Search Results (627)

Background: With the deepening coupling of multi-microgrids (MMGs) and transportation systems in smart cities, maintaining frequency stability under extreme conditions increasingly relies on vehicle-to-grid (V2G) flexibility. However, existing V2G dispatch strategies often overlook the noticeable battery degradation caused by high-frequency regulation and the vulnerability of extensive communication networks to false data injection attacks (FDIAs), while the high-dimensional coordination of EV routing and discharging makes classical algorithms struggle to converge. Methods: To address these challenges, this study proposes a quantum-empowered degradation-aware V2G coordination framework for smart-city MMGs considering communication security and user travel demands. At the physical layer, an equivalent RC circuit-based battery degradation model and a traffic flow model are established to quantify capacity loss and travel delays. At the cyber layer, quantum key distribution (QKD) ensures unconditionally secure communication, while a quantum reinforcement learning (QRL) algorithm is developed to achieve fast convergence in high-dimensional multi-objective optimization. Results: Simulation results demonstrate that the proposed framework completely immunizes the system against FDIAs, effectively suppresses frequency fluctuations, and significantly reduces battery degradation costs while preserving user mobility. Conclusions: This framework provides a highly secure and user-friendly pathway for resilient smart-city frequency regulation. Full article

Accessibility in bus-based public transport is essential for inclusive and sustainable urban mobility, yet bus stops and stations are often overlooked compared with vehicle modernization and network planning. This study presents a bibliometric and thematic review of research (2020–2026) on accessibility in bus stop and station infrastructure across European cities. Literature indexed in the Web of Science Core Collection was analyzed using thematic synthesis supported by bibliometric network analysis. A total of 685 publications were examined to identify research trends, barriers, regional differences, assessment methods, and planning implications. The findings show that accessibility is a multidimensional concept shaped by physical design, information systems, user experience, first- and last-mile connectivity, and governance capacity. Persistent barriers include poor boarding interfaces, inadequate maintenance, weak wayfinding, safety concerns, and uneven implementation of accessibility standards. The reviewed studies suggest that evidence of stronger integration and higher user satisfaction is more frequently reported in Northern and some Western European contexts, whereas rural and smaller cities often face lower service accessibility. Emerging topics include real-time passenger information, crowding management, electric buses, and smart mobility technologies. The review concludes that bus stops and stations should be treated as strategic mobility assets. Integrated planning, participatory design, and territorially differentiated policy support are essential to advance inclusive bus-based public transport across Europe. Full article

Fifth-generation advanced (5G-A) and sixth-generation (6G) networks in ultra-dense and smart cities offer an efficient solution for addressing the growing number of mobile users while ensuring high throughput and full coverage. However, the handover decision (HOD) remains a critical challenge in wireless networks, especially for high-speed users in millimeter-wave (mmWave) communication environments. The present paper proposes a mobility-awareness HOD algorithm that combines two decision algorithms: reference signal received power (RSRP) and signal-to-interference-plus-noise ratio (SINR). The algorithm makes decisions based on real-time user experiences collected every 40 milliseconds during the user’s mobility. The research was conducted using MATLAB 2023a and advanced 5G and 6G tools. All system settings, functions, mobility models, and simulation criteria were defined based on 3GPP specifications. The algorithm guarantees high-quality connectivity and smooth user transitions across 5G and 6G networks under diverse mobility conditions. The results show that Systems 2 and 4 minimize 96.97% and 99.99% of handover ping-pong (HOPP) compared with Systems 1 and 3. The simulation results reveal that the proposed algorithms in Systems 2 and 4 provide noticeable enhancements in network performance, with radio quality metrics of 16.32% and 8.04% for RSRP, 168.99% and 407.31% for SINR levels, and 55.11% and 16.04% improvements in throughput across various mobile speed scenarios compared with Systems 1 and 3. Full article

