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Rapid advances in new-generation information technologies have been the main driving force for the transformation of manufacturing enterprises in Industry 4.0. Digital twin (DT), as a key technology to promote intelligent manufacturing, has shown great potential for manufacturing enterprises to create an industrial intelligence-driven production equipment through in-depth integration of cyber-physical systems. However, the lack of a systematic effective DT modelling method with a supporting evaluation metric is the most important factor restricting the application of DT in manufacturing enterprises. To bridge the gap, this paper proposes a novel multi-level modelling and fidelity evaluation (MLM&FE) method of DT for creating smart production equipment in manufacturing enterprises, which could help enterprises establish an industrial intelligence-driven production environment to quickly respond to changes in the customised global market, thus greatly improving competitiveness of the enterprises. Specifically, this paper firstly designs a reference framework for DT-enhanced smart production equipment, on which an MLM&FE architecture is proposed. Then, key implementation methodologies and tools for MLM&FE are introduced from the perspective of data space modelling, virtual space modelling, knowledge space modelling, model integration and evaluation. Finally, the developed smart production equipment prototype demonstrates the feasibility and effectiveness of DT MLM&FE. [ABSTRACT FROM AUTHOR]

Unlike mass production manufacturing systems, where configurations are rarely changed after the initial design, reconfigurable cyber-physical systems (RCPMS) self-change their structures throughout missions and thus self-adjust production in response to demand requirements. Accordingly, such a paradigm requires enhancing selective maintenance strategy to optimise scheduling maintenance actions, selecting configuration layouts for capacity and product family changes, and achieving maintenance cost reduction and reliability maximisation. This paper is the first to propose a robust model for a selective maintenance problem with imperfect repairs in the RCPMS context. The model also integrates uncertainties originating from the imperfect observations of components' health status. The model's objectives are to maximise the expected reliability and minimise the variance and maintenance cost under maintenance resource constraints. Moreover, we propose a new deep reinforcement learning framework for solving the resulting multi-objective and combinatorial optimisation problem. In addition, we use decision values to enhance the scalarisation process by permitting the priorities of specific objectives to be adjusted after the learning process. Furthermore, we employ Analytical Hierarchy Process to adjust the static priorities with respect to the objective functions and the actual learning context. Finally, broad experiments are conducted to highlight the performance of the proposed model and resolution framework. [ABSTRACT FROM AUTHOR]

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

With the rise of Internet of Things (IoT) technology, the seamless connection between the physical and digital worlds has been realized. This review paper aims to conduct a science mapping review of IoT applications in industrial management and to identify mainstream research topics, research gaps, and future research directions. Using VOSviewer as a visualization tool, 142 articles retrieved from the Scopus database were quantitatively discussed using scientometric analysis. Additionally, a follow-up qualitative discussion was focused on mainstream research topics, existing research gaps, and future research directions as the main research goals. The results revealed influential findings for the co-occurrence of keywords, journals, countries, authors, and documents analyses. Moreover, it was found that the existing research mainly focused on four main research topics including (1) application of IoT in manufacturing based on cyber-physical systems, (2) IoT-related technologies on logistics and supply chain management, (3) The impact of IoT on business models, and (4) Industrial IoT (IIoT) in the context of Industry 4.0. On this basis, the existing research gaps and future research directions are proposed. This review paper would help relevant practitioners and researchers to better understand the existing body of knowledge and lay the foundation for further research. [ABSTRACT FROM AUTHOR]

The availability of cyber-physical systems (CPS) in modern manufacturing networks provides a multitude of interesting opportunities from a manufacturing control perspective. Providing sensors, data gathering, local computation and communication capabilities modern CPS fulfil the technical requirements to act completely autonomously in a manufacturing network. While the distribution of decision-making authority to autonomous entities is feasible given such requirements, practice often sees the monopolisation of decision-making authority for centralised control. However, distributed production control approaches might be better suited given current manufacturing challenges, ranging from unreliable supply chains over highly volatile markets, to the demand for increasingly efficient and highly customisable production. In this article, we extend an existing scheduling complexity framework which enables practitioners and researchers alike to assess the aptitude of given manufacturing networks for both centralised and distributed control. In particular, we study the influence of a manufacturing network's topology ranging from assembly line to job shops on the aforementioned aptitude, with total production costs as objective. We utilise a multi-agent-based discrete-event simulation comparing an MILP-based centralised control approach and an autonomy based distributed control approach with weighted costs as decision function to evaluate this framework. Our results provide novel insights regarding the influence of manufacturing network topologies on the scheduling complexity of manufacturing networks. [ABSTRACT FROM AUTHOR]

The metaverse and Web 3.0 have created a new digital world with specific properties and behaviours replicating and influencing the behaviours and processes of physical entities. This study aims to advance our understanding of how the metaverse will impact supply chain and operations management (SCOM). Using elements of a structured literature search and building on the concepts of cyber-physical systems, digital supply chain twins, cloud supply chains, and Industry 4.0/Industry 5.0, we propose a framework for metaverse SCOM encompassing multiple socio-technological dimensions. We conclude that further metaverse developments could result in a co-existence of physical SCOM, metaverse SCOM, and SCOM for coordination of the physical and metaverse worlds. We offer a structured future research agenda pointing to new research questions and topics stemming from metaverse-driven visibility, computational power for data analytics, digital collaboration, and connectivity. New research areas can emerge for the novel metaverse SCOM processes and decision-making areas (e.g. joint demand forecasting for metaverse and physical products, digital inventory allocation in the metaverse, integrated production planning for the metaverse and physical worlds, and pricing and contracting for digital products), as well as new performance measures (e.g. virtual customer experience level, availability of digital products, and digital resilience and sustainability). [ABSTRACT FROM AUTHOR]

Warehouse operations have been significantly improved because of the rapid advancement of cyber-physical system technologies, preparing for Work-of-the-Future. The challenge, however, is how to design a collaborative system to deliver optimal performance by multiple agents who are highly distributed but interconnected and operate with technologies that provide massive amounts of real-time data. To address the challenge, in this article, the Cyber Collaborative Protocol for Dual-Cycle Task in Future Warehouse is developed to minimise total operation cost and time. The problem is addressed in two phases; the Global and Local phases. The global phase has higher computational power, maintaining a mathematical model, while the local phase has limited computational power and time, utilising heuristics to deliver the outcome. Computer experiments are utilised for validating the designed protocol compared with other alternatives. The results show that, in all given scenarios, the newly designed protocol outperforms alternatives with statistical significance. The original contribution of this research is the design and control of Cyber Collaborative Warehouse operations with a new focus on collaborative multi-agent interactions. In addition, a major implication is that future warehouses can benefit competitively by operating with Task Administration Protocols such as the new Cyber Collaborative Protocol for Dual-Cycle Task in Future Warehouse. [ABSTRACT FROM AUTHOR]

