabetes at an early point.

Security attacks on Cyber-Physical Systems with operations that involve Unmanned Aerial Vehicles (UAVs) are a matter of great concern due to their major impact in the deployed systems, and a deal-breaker for their utilization; if a system is not perceived as secure, either it will not be used or its capabilities will be underutilized, regardless of how good they could be. This happens with particular intensity in missions with UAVs, as they can be hacked to tamper with their collected data or taken away by unauthorized parties. Development of security countermeasures is extensive both in theoretical and practical implementations, but the integration of these measures can be difficult, and performance might be affected because of it. This manuscript puts forward a SEDIBLOFRA (Secure, Distributed, Blockchain-based Framework) for remote maneuvering of UAVs, based on several distributed systems technologies that pile up to provide complementary functionalities. Asymmetric cryptography encrypts the data. Publish/Subscribe communications offer a way to enhance data delivery. Finally, blockchain provides a way to keep all the transferred data in a redundant and immutable manner. The proposed framework is also extensible to other kinds of unmanned vehicles, like Unmanned Ground Vehicles or Autonomous Underwater Vehicles.

Digital twins are quickly becoming a popular tool in several domains, taking advantage of recent advancements in the Internet of Things, Machine Learning and Big Data, while being used by both the industry sector and the research community. In this paper, we review the current research landscape as regards digital twins in the field of smart cities, while also attempting to draw parallels with the application of digital twins in Industry 4.0. Although digital twins have received considerable attention in the Industrial Internet of Things domain, their utilization in smart cities has not been as popular thus far. We discuss here the open challenges in the field and argue that digital twins in smart cities should be treated differently and be considered as cyber-physical "systems of systems", due to the vastly different system size, complexity and requirements, when compared to other recent applications of digital twins. We also argue that researchers should utilize established tools and methods of the smart city community, such as co-creation, to better handle the specificities of this domain in practice. This work was supported in part by the Project ‘‘I3T—Innovative Application of Industrial Internet of Things (IIoT) in Smart Environments’’ (MIS 5002434) implemented under the ‘‘Action for the Strategic Development on the Research and Technological Sector,’’ funded by the Operational Programme ‘‘Competitiveness, Entrepreneurship and Innovation’’ (NSRF 2014–2020), and in part by Greece and the European Union (European Regional Development Fund).

This paper is about the estimation of the cyber-resilience of CPS. We define two new resilience estimation metrics: $k$ -steerability and $\ell $ -monitorability. They aim at assisting designers to evaluate and increase the cyber-resilience of CPS when facing stealthy attacks. The $k$ -steerability metric reflects the ability of a controller to act on individual plant state variables when, at least, $k$ different groups of functionally diverse input signals may be processed. The $\ell $ -monitorability metric indicates the ability of a controller to monitor individual plant state variables with $\ell $ different groups of functionally diverse outputs. Paired together, the metrics lead to CPS reaching $(k,\ell)$ -resilience. When $k$ and $\ell $ are both greater than one, a CPS can absorb and adapt to control-theoretic attacks manipulating input and output signals. We also relate the parameters $k$ and $\ell $ to the recoverability of a system. We define recoverability strategies to mitigate the impact of perpetrated attacks. We show that the values of $k$ and $\ell $ can be augmented by combining redundancy and diversity in hardware and software, in order to apply the moving target paradigm. We validate the approach via simulation and numeric results.

Cyber-Physical Systems (CPS) underpin global critical infrastructure, including power, water, gas systems and smart grids. CPS, as a technology platform, is unique as a target for Advanced Persistent Threats (APTs), given the potentially high impact of a successful breach. Additionally, CPSs are targets as they produce significant amounts of heterogeneous data from the multitude of devices and networks included in their architecture. It is, therefore, essential to develop efficient privacy-preserving techniques for safeguarding system data from cyber attacks. This paper introduces a comprehensive review of the current privacy-preserving techniques for protecting CPS systems and their data from cyber attacks. Concepts of Privacy preservation and CPSs are discussed, demonstrating CPSs’ components and the way these systems could be exploited by either cyber and physical hacking scenarios. Then, classification of privacy preservation according to the way they would be protected, including perturbation, authentication, machine learning (ML), cryptography and blockchain, are explained to illustrate how they would be employed for data privacy preservation. Finally, we show existing challenges, solutions and future research directions of privacy preservation in CPSs.

