Cyber-physical Systems Research Articles

Educational Cyber–Physical Systems (ECPSs) for University 4.0

University 4.0 represents the adaptation of the Education 4.0 paradigm to the university context. The core principle is the automated supervision of the entire student learning process by an AI-driven computer assistant, allowing for timely adjustments based on the student’s progression. Critical to this process is the assistant’s ability to collect comprehensive information on all student activities within the curriculum, particularly overseeing pedagogical activities in real time to make necessary adaptations. This utilizes Educational Cyber–Physical Systems (ECPSs) to gather all relevant data and extract appropriate information effectively. This article examines a specific case of practical work involving students at two distinct geographical locations collaborating in a blended learning environment. A specialized ECPS is deployed to collect data from equipment at both sites, enabling the modeling of the pedagogical sequence, the cyber–physical system, and the data necessary for monitoring student progress in real time.

IMPLEMENTING IDENTITY AND ACCESS MANAGEMENT FOR IMPROVING CYBER SECURITY

Smart Banking Cyber-Physical Systems (SBCPS) face significant risks from various forms of financial fraud, including suspicious transactions, forgery, and identity theft. Traditional fraud detection methods often struggle to provide timely and reliable solutions. To address this, this study proposes an advanced fraud detection system based on a blockchain-enabled smart contract framework, integrating dual cryptographic algorithms: the Secure Asymmetric Hash Encryption Algorithm (SAHEA) and the Triple Integrity Twofish Algorithm (TITFA). The SAHEA ensures secure transaction data encryption using asymmetric encryption, while the TITFA employs triple layers of Twofish encryption to enhance data integrity and confidentiality. Together, these algorithms provide robust authentication and tamper-proof transaction records. By leveraging blockchain’s transparency and immutability, the proposed system creates a secure, auditable environment for financial transactions. This approach offers a more efficient, reliable, and scalable solution for detecting and preventing fraud in SBCPS, improving security while reducing computational overhead. The dual cryptographic system, integrated with blockchain, delivers a cutting- edge, trustworthy method for fraud detection in smart banking. Index Terms—Smart Banking Cyber-Physical Systems, Fraud Detection, Blockchain, Smart Contracts, Secure Asymmetric Hash Encryption Algorithm, Triple Integrity Twofish Algorithm, Cryptography

Formal Model Engineering of Synchronous CPSDesigns in AADL

Many cyber-physical systems (CPSs)-such as aircrafts, cars, robots, and manufacturing plants-have synchronous designs and are realized on platforms with bounded network delays and clock skews. This paper summarizes how we have: (i) defined modeling languages for synchronous CPS designs in the embedded systems modeling standard AADL, and (ii) integrated Maude-based formal model checking ("push-button") analysis of such AADL synchronous designs into the OSATE tool environment for AADL. This enables a "formal model engineering" approach which combines the convenience of domain-specific modeling with automatic "under-thehood" formal analysis. Furthermore, by the PALS synchronizers, the correctness of such synchronous designs implies the correctness of the much more complex and harder-to-analyze asynchronous implementations, greatly simplifying the task of designing and analyzing "virtually synchronous" CPSs.

BLESS Behavior Correctness Proof as ConvincingVerification Artifact

Safety-critical cyber-physical systems require evidence they are indeed safe. In practice, such evidence is results of system tests. Unfortunately, tests can only demonstrate the presence of software errors, not their absence, and can practically cover a tiny fraction of system state space. Valiant efforts to formally verify program correctness have either been excruciatingly difficult (theorem proving) or incomplete (static analysis, model checking, SAT or SMT solving). The BLESS Methodology was created specifically for engineers in industry to formally verify software controlling machines. The BLESS Methodology transforms programs that control machines, annotated with assertions to form proof outlines, into deductive proofs that every possible program execution will conform to its specification. To the extent that cyber-physical system specifications have been validated to express system safety and performance, a deductive proof can be a convincing argument to a person of program correctness. This paper uses a simple safety-critical system to argue that behavior correctness proof under the BLESS Methodology is a convincing verification artifact in addition to customary testing.

