Search Results (250)

Industry 4.0 is speeding up the move to connected, data-driven, and automated production, where the Internet of Things (IoT) enables sensing, communication, and real-time support for decisions. At the same time, rapid growth in industrial IoT studies has led to scattered technologies, architectures, and results. This paper fills this gap through a systematic literature review on IoT for Industry 4.0. It also helps readers compare methods and choose suitable building blocks for real deployments today. We focus on key technologies, integration architectures, application areas, challenges, trends, and reported benefits. Using PRISMA 2020, we searched five major databases (Scopus, MDPI, IEEE Xplore, ScienceDirect, and Web of Science) for 2020–2025 and found 584 records. After removing duplicates and screening, we kept 96 peer-reviewed studies for detailed analysis. Results show that most studies use a layered stack that combines sensing/actuation, industrial networking, data collection pipelines, and analytics across edge, fog, and cloud resources. MQTT, OPC UA, CoAP, LPWAN, and 5G connectivity are often used for communication, while RAMI 4.0, IIRA, and similar layered models guide system design. Many architectures follow an edge–cloud pattern, with growing focus on digital twin/CPS links and security-by-design. Applications are mainly smart manufacturing, predictive maintenance, and logistics, with added work in energy management, Construction 4.0, and agri-food monitoring. The key barriers remain interoperability, data quality and evaluation gaps, cybersecurity risks, legacy integration, and deployment limits. The review points to future work on edge AI/TinyML, deterministic connectivity, scalable digital twins, trusted data sharing, and sustainable industrial IoT. Full article

Digital twins (DTs) in the energy sector are operational-data-driven models of assets, installations, and networks. Their value grows alongside renewable expansion, electronic communications, and stricter resilience requirements for critical infrastructure. This study evaluates DT applications in Poland’s energy transition, identifying regulatory and cybersecurity determinants required for safe, scalable use. The methodology combines an international literature review, regulatory assessment, and qualitative desk research focusing on DT projects across four Polish energy groups: Enea, Energa, PGE, and Tauron. Each case is assessed using a DT maturity and governance framework covering scope, data coupling, decision support, and security posture. The study identifies four primary deployment types: (1) operational network twins for distribution system operators leveraging SCADA/ADMS, GIS, and state estimation; (2) AI-driven asset performance twins for wind turbines and CHP plants; (3) flexibility twins for hydropower system services; and (4) immersive training twins for the offshore wind sector. Main constraints include data quality, interoperability, fragmented data access regulations, and expanded cyber-attack surfaces from OT/IT convergence. DTs aid spatial planning, mitigating location and land use conflicts. Recommendations emphasize harmonized data governance, cybersecurity-by-design, special determinants, and the creation of regulatory sandboxes to support DT implementation within critical energy infrastructure. Full article

The increasing integration of distributed energy resources (DERs), including photovoltaic systems, battery energy storage systems, electric vehicles, and flexible loads, is transforming modern power systems and creating new challenges for coordination, control, and cybersecurity. Conventional Virtual Power Plant (VPP) architectures typically rely on centralized energy management systems, which may face scalability limitations, communication bottlenecks, cybersecurity risks, and reduced resilience to failures. This paper presents a blockchain-enabled decentralized Virtual Power Plant framework for secure and autonomous coordination of distributed energy resources. The proposed architecture combines blockchain technology, smart contracts, IoT-based communication infrastructure, and decentralized energy management functions within a unified multi-layer coordination framework. Smart contracts automate energy scheduling, peer-to-peer transaction validation, and settlement processes, reducing dependence on centralized control entities. Lightweight blockchain consensus mechanisms are employed to improve scalability while limiting computational overhead. The effectiveness of the proposed framework is evaluated through simulation-based case studies involving decentralized DER coordination, peer-to-peer energy trading, and resilience assessment under node-failure conditions. Its performance is compared with that of a conventional centralized VPP architecture in terms of scalability, coordination reliability, communication overhead, transaction transparency, and fault tolerance. The results indicate that the decentralized framework improves operational resilience, coordination transparency, and scalability under increasing DER participation while maintaining satisfactory energy balancing performance. Although blockchain-based coordination introduces additional transaction latency, the proposed approach enhances cybersecurity, reduces dependence on centralized control structures, and provides a flexible foundation for future intelligent smart-grid applications. Full article