The imperative for lightweighting technologies, paramount in mission-critical cyber-physical systems (CPSs) including aerospace, automotive and allied sectors, hinges upon optimizing energy efficiency and curbing product weight. Honeycomb structures, celebrated for their exceptional strength-to-weight ratio, have indisputably guided the pursuit of lightweight design. This paper expounds upon the versatility of honeycomb structures by scrutinizing their in-plane mechanical attributes. Leveraging finite element simulations and polynomial fitting, we enhance the prevailing equivalent elastic modulus model for uniform honeycomb structures, expanding its domain to encompass a broader spectrum of relative density values. Deliberations ensue concerning the model's constraints and its inapplicability to nonuniform honeycomb structures. The investigation introduces nodes as pivotal influencers in the mechanical comportment of nonuniform honeycomb structures, delineating the nexus between the equivalent elastic modulus and node dimensions through a fusion of finite element simulations and mechanical experimentation. Furthermore, this research delves into the tenets and constructs of density-based variable density methodologies within the ambit of topology optimization, with an overarching goal of maximizing stiffness. We furnish a holistic design protocol for optimizing honeycomb structures, underscored by a pragmatic instantiation of the density-based variable density approach. Scrutinizing the geometric interplay between honeycomb structures and design spaces, we posit an innovative paradigm employing concentric circles to approximate cellular envelopes, streamlining numerical cartography and the conversion of optimization outputs into variable density honeycomb configurations. Evaluation of the in-plane mechanical attributes of variable density honeycomb structures reveals that TPU material augments the resilience of both uniform and variable density honeycomb structures, whereas topology optimization amplifies specific stiffness and resilience modulus in variable density honeycomb structures relative to their uniform counterparts. This study sheds light on the complexities of honeycomb structures, providing valuable insights for their optimization in lightweight applications. [ABSTRACT FROM AUTHOR]

Prediction using computer vision is getting prevalent nowadays because of satisfying results. The vision of Internet of Vehicles (IoV) expedites Vehicle to everything (V2X) communications by implementing heterogeneous global networks. Road crack is one of the major factors that causes road mishaps and damage to vehicles. To ensure smooth and safe driving, avoiding road crack in transportation planning and navigation is significant. To address this issue, we proposed a novel convolutional neural network (CNN)-based smart transportation system. We showed how to quantify the severity of the cracks. We proposed a post-processing algorithm to provide option to the driver to select the safest road toward the destination. The communication system for the proposed smart transportation system has also been introduced. The performance comparison of a few popular CNN architectures has been investigated. Simulation results showed that Resnet50 algorithm provides significantly high accuracy compared with SqueezeNet and InceptionV3 algorithm in order to detect road cracks for the proposed transportation system. We demonstrated high accuracy of measuring the crack severity via numerical analysis. The integration of the proposed system in next generation smart vehicles can ensure accurate detection of road cracks earlier enough providing the option to select alternate safe route toward a destination as advanced driver assistance service. Moreover, the proposed system can also play a key role in order to reduce road mishaps notably by warning the driver about the updated road surface conditions. [ABSTRACT FROM AUTHOR]

This study employs the Latent Dirichlet allocation method, a topic modeling technique, to reveal hidden patterns in Industry 4.0 research. The dataset comprises 8584 articles published in the Scopus database from 2011 to the end of 2022. The analysis categorized the articles into 12 distinct topics. The three most prominent topics identified are "Smart Cyber-Physical Systems," "Digital Transformation and Knowledge Management" and "Data Science in Energy," respectively. The findings from this topic modeling provide a comprehensive overview for researchers in the field of Industry 4.0, offering valuable insights into current trends and potential future research directions. [ABSTRACT FROM AUTHOR]

Practical testing of distance relays under power swing conditions is becoming all the more essential. This is in view of the increasing use of power electronic apparatus due to penetration of renewable sources and/or FACTS controllers now-a-days. To perform such practical tests, a hardware platform emulating power system swing occurrences, as caused by electromechanical oscillation of alternators, is necessary. As a first step it is required to model the phenomenon before its implementation. In this paper an analytical small-signal model of the low-frequency electromechanical oscillations in a grid-connected alternator is presented. Off-line simulation-based validation for a Single Machine Infinite Bus (SMIB) system is done. To emulate an open-loop alternator, a prototype of a controlled grid synchronised VSI with AC-side reactor is developed and practically tested. A control scheme is proposed and implemented to emulate the active power swing phenomenon. Effects of machine inertia and initial power angle on the swing characteristics are investigated. In-process estimation of apparent impedances, as seen from the distance relay terminals downstream towards the grid, is done. Principle of estimating the apparent impedance during power swing is presented with corresponding experimental results. Both real-time and off-line simulation results are in close agreement with the experimental results thus establishing the accuracy of the model and the precision of the experiments. [ABSTRACT FROM AUTHOR]

For ensuring the proper operation of CPS, it is required to obtain a solution to the security problem. Applications for cyber-physical systems (CPS) have a big impact on several industrial sectors. Growing in both quantity and variety are cyber attacks try to manage both the industrial process itself as well as data collection from CPS. Attacks on CPS must be identified and stopped in order to avoid financial loss, production disruptions, and possible threats to national security. This paper models a cyber-attack detection and mitigation framework using a hybrid classifier (CADFHC) including the steps like (1) Pre-processing, (2) Feature Extraction (3) Feature Selection (4) Detection & (5) Mitigation. The initial data is preprocessed via z-score normalization. Higher-order statistical features, mutual information, correlation, and raw features, will be retrieved in the feature extraction step. From the extracted feature set, an improved PCA will be used for selecting the appropriate features. In the detection phase, the hybrid model combining improved Bi-GRU & RNN is used for detecting the presence of an attack. After the attack detection process, the mitigation process will be performed by eliminating the attackers from the network. For this, modified distribution entropy function-based mitigation is performed. Finally, the effectiveness of the accepted model is compared with the conventional models with different metrics. The CADFHC approach has attained the most excellent outcomes (0.99) under the best case scenario. [ABSTRACT FROM AUTHOR]

