Högskolan i Skövde

The convergence of cyber and physical systems through cyber–physical systems (CPSs) has been integrated into cyber–physical production systems (CPPSs), leading to a paradigm shift toward intelligent manufacturing. Despite the transformative benefits that CPPS provides, its increased connectivity exposes manufacturers to cyber-attacks through exploitable vulnerabilities. This paper presents a novel approach to CPPS security protection by leveraging digital twin (DT) technology to develop a comprehensive security model. This model enhances asset visibility and supports prioritization in mitigating vulnerable components through DT-based virtual tuning, providing quantitative assessment results for effective mitigation. Our proposed DT security model also serves as an advanced simulation environment, facilitating the evaluation of CPPS vulnerabilities across diverse attack scenarios without disrupting physical operations. The practicality and effectiveness of our approach are illustrated through its application in a human–robot collaborative assembly system, demonstrating the potential of DT technology. 

Critical infrastructure (CIs) such as power grids link a plethora of physical components from many different vendors to the software systems that control them. These systems are constantly threatened by sophisticated cyber attacks. The need to improve the cybersecurity of such CIs, through holistic system modeling and vulnerability analysis, cannot be overstated. This is challenging since a CI incorporates complex data from multiple interconnected physical and computation systems. Meanwhile, exploiting vulnerabilities in different information technology (IT) and operational technology (OT) systems leads to various cascading effects due to interconnections between systems. The paper investigates the use of a comprehensive taxonomy to model such interconnections and the implied dependencies within complex CIs, bridging the knowledge gap between IT security and OT security. The complexity of CI dependence analysis is harnessed by partitioning complicated dependencies into cyber and cyber-physical functional dependencies. These defined functional dependencies further support cascade modeling for vulnerability severity assessment and identification of critical components in a complex system. On top of the proposed taxonomy, the paper further suggests power-grid reference models that enhance the reproducibility and applicability of the proposed method. The methodology followed was design science research (DSR) to support the designing and validation of the proposed artifacts. More specifically, the structural, functional adequacy, compatibility, and coverage characteristics of the proposed artifacts are evaluated through a three-fold validation (two case studies and expert interviews). The first study uses two instantiated power-grid models extracted from existing architectures and frameworks like the IEC 62351 series. The second study involves a real-world municipal power grid.

To better optimize and control the renewable energy system and its integration with traditional grid systems and other energy systems, corresponding technologies are needed to meet its growing practical application requirements: decentralized management and control, support for decentralized decision-making, fine-grained and timely data sharing, maintain data and business privacy, support fast and low-cost electricity market transactions, maintain the security and reliability of system operation data, and prevent malicious cyberattacks. Blockchain is based on core technologies such as distributed ledgers, asymmetric encryption, consensus mechanisms, and smart contracts and has some excellent features such as decentralization, openness, independence, security, and anonymity. These characteristics seem to meet the technical requirements of future renewable energy systems partially. This chapter will systematically review how blockchain technology can potentially solve the challenges with decentralized solutions for future renewable energy systems and show a guideline to implement blockchain-based corresponding applications for future renewable energy. 

A comprehensive overview of blockchain-based smart grid management, control, and operation solutions. The report compares with related reviews and highlights the challenges in management, control, and operation for a blockchain-based smart grid, as well as future research directions in five categories: collaboration between actors, data analytics and management, control of network imbalances, decentralization of network management and operation, security and privacy.

The report reviews how blockchain technology can potentially solve the challenges of decentralized solutions for future renewable energy systems. As a result, several applications of blockchain for renewable energy are discussed, such as electric vehicles, decentralized P2P energy transactions, carbon certification and trading, physical information security, energy transfer, Energy-to-X, and the Internet of Energy.

A guideline for the implementation of blockchain to corresponding applications for future renewable energy is also presented in this report. This includes the different blockchain system architectures, the data flow from the power grid processed and recorded, the choice of the appropriate consensus, and the different blockchain frameworks.

Power generation, distribution, transmission, and consumption face ongoing challenges such as smart grid management, control, and operation, resulting from high energy demand, diversity of energy sources, and environmental or regulatory issues. To better optimize and control the renewable energy system and its integration with traditional grid systems and other energy systems, corresponding technologies are needed to meet its growing practical application requirements: decentralized management and control, support for decentralized decision-making, fine-grained and timely data sharing, maintain data and business privacy, support fast and low-cost electricity market transactions, and maintain the security and reliability of system operation data, and prevent malicious cyberattacks. Blockchain is based on core technologies such as distributed ledgers, asymmetric encryption, consensus mechanisms, and smart contracts and has some excellent features such as decentralization, openness, independence, security, and anonymity. These characteristics partially meet the technical requirements of future renewable energy systems.

