Cyber Physical System

Title of dissertation: STRUCTURAL PERFORMANCE EVALUATION AND OPTIMIZATION THROUGH CYBER-PHYSICAL SYSTEMS USING SUBSTRUCTURE REAL-TIME HYBRID SIMULATION Ruiyang Zhang, Doctor of Philosophy, 2017 Dissertation directed by: Assistant Professor, Brian M. Phillips Department of Civil and Environmental Engineering Natural hazards continue to demonstrate the vulnerability of civil infrastructure worldwide. Engineers are dedicated to improving structural performance against natural hazards with improved design codes and computational tools. These improvements are often driven by experiments. Experimental testing not only enables the prediction of structural responses under those dynamic loads but also provide a reliable way to investigate new solutions for hazard mitigation. Common experimental techniques in structural engineering include quasi-static testing, shake table testing, and hybrid simulation. In recent years, real-time hybrid simulation (RTHS) has emerged as a powerful alternative...

Objectives: This study examines the role of digital twin technology in optimizing maintenance within smart manufacturing. It explores the mediating effect of replacement maintenance on the relationship between digital twins and smart manufacturing outcomes in the Saudi industrial sector, focusing on energy efficiency, waste reduction, and operational performance. Theoretical Framework: Grounded in Industry 4.0 principles, this research highlights how digital twin technology enhances predictive maintenance and resource optimization. It aligns with Saudi Arabia's Vision 2030 by promoting advanced manufacturing solutions. Method: A quantitative approach is employed using structural equation modeling (SEM) to analyze data from 21 leading Saudi food industry companies, including Almarai, Savola, Al Mangal Foods, and Wataniya Poultry. The study evaluates digital twin-enabled maintenance strategies and their impact on manufacturing performance. Results and Discussion: The findings reveal that replacement maintenance partially mediates the relationship between digital twins and smart manufacturing. Digital twin technology enhances predictive maintenance, reduces downtime, and optimizes resource use, leading to improved energy efficiency and waste reduction. These outcomes support the broader adoption of Industry 4.0 technologies. Research Implications: The study underscores the need for further exploration of AI-driven analytics and deeper digital twin integration in smart manufacturing to improve efficiency and sustainability. Originality/Value: This research provides empirical evidence on the mediating role of replacement maintenance, offering valuable insights for businesses aiming to enhance production efficiency and align with Vision 2030 initiatives.

Cyber-physical systems (CPS) are recognized for their intelligence as they seamlessly interact with humans, enhancing the physical world through computation, communication, and control. Over the last few years, the evolution of CPS, including IoT components, has significantly impacted many facets of people's lifestyles. It has been immersed in a wide range of services and applications in various areas, including manufacturing, healthcare, and energy. However, the interrelationship between the cyber and physical worlds gives rise to a multitude of research problems and challenges. In the healthcare field, for instance, CPS introduces complexities related to interoperability, privacy, data security, and real-time data processing with critical implications for the reliability and safety of medical processes. To address these challenges and harness the full potential of CPS in healthcare, this paper, through a comprehensive literature review, aims to discuss cutting-edge CPS technologies and solutions that hold promise for healthcare applications. To this end, we propose a comprehensive architectural model that can serve as a benchmark for implementing CPS in healthcare applications. This model thoroughly details how services, components, and technologies can be integrated to transform massive raw data collected from the physical world into valuable information for an enhanced decision-making process. Finally, a use case on healthcare CPS is presented, outlining its characteristics, the role that different technologies have played in its development, and the major challenges in implementing such systems successfully. This study provides a cohesive understanding of the role CPS can play in empowering healthcare while offering insights into the challenges, and future research trends. INDEX TERMS Cyber-physical systems, Healthcare, Real-Time processing, IoT.

Cyber-physical systems bridge the gap between cyber components, typically written in software, and the physical world. Software written with traditional development practices, however, likely contains bugs or unintended interactions among components, which can result in uncontrolled and possibly disastrous physical-world interactions. Complete verification of cyber-physical systems, however, is often impractical due to outsourced development of software, cost, software created without formal models, or excessively large or complex models where the verification process becomes intractable. Rather than mandating complete modeling and verification, we advocate sandboxing of unverified cyber-physical system controllers by augmenting the system with a verified safety wrapper that can take control of the plant in order to avoid violations of formal safety properties. The focus of this work is an automatic method, based on reachability and time-bounded reachability of hybrid systems, to generate verified sandboxes. The method is shown to be both more general than previous work, and allows the trade-off of increased computation time for improved reachability accuracy. We also present an end-to-end toolkit which performs the low-level computation to generate the sandbox source code from Simulink/Stateflow models of a cyberphysical system.

The article describes semi-symbolic methods for the analysis of control and signal processing systems, including static and dynamic uncertainties. This above mentioned semi-symbolic description of uncertainties is based upon affine arithmetic. A short introduction to affine arithmetic is given. As affine arithmetic is only able to describe static uncertainties, an extension for effects of dynamic uncertainties is described and its feasibility is demonstrated by an example that delivers the frequency dependent noise of an output stage of a delta-sigma converter.