A lot of infrastructure upgrade and algorithms have been developed for the information technology driven smart grids over the past twenty years, especially with increasing interest in their system design and real-world implementation. Meanwhile, the study of detecting and preventing intruders in ubiquitous smart grids environment is spurred significantly by the possibility of access points on various communication equipment. As a result, there are no comprehensive security protocols in place preventing from a malicious attacker's accessing to smart grids components, which would enable the interaction of attackers and system operators through the power grid control system. Recently, dynamics of time-extended interactions are believed to be predicted and solved by reinforcement learning technology. As a descriptive advantage of the approach compared with other methods, it provides the opportunities of simultaneously modeling several human continuous interactions features for decision-making process, rather than specifying an individual agent's decision dynamics and requiring others to follow specific kinematic and dynamic limitations. In this way, a machine-mediated human-human interaction's result is determined by how control and physical systems are designed. Technically, it is possible to design dedicated human-in-the-loop societal control systems that are attack-resistant by using simulations that predict such results with preventive assessment and acceptable accuracy. It is important to have a reliable model of both the control and physical systems, as well as of human decision-making, to make reliable assumptions. This study presents such a method to develop these tools, which includes a model that simulates the attacks of a cyber-physical intruder on the system and the operator's defense, demonstrating the overall performance benefit of such framework designs. [ABSTRACT FROM AUTHOR]

The affine formation maneuver (AFM) control problem using the zero-sum differential game is studied for a second-order multi-agent system (MAS) against cyber-injection attacks on the actuators. First and foremost, the distributed control signals and the cyber-injected attack signals are viewed as two competing teams. Then, the leader-follower tracking error system is established where the followers aim to track the AFMs of the leaders. The leader-follower distributed optimal control policies and the cyber-attack attenuation policies are derived from the Nash equilibrium solution of the Hamilton-Jacobi-Isaac (HJI) equation. The solution of the HJI equation is estimated with the aid of the critic neural network. The weights of the critic system are learned online based on the adaptive dynamic programming (ADP) scheme. The uniformly ultimately bounded stability of the closed-loop system under the formulated zero-sum differential game control strategy has been proven by the Lyapunov stability theorem. A simulation example illustrates that the presented control method can attenuate cyber-injection attacks on the actuators and maintain the AFMs of the agents. [ABSTRACT FROM AUTHOR]

With advancements in communication systems and measurement technologies, smart grids have become more observable and controllable, evolving into cyberphysical-power systems (CPPS). The impact of network security and secondary equipment on power system stability has become more evident. To support the existing grid toward a smart grid scenario, smart metering plays a vital role at the customer end side. Cyber-Physical systems are vulnerable to cyber-attacks and various techniques have been evolved to detect a cyber attack in the smart grid. Weighted trust-based models are suggested as one of the most effective security mechanisms. A hardware-in-loop CPPS co-simulation platform is established to facilitate the theoretical study of CPPS and the formulation of grid operation strategies. This paper examines current co-simulation platform schemes and highlights the necessity for a real-time hard-ware-in-the-loop platform to accurately simulate cyber-attack processes. This consideration takes into account the fundamental differences in modeling between power and communication systems. The architecture of the co-simulation platform based on RT-LAB and OPNET is described, including detailed modeling of the power system, communication system, and security and stability control devices. Additionally, an analysis of the latency of the co-simulation is provided. The paper focuses on modeling and implementing methods for addressing DDOS attacks and man-in-the-middle at-tacks in the communication network. The results from simulating a 7-bus system show the effectiveness and rationality of the co-simulation platform that has been designed. [ABSTRACT FROM AUTHOR]

As the physical power information system undergoes continual advancement, mobile energy storage has become a pivotal component in the planning and orchestration of multi-component distribution networks. Furthermore, the evolution and enhancement of big data technologies have significantly contributed to enhancing the rationality and efficacy of various distribution network planning and layout approaches. At the same time, multi-distribution networks have also confronted numerous network attacks with increasing probability and severity. In this study, a Petri net is initially employed as a modeling technique to delineate the network attack flow within the distribution network. Subsequently, the data from prior network attacks are consolidated and scrutinized to evaluate the vulnerability of the cyber-physical system (CPS), thereby identifying the most critical network attack pattern for a multi-component distribution network. Following this, the defender-attacker-defender planning methodology is applied for scale modeling, incorporating rapidly evolving mobile energy storage into the prelayout, aiming to mitigate the detrimental impact of network attacks on the power grid. Ultimately, the column and constraint generation (C&CG) algorithm is utilized to simulate and validate the proposed planning strategy in a 33-node system with multiple control groups established to demonstrate the viability and merits of the proposed strategy. [ABSTRACT FROM AUTHOR]

Cyber-Physical Systems (CPS) are integral components of modern, interconnected environments, playing a crucial role in various applications such as smart infrastructure and autonomous systems. These systems operate in complex and ever-evolving settings, where resilience is an essential characteristic. Recently, several Machine Learning (ML) techniques have been proposed to design and implement resilience in CPS. However, this approach has often prioritized performance gains and adaptability enhancements, leading to a disregard for the potential dangers linked to centralized data processing, including data breaches and privacy infringements. To harness the full potential of ML in CPS, this paper introduces a distributed intelligence framework that equally prioritizes security, data privacy, and adaptability. The proposed framework is implemented through the integration of Federated Machine Learning techniques, where the CPS architecture is decentralized, allowing data processing to occur locally on individual nodes or devices. This decentralized approach facilitates the aggregation of insights from multiple sources without the need for centralized data processing, thereby minimizing the risks associated with data breaches and privacy violations. We further validate the viability of the proposed framework through its successful implementation in a real-world industrial CPS application, specifically focused on fault prediction within industrial CPS environments. In addition to its privacy and security benefits, our approach also achieved promising results in terms of accuracy and precision. [ABSTRACT FROM AUTHOR]

Digital substations have adopted a high amount of information and communication technology (ICT) and cyber–physical systems (CPSs) for monitoring and control. As a result, cyber attacks on substations have been increasing and have become a major concern. An intrusion-detection system (IDS) could be a solution to detect and identify the abnormal behaviors of hackers. In this paper, a Deep Neural Network (DNN)-based IDS is proposed to detect malicious generic object-oriented substation event (GOOSE) communication over the process and station bus network, followed by the multiclassification of the cyber attacks. For training, both the abnormal and the normal substation networks are monitored, captured, and logged, and then the proposed algorithm is applied for distinguishing normal events from abnormal ones within the network communication packets. The designed system is implemented and tested with a real-time IEC 61850 GOOSE message dataset using two different approaches. The experimental results show that the proposed system can successfully detect intrusions with an accuracy of 98%. In addition, a comparison is performed in which the proposed IDS outperforms the support vector machine (SVM)-based IDS. [ABSTRACT FROM AUTHOR]