Electrocardiogram (ECG) is sensitive to autonomic dysfunction and cardiac complications derived from ischemic or hemorrhage stroke and is supposed to be a potential prognostic tool in stroke identification and post-stroke treatment. ECG data generated cannot be real-time accumulated, processed, and used for enterprise-level healthcare and wellness services with the existing cardiovascular monitoring system used in hospitals. This study aims to assess the feasibility of a cyber-physical cardiac monitoring system to classify stroke patients with altered cardiac activity and healthy adults. Here, we propose Big-ECG, a cyber-physical cardiac monitoring system for stroke management, consisting of a wearable ECG sensor, data storage and data analysis in a big data platform, and health advisory services using data analytics and medical ontology. We investigated our proposed ECG-based patient monitoring system with 45 stroke patients (average age 70.8 years old, 68% men) admitted to the rehabilitation center of the hospital and 40 healthy elderly volunteers (average age 75.4 years old, 38% men). We recorded ECG at resting state using a single-channel ECG patch within three months of diagnosis of ischemic stroke (clinically confirmed). In statistical results, ECG fiducial features, RR-I, QRS, QT, ST, and heart rate variability (HRV) features, SDSD, LF/HF, LF/(LF + HF), and HF/(LF + HF) are observed as significantly distinctive biomarkers for the stroke group relative to the healthy control group. The Random Trees model presented the best classification performance (overall accuracy: 95.6%) utilizing ECG fiducial variables. This system may assist healthcare enterprises in prognosis and rehabilitation management during post-stroke treatment.

The paper seeks to present an innovative Bio-mimicking approach that utilizes Cyber-Physical Systems technology as a construction system based on a Knowledge-Based System that can ease the integration of this technology to the built environment. The paper starts by reviewing the relation between biological and architectural systems. Then it moves to bio-mimicking as a sustainability achieving approach and executes a review for growth phenomenon and self-organizing process. It then tracks the current paradigms of architectural robotics, with a specific focus on cyber-physical systems. After that, it comprehensively analyses an especially developed knowledge-based system that is architecturally oriented to control the construction process based on cyber-physical systems. Finally, the paper presents a successful construction process simulation for a complex case study based on a previously developed cyber-physical systems prototype to examine the functionality of the knowledge-based system, especially on generating and modifying building configurations. The paper represents a benchmark for creating a comprehensive and functional software for the using cyber-physical systems on the built environment field; however, there are some remaining obstacles, especially on the hardware level. Additionally, there is a critical need for real-time experiments to validate the cyber-physical systems as a reliable construction system.

Machine Learning (ML) is increasingly applied for the control of safety-critical Cyber-Physical Systems (CPS) in application areas that cannot easily be mastered with traditional control approaches, such as autonomous driving. As a consequence, the safety of machine learning became a focus area for research in recent years. Despite very considerable advances in selected areas related to machine learning safety, shortcomings were identified on holistic approaches that take an end-to-end view on the risks associated to the engineering of ML-based control systems and their certification. Applying a classic technique of safety engineering, our paper provides a comprehensive and methodological analysis of the safety hazards that could be introduced along the ML lifecycle, and could compromise the safe operation of ML-based CPS. Identified hazards are illustrated and explained using a real-world application scenario&mdash, an autonomous shop-floor transportation vehicle. The comprehensive analysis presented in this paper is intended as a basis for future holistic approaches for safety engineering of ML-based CPS in safety-critical applications, and aims to support the focus on research onto safety hazards that are not yet adequately addressed.

In this study, the authors consider the problem of exchanging secrete messages in cyber-physical systems (CPSs) without resorting to cryptographic solutions. In particular, they consider a CPS where the networked controller wants to send a secrete message to the plant. They show that such a problem can be solved by exploiting a Wyner wiretap-like encoding scheme taking advantage of the closed-loop operations typical of feedback control systems. Specifically, by resorting to the control concept of one-step reachable sets, they first show that a wiretap-like encoding scheme exists whenever there is an asymmetry in the plant model knowledge available to control system (the defender) and to the eavesdropper. The effectiveness of the proposed scheme is confirmed by means of a numerical example. Finally, they conclude the study by presenting open design challenges that can be addressed by the research community to improve, in different directions, the secrete message exchange problem in CPSs.