Reconfigurable and Scalable Honeynet forCyber-physical Systems

Industrial Control Systems (ICS) constitute the backbone of contemporary industrial operations, ranging from modest heating, ventilation, and air conditioning systems to expansive national power grids. Given their pivotal role in critical infrastructure, there has been a concerted effort to enhance security measures and deepen our comprehension of potential cyber threats within this domain. To address these challenges, numerous implementations of Honeypots and Honeynets intended to detect and understand attacks have been employed for ICS. This approach diverges from conventional methods by focusing on making a scalable and reconfigurable honeynet for cyber-physical systems. It will also automatically generate attacks on the honeynet to test and validate it. With the development of a scalable and reconfigurable Honeynet and automatic attack generation tools, it is also expected that the system will serve as a basis for producing datasets for training algorithms for detecting and classifying attacks in cyberphysical honeynets.

Task-to-Thread Mapping in OpenMP Using FuzzyDecision Making

The performance of shared-resource multi-core hardware platforms in complex cyber-physical systems (CPSs), e.g., automotive industry, can be improved using task-based parallelism through OpenMP. However, most CPS require certain level of predictability, which challenges the efficient implementation of the task-to-thread mapping process. This exploratory work build on the fact that existing mapping methods mostly use elementary or heuristic algorithms, and the idea that artificial intelligence (AI) algorithms can be used to enhance the efficiency of such processes. Accordingly, this paper (1) evaluates the suitability of AI-based techniques in improving the performance of task-to-thread mapping in the OpenMP framework, and (2) proposes a hypothesis to perform an intelligent mapping using fuzzy logic for multi-queue schedulers to improve the predictability of the system.

Cloud-Cyber Physical Systems: Enhanced Metaheuristics with Hierarchical Deep Learning-based Cyberattack Detection

Cyber-Physical Systems (CPS) integrate several interconnected physical processes, networking units, and computing resources, along with monitoring the processes of the computing system. The connection between the cyber and physical world creates threatening security problems, particularly with the growing complexities of transmission networks. Despite efforts to overcome this challenge, it remains challenging to analyze and detect cyber-physical attacks in CPS. This study mainly focuses on the development of Enhanced Metaheuristics with Hierarchical Deep Learning-based Attack Detection (EMHDL-AD) method in a cloud-based CPS environment. The proposed EMHDL-AD method identifies various types of attacks to protect CPS. In the initial stage, data preprocessing is implemented to convert the input dataset into a useful format. Then, the Quantum Harris Hawks Optimization (QHHO) algorithm is used for feature selection. An Improved Salp Swarm Algorithm (ISSA) is used to optimize the hyperparameters of the HDL technique to recognize several attacks. The performance of the EMHDL-AD algorithm was examined using two benchmark intrusion datasets, and the experimental results indicated improvements over other existing approaches.

Framework for detecting and resisting cyberattacks on cyber-physical systems in internet of things

Framework for detecting and resisting cyberattacks on cyber-physical systems in internet of things

Tribotronic components: A revolution transforming machine elements into cyber-physical systems

Tribotronic components: A revolution transforming machine elements into cyber-physical systems

Networking and need of complex literacy in the digital age

The digitalisation of society is a process that is constantly improving. The main reason for this is the development of contemporary technologies (Cloud Computing, Mobile Cloud, Internet of Tings, cyber-Physical Systems, Big Data, etc.) which impose new requirements on working conditions and the necessary knowledge for their use. This requires improving the digital literacy of society by expanding the knowledge and skills to use technologies and to know the need to comply with rules for information security, privacy and personal data protection. In this regard, the article discusses the problem of the need for complex literacy by adding new components to the established understanding of information (digital) literacy, covering the additional requirements for working in the global space. Based on a retrospective analysis of publications in the field of information literacy, the need for a new understanding of digital literacy is substantiated and a structure of the so-called "Complex Literacy" is proposed and the requirements for the new components "Technological Literacy" and "Security & Privacy Literacy” whit extended discussing the issues of information security, privacy and personal data protection in the digital age.

