Search Results (5,458)

This paper explores how blockchain technology, widely known as the backbone of cryptocurrencies, can be harnessed to address limitations of traditional water quality monitoring (WQM) systems. Blockchain offers a decentralized, tamper-proof ledger that enables secure, transparent, and traceable data management across distributed networks. When applied to water quality monitoring, blockchain facilitates real-time data acquisition, enhances data integrity, and enables smart contracts for automated regulatory compliance and alerts. These features not only improve the accuracy and efficiency of WQM systems but also build public trust in the reported data. Key insights from current research and pilot applications highlight blockchain’s capacity to integrate with IoT devices for real-time sensing, support adaptive water governance, and empower local stakeholders through decentralized control and transparent access to information. The implications for policy and practice are significant: blockchain-based WQM can support stronger regulatory enforcement, encourage cross-sector collaboration, and provide a robust digital foundation for sustainable water management in smart cities and rural areas alike. As such, this review paper positions blockchain as a transformative tool in the digital transition toward more resilient and equitable water management systems. Full article

The growing use of unmanned aerial vehicles (UAVs) for real-time video surveillance in smart city and smart region infrastructures requires reliable and delay-aware data transmission models. In urban environments, UAV communication links are subject to stochastic variability, leading to jitter, packet loss, and unstable video delivery. This paper presents a novel approach based on the Graphical Evaluation and Review Technique (GERT) for modeling the transmission of video frames from UAVs over uncertain network paths with probabilistic feedback loops and lognormally distributed delays. The proposed model enables both analytical and numerical evaluation of key Quality-of-Service (QoS) metrics, including mean transmission time and jitter, under varying levels of channel variability. Additionally, the structure of the GERT-based framework allows integration with artificial intelligence mechanisms, particularly for adaptive routing and delay prediction in urban conditions. Spectral analysis of the system’s characteristic function is also performed to identify instability zones and guide buffer design. The results demonstrate that the approach supports flexible, parameterized modeling of UAV video transmission and can be extended to intelligent, learning-based control strategies in complex smart city environments. This makes it suitable for a wide range of applications, including traffic monitoring, infrastructure inspection, and emergency response. Beyond QoS optimization, the framework explicitly accommodates security and privacy preserving operations (e.g., encryption, authentication, on-board redaction), enabling secure UAV video transmission in urban networks. Full article

The objective of this study is to explore the use of emerging technologies such as the Metaverse and Digital Twin to highlight how these can be used to analyse and improve the perception of security in urban parks. Through the proposed methodological approach, which combines real data collection, 3D modelling, immersive simulations, and user feedback, a virtual environment representative of the Quartieri Spagnoli Park in Naples, chosen as a case study, was developed and tested. The experimentation involved a heterogeneous group of users and consisted of two phases of questionnaire administration, one in person and one in a virtual environment, to compare the individual and collective perceptions of users in relation to issues such as disorientation, lighting, and maintenance. The results obtained made it possible to identify a correspondence between the data collected in the two environments, and to highlight any critical issues that emerged. Undoubtedly, the virtual experience proved to be useful, accessible, and immersive, demonstrating the potential of these tools not only in identifying issues but especially in supporting participatory design and urban planning with a view to a smart city. In urban design, as in many other fields, being able to intervene and test changes in a virtual environment before actually implementing them is a valuable opportunity, as it allows the feasibility to be assessed without compromising the real space. It is precisely this aspect that makes this type of approach extremely interesting and important. The distinctive feature of the proposed approach lies in the implementation of digital twins in the metaverse, which can perform a dual function: simulation and verification. In the first case, simulations within the virtual environment allow project planning to be tested in order to predict the outcome; in the second case, it is possible to investigate the state of affairs, thus assessing whether the planning put in place has achieved the desired results. Full article

