Search Results (2,282)

Digital twins are becoming vital decision-making infrastructures across critical infrastructure sectors such as smart city urban services, transportation, energy, and healthcare. As digital twins become autonomous and gain real-time intervention capabilities, their governance becomes increasingly essential. Yet existing governance mechanisms remain largely procedural: they emphasize compliance without operationalizing the cognitive practices required to question assumptions, detect algorithmic harms, or support legitimate multi-actor deliberation. Drawing on a systematic scoping review, this study synthesizes the literature on digital twin autonomy, algorithmic risks, epistemic foundations, and governance mechanisms. The review reveals a fundamental gap: current governance mechanisms lack institutionalized cognitive capacities for continuous validation, proactive detection of emerging harms, and structured multi-stakeholder deliberation. This gap is corroborated by a limited but growing body of empirical studies on governance in deployed DT settings. To address this gap, the paper proposes the skeptical intelligence framework, developed through design science research. The framework integrates three cognitive functions: validation, detection, and deliberation supported by operational principles, governance artifacts, and distributed accountability roles. The framework advances digital twin governance beyond compliance toward a model rooted in critical epistemology, reflexivity, transparency, and democratic legitimacy. Consistent with design science research, the framework is delivered and evaluated at design time; empirical implementation and outcome evaluation are planned across multi-actor digital twin infrastructure contexts, including smart city governance, port logistics, and energy networks, where DT-mediated decisions redistribute opportunities and risks across heterogeneous stakeholders. Empirical validation in an operational setting is planned as the next phase of this research. Full article

The transition towards smart megacities has deeply integrated Artificial Intelligence (AI) with power–transport networks. While AI empowers complex operations like multi-network coordinated dispatch and emergency rescue, current algorithm-centric perspectives largely ignore its massive physical energy costs. Accordingly, this Perspective examines the dual role of AI, considering it not only as an intelligent decision-support tool but also as a potential source of additional stress on physical infrastructure. First, through a structured synthesis of the representative literature, we deconstruct the functional dependencies between algorithms and physical infrastructures, identifying how AI reshapes the operational paradigms of power, ground transport, and aerial networks under routine and emergency scenarios. We then introduce the concept of the “Computation–Energy Paradox.” Integrating conceptual analysis with a quantitative case study of a typical community, we illustrate a plausible failure mechanism: during extreme disasters, intensified AI invocation for emergency management generates surging computational loads, which paradoxically exacerbate power shortages and reduce the operating margin of already weakened systems. In addition, we analyze core engineering bottlenecks, including spatiotemporal computation–energy mismatches and physical constraints in extreme edge environments. To address these challenges, we outline a prospective roadmap encompassing lightweight emergency AI and computation–power-coordinated offloading mechanisms. Finally, the sustainable development of such systems suggests a paradigm shift: AI must evolve from a purely virtual algorithm into a physical component of an integrated compute–power–transport system. Full article

A transition towards sustainable transportation requires efficient integration of electric vehicles (EVs) with renewable energy sources. This work proposes a two-stage Parking Lot Energy Management Scheme (PLEMS) to minimize charging costs while maximizing solar photovoltaic utilization in commercial parking facilities. In the first stage, the optimization phase is formulated using a mixed-integer linear programming (MILP) that minimizes the overall cost of EV charging while ensuring maximum utilization of locally available PV energy. In the second stage, a gated recurrent unit (GRU)-based deep learning model performs state of charge (SOC) forecasting for EVs parked in the parking lot. Using the predicted SOC for the next time step, the system decides whether each EV will be charged or discharged, ensuring consistency with the cost-optimal MILP strategy from the first stage. The proposed PLEMS achieves up to 62% daily cost savings in charging compared to uncoordinated direct grid charging. However, this cost saving is the outcome of proposed optimization as well as the integration of PV panels in power grid. When compared with nine similar vehicles to grid (V2G)-enabled approaches from the literature, which report cost savings ranging from 9.73% to 52%, the proposed framework shows an improvement of 10% to 52% over these methods. This hybrid MILP-GRU framework offers practical V2G operation and high scalability for large EV fleets in solar-powered smart parking lots. Full article