Large Language Models (LLMs) are rapidly transitioning from research concepts to transformative artificial intelligence components within the power and energy domain. Their ability to fuse diverse data, spanning SCADA logs, real-time sensor readings, and regulatory documentation enables unprecedented capabilities in forecasting, operator decision support, anomaly detection, and wide-area situational awareness for future intelligent grids. However, the integration of LLMs into safety-critical and highly regulated power systems introduces a convergence of novel and severe security risks. Beyond exhibiting model-intrinsic vulnerabilities like hallucination, prompt injection, and data poisoning, these models are susceptible to system-level threats that could compromise grid stability, distort energy market operations, or facilitate the leakage of sensitive operational data. Moreover, integrating LLM workloads into cloud or hybrid architectures necessitates strict compliance with critical standards and emerging governance frameworks like the EU AI Act. While existing surveys address AI security in power systems, general LLM security, and AI in smart grids separately, this paper bridges these threads by providing a unified treatment of LLM-specific risks, power-system deployment constraints, and emerging governance frameworks—a combination not covered in prior surveys. We provide a systematic taxonomy of risks across five dimensions: cybersecurity, privacy, robustness, explainability, and governance. We synthesize technological advances, clarify the complex interplay between LLM failure modes and grid security, and propose a forward-looking research agenda to guide future investigation. This work aims to be an indispensable resource for researchers, utility operators, and policymakers in designing resilient, trustworthy, and compliant AI-enabled energy infrastructures. Full article

The convergence of Software-Defined Networking (SDN) and Blockchain (BC) creates a symbiotic relationship in which SDN’s programmable global visibility complements BC’s decentralized, immutable trust model to address critical cybersecurity vulnerabilities and cyber attacks. Addressing the fragmentation in the current literature, this study rigorously investigates BC and SDN (B-SDN) integration with the primary objectives of: (1) differentiating impacts across varied sectors, including the Internet of Things (IoT), Smart Grids, and Vehicular Ad Hoc Networks (VANETs) and more; (2) analyzing critical performance metrics such as energy efficiency and scalability; (3) classifying mitigation, detection, and prevention schemes for specific threats; (4) examining novel Artificial Intelligence (AI) methods; and (5) identifying open challenges and future research directions. Methodologically, this study conducts a survey of state-of-the-art B-SDN studies to investigate six key areas: Industry-specific applications, security mechanisms, defense strategies, defenses against specific attacks, AI integration, and implementation performance. The findings demonstrate that B-SDN integration shows strong potential in simulated and prototype environments to mitigate specific high-impact threats, such as Distributed Denial of Service (DDoS), Man-in-the-Middle (MiTM), and spoofing, across various domains, including IoT, 5G/6G, VANETS, and Smart Grid. Despite the benefits and advantages promised by B-SDN, several limitations continue to exist, including the latency–security trade-off inherent to consensus protocols and scalability constraints in large-scale deployments. Finally, open research challenges persist in AI-driven automation, particularly in Federated Learning (FL) and in the development of standardized interoperability protocols required to enable the transition from conceptual models to operational systems. Full article

Industrial maintenance has increasingly evolved into a strategic function for improving asset reliability, extending asset lifecycle, and supporting sustainability objectives. However, the literature remains fragmented, with limited integration between digital twins, maintenance practices, and sustainability-oriented decision-making. To address this gap, the study performs a systematic review of 49 publications indexed in Scopus and Web of Science and introduces an integrative conceptual framework for Sustainable Maintenance 4.0. The analysis explores the role of digital twins, as a key enabling technology within the Industry 4.0 landscape, in supporting the shift from reactive and schedule-based maintenance toward predictive and prescriptive strategies. The findings suggest that digital twins can enhance maintenance decision-making, improve asset reliability, and contribute to lifecycle optimization. The reviewed studies also report improvements in operational and energy performance, although these effects vary according to digital maturity, system configuration, and implementation scope. In addition, digital twins may support safer operations and workforce development through data-driven and immersive environments. Despite these benefits, challenges remain, including high investment requirements, interoperability limitations, cybersecurity risks, and the need for interdisciplinary skills. The proposed framework positions digital twins as a mediating element between physical assets, data acquisition, advanced analytics, maintenance services, and sustainability outcomes. Full article