Rapid population aging in developing countries has intensified demand for accessible nursing home services, yet spatial disparities in service distribution remain insufficiently examined in secondary cities. This study investigates spatial distribution and multi-dimensional accessibility of nursing homes in Kunming, China, using comprehensive spatial analytical methods to inform sustainable urban development. We analyzed 205 nursing homes with 47,600 beds, evaluating spatial distribution patterns, economic accessibility, and spatial accessibility across different transportation modes. Our analysis reveals a pronounced monocentric pattern with nursing resources concentrated within central urban districts, creating a “primary core-multiple satellite” structure and spatial mismatch between service supply and older adult population needs. A distinct institutional dichotomy exists between publicly and privately operated facilities, establishing a dual-track system with different accessibility implications for social equity. Economic accessibility analysis demonstrates significant barriers in central urban and tourism-oriented districts dominated by higher-priced private facilities, where minimum prices frequently exceed average monthly pension. Spatial accessibility remains inadequate across all transportation modes, with only 24.3% of communities achieving normal or higher accessibility via private car, 21.5% via public bus, and merely 13.9% via walking. These limitations primarily stem from insufficient service capacity (34 beds per 1000 older adults) relative to demographic needs rather than transportation constraints. We recommend three sustainable interventions: implementing demand-based planning mechanisms, establishing progressive pricing policies, and developing older adult-friendly transportation networks. This framework supports sustainable urbanization by promoting spatial equity and efficient resource allocation, providing valuable insights for secondary cities pursuing sustainable development goals. Full article

Artificial intelligence (AI) for placemaking holds the potential to revolutionize how we conceptualize, design, and manage urban spaces to create more vibrant, resilient, and people-centered cities. In this context, integrating Human-Centered AI (HCAI) into public infrastructure presents an exciting opportunity to reimagine the role of urban amenities and furniture in shaping inclusive, responsive, and technologically enhanced public spaces. This review examines the state-of-the-art in HCAI for placemaking, focusing on some of the main factors that must be analyzed to guide future technological research and development, such as (a) AI-driven tools for community engagement in the placemaking process, including sentiment analysis, participatory design platforms, and virtual reality simulations; (b) AI sensors and image recognition technology for analyzing user behaviors within public spaces to inform evidence-based urban design decisions; (c) the role of HCAI in enhancing community engagement in the placemaking process, focusing on tools and approaches that facilitate more inclusive and participatory design practices; and (d) the utilization of AI in analyzing and understanding user behaviors within public spaces, highlighting how these insights can inform more responsive and user-centric design decisions. The review identifies current innovations, implementation challenges, and emerging opportunities at the intersection of artificial intelligence, urban design, and human experience. Full article

This paper contributes to the field of urban waste collection systems, which are crucial for advancing sustainability, urban cleanliness, and the aesthetic quality of cities. Specifically, it introduces a novel framework designed to support planners and decision makers in the design of efficient and responsive textile waste collection systems, aligned with both environmental objectives and citizen engagement. To this end, the framework exploits a hybrid simulation platform that realistically models the logistics infrastructure in a spatially explicit environment. Also, within the framework, citizens are represented as adaptive agents whose environmental attitudes evolve through personal experience, social influence, and perceived service quality. The behavioural layer is the core element of the framework. It enables dynamic analysis of the two-way feedback between citizen participation and service effectiveness to underscore the often-overlooked role of citizen behaviour in shaping overall system performance. The model was tested in a representative urban scenario under varying operational conditions. The results highlight how policy incentives and smart collection infrastructure can significantly boost participation, while social segregation may hinder the adoption of sustainable practices. The framework ultimately offers a generalisable decision-support tool to explore the behavioural dimension of circular economy initiatives and develop robust, scenario-based strategies. Full article

Efficient human detection in video streams is essential for various IoT applications, including surveillance, smart cities, intelligent transportation systems (ITSs), and industrial automation. However, resource-constrained IoT devices often face limitations in handling deep learning-based object detection. This study proposes a collaborative edge computing framework utilizing multiple Raspberry Pi-based IoT devices to improve YOLOv11-based human detection performance. By distributing video frames across multiple edge devices, the proposed system effectively balances the computational load, resulting in an increase in the FPS (Frames Per Second) for processed video outputs. The experimental results confirm that as more edge devices collaborate, overall video processing efficiency improves, demonstrating the feasibility of distributed object detection for scalable and cost-effective IoT-based video analytics. In particular, the proposed approach holds significant potential for ITS applications such as pedestrian monitoring at intersections, real-time incident detection, and enhancing traffic safety by enabling responsive and decentralized analysis at the edge. Full article