Background/Objectives: The rapid expansion of digital evidence in modern investigations has created pressing challenges for maintaining integrity, traceability, chain of custody, and meaningful analysis across heterogeneous forensic artefacts. Conventional evidence management approaches often fall short in scalability, transparency, and the ability to correlate diverse digital evidence. This study presents BlockFECS, a blockchain-based proof-of-concept system for metadata-driven evidence correlation in digital forensics. Methods: BlockFECS uses Hyperledger Fabric to support auditable and tamper-resistant evidence management while capturing structured forensic metadata, including timestamps, locations, device IDs, user IDs, and file hashes. An off-chain weighted correlation algorithm assigns similarity scores between evidence pairs and classifies relationships as Related, Supplementary, Duplicate, or Unrelated. The system was evaluated using a simulated smart city accident scenario and tested for correctness, transaction latency, throughput, scalability trends, and concurrency behaviour across four computing environments. Results: Within the controlled proof-of-concept dataset, the correlation algorithm achieved 1.00 precision and recall for clear Related and Duplicate evidence relationships and high precision (0.90) for Supplementary relationships, although recall in this category was lower due to incomplete or noisy metadata. Performance testing showed that Create, Transfer, and Delete operations completed with sub-second latency, while correlation throughput exceeded 60 comparisons per second across all tested environments. Conclusions: The findings demonstrate the feasibility of combining blockchain-backed evidence integrity with lightweight metadata-driven forensic intelligence. BlockFECS contributes a proof-of-concept model for automating metadata-based evidence analysis while preserving provenance integrity and auditability, highlighting a promising direction for trustworthy and intelligent digital forensic investigation support. Full article

The Social Internet of Things (SIoT) has emerged as a promising paradigm for addressing interoperability, adaptability, and intelligent collaboration challenges in smart city environments. However, existing solutions often provide only partial support for semantic interoperability, dynamic social relationships, and context-aware service coordination across heterogeneous IoT ecosystems. This paper presents SEMIWARE, a semantic social network-oriented middleware designed to support collaborative, interoperable, and context-aware SIoT applications. SEMIWARE adopts a layered architecture that combines a FIWARE-based middleware backbone with modular services for context management, semantic annotation, semantic reasoning, service discovery, social relationship management, profiling, security, and ontology alignment. Its semantic backbone is provided by an OWL2 ontology that models IoT entities, users, services, contextual information, and trust-aware social relationships. The middleware is validated through two representative applications in distinct domains: smart mobility, where semantic reasoning supports adaptive eco-friendly route computation, and healthcare, where semantically integrated wearable and environmental data support health-event detection for people with dementia. Experimental evaluation further examines the performance of semantic annotation, semantic reasoning, and context management services under increasing workloads. The results provide prototype-level evidence that SEMIWARE supports semantic interoperability, cross-domain adaptability, and graph-based processing under controlled workloads, indicating its potential suitability for complex, data-intensive SIoT applications. Full article

This narrative review examines how big data analytics supports geospatial decision-making in smart cities through the combined roles of spatial data foundations, GeoAI methods, and urban digital twins. Methodologically, the article follows a structured narrative and critical review design rather than a PRISMA-based systematic review, bibliometric analysis, or meta-analysis. The paper responds to fragmentation across GIScience, smart-city studies, urban analytics, geospatial data engineering, and digital twin research, where related contributions often remain technically rich but weakly integrated from a decision-oriented perspective. Rather than treating geospatial decision-making as an extension of GIS or as a general expression of data-driven governance, the review frames it as a layered socio-technical process through which heterogeneous urban data are transformed into decision-relevant knowledge. The analysis first clarifies the conceptual evolution from GIS to spatial decision support and urban governance, and then examines the spatial data sources, integration problems, and representational limits that shape smart-city evidence. It also reviews GeoAI and geospatial analytics methods, including spatial statistics, machine learning, spatiotemporal forecasting, graph-based modeling, optimization, and explainable GeoAI. Urban digital twins are then analyzed as decision infrastructures that connect sensing, data integration, synchronization, semantic modeling, simulation, visualization, user interaction, and feedback into planning or operations. The review further maps these capabilities across mobility, land use, utilities, risk management, environmental resilience, public health, and cross-domain decision contexts. Overall, the paper argues that the value of smart-city geoinformation systems depends not on data abundance or model sophistication alone, but on their capacity to support interpretable, accountable, and context-sensitive urban decisions. Full article