Improving the Structure of the Electricity Demand Response Aggregator Based on Holonic Approach

A demand response (DR) aggregator is a specialized entity designed to collaborate with electricity producers, facilitating the exchange of energy for numerous stakeholders. This application is a pivotal development within the Russian Energy System as it transitions to a Smart Grid. Its successful operation relies on the advancement and implementation of more efficient strategies to manage emerging energy assets and structures. The holonic approach is a distributed management model used to handle systems characterized by random and dynamic changes. This paper analyzes the specific aspects of the electricity demand management mechanism in Russia, primarily aimed at reducing peak load in the energy system by engaging active consumers who are outside the wholesale market. The DR-Aggregator is considered both a cyber-physical system (CPS) with a cluster structure and a business process. The DR-Aggregator exhibits essential holonic properties, enabling the application of a holonic approach to enhance the efficiency of the DR-Aggregator mechanism. This approach will facilitate greater flexibility in managing the load schedules of individual holon consumers, bolster the reliability of power supply by aligning load schedules among holon consumers within the super-holon cluster, and improve the fault tolerance of the DR-Aggregator structure, providing greater adaptability of demand management services.

Anomaly detection system based on deep learning for cyber physical systems on sensory and network datasets

Anomaly detection system based on deep learning for cyber physical systems on sensory and network datasets

Cyber Physical System of Wireless Communication Synchronizing to Mobility

Cyber Physical System of Wireless Communication Synchronizing to Mobility

Toward Precision-Aware Safe Neural-Controlled Cyber–Physical Systems

Toward Precision-Aware Safe Neural-Controlled Cyber–Physical Systems

Dynamic IoT deployment reconfiguration: A global-level self-organisation approach

The edge-cloud continuum provides a heterogeneous, multi-scale, and dynamic infrastructure supporting complex deployment profiles and trade-offs for application scenarios like those found in the Internet of Things and large-scale cyber–physical systems domains. To exploit the continuum, applications should be designed in a way that promotes flexibility and reconfigurability, and proper management (sub-)systems should take care of reconfiguring them in response to changes in the environment or non-functional requirements. Approaches may leverage optimisation-based or heuristic-based policies, and decision making may be centralised or distributed: this work investigates decentralised heuristic-based approaches. In particular, we focus on the pulverisation approach, whereby a distributed software system is automatically partitioned (“pulverised”) into different deployment units. In this context, we address two main research problems: how to support the runtime reconfiguration of pulverised systems, and how to specify decentralised reconfiguring policies by a global perspective. To address the first problem, we design and implement a middleware for pulverised systems separating infrastructural and application concerns. To address the second problem, we leverage aggregate computing and exploit self-organisation patterns to devise self-stabilising reconfiguration strategies. By simulating deployments on different kinds of complex infrastructures, we assess the flexibility of the pulverisation middleware design as well as the effectiveness and resilience of the aggregate computing-based reconfiguration policies.

Editorial: AI and ML for Resilience Assessment and Enhancement of Cyber-Physical Systems

Editorial: AI and ML for Resilience Assessment and Enhancement of Cyber-Physical Systems

Security Mechanism in MAMATA Healthcare System Using Rule based Algorithm for Maternal Hospitals and Pathology Laboratories

Recently, innovation in the digital health industry has grown steadily, trending with medical cyber-physical systems’ (MCPS) integrated solutions, regulations, real-time analytics, and software frameworks. Amid these advancements, the paramount concern is the security of medical information, particularly in maternity hospitals where data breaches must be rigorously avoided. While some researchers have explored security methods, they have encountered limitations and identified existing risks. In this research article, the authors propose a novel security mechanism/method called MAMATA Agent/BOT (i.e. Medical-data-Access-for-Maternal-Treat-ment-in-rural-Area) to discover and mitigate trending vulnerabilities, drawing reference from the OWASP (Open Web Application Security Project) Top 10 Report 2017/2020. The author’s approach leverages a rule-based algorithm, enhancing the robustness of the security mechanism. This MCPS system and security mechanism are implemented within PHP and Laravel environments, and comparative analysis is carried out against a few existing security methods commonly employed in general healthcare (like SOA/SOD Framework, Andrew Austin test, Marcelo test, etc..). Results demonstrate a quantified improvement, e.g., 60-70% improvement in detectable vulnerabilities compared to prior approaches. Additionally, the authors extend their previous research by depicting different turns of events and applying a more stable, secured structure tailored to medical applications. Quantitative data presented with comparative analysis in tabular format validates the effectiveness of the proposed methods by detecting and, or fixing 7 to 9 major vulnerabilities (out of the top 10) identified by security assessment tools like ‘Burp Suite Proxy’ and ‘Nikto’.