This paper provides a survey of the literature on the application of Multi-agent Systems (MAS) technology for Smartgrids. Smartgrids represent the next generation electric network, as communities are developing self-sufficient and environmentally friendly energy production. As a cyber-physical system, the development of the vision of Smartgrids requires the resolution of major technical problems; this has fed over a decade of research. Due to the stochastic, intermittent nature of renewable energy resources and the heterogeneity of the agents involved in a Smartgrid, demand and supply management, energy trade and control of grid elements constitute great challenges for stable operation. In addition, in order to offer resilience against faults and attacks, Smartgrids should also have restoration, self-recovery and security capabilities. Multi-agent systems (MAS) technology has been a popular approach to deal with these challenges in Smartgrids, due to their ability to support reasoning in a distributed context. This survey reviews the literature concerning the use of MAS models in each of the relevant research areas related to Smartgrids. The survey explores how researchers have utilized agent-based tools and methods to solve the main problems of Smartgrids. The survey also discusses the challenges in the advancement of Smartgrid technology and identifies the open problems for research from the view of multi-agent systems. [ABSTRACT FROM AUTHOR]

Recently, research has been conducted on mixed reality (MR), which provides immersive visualization and interaction experiences, and on mapping human motions directly onto a robot in a mixed reality (MR) space to achieve a high level of immersion. However, even though the robot is mapped onto the MR space, their surrounding environment is often not mapped sufficiently; this makes it difficult to comfortably perform tasks that require precise manipulation of the objects that are difficult to see from the human perspective. Therefore, we propose a system that allows users to operate a robot in real space by mapping the task environment around the robot on the MR space and performing operations within the MR space. [ABSTRACT FROM AUTHOR]

This research paper explores the realm of fault detection in distributed motors through the vision of the Internet of electrical drives. This paper aims at employing artificial neural networks supported by the data collected by the Internet of distributed devices. Cross-verification of results offers reliable diagnosis of industrial motor faults. The proposed methodology involves the development of a cyber–physical system architecture and mathematical modeling framework for efficient fault detection. The mathematical model is designed to capture the intricate relationships within the cyber–physical system, incorporating the dynamic interactions between distributed motors and their edge controllers. Fast Fourier transform is employed for signal processing, enabling the extraction of meaningful frequency features that serve as indicators of potential faults. The artificial neural network based fault detection system is integrated with the solution, utilizing its ability to learn complex patterns and adapt to varying motor conditions. The effectiveness of the proposed framework and model is demonstrated through experimental results. The experimental setup involves diverse fault scenarios, and the system's performance is evaluated in terms of accuracy, sensitivity, and false positive rates. [ABSTRACT FROM AUTHOR]

Industry 4.0 introduced new concepts, technologies, and paradigms, such as Cyber Physical Systems (CPSs), Industrial Internet of Things (IIoT) and, more recently, Artificial Intelligence of Things (AIoT). These paradigms ease the creation of complex systems by integrating heterogeneous devices. As a result, the structure of the production systems is changing completely. In this scenario, the adoption of reference architectures based on standards may guide designers and developers to create complex AIoT applications. This article surveys the main reference architectures available for industrial AIoT applications, analyzing their key characteristics, objectives, and benefits; it also presents some use cases that may help designers create new applications. The main goal of this review is to help engineers identify the alternative that best suits every application. The authors conclude that existing reference architectures are a necessary tool for standardizing AIoT applications, since they may guide developers in the process of developing new applications. However, the use of reference architectures in real AIoT industrial applications is still incipient, so more development effort is needed in order for it to be widely adopted. [ABSTRACT FROM AUTHOR]

There is a change in the traditional power system structure as a result of the increased incorporation of microgrids (MGs) into the grid. Multi‐area MGs will emerge as a result, and issues related to them will need to be addressed. Load frequency control (LFC) is a challenge in such structures, which are more complicated due to variations in demand and the stochastic characteristics of renewable energy sources. This paper presents a cascade fuzzy active disturbance rejection control technique to deal with the LFC problem. In order to tune different parameters of controllers, a newly developed heuristic algorithm called the Gazelle optimization algorithm (GOA) is also employed. Moreover, due to the fact that multi‐area MGs are regarded as cyber‐physical systems (CPSs), a relatively new concern for LFC problems is their resilience to cyberattacks such as false data injection (FDI) and denial of service (DoS) attacks. Therefore, this research also presents a novel machine learning approach called parallel attack resilience detection system (PARDS) to deal with the LFC problem in the presence of cyberattacks. The efficiency of the proposed strategy is investigated under different scenarios, such as non‐linearities in the power system or server cyberattacks. [ABSTRACT FROM AUTHOR]

As a result of recent advancements in electronics technology, wireless sensor networks (WSNs) may now make use of a vast array of tiny sensors (WSN). WSN are viewed as problematic networks because of the environments in which they may operate and the technology they rely on. For instance, routing protocols need to be prepared for and capable of recovering from unexpected events like node failures and packet losses. The dynamic nature of these routing environments necessitates that every WSN routing protocol have mechanisms for adapting to new conditions. In many applications, security affects performance and energy efficiency. To provide such security services, we require resilient key management techniques. Security Power Aware Routing-Salp Swarm Optimization (SPAR-SSO) protocol uses a unique cryptosystem. SPAR-SSO uses connection quality estimation and power aware routing to reduce power consumption and delay while increasing packet delivery ratio. WSNs must conserve energy because the nodes are battery-powered. If the route with the greatest energy savings is constantly chosen for delivery, the nodes along this route will be overused and drained quickly, leading to network partitioning. In SPAR-SSO, an optimal path to the sink node is chosen. This technology is interesting because to its ease in enterprise and tractability in network transportation administration to deliver integrated services. It delivers a collision-free medium and minimizes entire energy expended in attainment the terminus, dropping energy each unit or packet. Moreover, Salp Swarm Optimization (SSO) is applied to optimize the results. The Packet Delivery Ratio (PDR) and End to End Delay is used as parameters to compare the performance of SPAR-SSO protocol. The results are compared with existing results and presented the success of the proposed method. Results show the proposed algorithm is suitable and compatibility with cyber-physical systems. [ABSTRACT FROM AUTHOR]