This paper examines the role of Multimodal Journey Planners (MJPs) as a link between user-oriented personalization and the broader societal goals of sustainable urban mobility. In smart cities, MJPs may serve as digital decision-support tools that connect individual mobility choices with broader sustainability objectives. Although contemporary journey planners increasingly display multiple criteria, such as travel time, cost, CO₂ emissions, and number of transfers, they still generally rely on predefined and non-personalized criterion weights and rarely infer travellers’ actual preferences from observed choices. The paper therefore proposes a transparent methodological proof-of-concept that combines multicriteria decision-making and inverse optimization to discover individual preference weights and enable personalized, preference-aware planning of multimodal routes. The Weighted Sum Method (WSM) is adopted as the basic ranking framework, and the proposed approach is evaluated within a controlled methodological testbed based on multimodal journey scenarios in Vienna. The results indicate that, within the available methodological testbed, the preference-discovery-based model achieved closer in-sample agreement with user-provided route evaluations than the model based on explicitly rated criteria. This was observed in the ranking-agreement analysis, where a more favourable penalty-point ratio was obtained in 19/21 cases (90.5%) and in the numerical error comparison, where lower in-sample reconstruction errors were obtained for 18/21 users (85.71%) across all scenarios. The paper further considers the tension between individual and system-level goals, as well as a conceptual extension toward system-aware re-ranking of alternatives. Within the broader framework of smart mobility, the importance of interoperability and open data is also recognized, with National Access Points (NAPs) for multimodal travel information potentially representing an important precondition for the development of advanced and transparent MJP solutions. Full article

Inadequate management of municipal solid waste represents a critical challenge for the sustainability of modern cities, characterized by low citizen participation rates due to the lack of direct incentives. Unlike existing approaches that isolate hardware classification or fleet monitoring, this article presents RENOVA as a socio-technical closed-loop system based on the Internet of Things (IoT) and artificial intelligence (AI). This system integrates an IoT-enabled smart bin, a gamified mobile application for citizens, and an administrative web panel for merchant redemption, all interconnected via a REST API. The system employs computer vision through the GPT-4o (OpenAI, San Francisco, CA, USA) multimodal model for the automatic classification of recyclable materials (PET plastic and Aluminum) and integrates a gamified rewards program to incentivize citizen participation. The methodology follows an applied technological development approach under the agile Scrum framework. Prototype validation demonstrated successful real-time communication between the IoT device and the cloud platform, achieving classification accuracy exceeding 95% under controlled conditions. A diagnostic survey applied to a convenience sample of 51 participants revealed that 94.1% accepted the proposed gamification model, while user experience evaluation (n = 74; consisting primarily of university-affiliated individuals aged 15–24) yielded a mean overall satisfaction score of 4.77/5.0 (SD = 0.48), with 79.7% of participants assigning the maximum rating. These findings reflect stated user acceptance and behavioral intention under prototype conditions rather than observed long-term behavioral change, and should not be generalized to broader urban populations without further validation. The proposed solution directly contributes to Sustainable Development Goals 11 (Sustainable Cities) and 12 (Responsible Consumption), suggesting a potentially scalable framework. Full article

Urban traffic congestion remains a persistent global challenge, contributing to significant economic inefficiencies, elevated greenhouse gas emissions, and diminished quality of life. This paper presents a real-world video-based traffic monitoring study combined with a proposed adaptive signal control framework. In the monitoring component, YOLOv11 object detection was applied directly to footage recorded from an overhead bridge position on a 40 km/h road. The model successfully detected and tracked multiple road-user categories, including cars, trucks, buses, motorcycles, cyclists, and pedestrians, yielding 1041 vehicle detections across 25 unique tracked objects. Vehicle speeds were estimated from inter-frame centroid displacement, and a Region of Interest (ROI) occupancy model was used to classify congestion states as High, Medium, or Free Flow using thresholds grounded in Highway Capacity Manual (HCM) level-of-service criteria. The system detected 11 high-congestion frames (3.8%), 184 medium-congestion frames (63.9%), and 93 free-flow frames (32.3%), consistent with moderate congestion observed during the recording period. In the proposed control component, a Proximal Policy Optimisation (PPO)-based reinforcement learning signal controller is designed around the YOLOv11 detection outputs as its state representation. Based on comparable adaptive traffic signal control studies in the literature, the proposed framework is projected to achieve approximately 25% higher peak-hour throughput, 35% shorter queue lengths, and 32% lower average waiting times relative to a fixed-time signal baseline. The detection accuracy (mAP@0.5 = 93.2%) and inference speed (32 FPS) cited are published YOLOv11 benchmarks used as indicative performance references. This work bridges real-world perception and proposed intelligent control, providing a transparent and reproducible methodology for next-generation smart city traffic management. Full article