Enhancement of industrial information systems through AI models to simulate the vibrational and acoustic behavior of machining operations

Advanced simulation tools allow the optimization of processes prior to production implementation. Our study aims to integrate industrial information and data into a digital model based on artificial intelligence (AI) to simulate acoustic and vibration behavior during the production preparation phase. This model integrates real manufacturing conditions with generated vibrations and acoustic waves, creating a comprehensive simulation tool for acoustic and vibration behavior during the production preparation phase. By harnessing Internet of Things (IoT) sensors, Big Data, and Cyber-Physical Systems (CPS), our approach achieves a unified system that consolidates data from diverse sources, facilitating a seamless information flow within an Industry 4.0 framework. Small signal variations made it complex to model manufacturing operations using AI tools, as seen in recent studies. However, the proposed approach overcomes these challenges and has been successfully applied to a numerical lathe using sensors and advanced analytical tools, paving the way for a robust industrial information integration system to optimize and predict operational outcomes.

Lean Six Sigma practices supported by Industry 4.0 technologies: evidence from heavy vehicle manufacturers

PurposeThis paper aims to provide empirical evidence regarding Lean Six Sigma (LSS) practices supported by Industry 4.0 (I4.0) technologies in heavy vehicle manufacturing processes.Design/methodology/approachA two-case study was performed involving LSS specialists, leaders and managers of two heavy vehicle manufacturers in Brazil. The data analysis procedure combined content analysis techniques, conceptual maps and network analysis.FindingsThe results provide consistent evidence of synergies between LSS and I4.0, including digital mistake-proofing, digital andon, e-kanban, statistical monitoring as well as process mapping aided by cyber-physical systems (CPS) and big data analytics (BDA). To enable such interactions, companies need to invest in automation architectures, system integration, human–machine interfaces and analytical skills.Research limitations/implicationsThis study relies on data from a two-case study carried out in two companies from a single manufacturing sector in Brazil. For this reason, the findings cannot be generalized to the entire automotive industry.Originality/valueThere is still a lack of comprehensive research on the application of digital technologies in LSS practices. This is the first study which provides empirical evidence regarding the LSS practices supported by I4.0 technologies used by heavy vehicle manufacturers.

Dynamic carbon emissions accounting in the mixed production process of multi-pressure die-castingproducts based on cyber physical production system

Die-casting is an efficient and precise casting process, but it consumes significant energy and contributes to severe environmental pollution. The characteristic features of the die-casting process chain include high demand for energy and resources, dynamic synergy among multiple processing equipment, and mixed production of various products. These characteristics lead to challenges in carbon emission accounting, such as the problem of carbon emission data haze. To address this issue, this study analyzes the dynamic characteristics of carbon emissions in the die-casting process chain to identify the sources of carbon emissions. Subsequently, a multi-source carbon data collection scheme is developed based on these sources, and an information-physical fusion-based model for carbon source data collection and integration is established. Following this, the correlation between carbon sources in the die-casting process chain and the production process is elucidated, and a carbon emission accounting model for mixed production of multiple die-casting products is developed. For model parameterization, time-series power data are systematically integrated. Finally, using the dynamic characteristics of carbon emissions from typical die-casting production and the carbon source data model as a foundation, a case study is conducted on the carbon emissions from mixed production in the die-casting process chain. The results demonstrate the effectiveness, feasibility, and reliability of the proposed carbon emission accounting model. This study lays the foundation for optimizing carbon reduction in the die-casting process chain and supports the transition to a low-carbon die-casting workshop.