The Hyperconnected Architecture for High Cognitive Production Plants (HyperCOG) project aims at the process industry's complete digital transformation through an advanced Industrial Cyber-Physical Infrastructure. It is based on advanced technologies that allow a hyperconnected network of digital nodes to be created improving the classic automation hierarchy of communication layers. The nodes will collect data streams in real-time, offering cognitive sensing and information along with high performance computing capabilities making the process industry businesses solid in different scenarios. The system is validated in three fields of the process industry: steel, cement and chemical where optimization in the use of energy and raw materials is obtained, among other benefits.

The Hyperconnected Architecture for High Cognitive Production Plants (HyperCOG) project aims at the process industry's complete digital transformation through an advanced Industrial Cyber-Physical Infrastructure. It is based on advanced technologies that allow a hyperconnected network of digital nodes to be created improving the classic automation hierarchy of communication layers. The nodes will collect data streams in real-time, offering cognitive sensing and information along with high performance computing capabilities making the process industry businesses solid in different scenarios. The system is validated in three fields of the process industry: steel, cement and chemical where optimization in the use of energy and raw materials is obtained, among other benefits.

New generation of digitized factories in Industry 4.0 is based on a combination of cyber-physical systems (CPS) and digital technologies, to provide an integrated and intelligent synergy in production process, linking the physical, digital and virtual world. Industry 4.0 uses production systems, CPS, CPPS, SCPS and robots in combination with digital technologies IoS, IoP, IoT, IIoT, IoE, CC and etc. The digital infrastructure of a country can be evaluated using different development indices, such as: IDI, EGDI, TII, OSI and etc. The paper is given of trend analyzes of development indices (DI) for using ICT and the Internet., EGDI and TII in Georgia for the period 2003 to 2018.

Despite the recent increase in the popularity of blockchain, the technology suffers from the trilemma trade-off between security decentralisation and scalability prohibiting adoption, and limiting the efficiency and effectiveness of the induced system. Addressing the trilemma trade-off calls for dynamic management and configuration of the blockchain system. In particular, choosing an effective and efficient consensus protocol for balancing the trilemma trade-off when inducing the blockchain-based system is acknowledged to be a challenging problem given the dynamic and complex nature of the blockchain environment. DDDAS approaches are particularly suitable for this challenge, and in previous work, the authors presented a novel DDDAS-based blockchain architecture and demonstrated that it offers a promising approach for dynamically adjusting the parameters of a system and optimising for the trade-off. This paper presents a novel simulation tool that can support and satisfy the DDDAS requirements for a dynamically re-configurable blockchain system. The tool supports the simulation and the dynamic switching of consensus protocols, analysing their trilemma trade-off. The simulator design is modular and allows the implementation and analysis of a wide range of consensus protocols and their implementation scenarios, along with the ability for parallelization. The paper also presents a quantitative evaluation of the tool. • Computing methodologies → Simulation tools; • Computer systems organization → Peer-to-peer architectures.

Cyber-physical systems are hybrid networked cyber and engineered physical elements that record data (e.g. using sensors), analyse them using connected services, influence physical processes and interact with human actors using multi-channel interfaces. Examples of CPS interacting with humans in industrial production environments are the so-called cyber-physical production systems (CPPS), where operators supervise the industrial machines, according to the human-in-the-loop paradigm. In this scenario, research challenges for implementing CPPS resilience, promptly reacting to faults, concern: (i) the complex structure of CPPS, which cannot be addressed as a monolithic system, but as a dynamic ecosystem of single CPS interacting and influencing each other; (ii) the volume, velocity and variety of data (Big Data) on which resilience is based, which call for novel methods and techniques to ensure recovery procedures; (iii) the involvement of human factors in these systems. In this paper, we address the design of resilient cyber-physical production systems (R-CPPS) in digital factories by facing these challenges. Specifically, each component of the R-CPPS is modelled as a smart machine, that is, a cyberphysical system equipped with a set of recovery services, a Sensor Data API used to collect sensor data acquired from the physical side for monitoring the component behaviour, and an operator interface for displaying detected anomalous conditions and notifying necessary recovery actions to on-field operators. A context-based mediator, at shop floor level, is in charge of ensuring resilience by gathering data from the CPPS, selecting the proper recovery actions and invoking corresponding recovery services on the target CPS. Finally, data summarisation and relevance evaluation techniques are used for supporting the identification of anomalous conditions in the presence of high volume and velocity of data collected through the Sensor Data API. The approach is validated in a food industry real case study.