The convergence of the Internet of Things (IoT), edge computing, and artificial intelligence (AI) is reshaping cyber defense in distributed cyber–physical environments. IoT-edge systems expose heterogeneous, resource-constrained, and intermittently connected devices to threats that unfold close to sensing and control processes, making purely signature-based or rule-based defenses increasingly insufficient. This article presents a structured review of AI for cybersecurity in IoT-edge systems from a systems-oriented perspective. Rather than surveying AI for IoT security in general, it organizes the literature around four practical lenses: AI methods, datasets and benchmarks, evaluation practice, and deployment constraints. The review reconstructs a workspace-verifiable corpus of 96 references, emphasizes literature published between January 2023 and April 2026 while retaining foundational benchmark papers, and uses a conservative 26-paper empirical subset for paper-level gap coding. Because this subset was purposively sampled and the original retrieval logs were not preserved, coded counts are interpreted as recoverable reporting signals and comparability indicators rather than field-level prevalence estimates. The revised synthesis further stratifies the coded evidence by task, model family, dataset, application scenario, metric type, and deployment signal, and translates deployment feasibility into a minimum reporting checklist and edge-hardware decision matrix. Within this evidence boundary, recent work remains dominated by intrusion and anomaly detection, with continued use of traditional machine learning, deep learning, federated learning, explainable AI, and graph-based approaches. However, experimentation remains concentrated around a small set of public benchmarks, while latency, memory, energy, communication overhead, operational robustness, and reproducibility are reported inconsistently. The field is therefore constrained less by classifier novelty than by benchmark concentration, weak deployment reporting, limited response-and-mitigation analysis, undercoverage of authentication, access-control, and trust-management tasks, and limited reproducible edge-aware evaluation. Full article

This study presents a bibliometric and systematic review of 480 articles meeting the following inclusion criteria: English-language articles, reviews, or proceeding papers focusing on building topics with full text available, retrieved from the Web of Science Core Collection on 9 Jannary 2026 to map the intellectual landscape of smart-sustainable building (SSB) research. Employing the PRISMA framework combined with scientometric mapping (VOSviewer), thematic classification, and qualitative synthesis (no risk of bias assessment was performed as this was a bibliometric review), the analysis reveals exponential publication growth since 2022, identifying three dominant thematic clusters: digital enabling technologies (41.0%), energy systems (30.8%), and advanced building envelopes and materials (28.3%). Keyword analysis identifies “smart buildings,” “green buildings,” and “energy efficiency” as central conceptual anchors, while temporal trends indicate increasing attention to artificial intelligence, digital twins, and blockchain. Notably, 51.4% of articles address two or more themes simultaneously, confirming the field’s interdisciplinary character. Critical analysis reveals persistent fragmentation: sustainable building rating tools (e.g., BREEAM, LEED) and smart building evaluation methods (e.g., Smart Readiness Indicator). Seven challenges, including assessment fragmentation, high costs, and cybersecurity vulnerabilities, are identified as barriers to SSB adoption. Limitations include reliance on a single database (Web of Science) and subjective thematic classification. This review provides a roadmap for future research emphasizing integrated assessment frameworks and interdisciplinary collaboration. Registration: Not pre-registered. Funding: National Key R&D Program of China (2025YFF0521003). Full article

Critical infrastructures are increasingly Internet-connected cyber–physical systems whose recovery after cyber incidents must satisfy safety, timing, regulatory, and interdependency constraints. Yet, the use of large language models (LLMs) for generating recovery plans remains fragmented across cybersecurity, industrial control, digital twins, and AI assurance research. This review synthesizes that emerging field through a structured critical survey of studies on LLMs in incident response, OT/ICS resilience, and cyber–physical recovery, with a focused perspective on grounding, trust, and assurance mechanisms relevant to recovery-plan generation. It develops an architecture-centric taxonomy spanning prompt-only assistants, retrieval-augmented copilots, graph-aware planners, multi-agent systems, and hybrid verification/simulation pipelines; maps realistic applications across energy, water, manufacturing, transportation, healthcare, and telecommunications; and organizes limitations into technical, security, governance, and human-factor categories. Based on this synthesis, the paper proposes the Grounded Recovery Planning Stack as a reference architecture and outlines a staged roadmap from human-in-the-loop copilots to bounded orchestration. The main conclusion is that near-term value lies in grounded, auditable, compliance-aware copilots, whereas autonomous recovery execution remains premature without stronger validation, state-aware grounding, sector-specific benchmarks, and formal safeguards. Full article