Anomaly detection in industrial control and cyber-physical systems has gained much attention over the past years due to the increasing modernisation and exposure of industrial environments. Current dangers to the connected industry include the theft of industrial intellectual property, denial of service, or the compromise of cloud components; all of which might result in a cyber-attack across the operational network. However, most scientific work employs device logs, which necessitate substantial understanding and preprocessing before they can be used in anomaly detection. In this paper, we propose a network intrusion detection system (NIDS) architecture based on a deep autoencoder trained on network flow data, which has the advantage of not requiring prior knowledge of the network topology or its underlying architecture. Experimental results show that the proposed model can detect anomalies, caused by distributed denial of service attacks, providing a high detection rate and low false alarms, outperforming the state-of-the-art and a baseline model in an unsupervised learning environment. Furthermore, the deep autoencoder model can detect abnormal behaviour in legitimate devices after an attack. We also demonstrate the suitability of the proposed NIDS in a real industrial plant from the alimentary sector, analysing the false positive rate and the viability of the data generation, filtering and preprocessing procedure for a near real time scenario. The suggested NIDS architecture is a low-cost solution that uses only fifteen network-based features, requires minimal processing, operates in unsupervised mode, and is straightforward to deploy in real-world scenarios. [ABSTRACT FROM AUTHOR]

Advanced Persistent Threats (APTs) are stealthy, multi-step attacks tailored to a specific target. Often described as 'low and slow', APTs remain undetected until the consequences of the cyber-attack become evident, usually in the form of damage to the physical world, as seen with the Stuxnet attack, or manipulation of an industrial process, as was the case in the Ukraine Power Grid attacks. Given the increasing sophistication and targeted nature of cyber-attacks, especially APTs, this paper delves into the substantial threats APTs pose to critical infrastructures, focusing on power grid substations. Through a detailed case study, we present and explore a 2-stage APT attack on an IEC 61850 power grid substation, employing a Hardware-in-the-Loop (HIL) testbed to simulate real-world conditions. More specifically, this paper discusses two significant experiments conducted to assess vulnerabilities in the control protocols used in IEC 61850 substations: IEC 60870-5-104 and IEC 61850. The integration of findings from these experiments revealed a number of previously undiscussed potential threats to power grid infrastructure that could arise from attacking one or more substations. To better address these potential threats, the paper proposes an extension to the Industrial Control System (ICS) kill chain that explicitly accounts for the consequences of attacks on the physical aspects of Cyber-Physical Systems (CPSs). [ABSTRACT FROM AUTHOR]

In this article, a quality of service (QoS) dependent variable sampling dynamic event-triggered control method is designed for a cyber–physical system (CPS) with delays and packets dropout to cope with non-ideal network environments, maintain the desired control performance and improve the communication efficiency. To achieve the variable period sampling, a sampler is designed based on the QoS of the wireless network by using the delta operator discretization method. Then, a variable period sampling scheme for the delta operator system converted from the CPS is designed. Furthermore, a dynamic event-triggered mechanism (DETM) is proposed using the variable period sampling signal, which can reduce event triggered data calculations and increase event triggered intervals. By utilizing the average dwell time (ADT) approach, sufficient conditions contains the explicit variable sampling period are derived for the derived switched CPS. Finally, the effectiveness of the designed method is verified by numerical examples. • A novel QoS-based variable period sampling strategy is proposed to realize real-time adjustment to ensure the desired control performance of the CPS. • A DETM is derived using the variable period sampling signal for the CPS to improve the communication efficiency. • A switched delta operator system converted from the CPS is designed, and the sufficient conditions contain the explicit sampling period for system stability are obtained. [ABSTRACT FROM AUTHOR]

The W3C Web of Things (WoT) is a leading technology that facilitates dynamic information management in the Internet of Things (IoT). In most IoT scenarios, devices and their associated information change continuously, generating a large amount of data. Hence, to correctly use the information and the data generated by different devices, a new perspective of managing and ensuring data quality is recommended. Applying Data Science techniques to create the data model can help to manage and ensure data quality by creating a common schema that can be reused in future projects, as well as producing recommendations to facilitate Service Discovery. In addition, due to the dynamic devices that change over time or under specific circumstances, the data model created must be sufficiently abstract to add new instances and to support new requirements that devices should incorporate. The use of models helps to raise the abstraction level, adapting it to the continuous changes of devices by defining instances associated with the data model. This paper proposes two data models: one for Cyber‐Physical Systems (CPS) to define device information fetched by a Discovery Service, and another for applying Deep Learning in natural language problems through a Transformer approach. The latter matches user queries in natural language sentences with WoT devices or services. These data models expand the Thing Description model to help find similar CPSs by giving a confidence level to each CPS based on features such as security and the number of times the device was accessed. The results show how the proposed models support the search process of CPSs in syntactic and natural language searches. Furthermore, the four levels of the FAIR principles are validated for the proposed data models, thus ensuring the data's transparency, reproducibility, and reusability. [ABSTRACT FROM AUTHOR]

The development of advanced information technologies are paving the digital transformation of manufacturing systems, of which Digital Twin-based manufacturing system (DTMS) has become a prevailing topic attracted ever-increasing concerns from both industry and academia. As a cutting-edge smart manufacturing system, DTMS can improve manufacturing accuracy and efficiency based on high fidelity simulation, near real-time monitoring and control in a cyber-physical integrated manner. However, the connotation and boundary of DTMS lack a clear definition and systematic analysis. Therefore, this paper reviews the existing Digital Twin reference models and implementation architectures on manufacturing system, and further proposes a reference model of DTMS. Based on it, the characteristics and operational mechanism of DTMS are analyzed from three perspectives: hierarchy, dimension, and scale. Specifically, the composition of DTMS is described from a multi-level perspective, the specific characteristics of the DTMS are analyzed from a multi-dimensional perspective, and the temporal and spatial characteristics of the DTMS under different application scenarios are depicted from a multi-scale perspective, respectively. At last, the potential research directions of DTMS are highlighted in terms of reusability, interpretability and adaptability. It is envisioned that this work can provide a clear understanding with insightful knowledge to attract more in-depth research of DTMS. [ABSTRACT FROM AUTHOR]