As smart city approaches increasingly extend to public open spaces, understanding what urban park users expect from digital park applications has become a critical issue for sustainable urban management. This study examines park users’ expectations of smart park applications through an exploratory sequential mixed-methods design. In the first phase (Study I), semi-structured interviews were conducted with 32 purposively selected participants representing four user groups—parents with children, sport-oriented users, older adults, and general adults—in urban parks in Eskişehir, Türkiye. Thematic analysis identified eight user expectation themes, which were subsequently operationalized into a seven-factor quantitative structure. In the second phase (Study II), a seven-factor scale derived from the qualitative findings was administered to 374 participants. Confirmatory factor analysis demonstrated a good overall model fit, and the scale exhibited strong reliability and convergent validity. One-way ANOVA revealed significant between-group differences in six of the seven dimensions, with sport-oriented users consistently reporting higher expectations than older adults. Safety and Activity Diversity was the only dimension showing no significant group differences, indicating a universal expectation across all user profiles. Multiple regression analysis showed that Independent Functionality was the strongest predictor of use intention, followed by Centrality and Communal Function and Safety. Integration of both phases through a joint display revealed that expectations are both universal and user profile-specific, underscoring the need for user-sensitive smart park design. By linking digital park services to user expectations, well-being-oriented park design, and the sustainable use of urban green spaces, these findings contribute to the literatures on smart cities, urban green spaces, and well-being, providing an empirically informed and user-centred framework for digital park applications that may inform efforts toward healthier, more inclusive, and more sustainable urban public spaces in line with SDGs 3 and 11. Full article

The rapid expansion of intelligent technologies within urban commercial built environments has created an urgent need to understand how the quality attributes of infrastructure of smart parking space translate into sustained user behavioral engagement. This study proposes and empirically validates an integrated framework combining the Information Systems (IS) Success Model and the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) to examine continuance usage intention toward intelligent commercial parking space. Cross-sectional survey data from 610 users were analyzed using a hybrid structural equation modeling (SEM) and artificial neural network (ANN) methodology. Results confirm that information quality, service quality, and system quality differentially shape performance expectancy and effort expectancy, which in turn influence user satisfaction and continuance usage intention. Information quality emerged as the most consequential antecedent (normalized relative importance: 100% across both cognitive evaluation models), performance expectancy as the dominant cognitive mediator, and user satisfaction as the most proximal behavioral driver. The non-significant effect of system quality on effort expectancy is interpreted as reflecting a differentiated role of technical reliability in mature smart city environments. Findings provide theoretical contributions to IS success and technology acceptance scholarship in the intelligent built environment domain, and practical guidance for architects, facility managers, and urban planners. Full article

Rapid urbanization has intensified congestion, environmental pressures, and transport inequities, thereby increasing interest in Smart Urban Mobility (SUM) as an approach that combines digital technologies, sustainable transport strategies, and data-informed decision-making to respond to these challenges. However, the evaluation of SUM remains fragmented due to the absence of harmonized assessment frameworks and the diversity of methodologies applied across smart city contexts. This study presents a systematic literature review of evaluation approaches and indicator architectures for SUM in smart city contexts. Using a PRISMA-guided screening process, 33 eligible studies were selected from 412 retrieved records. Three main methodological groups were identified: quantitative approaches, multi-criteria decision-making methods, and qualitative or participatory frameworks. A total of 273 indicators were organized into eight factor categories, confirming the multidimensional nature of smart mobility assessment while also revealing limited consistency in indicator selection and application across studies. Across the selected studies, current evaluation practices are increasingly linked to project prioritization, planning, and decision support; however, their effectiveness remains constrained by data inconsistencies, governance fragmentation, and insufficient user inclusion. These findings highlight the need for assessment frameworks that are sufficiently comparable to enable cross-city learning, yet flexible enough to reflect local contexts and institutional realities. Full article