Management of large volumes of data, collected from modern Cyber-Physical Systems, is calling for models, tools and methods for data representation and exploration, in order to capture relevant properties of physical objects, and manage them in the cyber-space. In this context, the impact of big data disruptive characteristics (namely, volume, velocity and variety) on data modelling and information systems designneeds further investigation. In particular, data exploration is assuming an ever growing relevance, being a way users/operators can learn from data by inspecting it according to different perspectives. In this paper, we use conceptual modelling for (big) data exploration in a dynamic context of interconnected systems. We rely on a multi-dimensional model, that is suited for properly providing data organization for exploration. Furthermore, we propose a model-driven approach that guides the design of multiple exploration strategies according to different objectives. The model...

In modern Cyber Physical Production Systems (CPPS) workers interact with hybrid networked cyber and engineered physical elements that record data (e.g., using sensors), analyse them using connected services and support decision making, according to the Human-In-the-Loop paradigm. In this paper we present an approach for modelling Resilient Cyber Physical Production Systems (R-CPPS). The approach is conceived as: (i) data-driven, because recovery actions, modelled in a service-oriented architecture, are activated by sensor data measures collected on the CPPS subsystems and the surrounding production environment; (ii) context-based, since recovery services are associated with the steps of the production process as well as with the hierarchical organisation of the CPPS components involved in the recovery actions. The approach provides runtime selection of services, where the sensor data measures are used as service inputs and service outputs are displayed to the operators who supervise the CPPS subsystem on which recovery actions must be performed, enabling fast and effective resilience also in a Human-In-the-Loop scenario. The feasibility of the approach is demonstrated in a food industry case study.

Engineering safe and secure cyber-physical systems requires system engineers to develop and maintain a number of model views, both dynamic and static, which can be seen as algebras. We posit that verifying the composition of requirement, behavioral, and architectural models using category theory gives rise to a strictly compositional interpretation of cyber-physical systems theory, which can assist in the modeling and analysis of safety-critical cyber-physical systems.

As software to ensure they are resilient to unforeseen failures. Self provides a revolutionary approach by allowing software systems to independently identify, analyze, and rectify issues. This study investigates the combination of self cloud computing technologies to improve software recovery abilities. Utilizing the scalability and processing capabilities of cloud platforms, self monitoring, predictive analysis, and fault correction in distributed settings. The suggested framework utilizes machine learning techniques to forecast possible failures by examining past performance data and live metrics. Reinforc recovery strategies, striking a balance between system stability and operational effectiveness. The flexibility of cloud computing resources enables self power dynamically for Moreover, this document examines how microservices architectures and containerization contribute to granular self recovery. The research showcases experimental findings that illustrate the effectiveness of cloud-connected self dependability. The framework attained as much as a 92% decrease in mean time to recovery (MTTR) when compared to conventional reactive methods. Important challenges like data security, latency, and resource overhead are tackled, highlighting the significance of strong architectural design and data encryption methods. This study adds to the expanding under AI's adaptive learning abilities with the scalability offered by cloud computing. It offers a route for organizations to create robust software systems that can endure the challenges of fluctuating and un

In recent years, learning factories have expanded, especially in Europe. And they have acquired many forms of equipment that vary in size, scope, function and sophistication and aim to improve the learning experience of students and industry participants in multiple areas of manufacturing and transportation engineering knowledge. This article provides an overview of the concept of a learning factory involving manufacturing and transportation that uses Cloud platforms. To begin with, cyber physical systems need to be illuminated as a technology that connects physical objects with the virtual world. Cloud platforms provide promising prospects for future collaboration between universities. The described architecture allows the simulation of a learning factory. The explosion of the collected data requires a high concentration on the calculation in the Cloud.

Self-healing systems powered by machine learning signify a transformative change in software engineering, providing the capability to autonomously identify, analyze, and recover from faults in real-time, thus minimizing downtime and enhancing system dependability. These systems utilize the capabilities of machine learning algorithms to analyze historical data, detect unusual patterns, and forecast system failures prior to their happening. Through the integration of predictive analytics and automated recovery processes, self-healing systems can independently trigger corrective measures, like service restarts, resource reallocation, or patch application, without the need for human involvement. This study examines the function of machine learning in self-healing systems, emphasizing their structure, uses, and difficulties. We explore how different machine learning methods, such as supervised learning, unsupervised learning, and reinforcement learning, are employed to facilitate smart fault detection and recovery mechanisms. Additionally, we assess the efficacy of these systems across various software engineering settings, ranging from cloud computing services to distributed systems and IoT (Internet of Things) networks. The article additionally explores the advantages of self-healing systems, such as lower operational expenses, improved system availability, and better user satisfaction. Nonetheless, it also tackles the challenges, including model precision, scalability, and the difficulties of incorporating machine learning models into existing systems. The paper wraps up by detailing future pathways for self-healing systems, highlighting the incorporation of deep learning and edge computing for enhanced and scalable solutions.