The increasing complexity and criticality of industrial automation systems, embodied by the concept of Industry 4.0, which brings together concepts, such as cyber–physical systems, the Internet of Things, big data, and artificial intelligence, call for more formality in specifying the technology standards underlying these systems. We define in this article a formal model of an Industry 4.0 machine-to-machine communication protocol, Hermes, used in specifying electronic board transfers in an assembly line. Our analysis of the formal specification reveals that despite the robustness of the protocol, many testing scenarios have been ignored in the protocol standard and in particular, scenarios that include simultaneous machine errors. Therefore, this article paves the way for a better informed testing strategy in Industry 4.0 systems that implement the protocol.

Cloud Manufacturing (CMFg) is a novel production paradigm that benefits from Cloud Computing in order to develop manufacturing systems linked by the cloud. These systems, based on virtual platforms, allow direct linkage between customers and suppliers of manufacturing services, regardless of geographical distance. In this way, CMfg can expand both markets for producers, and suppliers for customers. However, these linkages imply a new challenge for production planning and decision-making process, especially in Scheduling. In this paper, a systematic literature review of articles addressing scheduling in Cloud Manufacturing environments is carried out. The review takes as its starting point a seminal study published in 2019, in which all problem features are described in detail. We pay special attention to the optimization methods and problem-solving strategies that have been suggested in CMfg scheduling. From the review carried out, we can assert that CMfg is a topic of growing interest within the scientific community. We also conclude that the methods based on bio-inspired metaheuristics are by far the most widely used (they represent more than 50% of the articles found). On the other hand, we suggest some lines for future research to further consolidate this field. In particular, we want to highlight the multi-objective approach, since due to the nature of the problem and the production paradigm, the optimization objectives involved are generally in conflict. In addition, decentralized approaches such as those based on game theory are promising lines for future research. Fil: Halty, Agustín. Universidad de la República; Uruguay Fil: Sánchez, Rodrigo. Universidad de la República; Uruguay Fil: Vázquez, Valentín. Universidad de la República; Uruguay Fil: Viana, Víctor. Universidad de la República; Uruguay Fil: Piñeyro, Pedro. Universidad de la República; Uruguay Fil: Rossit, Daniel Alejandro. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina

As the worst-case interacting false data to the power system state estimation (SE), cyber data attacks can avoid being filtered out by most bad data detectors. In this study, coordinated attacks (unobservable attack and logic bomb attack) and coordinated defences (honeypot and weakening vision) are used to analyse attackers’ and defenders’ behaviours, respectively. To quantify the potential physical influences (attack-and-defence) benefits, the residual of the expected state is devised. Subsequently, a zero-sum stochastic game is utilised to model the interaction between the cyber-physical power system and the external attack-and-defence actions. This game is demonstrated to admit a Nash equilibrium and the minimax Q-learning algorithm is introduced to enable the two players to reach their equilibrium strategies while maximising their respective minimum rewards in a sequence of stages. Numerous simulations of the stochastic game model on the IEEE 14-bus system show that while resisting the isolated or coordinated attacks, the optimal coordinated defences are more effective than those of isolated attacks.

This paper reviews the use of computational models to support the functioning of cyber-physical systems (CPS) in the parallel world of the Internet of Things (IoT). Existing models, methods, techniques and their implementation in this direction are studied. The necessity of using machine learning methods due to inaccuracy, fuzziness, incompleteness of the transmitted data from sensors of physical systems is substantiated. The task is to make informed decisions in a timely manner to support the functioning of real objects of a particular cyber-physical system in real time conditions.

The increasing coupling between the physical and communication layers in the cyber-physical system (CPS) brings up new challenges in system monitoring and control. Smart power grids with the integration of information and communication technologies are one of the most important types of CPS. Proper monitoring and control of the smart grid are highly dependent on the transient stability assessment (TSA). Effective TSA can provide system operators with insightful information on stability statuses and causes under various contingencies and cyber-attacks. In this study, a real-time stability condition predictor based on a feedforward neural network is proposed. The conjugate gradient backpropagation algorithm and Fletcher–Reeves updates are used for training, and the Kohonen learning algorithm is utilised to improve the learning process. By real-time assessment of the network features based on the minimum redundancy maximum relevancy algorithm, the proposed method can successfully predict transient stability and out of step conditions for the network and generators, respectively. Simulation results on the IEEE 39-bus test system indicate the superiority of the proposed method in terms of accuracy, precision, false positive rate, and true positive rate.

