Search Results (1,813)

Federated learning (FL) has emerged as an acceptable approach for training machine learning (ML) models across distributed devices while preserving their private data. Recently, Internet of Things (IoTs) has been widely used across three application domains: smart healthcare, homes, and cities (SHHCs). In this study, we present a systematic literature review (SLR) using the PRISMA framework to comprehensively analyze existing research on FL-IoT smart healthcare, homes, and cities (FL-IoT SHHC). We identified and categorized 84 studies into three application domains (smart healthcare, homes, and cities) and critically analyzed them to identify three frequently used FL architectures: centralized, decentralized, and hierarchical FL. Furthermore, we discuss 17 optimization algorithms, seven hyperparameters, 76 datasets, and 13 evaluation metrics. Four explainable AI approaches, five privacy attacks, the limitations of this research, and future directions are also discussed. This SLR not only provides a comprehensive overview of the state of the art in FL-IoT smart healthcare, homes, and cities but also provides a roadmap for researchers and professionals seeking to advance the field and design more robust and resilient FL-IoT systems for user privacy domains. Full article

Urban environments are characterized by complex traffic conditions, which can significantly affect ambulance dispatch performance. This study presents a graph-based optimization approach for ambulance dispatch, using the city of Prague as a case study. The road network is modeled as a directed weighted graph based on OpenStreetMap data and constructed using the OSMnx library, where edge weights represent travel time. Traffic uncertainty is incorporated through variable edge weights to simulate fluctuating conditions. A simulation environment generates ambulance locations and emergency incidents across the network. The proposed approach combines shortest-path routing with global assignment optimization and is evaluated against a baseline nearest-ambulance strategy. The results show a relationship between coordination mechanisms and computational behavior. The Greedy and Bidirectional strategies produced similar average response times of 6.08 min and 5.77 min, respectively. The Greedy strategy achieved a higher fairness score (Gini index of 0.383) and lower maximum response time. The Bidirectional strategy reduced assignment computation time to 0.28 s per request and decreased total computation time from 85.35 s to 11.08 s, corresponding to an 87.02% reduction. The findings support the applicability of the approach in smart city emergency response systems. Full article

The energy conservation, emission reduction, and electricity peak-load regulation requirements of smart cities have driven the establishment of virtual power plants (VPPs). Peer-to-peer (P2P) trading is applicable to distributed clean energy resources in VPPs and affects the transformation of the energy trading paradigm. To address the privacy protection and robustness issues of P2P trading, this paper presents a unified framework for point-to-point energy trading in VPPs. We comprehensively consider the multiple stages of verification and consensus in transactions and propose a complete framework. The proposed system can effectively protect participants’ private information and achieve identity authentication. Furthermore, we design a progressive settlement mechanism to ensure time-slot-level transaction parallelism. To verify the effectiveness of the method, we implemented the proposed system on IEEE bus benchmarks. The experiments covered the complete stages of authentication, consensus, and clearing. The overhead measurements showed that the overhead of the cryptographic components was reasonable and that the final signature adaptation introduced only a small computational overhead. Full article

Several potential applications of rare-earth-free magnets have been identified in emerging technological domains such as renewable energy and e-mobility in smart cities, with emphasis on electric bikes and scooters, as well as in the field of autonomous vehicles. In all these applications, with operability at high temperatures, there is considerable interest in nanocomposite magnetic alloys based on MnAl binary systems. The reason for such interest is represented by the occurrence of the τ-MnAl phase, which is magnetic and structurally compatible with L1₀ tetragonal phases. While the research on MnAl and MnAl-derived magnets has been extensive, alloys with the addition of post-transitional metals with an orthorhombic structure, such as Ga, are less often investigated. The purpose of such additions is to promote and preserve the formation and stability of the τ-MnAl phase, which has promising magnetic properties. The paper illustrates the thermal stability and optimized magnetic properties of τ-MnAl in an alloy based on an off-equiatomic MnAl system with 4 at% Ga addition. A thorough thermal analysis involving a differential scanning calorimetry study is reported, powered by kinetic analysis using two complementary iso-conversional methods: Friedman and Ozawa–Flynn–Wall. Structural characterization is performed using XRD, with the results supported through full-profile MAUD analysis (version 2.99, University of Trento, Italy), revealing that the hexagonal ε phase, predominant in the as-cast sample, transforms massively into the tetragonal τ phase, which becomes predominant upon annealing. Magnetic measurements are employed to fully characterize the alloy’s magnetic properties. It is shown that the phase transformation creates very good conditions for achieving good remanence values of about 210 kA/m and large intrinsic coercivities of about 446 kA/m at ambient temperatures. Full article

Standardized urban indicators are essential for assessing smart and sustainable cities, yet their application in developing countries remains limited. This study addresses the critical gap regarding the availability of ISO 3712x standard indicators among local governments in Brazil—a nation marked by territorial and socioeconomic heterogeneity. Urban indicators based on open government data across all 5570 Brazilian municipalities were analyzed in this study using the ISO 37120, 37122, 37123, and 37125 standards. A synthetic smart and sustainable cities index was drawn up, supported by Factor Analysis and Principal Component Analysis (PCA). Considering the Local Indicator of Spatial Association (LISA), clustering patterns of municipal sustainability and smartness performance were identified: LISA analysis identified pronounced spatial clusters, with high-performance municipalities concentrated in the Southeast and South-Central regions, while low-performance clusters spread across the North and Northeast of Brazil. Despite the diversity of characteristics among Brazilian cities, 22 of the 302 ISO 3712x indicators (7.3%) could be collected with complete national data for all 5570 Brazilian municipalities; data for 59 indicators were available from open sources, though with gaps. Since the ISO 3712x standards offer a voluntary portfolio of indicators rather than a mandatory set, partial data coverage does not make the framework inapplicable; rather, they delimit the scope of the synthetic index presented here and highlight where open-data infrastructure must be strengthened. Municipal smartness and sustainability result from the multidimensional integration of technological innovation, human capital, social protection, and responsible environmental management. Despite the availability of ISO indicators, observations based on the authors’ institutional experience as members of ABNT/CEE-268—Brazilian mirror committee of ISO/TC 268—“Sustainable cities and communities”—reveal that local governments face challenges in systematically integrating them into strategic planning, presenting a significant opportunity for capacity building and evidence-based governance. In addition to the innovative aspect of this research—which involves using ISO-standardized indicators and collecting numerical data from official open data sources—this article presents a comprehensive index calculation for sustainable and smart cities based on these indicators, followed by the identification of its spatial clusters. Full article