A flexible manufacturing system (FMS) is an integral part of a smart factory of Industry 4.0 in which every machine is interconnected and works autonomously. Robots are in the process of replacing humans in every industrial sector. As the cyber-physical system (CPS) and artificial intelligence (AI) are advancing, the manufacturing industry is getting more dependent on computers than human brains. This modernization has boosted production with high quality and accuracy and shifted from classic production to smart manufacturing systems. However, workpiece handling for such an automated production system is a challenge and needs to be addressed with the best possible solution. Conventional clamping systems are designed for manual work and are not suitable for highly automated production lines. Researchers and engineers are trying to find the most economical solution for loading/unloading and transportation of workpieces from a warehouse to a machine shop for machining operations and ba...

Runtime verification or runtime monitoring equips safetycritical cyber-physical systems to augment design assurance measures and ensure operational safety and security. Cyber-physical systems have interaction failures, attack surfaces, and attack vectors resulting in unanticipated hazards and loss scenarios. These interaction failures pose challenges to runtime verification regarding monitoring specifications and monitoring placements for in-time detection of hazards. We develop a well-formed workflow model that connects system theoretic process analysis, commonly referred to as STPA, hazard causation information to lower-level runtime monitoring to detect hazards at the operational phase. Specifically, our model follows the DepDevOps paradigm to provide evidence and insights to runtime monitoring on what to monitor, where to monitor, and the monitoring context. We demonstrate and evaluate the value of multilevel monitors by injecting hazards on an autonomous emergency braking system model.

Advanced embedded system technology is one of the key driving forces behind the rapid growth of Cyber-Physical System (CPS) applications. Cyber-Physical Systems are comprised of multiple coordinating and cooperating components, which are often software intensive and interacting with each other to achieve unprecedented tasks. Such complex CPSs have multiple attack surfaces and attack vectors that we have to secure against. Towards this goal, we demonstrate a multilevel runtime safety and security monitor framework where there are monitors across the CPS for detection and isolation of attacks. We implement the runtime monitors on FPGA using a stream-based runtime verification tool called TeSSLa. We demonstrate our monitoring scheme for an Autonomous Emergency Braking (AEB) CPS system.

Cyber-Physical Systems (CPS) are heterogeneous in nature and are composed of numerous components and embedded subsystems that are interacting with each other and with the physical world. The interaction of hardware and software components at each level, expose them to attack surfaces, which need novel methods to secure against. To ensure safety and security of high integrity CPSs, we present a multilevel runtime monitor approach where there are monitors at each level of processing and integration. In the proposed multi-level monitoring framework, some monitoring properties are formally defined using Event Calculus. We then demonstrate the need for multilevel monitors for faster detection and isolation of attacks by performing data attack and fault injection on a Simulink CPS model.

Runtime verification or runtime monitoring equips safetycritical cyber-physical systems to augment design assurance measures and ensure operational safety and security. Cyber-physical systems have interaction failures, attack surfaces, and attack vectors resulting in unanticipated hazards and loss scenarios. These interaction failures pose challenges to runtime verification regarding monitoring specifications and monitoring placements for in-time detection of hazards. We develop a well-formed workflow model that connects system theoretic process analysis, commonly referred to as STPA, hazard causation information to lower-level runtime monitoring to detect hazards at the operational phase. Specifically, our model follows the DepDevOps paradigm to provide evidence and insights to runtime monitoring on what to monitor, where to monitor, and the monitoring context. We demonstrate and evaluate the value of multilevel monitors by injecting hazards on an autonomous emergency braking system model.

The synergy of computational and physical network components leading to the Internet of Things, Data and Services has been made feasible by the use of Cyber Physical Systems (CPSs). CPS engineering promises to impact system condition monitoring for a diverse range of fields from healthcare, manufacturing, and transportation to aerospace and warfare. CPS for environment monitoring applications completely transforms human-to-human, human-to-machine and machine-to-machine interactions with the use of Internet Cloud. A recent trend is to gain assistance from mergers between virtual networking and physical actuation to reliably perform all conventional and complex sensing and communication tasks. Oil and gas pipeline monitoring provides a novel example of the benefits of CPS, providing a reliable remote monitoring platform to Sensors 2015, 15 7173 leverage environment, strategic and economic benefits. In this paper, we evaluate the applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective and review the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes. Related challenges and issues in network architecture design and relevant protocols are also provided with classification. This is supported by a case study on implementing reliable monitoring of oil and gas pipeline installations. Network parameters like node-discovery, node-mobility, data security, link connectivity, data aggregation, information knowledge discovery and quality of service provisioning have been reviewed.

This live demonstration showcases a cyber-physical system tailored for inexpensive remote bird monitoring. A comprehensive analysis of the application requirements along with a tight system integration have given rise to a smart autonomous nest-box ready for deployment. This nest-box includes radiofrequency identification (RFID), a weighing scale, two temperature sensors, passive infrared devices (PIR), massive data storage and internet connection via mobile infrastructure. It is powered through a solar panel. The bill of materials has been diminished 77% with respect to the previous version of the nest-box whereas the power consumption has been reduced 84%.

