Search Results (6,252)

The rapid decarbonisation of urban districts is accelerating the deployment of rooftop photovoltaic (PV) systems, household battery energy storage systems (BESSs) and electric vehicles (EVs). However, the high variability of urban micro-generation creates substantial forecasting errors at the urban–grid interface, imposing high balancing costs on distribution system operators (DSOs) and local energy communities. Traditional metrics fail to assess how effectively internal peer-to-peer (P2P) flexibility offsets these forecast mismatches prior to grid settlement. To address this gap, this paper presents a novel methodological framework and a non-parametric indicator: the Community Imbalance Neutralisation Index (CINI). Formulated in a generalised, scalable matrix structure applicable to any heterogeneous urban neighbourhood (N households), CINI quantifies the relative reduction in net community-level imbalance relative to the cumulative sum of uncoordinated individual forecast errors. CINI ranges from 0 per cent (no collective mitigation) to 100 per cent (perfect internal neutralisation). Complementing this index, an adaptive day-ahead scheduling algorithm is introduced to determine the optimal community energy purchase requirement (E_forecast). The proposed framework is numerically evaluated using a high-resolution synthetic benchmark annual dataset with 15 min intervals (35,040 intervals) representing a Central European urban residential cluster equipped with diverse combinations of PV, BESS and managed EV charging infrastructure. The simulation results demonstrate that active cVPP coordination reduces annual grid-facing imbalance energy from 188.73 MWh to 143.88 MWh, increasing the annual CINI score from 57.22% to 67.39% (+10.17 percentage points) compared with the uncoordinated baseline. Notably, the framework reveals a ‘Winter Flexibility Paradox’, achieving its highest relative efficacy during the winter months (+13.88 percentage points in December). Furthermore, sensitivity analyses show that scaling flexibility up to 40 kW achieves a CINI score of 91.22%, revealing diminishing marginal returns and critical technological saturation thresholds. The proposed CINI metric and E_forecast dispatch algorithm provide city planners, municipal energy managers and DSOs with a transparent diagnostic tool to design dynamic socio-economic tariff incentives, optimise urban micro-grid sizing, prevent free-rider dynamics, and foster resilient, self-balancing smart cities. Full article

Intelligent systems deployed in smart cities, smart grids, environmental monitoring, and other data-driven applications increasingly depend on reliable multivariate time-series forecasting. Recent deep forecasting models have achieved strong performance on various benchmarks, but their final predictions are often generated through a fixed forecast-generation process. This one-size-fits-all approach may be suboptimal because input windows exhibit different values, trends, and periodic patterns. We propose gap-aware residual selection (GARS), a plug-in correction module for time-series forecasting that is attached to a base forecasting model and adjusts its initial forecast rather than replacing the model. From the observed input window and the initial forecast alone, GARS constructs deterministic reference forecasts, uses their differences from the initial forecast as gap-aware residual information, and forms three component forecasts: the initial forecast, a gap-aware residual component, and a direct residual component. GARS combines these components with soft weights over segments of the forecast horizon, and future target values are used only for training and evaluation. Experiments on five multivariate benchmark datasets related to solar energy, weather, electricity consumption, traffic, and exchange rates, using four representative forecasting models trained on the complete chronological training splits, show that GARS reduces the normalized-scale mean squared error by an average of $6.6\%$ across the 80 evaluated settings. This comparison is between GARS trained jointly with each base model and the same base models trained without it. The mean absolute error is not consistently improved, and the difference between the two metrics is associated with a redistribution of error across the test samples. Under the same protocol, a direct conditional mixture without the gap signal performs at least as well as GARS on average and a parameter-matched control also improves on the base models, and thus the gain cannot be attributed to the gap-based construction. On two datasets not used elsewhere in this study, the same configuration increased the mean squared error, and thus the improvements are not established beyond the evaluated benchmarks. Full article

This systematic review aims to analyze Industry 5.0-enabled smart education in smart city ecosystems. A search was conducted in Scopus, Web of Science, and ERIC using a predefined search string. In total, 4631 records were identified, and 40 peer-reviewed articles published between 2022 and April 2026 were selected for descriptive and thematic analyses. Results indicate that Industry 5.0-based smart education forms an integrated ecosystem that includes human-centered approaches, intelligent technologies, stakeholders’ collaboration, and sustainable governance. The reviewed literature suggests that Industry 5.0-enabled smart education may support educational quality, human-capital development, digital inclusion, and sustainable smart-city development. The main technological enablers are AI, IoT, Digital Twins, immersive technologies, learning analytics, blockchain, and human–AI collaboration. Smart education also faces technological, organizational, ethical, and policy barriers. The review provides an integrated conceptual framework for Industry 5.0, smart education, and smart city ecosystems, and provides recommendations for researchers, educators, educational institutions, policymakers, technology providers, and other stakeholders involved in smart cities to design future-proof educational systems that prepare learners to address the challenges of sustainable development and rapidly changing societies by acquiring the competencies required in these environments. Full article