The nuclear energy sector can benefit from mobile robots for remote inspection and handling, reducing human exposure to radiation. Advances in cyber–physical systems have improved robotic platforms in this sector through digital twin (DT) technology. DTs enhance situational awareness for robot operators, crucial for safety in the nuclear energy sector, and their value is anticipated to increase with the growing complexity of cyber–physical systems. The primary motivation of this work is to rapidly develop and evaluate a robot fleet interface that accounts for these benefits in the context of nuclear environments. Here, we introduce a multimodal immersive DT platform for cyber–physical robot fleets based on the ROS‐Unity 3D framework. The system design enables fleet monitoring and management by integrating building information models, mission parameters, robot sensor data, and multimodal user interaction through traditional and virtual reality interfaces. A modified heuristic evaluation approach, which accounts for the positive and negative aspects of the interface, was introduced to accelerate the iterative design process of our DT platform. Robot operators from leading nuclear research institutions (Sellafield Ltd. and the Japan Atomic Energy Agency) performed a simulated robot inspection mission while providing valuable insights into the design elements of the cyber–physical system. The three usability themes that emerged and inspired our design recommendations for future developers include increasing the interface's flexibility, considering each robot's individuality, and adapting the platform to expand sensor visualization capabilities. [ABSTRACT FROM AUTHOR]

Purpose: The main aim of this study is to review different aspects of Industry 5.0 (I5.0) along with Kaizen measures to foster this novel aspect of industrial sustainability. The study makes a comprehensive study to explore the implementation status of I5.0 in industries, key technologies, adoption level in different nations and barriers to I5.0 adoption together with mitigation actions. Design/methodology/approach: To do a systematic study of the literature, the authors have used preferred reporting items for systematic reviews and meta-analysis (PRISMA) methodology to extract articles related to the field of the study. Findings: It has been found that academic literature on the I5.0 is continuously growing as the wheel of time is running. Most of the studies on I5.0 are conceptual-based, and manufacturing and medical industries are the flag bearer in the adoption of this novel aspect. Further, due to I5.0's infancy, many organizations face difficulty to adopt the same due to financial burden, resistive nature, a well-designed standard for cyber-physical systems (CPS) and an effective mechanism for human–robot collaboration. Further studies also provide avenues for future research in terms of the identification of collaborative mechanisms between machines and wells, the establishment of different standards for comparison and the development of I5.0-enabled models for different industrial domains. Originality/value: The study is the first of its kind that reviews different facets of I5.0in conjunction with Kaizen's measures and application areas and provides avenues for future research to improve an organization's environmental and social sustainability. [ABSTRACT FROM AUTHOR]

Summary In this article, the issue of resilient filtering for descriptor nonhomogeneous Markovian jump cyber‐physical systems under hybrid cyber‐attacks has been studied. First, occurrences of both deception attacks and replay attacks are modeled as two mutually independent Bernoulli distributed random variables. To depress the ratio of sampled data transmissions over the network, the dynamic version of event‐triggered transmission policy is properly proposed. Then, criteria of stochastically admissible for the filtering error systems guaranteeing mixed H∞$$ {H}_{\infty } $$ and passive performance are addressed via linear matrix inequalities (LMIs). The gains of resilient filter and the dynamic event‐based transmission policy can be simultaneously gained by optimizing the LMIs. Finally, two examples that include a numerical example and a direct current motor system are provided to testify the feasibility and effectiveness of the developed technique. [ABSTRACT FROM AUTHOR]

This paper investigates the sliding mode control problem for cyber‐physical systems subject to multi‐source disturbances and actuator false data injection attacks simultaneously. Firstly, a series of extended state observers are designed to estimate the unknown multi‐source disturbances. For the actuator false data injection attacks, a sliding mode observer is designed to online estimate the values of attacks. Then, by introducing the disturbance and attack estimations, a dynamic sliding manifold and a composite sliding mode controller are constructed. Under the proposed approach, the unknown disturbances and false data injection attacks can be timely estimated and compensated, which can improve the disturbance rejection ability and guarantee security of the cyber‐physical system. Lyapunov theory is utilized to prove the stability of the closed‐loop system. Finally, simulations of both the numerical example and application to a power converter system demonstrate the effectiveness and superiority of the proposed composite sliding mode control approach. [ABSTRACT FROM AUTHOR]

AbstractThe construction industry has been slow to adopt advanced technologies like Cyber-Physical Systems (CPS), underscoring the necessity for a human-centered approach to their development. This study aims to understand the significance and impact of human-CPS interaction modalities on human perceptions of CPS, specifically in the use case of construction progress monitoring. A mixed-method approach comprising quantitative observations and qualitative interviews was employed to evaluate the system’s usability and performance with different human-CPS interaction modalities. The findings reveal variable levels of usability and performance across different human-CPS interaction modalities. Users expressed a preference for the three-dimensional immersive environment compared to a two-dimensional setting due to a better sense of presence. However, they experienced heightened cognitive load when utilizing novel interaction methods such as hand gestures and voice commands. Interestingly, despite this increased cognitive demand, users exhibited comparable performance in the assigned task, irrespective of their familiarity with the interaction modality. The results underscore the importance of human-centered design in CPS development. It contributes empirical evidence crucial for understanding and improving human-CPS interactions, thereby advancing the implementation of CPS in construction progress monitoring. [ABSTRACT FROM AUTHOR]

A serious threat to filter design is the difficulty in resisting important‐data‐based (IDB) Denial of Service (DoS) attacks. In this article, a new approach to filter design that is more relaxed and flexible in resisting IDB DoS policies is proposed. The proposed approach introduces the technique of homogeneous polynomials to develop a more general relaxed fuzzy filter design that can reduce conservatism. Moreover, it is guaranteed that the new method will obtain sets of more relaxed filter gains, which are more capable of responding to the IDB DoS policy. This enables the filter to perform more accurate signal estimation and ensures the filtering error system's H∞$$ {H}_{\infty } $$ performance. Finally, the reliability and effectiveness of the proposed filter method are demonstrated by two comparative examples. [ABSTRACT FROM AUTHOR]

Summary In the era of digitalization boom, cyber‐physical system (CPS) has been widely used in several fields. However, malicious data tampering in communication networks may lead to degradation of the state estimation performance, which may affect the control decision and cause significant losses. In this paper, for the identification of finite impluse response (FIR) systems with binary‐valued observations under data tampering attack, an optimal attack strategy based on the average entropy is designed from the perspective of the attacker. In the case of unknown parameters, the regression matrix is used to give the estimation method of the system parameters, the algorithmic flow of the data tampering attack for the implementation of the on‐line attack is designed. Finally, the effectiveness of the algorithm and the reliability of the conclusions is verified through the examples. [ABSTRACT FROM AUTHOR]