Effective thermal management is a cornerstone of safe, long-life lithium-ion battery operation, especially under high-rate charge–discharge and dynamic driving conditions. Conventional active cooling technologies face inherent trade-offs between heat dissipation efficiency, system complexity, and temperature uniformity, while phase change materials (PCMs) provide a promising passive alternative by absorbing latent heat during phase transition to buffer temperature spikes, improve temperature uniformity, and delay thermal runaway propagation. This paper presents a comprehensive review of recent advances in PCM-based lithium-ion battery thermal management, systematically covering the full scope from fundamental battery heat generation mechanisms to material synthesis optimization and hybrid system integration. At the material level, we analyze state-of-the-art strategies to address the intrinsic drawbacks of organic PCMs—low thermal conductivity, mismatched phase transition temperatures, and high flammability—including the construction of carbon/metal conductive skeletons, compositional tuning of phase change behavior, and flame-retardant modifications. These approaches have yielded composite PCMs with significantly improved heat transport capability and fire safety, while preserving high latent heat storage capacity. At the system level, we evaluate the thermal performance of pure passive PCM configurations, which excel at peak temperature suppression and inter-cell temperature uniformity, as well as hybrid designs that combine PCMs with air or liquid cooling to resolve heat accumulation issues and maintain stable performance under prolonged, demanding operating cycles. Despite these advances, key challenges remain: balancing high thermal conductivity with high latent heat capacity, developing climate-adaptable phase transition temperatures, and integrating multiple functionalities without compromising core thermal storage properties. Looking forward, future research directions include multifunctional integrated composites, smart adaptive PCMs, cost-effective scalable manufacturing, and precision structural engineering. This review also summarizes quantified performance trade-offs and provides actionable design guidelines for both material development and system-level integration. Full article

Urban mobility systems generate large volumes of heterogeneous geospatial data that differ in temporal resolution, spatial coverage, update frequency, and semantic structure, making integrated near-real-time analysis difficult. This paper presents a geospatial big-data framework for integrating and analyzing multimodal urban mobility data from the Norwegian transport ecosystem, including public transport, micromobility, road infrastructure, weather sensing, and civil aviation. The framework is implemented as a modular pipeline for data ingestion, source-specific normalization, temporal alignment, and analytical processing, enabling minute-level comparison across heterogeneous operational feeds. The proposed approach preserves source-level semantics while supporting unified spatiotemporal analysis across transport modes with different operational characteristics. The framework is evaluated through analytical scenarios focused on peak and off-peak mobility dynamics, weather-related multimodal variability, and spatial autocorrelation of public transport activity and delay across four analysis windows and six Norwegian cities. The results show that mobility–weather relationships vary across transport modes and temporal windows, particularly in public transport activity, cycling behavior, and delay patterns, and that spatial clustering of public transport activity and delay is itself city- and window-dependent, with some cities showing strong, stable clustering and others showing none. The findings indicate that multimodal urban mobility should be interpreted as a context-dependent and interconnected spatiotemporal system rather than through isolated modal indicators. The study demonstrates how geospatial big-data integration can support near-real-time urban mobility monitoring and operational analytics in smart-city environments. Full article

Electric Vehicle Charging Systems (EVCSs) have become a critical component of the global transition toward sustainable and intelligent transportation. However, their tight integration with heterogeneous cyber–physical, vehicular, and cloud-based infrastructures exposes them to an expanding attack surface, including data poisoning, malware injection, denial-of-service, and man-in-the-middle (MITM) attacks. Existing security solutions largely rely on isolated detection mechanisms and lack a unified semantic representation of EVCS assets, attack propagation paths, and mitigation dependencies, limiting their effectiveness in complex and evolving threat scenarios. To address these challenges, this paper proposes EVCS-SecOnt, an ontology-driven cybersecurity framework for modeling, reasoning, and mitigating security threats in EVCS infrastructures. The proposed ontology formalizes relationships across four core modules, namely Attack Surface, Attack Classification, Protection Mechanisms, and Risk and Mitigation, enabling holistic threat representation and TARA-based risk assessment. EVCS-SecOnt incorporates standard semantic namespaces (em:, seas:, uiote:, sch:, and time:) to ensure interoperability and is instantiated using the CICEVSE2024 dataset to support observation-level security reasoning. A unified SPARQL-based analytical workflow is employed to perform global ontology validation, attack–risk–severity correlation, mitigation prioritization, and observation-level inference using statistical feature vectors. Experimental results demonstrate that the ontology captures multiple attack classes, risk levels, severity categories, and mitigation strategies, enabling automated identification of critical attack scenarios and context-aware defense recommendations. The validation demonstrates logical consistency, semantic traceability, and query-based coverage of the ontology across attack classes, risk levels, severity categories, and mitigation strategies. EVCS-SecOnt enhances the interpretability, reusability, and explainability of EVCS cybersecurity management by bridging operational data with semantic intelligence. The proposed framework supports adaptive protection, risk-aware decision-making, and ontology-driven security analytics, providing a semantic foundation for next-generation e-mobility and smart charging infrastructures. Full article