The increasing demand for sustainable outdoor lighting has accelerated the development of solar-powered lighting systems. However, conventional solar lamps typically employ fixed illumination levels and simple day–night switching mechanisms, resulting in inefficient battery utilization and limited adaptability to changing environmental conditions. This study proposes a TRIZ-guided intelligent solar-powered lighting system that integrates photovoltaic energy harvesting, adaptive pulse-width modulation (PWM)-based illumination control, ultrasonic sensing, wireless communication, and embedded control into a unified standalone platform. The TRIZ contradiction matrix was employed during the conceptual design stage to systematically resolve key engineering contradictions involving illumination performance, energy efficiency, hardware complexity, battery lifetime, and user convenience. The proposed prototype was developed using an AT89S51 microcontroller to coordinate battery charging protection, environmental sensing, adaptive brightness regulation, and manual wireless operation. Experimental validation demonstrated stable photovoltaic charging with a regulated battery charging voltage of 14.4 V, reliable execution of embedded control functions, seamless transition between manual and autonomous operating modes, and adaptive LED brightness regulation according to real-time environmental conditions. The integrated PWM control strategy reduced unnecessary energy consumption by dynamically adjusting illumination intensity based on object detection rather than maintaining constant full-power operation. The experimental results further verified the feasibility of combining software-driven adaptive control with renewable energy harvesting to achieve intelligent energy management without increasing hardware complexity. Overall, the proposed system demonstrates that the integration of TRIZ-based systematic innovation with embedded intelligent control provides a practical, energy-efficient, and cost-effective solution for autonomous outdoor lighting. The proposed architecture offers valuable engineering insights for future smart lighting applications in off-grid infrastructure, sustainable communities, and smart city environments. Full article

This paper addresses the task of binary violence classification in short surveillance video clips, i.e., deciding whether a given clip contains violent interactions (Fight) or not (Non-Fight). Although this task is often discussed in the broader context of video-based abnormal behavior detection driven by smart cities, Closed-Circuit Television (CCTV) networks, and intelligent surveillance systems, existing methods for violence classification largely rely on appearance features from single frames or global motion cues across whole scenes and therefore fail to adequately capture the interactions among multiple objects and the structural changes in their relationships that arise in real surveillance environments. In particular, violent behavior is rarely defined by a specific pose or a single moment; rather, it emerges as a cumulative process in which relational changes—such as inter-person approach, distance variation, collision, and repeated contact—unfold over time. Detecting such behavior accurately therefore requires an approach that can analyze inter-object relationships in a spatiotemporal manner. To this end, this paper proposes a violence detection method that combines multi-object tracking with spatiotemporal graph-based relational pattern mining. The proposed method first detects and tracks person objects using YOLO and DeepSORT, and extracts time-series features—including position, velocity, pose, and inter-object distance variation—to construct a spatiotemporal graph. Relational event sequences are then generated from the edge features of the graph, and class-representative relational patterns are automatically extracted based on discriminative power through PrefixSpan-based frequent sequential pattern mining. In parallel, the spatiotemporal graph is fed into a Spatial Temporal Graph Convolutional Network (ST-GCN) to learn the structural relationships among objects and their temporal evolution. Finally, the pattern-matching score and the ST-GCN classification score are combined to classify each input video as either violent or non-violent. By jointly exploiting interpretable relational pattern information and graph-based structural learning, the proposed approach compensates for the limitations of appearance-centric anomaly detection and demonstrates its applicability to complex real-world surveillance environments. Performance is evaluated in terms of Accuracy, Precision, Recall, and F1-score, with Recall considered a primary metric to reflect the importance of not missing violent events. Full article