Powered by the technologies that have originated from manufacturing, the fourth revolution of healthcare technologies is happening (Healthcare 4.0). As an example of such revolution, new generation homecare robotic systems (HRS) based on the cyber-physical systems (CPS) with higher speed and more intelligent execution are emerging. In this article, the new visions and features of the CPS-based HRS are proposed. The latest progress in related enabling technologies is reviewed, including artificial intelligence, sensing fundamentals, materials and machines, cloud computing and communication, as well as motion capture and mapping. Finally, the future perspectives of the CPS-based HRS and the technical challenges faced in each technical area are discussed.

Recently, public attention is thoroughly aroused as to the security threats of Cyber-Physical Systems (CPS), which can seriously disrupt the system operation. In order to analyze the impact of potential cyber-attacks, one needs to investigate the attack scheduling strategy from the attacker's perspective. In this paper, we investigate the optimal attack schedules to degrade the system performance through multiple interactive channels against remote state estimation, which is different from the studies through multiple independent channels. Specifically, a channel is affected when the attacker launches an attack on another one, causing two different loss rates of the data package. Based on this, we represent the estimation error covariance of the remote estimator. Then we give the optimal jamming attack schedule theoretically. Finally, the effectiveness of the theoretical results is shown by numerical simulations.

The rapid development and implementation of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) in the engineering and manufacturing field have embraced a virtual identity to ensure nearly real-time adjustment. Warehouses are challenged to reassess its order fulfillment operations while simultaneously being provided with the opportunity to develop its own cloud-based CPS with the aid of IoT devices. Robotic Mobile Fulfillment System (RMFS) is a system controlling mobile robots, mobile storage rack, putaway and picking workstations, charging stations, and wireless communication infrastructure in the context of robotic-assisted warehouse. This paper addresses the value creation utilizing cloud-based CPS in RMFS. By providing an analysis of cloud services and IoT enhancement, theoretical concepts from the literatures are consolidated to solve the research que-stions on how RMFS offering better order fulfillment can gain benefits in terms of operational efficiency and system reliability. The paper also proposes a cloud-based CPS architecture, providing a comprehensive understanding on conflict avoidance strategy in the multi-layers multi-deeps warehouse layout. This research presents six conflict classifications in RMFS and provides a case study in the real-life context. Dock grid conflict is a new type of conflict appearing in multi-deeps RMFS. A scenario analysis with real customer orders is applied to present the collision detection and solution. Nanyang Technological University National Research Foundation (NRF) Published version This work was supported in part by the Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University,Hong Kong under Grant RK2F and Grant K-ZM31, in part by the School of Mechanical and Aerospace Engineering, NanyangTechnological University, Singapore, in part by the Research Committee and the Department of Industrial and Systems Engineering, TheHong Kong Polytechnic University, Hong Kong and The Innovation and Technology Commission, The Government of the Hong KongSAR, Hong Kong under Project PRP/002/19FX/K.ZM31, and in part by the Delta Electronics Inc., and the National Research Foundation (NRF) Singapore through the Corporate Laboratory @ University Scheme, Nanyang Technological University, Singapore under Grant RCA-16/434 and Grant SCO-RP1.

Nowadays, the increasing penetration of renewable energies often leads to system frequency variation due to the intermittent outputs, which limits the utilization of renewable electricity and brings higher requirements to frequency regulation. Aggregated electric vehicles (EVs) are suggested to be used as good regulation resources because of the vehicle-to-grid (V2G) capability and quick response characteristic. However, the participation of EVs inevitably leads to cyber delays due to their underlying communication infrastructure and scheduling, which may cause instability. To guarantee the system stability, this paper investigates the stability of a grid frequency regulation system with electric vehicle (EV) aggregators embedded by cyber delays in the cyber system. A comprehensive stability assessment method is proposed in this paper based on the discrete spectrum iteration. A stability assessment method flow is designed with only five stages: 1) system initialization and modeling; 2) spectrum transformation; 3) Chebyshev discretization; 4) Krylov subspace projection dimension reduction; 5) Newton correction. Comprehensive case studies are performed on the cyber regulation systems with single, double, and three EV aggregators to extract the stability region and to validate the proposed approach. It is revealed that the controller gain, the ratio between the participation ratios of each aggregator, and that between EV and grid are important factors for determining the stability of the regulation system at a given controller gain. Besides, the criterion of the stability region changing with the three factors is proposed. It is found that the mass utilization of electric vehicles in the frequency control disrupts system stability due to the aggregation delay and thus the stability region can be divided into several intervals and presents periodicity. It is expected that the proposed criteria can help to guide the determination of delay requirements for EV aggregators participating in frequency regulation service.