Smart-building systems need reliable sensing, long-term storage, and remote control, but many studies mix up fast network response with slow physical changes in the building. We report measurements from an openHAB deployment at University Lab 1 and University Lab 2 in Greece over 19 months (April 2024–October 2025), with Z-Wave and ZigBee devices and Parquet exports for offline analysis. We treat sensing availability, Message Queuing Telemetry Transport (MQTT) command latency, and heating, ventilation, and air conditioning (HVAC) behavior as separate questions. In March–June 2024, raw record-level temperature field availability was 79.7% and 74.2% at University Lab 1 and University Lab 2, respectively, increasing to 99.7% and 98.5% after the <30 min linear field interpolation used in golden-dataset construction; assessable device-month cadence-normalized reading-count ratios were far lower (7.9% temperature and 13.0% humidity under the 300 s assumption at University Lab 1; humidity 7.8–26.0% across 180–600 s), reflecting heterogeneous archival participation rather than a fully populated expected-cadence denominator over the full archive span. In May–June 2024, 1247 MQTT commands yielded 1246 successful correlated responses; the archived application log contained no repeated correlation identifiers or duplicate response records (broker DUP flags are not archived); median, p90, and p99 latencies were 287 ms, 487 ms, and 1.66 s. These times describe the MQTT–edge-agent–openHAB path only, not physical device action. HVAC figures are illustrative; we do not infer settling times. The results show why availability metrics, archive denominators, and response-time boundaries must be defined separately in smart-building evaluations. Full article

Maritime transport entered the EU Emissions Trading System (EU ETS) in 2024, turning a route’s carbon performance into an economic variable. Existing studies examine this on established routes; the case for a new (greenfield) short-sea corridor under the post-2024 regime remains unaddressed, particularly in the under-served south-eastern Baltic. This study proposes a transparent, transferable methodology linking multi-criteria route selection, a lane-metre demand model, a speed-dependent fuel-consumption model and a consignment-level modal-shift carbon balance, applied to a prospective Gdynia–Liepāja Ro-Pax connection (148 nautical miles). At high deck utilization, each freight unit shifted from the 850 km road alternative avoids roughly 300–380 kg of CO₂ (a 44–55% reduction), because a short-sea leg replaces a long road haul rather than because the ferry is cleaner per tonne-kilometre. The benefit is conditional: the corridor is climate-beneficial only above a break-even freight-deck occupancy of about 45% at design speed, falling to about 34% under slow steaming. Across the demand scenarios (about 17,900–35,900 units per year), it avoids on the order of 10,000–13,400 t of CO₂ annually under high demand, while under low demand the annual balance ranges from a small net increase at design speed to a modest saving under slow steaming. The corridor relieves the congested Suwałki Gap and aligns with smart-port enablers, providing a replicable decision tool for operators and port authorities. Full article

The increasing complexity of residential energy systems and the growing penetration of distributed resources require practical energy-management solutions that extend beyond conventional metering. This paper presents the design and implementation of a real-time Internet of Things (IoT)-based energy-management system for monitoring and controlling household energy consumption under different operating conditions. The proposed system adopts a dual-processor architecture, in which a primary microcontroller performs time-critical electrical measurements and low-level load switching, while a secondary processor operates as a local IoT gateway for data handling, rule-based control decisions, local visualization, and message queuing telemetry transport (MQTT)-based cloud communication through a 4G link. The contribution of this work is not associated with the individual use of dual processing, cellular communication, cloud monitoring, load shedding, or backup power, as these technologies have been previously reported in smart-metering and home energy-management systems. Instead, the study focuses on their coordinated integration within a residential-scale prototype that combines calibrated per-load monitoring, priority-based load control, outage-resilient reporting, and credit-aware load restriction. The system measures voltage, current, active and apparent power, power factor, and energy consumption for individual loads and supports centralized visualization through a cloud-based dashboard. The prototype was experimentally evaluated under three representative scenarios: overload, main power outage, and low-credit operation. In the overload scenario, automatic priority-based load shedding reduced the total load by up to 75%. During power outages, a battery-supported subsystem maintained monitoring and communication for real-time outage reporting. In the low-credit scenario, non-essential loads were disconnected when the user balance fell below a predefined threshold, while essential loads remained energized. The results demonstrate that the implemented prototype can provide integrated monitoring, local rule-based control, cloud reporting, and backup-supported operation within a unified residential energy-management platform. Full article