This research examines advanced techniques for cyber threat intelligence (CTI), focusing on methods to collect, analyse and leverage data for detecting, preventing, and mitigating cyber threats. We evaluate and compare multiple machine learning and data mining approaches for threat detection and analysis, including supervised and unsupervised learning models. Experimental results on real-world cyber threat datasets demonstrate the effectiveness of ensemble methods combining deep learning and traditional anomaly detection techniques. The proposed hybrid model achieves 96.3% accuracy in identifying threats, outperforming individual models. Implementation of the CTI system resulted in a 42% reduction in successful attacks and 35% decrease in mean time to detect threats. Key challenges and limitations in operationalizing CTI are discussed, along with future research directions.

Simulation is critical when studying real operational behavior of increasingly complex Cyber-Physical Systems, forecasting future behavior, and experimenting with hypothetical scenarios. A critical aspect of simulation is the ability to evaluate large-scale systems within a reasonable time frame while modeling complex interactions between millions of components. However, modern simulations face limitations in provisioning this functionality for CPSs in terms of balancing simulation complexity with performance, resulting in substantial operational costs required for completing simulation execution. Moreover, users are required to have expertise in modeling and configuring simulations to infrastructure which is time consuming. In this paper we present SEED (Simulation EnvironmEnt Distributor), a novel approach for simulating large-scale CPSs across a loosely-coupled distributed system requiring minimal user configuration. This is achieved through automated simulation partitioning and instantiation while enforcing tight event messaging across the system. SEED operates efficiently within both small and large-scale OTS hardware, agnostic of cluster heterogeneity and OS running, and is capable of simulating the full system and network stack of a CPS. Our approach is validated through experiments conducted in a cluster to simulate CPS operation. Results demonstrate that SEED is capable of simulating CPSs containing 2,000,000 tasks across 2000 nodes with only 6.89x slow down relative to real time, and executes effectively across distributed infrastructure.

Unmanned Aerial Systems (UASs) are expected to see the largest application within the agricultural market. These versatile platforms have shown promise ushering in the next wave of precision agriculture. While remote sensing applications have been well documented and developed, there remains significant advances left to be made in improving the use of UASs for precision application of pesticides. Although the current mode of aerial spraying or application of pesticides within the U.S. is done using full-sized aircraft, future implementations for autonomous crop dusting are likely to utilize small unmanned aircraft systems (SUASs) due to their much lower cost and ease of use. However, these smaller platforms have limited payload capacities, thus the development of cooperative swarms of SUASs is necessary. In this paper, a swarm of cooperative SUASs are utilized within a cyber-physical system framework to optimize the use of pesticides with precision spraying. Through the use of Centroid Voronoi Tessellation, the swarm makes the most efficient use of pesticides to attack an invasive pathogen. Simulation results are presented to validate the performance of the proposed method.

This paper presents the morphological characteristics and original drawings of 16 species of aphid newly determined in Croatia from samples collected with the use of yellow water trap and the method of aspiration at an suction trap from 2001 to 2004. Aphids, newly determined species, yellow water trap, suction trap, Croatia Važnost lisnih uši u poljoprivrednoj je proizvodnji vrlo velika. Osim izravnih šteta koje uzrokuju sisanjem na biljci, katkad su puno važnije neizravne štete koje se očituju u prenošenju biljnih viroza. Sastav lisnih uši stalno se mijenja i stoga ga je potrebno stalno pratiti. Vrste koje su nekada bile dominantne sada imaju nisku gustoću populacije, i obrnuto. Nadalje, stalnim se faunističkim istraživanjima otkrivaju novoutvrđene vrste kojih do sada nije bilo u našoj fauni, a mogu biti potencijalni štetnici na poljoprivrednim i šumskim kulturama i ukrasnom bilju (npr. Diuraphis noxia).

U radu su prikazani rezultati istraživanja dinamike leta i dominantnost vrste lisne usi Drepanosiphum platanoidis Schrank kao važnog stetnika gradskog zelenila. Istraživanje je provedeno u razdoblju od 1996. do 2003. dvijema metodama: uporabom modificirane Moerikeove posude i stacionarne usisne postaje smjestene u parku Maksimir (Zagreb). U svim je godinama istraživanja visi ulov zabilježen uporabom žute posude. Tijekom oba leta, i kontaminacijskog i remigracijskog, zabilježen je jedan maksimum. Prema vremenu prvih i zadnjih ulova vrste D. platanoidis vidljivo je da vrsta može letjeti na temperaturama nižim od 15 o C. Vrsta je s obzirom na udio u ukupnoj populaciji 1996. u usisnoj postaji oznacena kao subdominantna, 1997. i 2000. subrecentna, a 1998., 1999., 2001., 2002. i 2003. kao recentna vrsta. S obzirom na udio u ukupnoj populaciji u žutoj posudi vrsta je oznacena kao eudominantna 1996., 1997., 1998., 1999., 2002. i 2003., dok je 2000. i 2001. oznacena kao dominantna vrsta.