This report provides a comprehensive overview of blockchain-based smart grid management, control, and operation solutions. The report compares with related reviews and highlights the challenges in management, control, and operation for a blockchain-based smart grid, as well as future research directions in five categories: collaboration between actors, data analytics and management, control of network imbalances, decentralization of network management and operation, security and privacy.

The report reviews how blockchain technology can potentially solve the challenges of decentralized solutions for future renewable energy systems. As a result, several applications of blockchain for renewable energy are discussed, such as electric vehicles, decentralized P2P energy transactions, carbon certification and trading, physical information security, energy transfer, Energy-to-X, and the Internet of Energy.

A guideline for the implementation of blockchain to corresponding applications for future renewable energy is also presented in this report. This includes the different blockchain system architectures, the data flow from the power grid processed and recorded, the choice of the appropriate consensus, and the different blockchain frameworks.These are the results and conclusions of a project, which is part of a research programme run by Energiforsk. The author/authors are responsible for the content.

Elproduktion, -distribution, -överföring och -förbrukning står inför pågående utmaningar som smart näthantering, kontroll och drift, som är ett resultat av hög energiefterfrågan, mångfald av energikällor och miljö- eller regelfrågor. För att bättre optimera och kontrollera det förnybara energisystemet och dess integration med traditionella nätsystem och andra energisystem, behövs motsvarande teknik för att möta dess växande praktiska tillämpningskrav: decentraliserad förvaltning och kontroll, stöd för decentraliserat beslutsfattande, finkornigt och aktuellt datadelning, upprätthålla data och affärsintegritet, stödja snabba och billiga elmarknadstransaktioner och upprätthålla säkerheten och tillförlitligheten för systemdriftsdata och förhindra skadliga cyberattacker. Blockkedja är baserad på kärnteknologier som distribuerade reskontra, asymmetrisk kryptering, konsensusmekanismer och smarta kontrakt och har några utmärkta funktioner som decentralisering, öppenhet, oberoende, säkerhet och anonymitet. Dessa egenskaper ska delvis uppfylla de tekniska kraven för framtida förnybara energisystem.

Den här rapporten ger en omfattande översikt över blockkedjebaserade lösningar för smart näthantering, kontroll och drift. Rapporten jämför med relaterade recensioner och belyser utmaningarna inom förvaltning, kontroll och drift för ett blockkedjebaserat smart nät, såväl som framtida forskningsriktningar inom fem kategorier: samarbete mellan aktörer, dataanalys och hantering, kontroll av nätobalanser, decentralisering av nätförvaltning och drift, säkerhet och integritet.

Rapporten går igenom hur blockkedjeteknik potentiellt kan lösa utmaningarna med decentraliserade lösningar för framtida förnybara energisystem. Som ett resultat diskuteras flera tillämpningar av blockkedja för förnybar energi såsom elektriska fordon, decentraliserade P2P-energitransaktioner, certifiering och handel med koldioxidutsläpp, fysisk informationssäkerhet, energiöverföring, Energi-to-X och Internet för Energi.

En riktlinje för implementering av blockkedja till motsvarande tillämpningar för framtida förnybar energi presenteras också i denna rapport. Detta inkluderar olika blockkedja -systemarkitektur, dataflöde från elnätet bearbetas och registreras, val av lämplig konsensus samt de olika blockkedja -ramverken

Power generation, distribution, transmission, and consumption face ongoing challenges such as smart grid management, control, and operation, resulting from high energy demand, the diversity of energy sources, and environmental or regulatory issues. This paper provides a comprehensive overview of blockchain-based solutions for smart grid management, control, and operations. We systematically summarize existing work on the use and implementation of blockchain technology in various smart grid domains. The paper compares related reviews and highlights the challenges in the management, control, and operation for a blockchain-based smart grid as well as future research directions in the five categories: collaboration among stakeholders; data analysis and data manage-ment; control of grid imbalances; decentralization of grid management and operations; and security and privacy. All these aspects have not been covered in previous reviews. 

Modern security practices promote quantitative methods to provide prioritisation insights and support predictive analysis, which is supported by open-source cybersecurity databases such as the Common Vulnerabilities and Exposures (CVE), the National Vulnerability Database (NVD), CERT, and vendor websites. These public repositories provide a way to standardise and share up-to-date vulnerability information, with the purpose to enhance cybersecurity awareness. However, data quality issues of these vulnerability repositories may lead to incorrect prioritisation and misemployment of resources. In this paper, we aim to empirically analyse the data quality impact of vulnerability repositories for actual information technology (IT) and operating technology (OT) systems, especially on data inconsistency. Our case study shows that data inconsistency may misdirect investment of cybersecurity resources. Instead, correlated vulnerability repositories and trustworthiness data verification bring substantial benefits for vulnerability management. 