Grasping and object manipulation have been considered key domains of Cyber-Physical Systems (CPS) since the beginning of automation, as they are the most common interactions between systems, or a system and its environment. As the demand for automation is spreading to increasingly complex fields of industry, smart tools with sensors and internal decision-making become necessities. CPS, such as robots and smart autonomous machinery, have been introduced in the meat industry in recent decades; however, the natural diversity of animals, potential anatomical disorders and soft, slippery animal tissues require the use of a wide range of sensors, software and intelligent tools. This paper presents the development of a smart robotic gripper for deployment in the meat industry. A comprehensive review of the available robotic grippers employed in the sector is presented along with the relevant recent research projects. Based on the identified needs, a new mechatronic design and early development process of the smart gripper is described. The integrated force sensing method based on strain measurement and magnetic encoders is described, including the adjacent laboratory and on-site tests. Furthermore, a combined slip detection system is presented, which relies on an optical flow-based image processing algorithm using the video feed of a built-in endoscopic camera. Basic user tests and application assessments are presented. [ABSTRACT FROM AUTHOR]

This paper emphasises the study of switched nonlinear cyber-physical systems (CPSs) subjected to input delay, actuator faults and deception attacks by a $ H_\infty $ H ∞ -based truncated predictive control design. Particularly, the predictive controller uses delayed state information in addition to the traditional feedback control to estimate the present state for the feedback. Furthermore, the proposed control design is modelled with a truncated predictive approach which is also used to compensate the effects of actuator faults and attacks that also satisfy a predefined $ H_\infty $ H ∞ index simultaneously. With the assistance of the Lyapunov stability theory, a novel set of adequate criteria are acquired in terms of LMIs which assure the asymptotical stability of the switched nonlinear CPS under prescribed predefined $ H_\infty $ H ∞ performance levels. Eventually, the proposed theoretical results are validated through two numerical examples. [ABSTRACT FROM AUTHOR]

Against the background of the construction of new power systems, power generation, transmission, distribution, and dispatching services are open to the outside world for interaction, and the accessibility of attack paths has been significantly enhanced. We are facing cyber-physical cross-domain attacks with the characteristics of strong targeting, high concealment, and cross-space threats. This paper proposes a quantitative analysis method for the influence of power cyber-physical cross-domain attack paths based on graph knowledge. First, a layered attack graph was constructed based on the cross-space and strong coupling characteristics of the power cyber-physical system business and the vertical architecture of network security protection focusing on border protection. The attack graph included cyber-physical cross-domain attacks, control master stations, measurement and control equipment failures, transient stable node disturbances, and other vertices, and achieved a comprehensive depiction of the attack path. Second, the out-degree, in-degree, vertex betweenness, etc., of each vertex in the attack graph were comprehensively considered to calculate the vertex vulnerability, and by defining the cyber-physical coupling degree and edge weights, the risk of each attack path was analyzed in detail. Finally, the IEEE RTS79 and RTS96 node systems were selected, and the impact of risk conduction on the cascading failures of the physical space system under typical attack paths was analyzed using examples, verifying the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With the large scale and high proportion of distributed generation connected to the power grid, the distributed economic dispatch system has attracted more attention because of its significant advantages. However, the distributed economic dispatch system faces more serious information security issues due to the variation of communication topology. Therefore, the purpose of this paper is to review the information security issues that may occur in the distributed economic dispatch system and the defense measures. By summarizing the existing literature on information security issues and defense measures, it can be found that the current research focuses on the cyber-side defense for information security, and lacks consideration of the cyber-physical coupling characteristics. Additionally, the separate cyber-side defense measures still have a defense blind spot and cannot respond in a timely manner to the physical-side actions caused by information security issues. Finally, the establishment of the information security issues model and the construction of the integrated security defense system are discussed from the perspective of the power cyber-physical system. This research will be helpful in the construction of the security defense system for information issues in distributed economic dispatch systems. [ABSTRACT FROM AUTHOR]

Driven by the concept of mass customization, manufacturing enterprises are now required to transform their traditional workshops into smart workshops. The integration of cyber-physical systems (CPS), which serves as the core of Industry 4.0, is crucial in achieving smart manufacturing. By implementing CPS, the conventional workshop can transition into a new paradigm characterized by intelligence and flexibility. However, the implementation of CPS in workshops is a complex undertaking that is still in its early stages. Therefore, this research paper aims to provide a comprehensive perspective on CPS-based workshops, with the intention of facilitating their implementation in the industry. Initially, the paper identifies seven key features of CPS-based workshops, namely self-sensing, self-awareness, self-assessment, self-optimization, self-adjustment, self-configuration, and self-control. Subsequently, the paper proposes the architectural framework of CPS-based workshops from a technical standpoint. Furthermore, a conceptual model of CPS-based workshops is developed, which highlights the three fundamental elements and closed-loop mechanism of such workshops. Finally, a case study of a Rolling stock manufacturing company in Indonesia is presented to demonstrate the feasibility of implementing CPS-based workshops in the industry. [ABSTRACT FROM AUTHOR]

The main goal of this work is to design an optimized sensor-fault identification and diagnostic system for the Internet of Things (IoT) and Cyber-Physical Systems (CPS). The challenge is to accomplish this task within the sensors' limited computing, memory, and energy capabilities. More importantly, identifying errors is time-sensitive, even though the diagnosis does not have to be made quickly. This project aims to provide an enhanced sensor-fault detection and diagnostic system for the IoT and CPS with constrained energy, memory, and computation resources. The system's goals are to promptly detect defects, lessen the computing burden on sensors, and enhance the recall and accuracy of fault detection. This study used a hybrid approach that combined principal component analysis, autoencoder, and gated recurrent unit to create an optimal sensor-fault detection and diagnostic system. There were 1001 sensor readings in the dataset; 112 were defective, while the remaining 888 were normal. The investigation showed that the suggested method, which detected faults with an accuracy of 95% and recall of 92%, achieved high accuracy and recall in recognizing defects in IoT and CPS. With significantly shorter processing times, the system's potential to reduce computational strain on sensors was also proved. The findings of this study indicate that the suggested optimized sensor-fault detection and diagnosis system successfully detects faults in IoT and CPS with limited computation, memory, and energy resources. The system's ability to reduce the computing burden on sensors while improving accuracy and recall makes it an appealing choice for industrial and commercial applications. [ABSTRACT FROM AUTHOR]