В статье рассматриваются вопросы, связанные с влиянием процессов стандартизации производства на формирование и модернизацию глобальных цепочек стоимости (ГЦС). В настоящее время торговля в терминах добавленной стоимости является одной из наиболее остро обсуждаемых тем, однако неоправданно мало внимания уделяется стандартизации, в частности международной. В ходе исследования рассматривается история развития стандартов разных типов, сравниваются различные подходы к моделированию процессов стандартизации и вырабатываются рекомендации по проведению экономической политики России с учетом ее участия в ГЦС в настоящее время. На данный момент можно отметить, что стандартизация приводит к перемещению фирм из областей чистого производства (начальные стадии) в области дизайна и маркетинга (конечные стадии), где больше шансов реализовать конкурентные преимущества. Для России это означает необходимость развития крупного и мелкого предпринимательства в отраслях, не имеющих отношения к энергетике, стимулирование компаний к созданию сетей поставок и цепочек стоимости, ориентация на потребителя. Помимо позитивных эффектов стандартизации, в статье рассматриваются также и негативные тренды. Прежде всего, они возникают при внедрении стандартов мелкой фирмой, когда они не гармонизированы с международными стандартами; когда отсутствуют ресурсы для модернизации внутри глобальной цепочки создания стоимости; когда нет возможностей для перехода на новые рынки, где стандартизация развита слабее и т.п. Тем не менее во всех этих случаях существуют условия, выполнение которых позволит сделать стандартизацию максимально эффективной. В качестве примера можно привести сбалансированное соотношение между количеством используемого в производстве фактора и сложностью технологии, при которой он используется. Данное условие в работе объясняется и математически, и на примере Китайской национальной программы по сертификации органических продуктов. Таким образом, важность международной стандартизации для развития глобальных цепочек стоимости невозможно переоценить, несмотря на возможные негативные эффекты. Поэтому России необходимо активно включаться в эти процессы с целью стимулирования экономического роста.

This abstract investigates the convergence of collection tree protocols, cyber-physical systems (CPS), dependability, epidemic modelling, the Fourth Industrial Revolution (4IR), and the Internet of Things (IoT). Our research focuses on improving the dependability of Cyber-Physical Systems (CPS) by examining the incorporation of Internet of Things (IoT) monitoring and collecting tree protocols. The objective of the study is to develop a strong framework for collecting and analysing data in real-time. This will be done by using epidemic modelling to forecast and avoid system breakdowns. Within the framework of the Fourth Industrial Revolution, the study focuses on highlighting adaptive and fault-tolerant designs for Cyber-Physical Systems (CPS). By integrating these technologies, we tackle issues related to the reliability of systems, providing valuable knowledge on flexible adjustment, robustness, and uniform communication protocols. This book makes a valuable contribution to the changing field of Cyber-Physical Systems (CPS), providing a comprehensive methodology that is in line with the revolutionary possibilities of the Fourth Industrial Revolution.

Cyber-Physical Systems (CPS) usually consist of a set of embedded systems (CPS nodes) connected through wireless communication, providing multiple functionalities that support different types of applications. During CPS deployment, application tasks are mapped on the CPS nodes with the objective of enhancing real-time performance, energy efficiency, and execution reliability. To satisfy these requirements, effective task mapping approaches should be designed based on different types of tasks, platforms, application and system requirements. In this paper, we provide a comprehensive survey regarding the task mapping methods in CPS.

Cyber-Physical Systems (CPS) usually consist of a set of embedded systems (CPS nodes) connected through wireless communication, providing multiple functionalities that support different types of applications. During CPS deployment, application tasks are mapped on the CPS nodes with the objective of enhancing real-time performance, energy efficiency, and execution reliability. To satisfy these requirements, effective task mapping approaches should be designed based on different types of tasks, platforms, application and system requirements. In this paper, we provide a comprehensive survey regarding the task mapping methods in CPS.

By allocating a set of tasks onto a set of nodes and adjusting the execution time of tasks, task mapping is an efficient approach to realize distributed computing. Cyber-Physical Systems (CPS), as a particular case of distributed systems, raise new challenges in task mapping, because of the heterogeneity and other properties traditionally associated with Wireless Sensor and Actuator Networks (WSAN), including shared sensing, acting and real-time computing. In addition, many of the real-time tasks of CPS can be executed in an imprecise way. Such systems accept an approximate result as long as the baseline Quality-of-Service (QoS) is satisfied and they can execute more computations to yield better results, if more system resources is available. These systems are typically considered under the Imprecise Computation (IC) model, achieving a better tradeoff between QoS and limited system resources. However, determining a QoS-aware mapping of these real-time IC-tasks onto the nodes of a CPS creates a set of interesting problems. In this paper, we firstly propose a mathematical model to capture the dependency, energy and real-time constraints of IC-tasks, as well as the sensing, acting, and routing in the CPS. The problem is formulated as a Mixed-Integer Non-Linear Programming (MINLP) due to the complex nature of the problem. Secondly, to efficiently solve this problem, we provide a linearization method that results in a Mixed-Integer Linear Programming (MILP) formulation of our original problem. Finally, we decompose the transformed problem into a task allocation subproblem and a task adjustment subproblem, and, then, we find the optimal solution based on subproblem iteration. Through the simulations, we demonstrate the effectiveness of the proposed method.