The Internet of Vehicles (IoV) is revolutionizing intelligent transportation systems by ubiquitous connectivity of vehicles, roadside infrastructure, pedestrians, edge/cloud platforms, and smart-city services. With the IoV evolving towards highly connected, autonomous and data-driven mobility ecosystems, it needs to meet challenging requirements for low latency, scalability, interoperability, security, privacy and trust. This paper presents a comprehensive cross-layer approach for intelligent, secure and privacy-preserving IoV systems. It is built upon an analytical framework and systematically studies the perception, communication, edge/cloud computing, blockchain-enabled trust and application layers of IoV technologies. In addition, the paper presents an in-depth review of the enabling techniques such as machine learning (ML), deep learning (DL), reinforcement learning (RL), federated learning (FL), blockchain, cybersecurity mechanisms, digital twins, edge computing, 6G integration, and resource allocation. Moreover, it discusses the interplay and trade-offs between intelligence, security, privacy, computation, latency, and scalability. The survey also covers other significant challenges like intrusion detection, decentralized authentication, privacy-preserving learning, blockchain overhead, semantic interoperability, post-quantum security, and standardized datasets. This study is intended to serve as a structured reference for the development of scalable, trustworthy, and intelligent IoV systems by highlighting state-of-the-art techniques, open research gaps, and future directions. Full article

Human presence detection is critical when building smart cities with use cases in sectors like smart homes, emergency evacuation, health-care monitoring and others. Existing human detection systems predominantly rely on camera-based imaging, raising privacy concerns. Moreover, conventional FMCW radar approaches are primarily motion-based, thus often failing to detect the presence of unconscious individuals, as in the case of search and rescue (SAR) operations. Some radar approaches use Doppler or spectral peak analysis to estimate respiration but fail to exploit phase coherence to resolve sub-millimeter chest displacement and higher-order physiological harmonics. This paper presents an interferometric radar framework that models multi-feature vital-sign signatures for human detection under controlled clinical settings using respiratory harmonic relationships, inter-harmonic consistency, chest-displacement spectral characteristics, and radar-derived cardiac mechanical signatures. Physiological relationships are used to establish the expected structure of the extracted features, while subject-to-subject variability and measurement uncertainty are used to determine practical acceptance regions from the training cohort. Experimental data from 30 healthy subjects were analyzed using a single interferometric radar sensor under controlled clinical conditions. The resulting signatures were subsequently evaluated using a machine-learning validation pipeline. With 243 test cases, the proposed framework achieved 89.71% accuracy, 95.26% precision, 94.15% F1-score, and 93.06% sensitivity. The study demonstrates that interferometric chest-displacement sensing can provide a privacy-preserving physiological feature space for human presence detection, while also identifying the limitations associated with unresolved multi-person signal superposition and hardware-induced phase uncertainty. Moreover, interferometric sensing by principle will work better than conventional radar approaches for SAR operations. Although validated in a controlled clinical environment, the framework establishes a foundational pathway towards future research for eventual deployment in next-generation smart systems. Full article

Urban anomalies such as rare facilities, unusual spatial configurations, and atypical functional patterns can provide valuable insights into urban dynamics and planning processes. However, graph-based anomaly discovery methods often suffer from instability, producing substantially different results across training runs and making the detected anomalies difficult to reproduce and interpret. In this paper, we use the term anomaly to denote automatically detected abnormal urban entities, while the term urban irregularity refers to their interpretation within the urban context. We present a consensus-driven framework for stable anomaly discovery using multi-relational point-of-interest (POI) graphs. Urban facilities are represented as nodes connected through multiple spatial and semantic relations, including geographic proximity, shared categories, shared facility types, and region-based associations. Four graph autoencoder architectures (GAE, ResGAE, VGAE, and SAGEAE) are employed to learn node representations, while reconstruction-, cluster-, neighborhood-, and relation-based anomaly scoring strategies are combined with multi-seed stability analysis to identify consensus anomalies. Experiments conducted on five large-scale cities (Baku, Turin, Vienna, Prague, and Kuala Lumpur) show that the proposed framework identifies recurring anomaly patterns across repeated runs and analytical configurations. Comparisons with representative anomaly detectors reveal partial but method-dependent overlap, while cross-city control experiments indicate that a subset of the detected anomalies exhibits non-random semantic and structural correspondence across different urban environments. Additional analyses suggest that consensus anomalies are frequently associated with semantically distinctive urban entities, including recreational areas, utility infrastructure, institutional facilities, specialized services, and cultural landmarks. Overall, the results indicate that stability-aware consensus provides a more reproducible and consistent basis for graph-based urban anomaly discovery and supports the interpretation of recurrent anomaly patterns in large-scale urban POI graphs, without requiring ground-truth anomaly labels. Full article