Internet of Things (IoT), Smart Health, and Smart City applications generate continuous data streams that may contain sensitive personal information. Decentralized storage systems such as Solid provide data ownership, access control, and interoperable sharing but offer limited support for real-time stream processing. We present Heimdall, an intermediary analytics service that registers RSP-QL queries over streams stored in Solid Pods to produce query results and reuses existing query executions when supported reuse conditions are satisfied. We evaluate Heimdall against Client-Side Processing and a notification intermediary using a wearable-sensor workload with concurrent client scaling, query and data heterogeneity, and concurrent non-reusable queries. For equivalent queries, Heimdall maintains nearly constant latency as client count increases, while Client-Side Processing shows substantial latency growth and instability at higher concurrency. Heimdall also reduces accumulated CPU and memory consumption and client-side network traffic by sharing stream retrieval and query execution. When query execution cannot be reused, shared stream acquisition still improves scalability, although degradation appears as the number of independent queries and physical streams increases with no-reuse. These results show that shared stream acquisition and continuous-query execution can reduce duplicated computation and communication in decentralized stream processing. Full article

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

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

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

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

Urban traffic environments can elevate physiological stress, yet most existing studies assess this indirectly through infrastructural or traffic-related proxies rather than direct physiological measurement. This pilot study proposes a geographic information system (GIS)–Analytical Hierarchy Process (AHP) framework that integrates wearable heart-rate sensing with spatial analysis to identify localized physiological activation patterns at urban intersections. The proposed framework is presented as a methodological proof-of-concept and is not yet validated as a decision-support tool; application to urban health assessment or smart-city planning would require testing on a substantially larger and independently sampled spatial dataset. Data were collected from ten participants across 118 repeated commuting passes by private automobile at six intersections in Kragujevac, Serbia. An AHP-weighted urban stress index combining heart rate, the traffic-intensity proxy, time of day, and acceleration events (CR = 0.0115) was computed and mapped using inverse-distance-weighted interpolation. A linear mixed-effects model showed a significant positive association between an ordinal, time-of-day-based traffic-intensity proxy and heart rate across the 118 passes (8.90 bpm per ordinal unit, p < 0.001); because this proxy is derived from time-of-day categories, the association is best interpreted as an exploratory time-of-day–heart-rate relationship rather than a validated causal effect of traffic, and a sensitivity analysis confirmed that the same three intersections ranked highest across alternative weighting scenarios. The results indicate a consistent spatial relationship between intersections associated with higher traffic-intensity proxy values and elevated physiological activation. Although based on a limited pilot-scale dataset, the proposed framework demonstrates the feasibility of combining wearable physiological sensing with GIS–AHP spatial analysis and offers a methodological proof-of-concept for smart-city and urban-health research in medium-sized cities, pending validation on larger, independently sampled spatial datasets. Full article

Rapid urbanization, increasing demand variability, and digitalisation of urban infrastructure require intelligent decision-support systems that can improve sustainability, resilience, and operational efficiency in smart city operations. Artificial intelligence (AI) and data-driven optimization offer strong potential for adaptive urban services, including dynamic pricing, demand response, resource allocation, and energy-aware management. However, many reinforcement learning applications still focus mainly on short-term performance while giving limited attention to transparency, fairness, stability, and accountability. This study proposes a trustworthy reinforcement learning framework for AI-driven urban decision-making, using sustainable dynamic pricing and resource optimization as mechanisms for adaptive and responsible decision-making. A custom reinforcement learning environment was developed using historical e-commerce transactional data as a methodological proxy to simulate interactions among demand, resource or inventory availability, service categories, price elasticity, and changing market conditions. Three reinforcement learning algorithms, namely Deep Q-Network, Proximal Policy Optimization, and Advantage Actor–Critic, were evaluated under comparable experimental conditions. Performance was assessed using profitability, decision stability, fairness-oriented pricing behavior, decision consistency, and interpretability. To improve transparency, trajectory-based policy audits and SHapley Additive exPlanations were applied to identify the main factors influencing pricing decisions. The results show that the Deep Q-Network agent achieved the most balanced performance, increasing total profit by 12.58% while recording no unethical price increases under low-demand conditions. Explainability analysis showed that stock or resource levels, demand shifts, and price elasticity were the strongest positive drivers of pricing actions, whereas inventory hoarding and unfavorable price increases reduced decision quality. The findings indicate that reinforcement learning can support sustainable and resilient urban decision-making when optimization objectives are combined with trustworthy AI principles. The proposed framework provides a practical basis for accountable AI-based decision-support systems in smart city operations, including demand-responsive services, resource optimization, sustainable dynamic pricing, and energy-aware management. Full article