Green-building research has examined energy efficiency and indoor environmental quality in depth, whereas site-related strategies are often represented through aggregate certification outcomes with limited visibility into criterion-level evidence and weighting assumptions. This study develops the Climate-Referenced SS–LT Site-Performance Assessment Framework (CR-SSAF) for an exploratory documentation-based comparison of a combined Sustainable Sites, Location, and Transportation (SS–LT) construct across five green-certified smart office buildings in three Köppen climate zones. Six SS–LT criteria were assessed using a four-level operational rubric, an exact-normalized weighted Site-Performance Index (SSPI), a descriptive Technology Enablement Factor (TEF), ordinal inter-rater agreement analysis, three weighting schemes, and a TOPSIS ranking-concordance check. SSPI values ranged from 2.00 to 2.65. The Af case recorded the highest SSPI in this sample, while the lowest scores occurred where no qualifying project-specific heat-mitigation or green-/open-space evidence could be verified. The first, second, and last ranks remained unchanged across the three weighting schemes, while The Edge and Shanghai Tower were tied under the equal and ecology-sensitive schemes. Because the sample is small and heterogeneous, and does not control for urban form, building scale, infrastructure, certification system, or documentation availability, differences cannot be attributed independently to climate. CR-SSAF is therefore presented as a transparent exploratory workflow, not as a validated climate effects model or as evidence of transferability. Full article

Energy planning is a growing challenge driven by the global push for decarbonization and the need to modernize aging power grids. This issue is particularly critical for islands, which often endure energy vulnerability and a high dependency on imported fossil fuels. While the electrification of transport is a popular option to reduce emissions, the integration of Electric Vehicles into weak island grids presents significant stability challenges due to the intermittent nature of renewable sources. This article provides a systematic bibliometric analysis to identify the advances made in bridging the gap between long-term energy balance and short-term dynamic stability in isolated systems. This paper analyzes islands’ stability needs and showcases smart charging systems, exploring their roles as distributed energy storage and as providers of ancillary services. First, the most relevant international scientific journals are identified to allow the subsequent selection and quantitative and qualitative analysis of articles dealing with EV-island stability pathways. A total of 7469 publications were screened, of which 284 articles were finally selected. Full article

Flexible capacitive humidity sensors are pivotal for next-generation wearable electronics and Internet of Things (IoT) applications. However, conventional devices suffer from severe salt leaching and delamination of hygroscopic sensing materials, alongside poor interfacial adhesion and mechanical fragility of metallic electrodes. Herein, an innovative in situ strategy is reported for constructing LiCl-CH₃COOK/laser-induced graphene (LIG) composite flexible electrodes via single-step laser direct writing. This approach simultaneously patterns three-dimensional (3D) porous LIG interdigitated networks on polyimide substrates and drives deep infiltration of the LiCl-CH₃COOK hygroscopic phase within the graphene pores. The 3D interconnected LIG skeleton not only provides abundant physical anchoring sites and rapid water vapor transport channels but also effectively suppresses the physical loss and leaching of the deliquesced salts through micro-nanoscale spatial confinement, yielding remarkable interfacial stability and cycling lifetime. Benefiting from the synergistic deliquescence of the composite salts, the sensor delivers an exceptional sensitivity of 65,570% (ΔC/C₀), moderate response/recovery times of 75/90 s, and ultralow hysteresis of 0.981%. Furthermore, the streamlined laser-scribing route replaces conventional costly microfabrication sequences, enabling low-cost, high-precision customization. Demonstrations in human respiration monitoring and smart agriculture validate the sensor’s superior reliability and practical applicability, establishing a novel pathway for miniaturized, highly integrated, and robust flexible humidity detection systems. Full article

Automated Guided Vehicles (AGVs) are a key part of today’s industrial automation, especially for material handling and intralogistics operations. The conventional AGV designs, such as differential-drive vehicles and fixed-steering AGVs, tend to exhibit limited maneuverability in restricted spaces. Such behavior can be attributed to comparatively large turning radii in classic designs, which limit the possibility of efficient movement. Thus, the production of affordable AGVs with high motion flexibility and load stability remains a challenge in AGV development. To resolve this issue, PID-controlled reverse-phase steering method is suggested. Experimental evaluation with 12 trials demonstrated a decreased turning radius for the designed AGV from 1.5 ± 0.08 m (literature-reported value) to 0.84 ± 0.05 m (current study finding), corresponding to an approximately 46.7% reduction. Results demonstrate the proposed AGV’s improved cornering capabilities. In addition, the lateral deviation achieved from the designed AGV stands at an average of 3.1 ± 0.5 cm, while the Root Mean Square Error (RMSE) is 3.5 cm, resulting in an overall accuracy rate of 96% ± 1.2%. Obstacle avoidance tests confirm successful performance within an obstacle range of up to 80 cm. Overall, the developed AGV represents a scalable and economical system for intelligent material handling within the industrial environment. Full article