BACKGROUND: Sports have been a fundamental component of any culture and legacy for centuries. Athletes are widely regarded as a source of national pride, and their physical well-being is deemed to be of paramount significance. The attainment of optimal performance and injury prevention in athletes is contingent upon physical fitness. Technology integration has implemented Cyber-Physical Systems (CPS) to augment the athletic training milieu. OBJECTIVE: The present study introduces an approach for assessing athlete physical fitness in training environments: the Internet of Things (IoT) and CPS-based Physical Fitness Evaluation Method (IoT-CPS-PFEM). METHODS: The IoT-CPS-PFEM employs a range of IoT-connected sensors and devices to observe and assess the physical fitness of athletes. The proposed methodology gathers information on diverse fitness parameters, including heart rate, body temperature, and oxygen saturation. It employs machine learning algorithms to scrutinize and furnish feedback on the athlete's physical fitness status. RESULTS: The simulation findings illustrate the efficacy of the proposed IoT-CPS-PFEM in identifying the physical fitness levels of athletes, with an average precision of 93%. The method under consideration aims to tackle the existing obstacles of conventional physical fitness assessment techniques, including imprecisions, time lags, and manual data-gathering requirements. The approach of IoT-CPS-PFEM provides the benefits of real-time monitoring, precision, and automation, thereby enhancing an athlete's physical fitness and overall performance to a considerable extent. CONCLUSION: The research findings suggest that the implementation of IoT-CPS-PFEM can significantly impact the physical fitness of athletes and enhance the performance of the Indian sports industry in global competitions. [ABSTRACT FROM AUTHOR]

This paper focuses on policy selection and scheduling of sensors and attackers in cyber-physical systems (CPSs) with multiple sensors under denial-of-service (DoS) attacks. DoS attacks have caused enormous disruption to the regular operation of CPSs, and it is necessary to assess this damage. The state estimation of the CPSs plays a vital role in providing real-time information about their operational status and ensuring accurate prediction and assessment of their security. For a multi-sensor CPS, this paper is different from utilizing robust control methods to characterize the state of the system against DoS attacks, but rather positively analyzes the optimal policy selection of the sensors and the attackers through dynamic programming ideology. To optimize the strategies of both sides, game theory is employed as a means to study the dynamic interaction that occurs between the sensors and the attackers. During the policy iterative optimization process, the sensors and attackers dynamically learn and adjust strategies by incorporating reinforcement learning. In order to explore more state information, the restriction on the set of states is relaxed, i.e., the transfer of states is not limited compulsorily. Meanwhile, the complexity of the proposed algorithm is decreased by introducing a penalty in the reward function. Finally, simulation results show that the proposed algorithm can effectively optimize policy selection and scheduling for CPSs with multiple sensors. [ABSTRACT FROM AUTHOR]

Cyber‐Physical Systems (CPS) are at the forefront of the intersection between information technology and physical operations, revolutionizing industries such as communication, automation, control, and intelligent transportation. However, evaluating the reliability of CPS, especially in the presence of multilateral random attacks, poses a complex challenge. This paper introduces an inventive probabilistic modeling approach to enhance the analysis of CPS reliability under the influence of multilateral random attacks. We provide an overview of fundamental CPS concepts and their significant role in modern society, followed by a comprehensive exploration of multilateral random attacks. Our novel probabilistic modeling approach is presented as a means to improve the accuracy and robustness of CPS reliability assessments when faced with multilateral random attacks. Numerical experiments and simulations validate the effectiveness of our innovative approach, emphasizing its potential to fortify the reliability of Cyber‐Physical Systems. [ABSTRACT FROM AUTHOR]

The digital evolution of Industry 4.0 enabled Operational Technology infrastructures to operate and remotely maintain cyber-physical systems bridging over IP communications. It has also expanded new attack surfaces and steadily increased the number of malicious cyber incidents for the interconnected smart critical systems. Within Industrial Control System (ICS), Programmable Logic Controller plays a crucial function to bridge between cyber and physical environments which made them the victim of sophisticated cyber-attacks that are designed to interrupt and damage their operations. Honeypots have been used as a key tool for aggregating real threat data e.g., malicious activities and payloads, to observe and determine different attack methods and strategies that can easily affect poorly secured cyber-physical systems. In this study, we deployed scalable low-interaction honeypot in Amazon Elastic Compute Cloud (AWS EC2) instance across six different regions to determine the current threat landscape as well as how knowledgeable and ingenious threat actors could be in compromising internet-facing ICS. This work is an extended version of our work published in 14th EAI International Wireless Internet Conference. [ABSTRACT FROM AUTHOR]

As a classic cyber-physical system, smart grids often suffer from various types of attacks, one of which the most threatening attacks is coordinated cyber-physical attack (CCPA). In order to improve the robustness of the smart grids against CCPA, we predict and simulate various possible attack scenarios, and propose three attack strategies, such as optimal attack strategy (OAS), saturation attack strategy (SAS) and decentralized random selective attack strategy (DRSAS). The OAS is used to get the maximum attack effect by attacking a small of number of targets in the case of limited resources. The SAS is designed to destroy the entire network with sufficient resource while avoiding repeated or redundant attacks. The DRSAS is proposed to random select decentralized cyber and physical targets according to attacker acknowledgement, real-time vulnerabilities, network characteristics, which is divided into implicit attack and explicit attack. In this paper, attack goals (AGs) function is firstly constructed to identify important cyber and physical targets. Secondly, based on these AGs, OAS and SAS are designed and applied to CCPA for analyzing the effect of those attack strategies on smart grids. Thirdly, The DRSAS is used to study the influence of decentralized random attack on cascading failure of smart grid, which applied to CCPA is to compare with OAS and SAS. Finally, node loss is used as an evaluation index to compare the attack effect of cyber attack (CA), physical attack (PA), CCPA based on OAS, SAS and DRSAS. The experimental results show that: (I) when the same proportion of nodes are removed, the CCPA based on the OAS or SAS has better attack effect rather than CA and PA; (II) CCPA based on DRSAS has different effects at different times, and explicit attack has better effect than implicit attack regardless of α = 0.3 , 0.4 , 0.5 and times t1, t2, t3. [ABSTRACT FROM AUTHOR]