The standard for effective communication between Internet of Things (IoT) devices has been demonstrated by the increasing demand for IoT technologies in Industry 5.0, along with the growing use of actuators, sensors, and automated processes in these settings. De-vice-to-device interactions controlled by communication protocols that specify data sharing are essential to effective operation. By establishing a single standard that permits plug-and-play integration and improves flexibility across various IoT devices, the IEEE 1451 standard represents an approach. This standard ensures interoperability and enables smooth communication with devices from various companies, regardless of their features. By addressing major obstacles to system integration, the IEEE 1451 standard enables IoT technologies to reach their full potential. By integrating information technology (IT) through automation and industrial control systems (ICSs), the Industrial IoT (IIoT) is transforming many industries, especially essential sectors such as energy, chemicals, oil and gas, and water plants. Although drinking water is an essential resource for life and an aspect of technological progress, little is known about the potential for cyberattacks, including the disastrous consequences they could have for water treatment plants. This re-view identifies and documents several adversarial cyberattacks targeting the water distribution and purification sector. Understanding the range of risk factors in this sector is our primary objective. This study presents a technical assessment from an IIoT perspective that addresses attack scenarios, real-world instances of cyberattacks in the water industry, a range of security challenges, and security measures. The contribution is an informative, up-to-date resource that benefits both prospective scholars and industrial practitioners. By integrating key findings to build a secure and reliable digital future, this work will advance a comprehensive understanding of the cybersecurity environment in water plants in Industry 5.0 and smart cities. Full article

Smart cities increasingly depend on electrical grid infrastructures capable of operating under high levels of digitalization, decentralization, and intelligence while maintaining reliability, security, and governance at the city scale. However, conventional power systems, historically designed for centralized generation and passive operation, are poorly aligned with the operational complexity, multi-actor coordination, and cross-sector integration characteristic of urban energy systems. This review develops an architecture-first perspective on smart-city electrical grids, tracing their evolution from digitalized power networks to decentralized and AI-enabled urban energy ecosystems. Rather than focusing on individual technologies, the study evaluates grid architectures using a multi-layer framework that integrates physical grid infrastructure, distributed energy resources and microgrids, communication and data platforms, intelligence placement, cybersecurity exposure, and governance accountability. Smart-city grid architectures are assessed using deployability beyond pilot projects, auditability, and regulatory alignment as primary evaluation criteria alongside conventional technical considerations. Through this perspective, the review explains a recurring pattern observed in the literature: many technically mature smart-grid solutions fail to scale in real urban deployments due to architectural fragmentation and governance constraints. By synthesizing insights from power systems engineering, information and communication technologies, and smart-city research, the paper highlights architectural trade-offs related to decentralization, interoperability, resilience under compound threats, and assisted autonomy. The resulting framework supports researchers, system designers, and policymakers in the coordinated development of resilient, secure, and governable electrical grids for future smart-city energy systems. Full article

The rapid growth of renewable energy is driving a profound transformation of electricity grids toward architectures characterized by high shares of inverter-based generation, increased decentralization, and extensive digitalization. While wind and solar technologies have matured at the component level, their large-scale integration introduces technical, operational, and institutional challenges that extend beyond conventional power-system design paradigms. This review provides an integrated synthesis of the technologies, control strategies, and management processes that enable renewable energy integration into emerging electricity grids. Key challenges are analyzed across multiple timescales: fast frequency and voltage dynamics in low-inertia systems (milliseconds to seconds), forecasting, optimization, and automated control (real-time to near-real-time), and long-term planning of transmission, storage, and flexibility resources (years to decades). The synthesis covers grid-forming and grid-following inverter control, with quantitative comparison across short-circuit-ratio regimes; HVDC and HVAC transmission technologies; energy storage systems, including emerging electrochemical and mechanical solutions; smart-grid digitalization through EMS, SCADA, and digital twins; artificial intelligence and machine-learning deployments at major transmission system operators; sector coupling involving hydrogen and carbon capture; and cybersecurity considerations. Real-world case studies are used to illustrate practical lessons, with explicit attention to the brownfield–greenfield distinction between modernization of legacy systems and the design of new networks in developing regions. The review concludes by identifying key research and development priorities for achieving reliable, resilient, and economically efficient high-renewable energy systems. Full article