Cyber-Physical Systems (CPS) refer to a new generation of intelligent systems with integrated computational performance and physical capabilities. However, with the expansion of system complexity and the enhancement of system openness, most CPS become not only safety-critical but also security-critical since they involv physical objects, computer networks and communications. In the past decade, it is no longer rare to see safety incidents and security attacks happening in industries. Identifying risks in CPS is critical to ensure the normal operation of these systems. The Cyber-to-Physical (C2P) risk is defined as the impact of cyber threats on physical process safety. In this paper, a C2P risk assessment model based on hierarchical Bayesian Network (BN) is proposed, and the feasibility of this model is verified by constructing two undesired event scenarios on a typical CPS. The quantitative risk values are explained and compared through qualitative risk analysis and assessment.

Cyber-Physical Systems (CPS) and Internet of Thing (IoT) generate large amounts of data spurring the rise of Artificial Intelligence (AI) based smart applications. Driven by rapid advancements in technologies that support smart devices, agriculture and farming sector is shifting towards IoT connected ecosystem to balance the increase in demand for food supply. As the number of smart farms reach critical mass, it is now possible to include AI assisted systems at a cooperative (co-op) farming level. Today, in the United States alone there are about 1,871 co-ops serving 1,890,057 member farmers. Hence, such advanced technologies and infrastructure when incorporated in the co-op farming ecosystem can immensely benefit small member farmers who operate and maintain these independent co-op entities. In this paper, we develop a connected cooperative ecosystem which defines sensors and their communication among different entities along with cloud supported co-op hub. We develop member farm and co-op ontologies to capture data and various interactions that happen between shared resources, member farms, and the co-op that are stored in the cloud. These can then help generate AI supported insights for farmers and the cooperative. Several co-op farming use case scenarios have been discussed to demonstrate the functioning of our smart cooperative ecosystem. Finally, the paper describes various AI applications that can be deployed at the co-op level to aid member farmers.

Turbines are typical engineering-to-order products which can be highly customized. This paper considers the final assembly control under uncertainty in turbine assembly workstations. A cyber-physical framework based on Internet of Things (IoT) is proposed for the turbine assembly, which consists of physical components, cyber components and an IoT-based monitoring system. The IoT-based monitoring modules capture real-time data of the physical components, including the workers, tools, parts and the actual assembly process. These cyber modules can generate the original scheduling for the assembly tasks, re-sequence the assembly tasks, re-assign the workers and control the logistics when an unexpected event occurs. Physical activities are undertaken in a turbine assembly workstation based on assembly instructions and guidance from the cyber components. The proposed framework can be implemented in the large turbine assembly system, which can facilitate real-time information driven assembly process monitoring and collaborative control of the task sequence, the worker assignments and logistics in a closed-loop environment. The experimental results show that our method can significantly improve the quality and the efficiency of a turbine assembly system.

Modular Multilevel Converters (MMCs) have become one of the most popular power converters for medium/high power applications, from transmission systems to motor drives. However, to operate properly, MMCs require a considerable number of sensors and communication of sensitive data to a central controller, all under relevant electromagnetic interference produced by the high frequency switching of power semiconductors. This work explores the use of neural networks (NNs) to support the operation of MMCs by: i) denoising measurements, such as stack currents, using a blind autoencoder NN; and ii) estimating the sub-module capacitor voltages, using an encoder-decoder NN. Experimental results obtained with data from a three-phase MMC show that NNs can effectively clean sensor measurements and estimate internal states of the converter accurately, even during transients, drastically reducing sensing and communication requirements.