Growing urbanization and increasing traffic intensity create a need for transparent tools that support urban transport management and city logistics planning. The aim of this study is to propose an interpretable rule-based decision-support framework that transforms short-term traffic-count data into relative traffic-load categories for preliminary traffic assessment and planning. A pilot case study was carried out using minute-level traffic count data from four selected locations in the Prague metropolitan area: Stodůlky, Barrandov, Radlice, and Velká Chuchle. The data were aggregated into hourly intervals and subsequently evaluated according to predefined daily traffic periods. To assess relative traffic intensity within each monitored location, the Relative Traffic Intensity Index was calculated using a percentile-based reference value. Based on predefined analytical thresholds, each daily period was classified into one of four relative traffic-load categories: low, medium, high, or critical. Within the Barrandov dataset, both the morning and afternoon peak periods showed high relative traffic intensity and were classified as critical under the primary reference setting. Sensitivity analysis using alternative percentile references showed that these peak-period classifications remained stable at Barrandov, whereas some classifications, particularly at Velká Chuchle, were more sensitive to the selected reference value. The proposed framework provides a transparent and interpretable approach for converting traffic count data into operationally understandable information for preliminary traffic assessment and planning. Due to the limited temporal and spatial scope of the available data and the absence of independent congestion indicators, the results should be interpreted as a pilot demonstration of the proposed analytical procedure rather than as an externally validated or fully generalized predictive traffic management system. Full article

Forecasting power consumption is essential for intelligent power management in IoT-enabled smart environments, where heterogeneous behaviors appear from diverse building usages. University campuses are considered environments that share similarities with smart cities, making them suitable for power dynamics analysis. We build upon an IoT-based Advanced Metering Infrastructure (AMI) we deployed at our Engineering School’s Campus (INPT, Morocco), and an optimized XGBoost pipeline enhanced via Genetic Algorithms. Limited modeling granularity is addressed in heterogeneous consumption patterns. We propose and justify a cluster-aware approach partitioning data $\left(D\right)$ into K regimes such that $D={\bigcup }_{c=1}^K{C}_c$. Each cluster is treated as a homogeneous behavioral profile and modeled using a GA-XGBoost model, enabling an intermediate granularity between global and meter-level learning. Experiments on real-world campus AMI data show that our proposed GA-XGBoost model consistently outperforms SVR and LSTM baselines across all clusters. In addition, cluster-specific models further improve performance compared to a single GA-XGBoost model trained without clustering, achieving a 48.42% improvement in MASE. Overall, beyond improving forecasting accuracy, cross-cluster generalization shows performance degradation and distributional shift when models are transferred across clusters, while residual diagnostics reveal differences in variance, temporal dependence, and non-Gaussianity. Full article

Smart-city energy platforms—peer-to-peer markets and tokenized renewable energy certificates—settle against metered generation, yet never check the physical plausibility of those claims. For wind, attainable energy is not measured but derived from anemometry through a vertical extrapolation whose exponent—the wind-shear coefficient—is a discretionary modeling choice. Using a five-height, 52,192-record campaign from Phangan Island, Thailand—reproduced here as a statistically anchored reconstruction, the raw archive not being redistributable—we show that the conventional $1/7$ rule understates attainable energy by $29.8\%$, and that an adversary asserting the exponent could inflate a resource claim by $87.4\%$. WindChain sits beneath the smart-city transactive layer rather than beside it: it does not mint certificates from wind data but bounds what a revenue meter may claim. It enforces boundary-layer, kinematic, and thermodynamic admissibility as a consensus predicate and commits wind statistics to a hierarchical Merkle–Weibull accumulator whose 104-byte root lets any verifier recompute the Weibull parameters, power density, and shear exponent in constant time. We prove that an epoch-level admissibility gate bounds over-issuance, and that attestation windows must be thirty-six times longer than independence assumes. On the reconstruction, WindChain detects six of eight manipulation classes—four of them with recall $\ge 0.99$—at a $1.75\%$ false-positive rate; we also report a camouflage regime defeating every per-record test, and a sustained bias at or below $2.6\%$ that the epoch detector does not see. Consensus performance is modeled, not deployed. WindChain narrows the trust boundary rather than removing it: the guarantee is conditional on an independently certified site reference and on physical calibration of the mast and is best read as an auditable plausibility layer beneath settlement rather than as a trustless one. Full article