The expansion of accessible, fine-grained city data has significantly increased opportunities for evidence-based and informed policy-making. Despite this evolution, extracting actionable insights from heterogeneous data sources and effectively communicating findings remain persistent challenges. Most existing visualisation approaches and research prioritise technical implementation by focusing on how to visualise, often neglecting the importance of policy-driven visualisation questions and data contexts. This led to flawed analyses, particularly in complex domains such as smart cities and urban policy-making using digital twins. This article presents a novel, practical, step-by-step policy visualisation methodology grounded in empirical smart city research, shifting the emphasis toward policy-element-based questions informed by data-informed evidence. The methodology was successfully applied, tested, and adapted, resulting in an implementable, structured, and integrative approach that aligns with policymakers’ established policy design, implementation, and evaluation cycles. Through this approach, 20 user-driven smart city policy visualisations were operationalised and implemented in strategic policy decision-making contexts across smart city domains, including mobility, spatial planning, and environment. The results demonstrate how dashboards, algorithmic simulations, and digital twins visualisations can be systematically deployed to support evidence-informed decision-making. Full article

With the rapid electrification of urban transportation and increasing penetration of renewable energy, maintaining frequency stability in smart-city multi-microgrids (MMG) systems increasingly depends on coordinated vehicle-to-grid (V2G) flexibility. However, existing load frequency control strategies typically treat electric vehicles (EVs) as homogeneous resources and overlook the impacts of charging-infrastructure diversity, user mobility constraints, and extreme weather conditions on regulation availability. To address these challenges, this study proposes a weather-adaptive intelligent load frequency control strategy for smart-city MMG considering heterogeneous charging stations and energy requirements of EV users. Fast and slow charging infrastructures are modeled separately to reflect their distinct regulation characteristics, while time-varying charging and discharging margins are derived from travel demand, parking duration, and state-of-charge preferences and further adjusted under extreme weather scenarios. Based on these dynamic constraints, an enhanced multi-agent soft actor–critic (MA-SAC) controller coordinates micro gas turbines and charging stations for distributed frequency regulation. Simulations demonstrate MA-SAC outperforms PID, Fuzzy, and MA-DDPG methods, achieving a 98.51% frequency excellent rate normally and 91.47% during extreme weather. It reduces maximum deviations by up to 80% versus PID, while preserving user travel requirements. The proposed framework provides a practical pathway for integrating electrified mobility into resilient smart-city MMG frequency regulation. Full article

UAV aerial cinematography has become increasingly important in film production, surveying, and smart-city applications due to its efficiency and creative potential. However, existing UAV filming workflows still rely heavily on manual operation and professional piloting skills, resulting in complex mission design, limited planning autonomy, and inconsistent visual quality. To address these challenges, this paper proposes a unified aesthetics-aware trajectory planning framework for multi-region-of-interest (multi-ROI) UAV aerial cinematography that automatically generates safe, efficient, and visually coherent flight paths from user-specified ROIs. The proposed framework consists of three main components. First, for each ROI, candidate viewpoints are sampled using a spiral trajectory, and a learning-based aesthetic evaluation network is applied to select visually optimal viewpoints for local trajectory generation. Second, transition trajectories between ROIs are generated using a Goal-biased Bidirectional Rapidly exploring Random Tree Star (Goal-biased BiRRT*) planner and evaluated through a multi-objective cost function to determine the most suitable transition paths. Third, the global connection of multiple ROIs is formulated as a Set Traveling Salesman Problem (STSP) to obtain an efficient visiting sequence. By integrating learning-based aesthetic evaluation with hierarchical trajectory planning and coordinated multi-ROI route organization, the proposed framework jointly considers flight feasibility, planning efficiency, visual composition quality, and trajectory continuity within a unified planning pipeline. Experimental results demonstrate that the proposed method generates more visually appealing and coherent aerial trajectories than traditional manual or rule-based approaches, while significantly reducing operational complexity. The proposed system provides an effective solution for autonomous UAV aerial cinematography with improved global consistency, aesthetic performance, and practical planning capability in complex environments. Full article