Relevant research has shown the potential to understand the game user experience (GUX) more accurately and reliably by measuring the user’s psychophysiological responses. However, the current studies are still very scarce and limited in scope and depth. Besides, the low-detection accuracy and the common use of the professional physiological signal apparatus make it difficult to be applied in practice. This article analyzes the GUX, particularly flow experience, based on users’ physiological responses, including the galvanic skin response (GSR) and heart rate (HR) signals, captured by low-cost wearable devices. Based on the collected data sets regarding two test games and the mixed data set, several classification models were constructed to detect the flow state automatically. Hereinto, two strategies were proposed and applied to improve classification performance. The results demonstrated that the flow experience of game users could be effectively classified from other experiences. The best accuracies of two-way classification and three-way classification under the support of the proposed strategies were over 90% and 80%, respectively. Specifically, the comparison test with the existing results showed that Strategy1 could significantly reduce the negative interference of individual differences in physiological signals and improve the classification accuracy. In addition, the results of the mixed data set identified the potential of a general classification model of flow experience.

Criticalmanufacturingprocessesinsmartnetworkedsystems such as Cyber-Physical Production Systems (CPPSs) typically require guaranteed quality-of-service performances, which is supported by cyber- security management. Currently, most existing vulnerability-assessment techniques mostly rely on only the security department due to limited communication between di↵erent working groups. This poses a limitation to the security management of CPPSs, as malicious operations may use new exploits that occur between successive analysis milestones or across departmental managerial boundaries. Thus, it is important to study and analyse CPPS networks’ security, in terms of vulnerability analysis that accounts for humans in the production process loop, to prevent potential threats to infiltrate through cross-layer gaps and to reduce the magnitude of their impact. We propose a semantic framework that supports the col- laboration between di↵erent actors in the production process, to improve situation awareness for cyberthreats prevention. Stakeholders with dif- ferent expertise are contributing to vulnerability assessment, which can be further combined with attack-scenario analysis to provide more prac- tical analysis. In doing so, we show through a case study evaluation how our proposed framework leverages crucial relationships between vulner- abilities, threats and attacks, in order to narrow further the risk-window induced by discoverable vulnerabilities.

The extensive use of Simulation-based Serious Games (SSGs) has made a revolution in educational techniques. As a potentially significant feature for SSG design and evaluation, the term fidelity (the similarity between an SSG and its real reference) emerges and attracts increasing attention. The study of fidelity not only benefits the design, development, and analysis of an SSG with the consideration of improving the learning effect but also contributes to the investment reduction of an SSG. However, the term fidelity is used inconsistently in current literature. The introduction of new technologies (e.g. virtual reality) and the blend of multiform SSGs also facilitate the extension of fidelity with new connotations. All lead to confusing concepts and vague measure metrics. Besides, the relationship between fidelity and learning effect is still uncertain. A new vision and a comprehensive conceptual framework of fidelity for more general applications are in need. In this paper, further exploration and discussion of these issues in relation to fidelity of SSGs are presented through a systematic review. A general conceptual framework considering both aspects of the SSG system itself and the learners is developed and applied to analyze fidelity in SSGs. Based on that, a discussion on fidelity related issues of SSG design and development is presented.

Cutting-edge sensors and devices are increasingly deployed within urban areas to make-up the fabric of transmission control protocol/internet protocol con- nectivity driven by Internet of Things (IoT). This immersion into physical urban environments creates new data streams, which could be exploited to deliver novel cloud-based services. Connected vehicles and road-infrastructure data are leveraged in this article to build applications that alleviate notorious parking and induced traffic-congestion issues. To optimize the utility of parking lots, our proposed SmartPark algorithm employs a discrete Markov-chain model to demystify the future state of a parking lot, by the time a vehicle is expected to reach it. The algorithm features three modular sections. First, a search pro- cess is triggered to identify the expected arrival-time periods to all parking lots in the targeted central business district (CBD) area. This process utilizes smart-pole data streams reporting congestion rates across parking area junc- tions. Then, a predictive analytics phase uses consolidated historical data about past parking dynamics to infer a state-transition matrix, showing the transfor- mation of available spots in a parking lot over short periods of time. Finally, this matrix is projected against similar future seasonal periods to figure out the actual vacancy-expectation of a lot. The performance evaluation over an actual busy CBD area in Stockholm (Sweden) shows increased scalability capa- bilities, when further parking resources are made available, compared to a baseline case algorithm. Using standard urban-mobility simulation packages, the traffic-congestion-aware SmartPark is also shown to minimize the journey duration to the selected parking lot while maximizing the chances to find an available spot at the selected lot.