Cyberterrorism, espionage, and warfare are serious threats to national security. These attacks can harm people or destroy critical infrastructures like the data centres, computer networks, and systems. Surveillance systems currently used in monitoring critical infrastructures, national key points, and military exclusion zones (MEZ) are ineffective in detecting unauthorised intrusions. These issues compromise the stability of the countries, and the safety of the citizens and result in the loss of important assets. This experimental research study developed a Cyber Physical Security (CPS) defense gaitrecognition monitoring system. Autonomous Machine Learning (ML) technology was employed to enhance the precision and reliability of the system against CPA, in tracking access, managing security clearances, and triggering alerts in the event of unauthorized entries to restricted areas.

Cyber-physical systems (CPSs) are softwareintensive systems that are embedded in the physical world to monitor, control and coordinate a variety of processes in both the physical and the digital world. As a result, they often operate in complex, dynamic, and unanticipated environments with various potential sources of run-time changes and uncertainties, that could potentially lead the CPSs to faults, and even to complete system failures. To cope with these changes, the systems should have the capabilities to self-adapt in order to continue meeting their functional specifications. In this paper, we investigate how creating self-adaptive CPSs which are able to collaborate and learn in a dynamic, partially observable, multi-agent context, can not only preserve but also improve the performance, despite all the changes introduced to the system at run-time. We evaluate the proposed methodology on an in-house developed, multi-agent system from the robotics domain. Keywords—self-adaptive sy...

Nowadays, cyber-physical systems (CPS) are becoming ubiquitous in various application domains. The variety of design and implementation methodologies utilized for cyber-physical systems, as well as the dynamic interaction of its components, make the resilience of these systems a major challenge. We aim to increase the resilience of these systems in a decentralized way by leveraging knowledge of the social sciences and humanities (SSH) and especially emotional processes. Both individual decision-making processes and social coordinating mechanisms are based on emotional inspiration. Our hypotheses and studies on resilience approaches, cyber-physical systems and emotional processes allowed us to choose the multi-agent paradigm. In this paper, we present the results of our research on resilience, which includes an emotion-inspired anomaly detection approach for improving CPS resilience. This approach is integrated into an agent architecture, compared to the literature, and validated through the development of proof-of-concept scenarios. The experimental results prove its advantages in terms of resilience properties.

For a long time, and especially today, the energy crisis has been a limiting factor for the growth and development of the world economy. On the other hand, improving the reliability and readiness of energy production systems is becoming a first class priority for research and development institutions around the world. Therefore, the process of production, transport, distribution and usage of energy is increasingly becoming a very important part of smart systems, whose basic framework is Industry 4.0. Thus, starting from the analogies between industrial manufacturing and mining (i.e. “ore production”), the concept of smart mining is developed. This model has three dimensions: (i) application of advanced digital technologies (Cloud Computing and Internet of Things) with automated Cyber-Physical Systems (CPS), Adaptive Manufacturing Processes (depending on working conditions) and Control of Manufacturing Processes (with optimal resource usage); (ii) Smart Maintenance of CPS (for machin...

The paper shows the concept of Serbian Industry 4.0 Model based on cyber-physical manufacturing metrology model (CPM 3 ) and an integrated approach to manufacturing quality. The paper presents two directions of research: Virtual optimization of CAI process parameters for the sculptured surface inspection and Intelligent model for Inspection Planning on CMM.

In order to provide more advanced and intelligent energy management systems (EMS), generic and scalable solutions for interfacing with numerous proprietary monitoring equipment and legacy SCADA systems are needed. Such solutions should provide for an easy, plug-and-play coupling with the existing monitoring devices of target infrastructure, thus supporting the integration and interoperability of the overall system. One way of providing such holistic interface that will enable energy data provision to the EMS for further evaluation is presented in this paper. The proposed solution was based on the OpenMUC framework implementing a web-service based technology for data delivery. For testing the system prior to an actual deployment, data simulation and scheduling component was developed to provide a low-level data/signal emulation which would replace signals from the field sensors, monitoring equipment, SCADAs, etc. Furthermore, the proposed interface was implemented in a scalable and adaptable manner so it could be easily adjusted and deployed, practically, within any complex infrastructure of interest. Finally, for demonstration and validation purposes, the campus of the Institute Mihajlo Pupin in Belgrade was taken as a test-bed platform which was equipped with SCADA View6000 system serving as the main building management system.