The rapid deployment of Internet of Things (IoT) devices across smart cities, healthcare systems, industrial automation, transportation networks, smart grids, and cyber-physical infrastructures has expanded the modern cyberattack surface. IoT devices are often constrained by limited processing capacity, memory, battery power, and communication bandwidth, making conventional security mechanisms difficult to deploy consistently at scale. Intrusion detection systems (IDSs) provide an important defensive layer; however, many machine-learning-based IDSs are developed under static assumptions and may experience performance degradation as traffic distributions evolve due to firmware changes, device onboarding, protocol updates, user behavior variation, or adaptive attacks. This paper presents a hybrid IDS framework that integrates supervised Random Forest classification, unsupervised Isolation Forest anomaly monitoring, and Kolmogorov–Smirnov (KS)-based concept drift monitoring. In the experimental pipeline, Isolation Forest is trained exclusively on benign traffic to ensure that the anomaly detector models normal behavior rather than an attack-dominated training distribution. The evaluation uses a large-scale chronologically sampled subset of the CICIoT2023 dataset containing 3,890,621 records while preserving the natural class distribution of 2.35% benign traffic and 97.65% attack traffic. The chronological 80/20 train/test split is established first at the file level, followed by systematic sampling within each split to reduce the risk of leakage across the evaluation boundary. On the 746,094-record test set, the proposed hybrid IDS achieved 99.73% accuracy, 99.89% precision, 99.83% recall, 99.86% F1-score, and a false positive rate of 4.77%. The corresponding confusion matrix contains TN = 16,683, FP = 836, FN = 1205, and TP = 727,370, yielding 95.23% specificity and 97.53% balanced accuracy. Standalone Random Forest marginally outperformed the hybrid model in raw accuracy and false positive rate; therefore, the contribution of the proposed framework is centered on deployment-oriented anomaly monitoring, drift awareness, and generalization rather than absolute superiority in static classification metrics. A leave-one-attack-family-out experiment withholding MITM-ArpSpoofing from training showed that the hybrid model detected 85.26% of the unseen attack-family samples, compared with 85.18% for Random Forest alone and 7.05% for Isolation Forest alone. These findings provide initial evidence of generalization to one held-out attack family but should not be interpreted as proof of broad zero-day detection capability. The framework is therefore positioned as a competitive IDS that combines supervised detection with anomaly monitoring and concept drift awareness for deployment-oriented IoT security. Full article

The rapid adoption of Internet of Things (IoT)-enabled applications has significantly expanded the cyberattack surface across a wide range of critical systems such as industrial IoT (IIoT), smart grids, transportation, healthcare, industrial control systems, and smart cities. False data injection attack (FDIA) has emerged as a serious security threat to these applications due to its stealthiness and adversarial nature, silently corrupting the data integrity of critical operational processes without triggering conventional detection mechanisms. Existing FDIA solutions rely on single-model architectures that are built based on classical or limited predefined attack scenarios. Such solutions often fail to achieve robust detection under adversarial and evolving attack conditions; accordingly, they lack generalisability and are insufficient to capture the broader scope of FDIAs. In this study, a hybrid detection framework is proposed that integrates a Random Forest classifier with an unsupervised anomaly detection model based on a deep autoencoder combined through a Logistic Regression metaclassifier. The proposed framework addresses the gap in single-model detectors that either rely on fixed decision boundaries that struggle with gradually evolving stealthy FDIA patterns or on anomaly detection that lacks strong discriminative power in separating subtle adversarial deviations from normal operational variability. Different types of stealthy and adversarial FDIA have been modelled and injected into the dataset samples for use in training the proposed model. The results show that the overall detection performance of the proposed architecture improved by 2.39 percentage points in terms of F1-score while maintaining a low false-positive rate of 0.49%. These findings reflect the effectiveness of feature representation learning via autoencoders and hybrid classification strategies against stealthy and adversarial FDIA patterns. Future work should include temporal modelling for further advancing robust detection against evolving adversarial threats. Full article