Short-term multimodal travel demand forecasting must reconcile shared mobility patterns with mode-specific dynamics and uneven spatial coverage. This paper proposes Multimodal Disentangled Dynamic Fusion (MDDF), which combines three mechanisms: dual-branch encoding with an orthogonality constraint to separate shared and modality-specific representations; Jensen–Shannon divergence regularization with coverage-aware masking to align shared features without introducing structurally absent observations; and attention-based dynamic fusion with an auxiliary ranking loss to adapt cross-modal information exchange. We evaluate MDDF over five random seeds on two multimodal datasets with distinct settings: 10 min bus, metro, and taxi demand over 491 traffic analysis zones in Shenzhen, and hourly bus, metro, and bike demand over 69 zones in Manhattan. MDDF achieves the lowest mean MAE in all nine mode–horizon settings on both datasets and the lowest mean RMSE in eight of nine settings on each dataset. On Shenzhen, MDDF improves upon the strongest competing baseline in all nine mode–horizon settings; at the 60 min horizon, the mean MAE reductions are 19.5% for bus, 18.3% for metro, and 5.9% for taxi. Ablation and representation analyses indicate broad, though not universal, benefits from disentanglement, alignment, and dynamic fusion. Full article

This empirical study examines how artificial intelligence-enabled heritage intelligence (AIHI) and green technology integration (GTI) influence sustainable heritage development performance (SHDP) through participatory heritage governance (PHG) in Kaifeng. A quantitative design was adopted using a structured questionnaire. Anonymized data with a sample size of 360 were collected via five-point Likert-scale responses from heritage-governance stakeholders in Kaifeng. Measurement properties and structural relationships were examined using partial least squares structural equation modeling with bootstrapping procedures. AIHI and GTI both exert significant positive effects on PHG. PHG significantly improves SHDP and partially mediates the relationship between AIHI and sustainability outcomes. The interaction between PHG and GTI is positive, indicating that green technologies reinforce the link between governance processes and sustainability performance. This study connects smart-city governance literature with emerging research on heritage governance and green transition by identifying PHG as the governance mechanism through which AI and green technologies contribute to sustainable outcomes. The findings provide guidance for coordinating AI applications, green-retrofitting initiatives, and stakeholder participation within a heritage-sensitive governance structure. Full article

The supply chain ecosystem (SCE) operating within an urban environment is characterised by continuous interactions among manufacturing, logistics, service, information, and energy flows across multiple smart city manufacturing-service clusters (SCMSCs). Within the SCE, intelligent production buildings (IPBs) emerge as multifunctional multistorey production-service infrastructures developed under conditions of limited urban land availability and increasing demand for localised manufacturing and service integration. These buildings operate under heterogeneous and dynamically changing energy-demand conditions, substantially complicating energy consumption management. This study develops a knowledge-driven coordination approach for the energy consumption management of IPBs operating within SCMSCs from the perspective of the urban SCE. IPBs are conceptualised as distributed environments with finite building-level power supply system capacity, where manufacturing, logistics, service, and digital processes dynamically compete for shared energy resources. A hierarchical representation of the SCMSC energy environment is proposed, capturing distributed interactions and heterogeneous electricity-demand profiles across interconnected clusters. An information-analytical system integrating monitoring, data acquisition, analysis, ML-based demand prediction, planning, and decision-support functions is developed to support predictive electricity-demand coordination. The proposed framework combines IoT-enabled monitoring with digital-twin-supported synchronisation of energy states for distributed coordination among IPBs. The proposed approach is evaluated through scenario-based analysis of an IPB operating within an urban manufacturing-service environment. The results indicate the potential of knowledge-driven coordination to improve energy-capacity utilisation, mitigate peak-load formation, and enhance operational stability within SCMSCs. Full article

Smart city mobility systems face a persistent gap between digital innovation and large-scale deployment. While Mobility-as-a-Service platforms and Mobility Living Labs have generated promising local solutions, transferring these solutions across cities remains constrained by data fragmentation, proprietary platforms, and divergent governance frameworks. This study proposes and operationalizes a policy-oriented framework for assessing the transferability of smart mobility solutions across urban contexts, drawing on the GEMINI project spanning seven European cities: Amsterdam, Copenhagen, Helsinki, Munich, Paris-Saclay, Porto, and Turin. A structured dataset covering application features, interoperability characteristics, technical specifications, and implementation challenges was developed and analysed using comparative indicators including Ease of integration, interoperability level, and replication potential. The results demonstrate that transferability depends on the interaction of technical, governance, and institutional conditions rather than technical compatibility alone. Applications built on open standards and modular architectures show significantly higher replication potential, while legacy systems, fragmented governance structures, and restrictive data-sharing arrangements remain the most persistent barriers. To operationalize the framework, a web-based Knowledge Hub was developed and deployed as a live decision-support platform, currently serving all seven Mobility Living Labs and openly accessible to cities across Europe. The framework offers a replicable model for smart city mobility governance. Full article