As requirements of manufacturing industries continue to grow, whether for electrical systems or mechanical systems, improved mechanical efficiency must be found. Integration of computing, network and physical process, or so called Cyber Physical System (CPS) is proposed way of managing an industry. This paper describes a model of CPS in traffic management system in detail. It also provides basic information about the basic structure, standards, applications and some recent developments in CPS. . The paper also presents a detailed analysis of a CPS implemented in a bending machine. A model is established to propose the solution to improving efficiency and lifespan of a bending machine. The research results are very important for future works that might upgrade industries to Industry 4.0.

Big data is fueling the digital revolution in an increasingly knowledge driven and connected society by offering big data analytics and computational intelligence based solutions to reduce the complexity and cognitive burden on accessing and processing large volumes of data. In this paper, we discuss the importance of big data analytics and computational intelligence techniques applied to data produced from the myriad of pervasively connected machines and personalized devices offering embedded and distributed information processing capabilities. We provide a comprehensive survey of computational intelligence techniques appropriate for the effective processing and analysis of big data. We discuss a number of exemplar application areas that generate big data and can hence benefit from its effective processing. State of the art research and novel applications in health-care, intelligent transportation and social network sentiment analysis, are presented and discussed in the context of Big data, Cyber Physical Systems (CPS), and Computational Intelligence (CI). We present a data modelling methodology, which introduces a novel biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). The HSTSM modelling approach incorporates a number of soft computing techniques such as: deep belief networks, auto-encoders, agglomerative hierarchical clustering and temporal sequence processing, in order to address the computational challenges arising from analyzing and processing large volumes of diverse data to provide an effective big data analytics tool for diverse application areas. A conceptual cyber physical architecture, which can accommodate and benefit from the proposed methodology, is further presented.

The synergy of computational and physical network components leading to the Internet of Things, Data and Services has been made feasible by the use of Cyber Physical Systems (CPSs). CPS engineering promises to impact system condition monitoring for a diverse range of fields from healthcare, manufacturing, and transportation to aerospace and warfare. CPS for environment monitoring applications completely transforms human-to-human, human-to-machine and machine-to-machine interactions with the use of Internet Cloud. A recent trend is to gain assistance from mergers between virtual networking and physical actuation to reliably perform all conventional and complex sensing and communication tasks. Oil and gas pipeline monitoring provides a novel example of the benefits of CPS, providing a reliable remote monitoring platform to Sensors 2015, 15 7173 leverage environment, strategic and economic benefits. In this paper, we evaluate the applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective and review the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes. Related challenges and issues in network architecture design and relevant protocols are also provided with classification. This is supported by a case study on implementing reliable monitoring of oil and gas pipeline installations. Network parameters like node-discovery, node-mobility, data security, link connectivity, data aggregation, information knowledge discovery and quality of service provisioning have been reviewed.

Several cyber-attacks on the cyber-physical systems (CPS) that monitor and control critical infrastructure were publically announced over the last few years. Almost without exception, the proposed security solutions focus on preventing unauthorized access to the industrial control systems (ICS) at various levels -the defense in depth approach. While useful, it does not address the problem of making the systems more capable of responding to the malicious actions of an attacker once they have gained access to the system. The first step in making an ICS more resilient to an attacker is identifying the cyber security vulnerabilities the attacker can use during system design. This paper presents a method that reveals cyber security vulnerabilities in ICS through the formal modeling of the system and malicious agents. The inclusion of the malicious agent in the analysis of an existing systems identifies security vulnerabilities that are missed in traditional functional model checking.

Smart grid reliability and efficiency are critical for uninterrupted service, especially amidst growing demand and network complexity. Wide-Area Measurement Systems (WAMS) are valuable tools for mitigating faults and reducing fault-clearing time while simultaneously prioritizing cybersecurity. This review looks at smart grid WAMS implementation and its potential for cyber-physical power system (CPPS) development and compares it to traditional Supervisory Control and Data Acquisition (SCADA) infrastructure. While traditionally used in smart grids, SCADA has become insufficient in handling modern grid dynamics. WAMS differ through utilizing phasor measurement units (PMUs) to provide real-time monitoring and enhance situational awareness. This review explores PMU deployment models and their integration into existing grid infrastructure for CPPS and smart grid development. The review discusses PMU configurations that enable precise measurements across the grid for quicker, more accurate decisions. This study highlights models of PMU and WAMS deployment for conventional grids to convert them into smart grids in terms of the Smart Grid Architecture Model (SGAM). Examples from developing nations illustrate cybersecurity benefits in cyber-physical frameworks and improvements in grid stability and efficiency. Further incorporating machine learning, multi-level optimization, and predictive analytics can enhance WAMS capabilities by enabling advanced fault prediction, automated response, and multilayer cybersecurity.