Urban flexibility research is expanding across buildings, electric vehicles (EVs), distributed energy resources (DERs), storage, positive energy districts (PEDs), digital twins, and interoperability platforms. These strands are often reviewed separately, although urban distribution operators must manage their combined impacts on the same feeders. This paper presents a PRISMA 2020-aligned systematic review with evidence mapping and narrative synthesis of feeder-aware coordination in smart-city electricity systems. Searches of Scopus, Web of Science, IEEE Xplore, ScienceDirect, and citation chasing identified 312 records; 127 studies were included after screening and eligibility assessment, 101 entered the quantitative mapping sample, and 31 formed the deep-synthesis anchor core. Sparse contingency tables were analyzed with Monte-Carlo permutation chi-square tests and bootstrap confidence intervals for Cramér’s V, while ordinal variables were summarized with medians and interquartile ranges. Explicit feeder grounding was concentrated in grid-oriented and EV-oriented studies, whereas many AI/digital-twin and interoperability studies were less often validated against distribution-network operation. Economic and peak-flexibility indicators were reported far more often than interoperability, cybersecurity, or validation-maturity indicators in the anchor core. The synthesis also showed that deployment-oriented work depends on clearer treatment of standards, co-simulation workflows, regulatory instruments, and stakeholder roles. The evidence base is heterogeneous, English-only, and single-coded, so the quantitative results are descriptive rather than population-level. The review contributes a transparent three-layer corpus design (127 included/101 mapped/31 anchor), a domain-specific specialization of SGAM/IEEE 2030 for urban feeder orchestration, an operational digital-twin definition and validation ladder, a retrofittable benchmarking framework, and a practical roadmap for DSOs, municipalities, aggregators, EV operators, building managers, and ICT providers. Full article

Modern power systems are undergoing a structural transformation driven by the rapid integration of renewable energy sources, distributed energy resources, electrification, and increasing operational uncertainty. These developments expose the limitations of traditional centralized energy management and rule-based automation in highly distributed, data-intensive, and dynamically coupled energy infrastructures. In response, recent advances in artificial intelligence offer new opportunities for improving prediction, coordination, and adaptive control. This paper develops a reference architecture for Autonomous Energy Systems based on the integration of generative AI, agentic AI, digital twins, and distributed cyber–physical energy infrastructures. Rather than treating forecasting, control, simulation, and market coordination as separate research tracks, the paper organizes them within a common architectural perspective. Generative AI is positioned as a source of scenario intelligence, synthetic data generation, and uncertainty-aware forecasting, while agentic AI is framed as a bounded decision layer for perception, reasoning, planning, and coordinated action under operational constraints. The paper further clarifies the distinction between agentic AI, conventional multi-agent systems, and multi-agent reinforcement learning in energy applications. Representative application domains are discussed, including self-healing power grids, autonomous energy markets, and digital twin training environments. Major open challenges are identified in relation to scalability, physical consistency, safety verification, sim-to-real transfer, cybersecurity, interoperability with legacy infrastructures, and governance. The paper concludes by outlining a research agenda for the staged and safe development of increasingly autonomous energy systems. Full article

A microgrid is a localized distribution network composed of electricity users who have access to local renewable and other energy sources. While the utility grid plays a critical role in the nation’s economy, security, and the well-being of its residents, connecting microgrids to the wider network via utility substations can introduce significant cybersecurity risks. Unlike most existing studies that rely on simulation, this research designs and implements a physical microgrid testbed to examine cybersecurity vulnerabilities in microgrid systems. We examine the impact of various cyberattacks—including denial of service (DoS) and communication hijacking—on microgrid operations, with a particular focus on system stability and communication networks. The findings reveal critical weaknesses within the existing communication infrastructure, providing valuable insights for designing more resilient and secure microgrids. This work offers a practical framework for addressing cybersecurity challenges in real-world industrial utility networks. Full article