As mental health issues such as stress, anxiety, and depression become increasingly prevalent in urban populations, there is a critical need to embed restorative functions into the built environment. Urban parks, as integral components of ecological infrastructure, play a vital role in promoting psychological well-being. This study explores how diverse park environments facilitate mental health recovery through multi-sensory engagement, using integrated psychophysiological assessments in a wetland park in Zhengzhou, China. Electroencephalography (EEG) and perceived restoration scores were employed to evaluate recovery outcomes across four environmental types: waterfront, wetland, forest, and plaza. Key perceptual factors—including landscape design, spatial configuration, biodiversity, and facility quality—were validated and analyzed for their roles in shaping restorative experiences. Results reveal significant variation in recovery effectiveness across environments. Waterfront areas elicited the strongest physiological responses, while plazas demonstrated lower restorative benefits. Two recovery pathways were identified: a direct, sensory-driven process and a cognitively mediated route. Biodiversity promoted physiological restoration only when mediated by perceived restorative qualities, whereas landscape and spatial attributes produced more immediate effects. Facilities supported psychological recovery mainly through cognitive appraisal. The study proposes a smart park framework that incorporates environmental sensors, adaptive lighting, real-time biofeedback systems, and interactive interfaces to enhance user engagement and monitor well-being. These technologies enable urban parks to function as intelligent, health-supportive infrastructures within the broader built environment. The findings offer evidence-based guidance for designing responsive green spaces that contribute to mental resilience, aligning with the goals of smart city development and healthy life-building environments. Full article

The urgent need for sustainable food systems in Indonesia is hindered by urban planning policies that are disconnected from food security priorities. Smart city planning policies in Indonesia have been subject to numerous misconceptions compared to successful implementations in developed countries. This study examines the relationship between urban planning policies and architectural design in fostering sustainable food systems, employing a mixed-methods approach that combines multiple linear regression analysis with a sample of 75 smart cities, correlation analysis, and case studies from six representative cities that demonstrate best practices. Key findings reveal that food security is significantly undermined by the Gross Regional Domestic Product (GRDP), indicating distributional inequalities, high food expenditure, and a lack of clean water, while access to electricity improves resilience. Case study analysis showed that Semarang is the city with the highest readiness level (97%), followed by Makassar (91%), which employs a Holistic Benchmark approach, Jakarta (91%), which follows a Technological—fragmented approach, Samarinda (86%) and Medan (79%), which are in a Developing Transition phase, and Surabaya (66%), which utilizes a Community and Local Initiatives approach. Each city adopted a different approach, which means the national strategy for developing Smart Cities will also differ; however, they must prioritize equitable infrastructure and architectural innovation, such as urban farming integration and a water–energy–food nexus system. Smart cities extend beyond technological innovations, encompassing integrated urban planning policies and architectural practices that foster sustainable food systems through infrastructure management and environmental sustainability. Full article

Digital transformation (DT) has become a strategic priority for public administrations, particularly due to the need to deliver more efficient and citizen-centered services and respond to societal expectations, ESG (Environmental, Social, and Governance) criteria, and the United Nations Sustainable Development Goals (UN SDGs). In this context, the main objective of this study is to propose an innovative methodology to automatically evaluate the level of digital transformation (DT) in public sector organizations. The proposed approach combines traditional assessment methods with Artificial Intelligence (AI) techniques. The methodology follows a dual approach: on the one hand, surveys are conducted using specialized staff from various public entities; on the other, AI-based models (including neural networks and transformer architectures) are used to estimate the DT level of the organizations automatically. Our approach has been applied to a real-world case study involving local public administrations in the Valencian Community (Spain) and shown effective performance in assessing DT. While the proposed methodology has been validated in a specific local context, its modular structure and dual-source data foundation support its international scalability, acknowledging that administrative, regulatory, and DT maturity factors may condition its broader applicability. The experiments carried out in this work include (i) the creation of a domain-specific corpus derived from the surveys and websites of several organizations, used to train the proposed models; (ii) the use and comparison of diverse AI methods; and (iii) the validation of our approach using real data. Based on the deficiencies identified, the study concludes that the integration of technologies such as the Internet of Things (IoT), sensor networks, and AI-based analytics can significantly support resilient, agile urban environments and the transition towards more effective and sustainable Smart City models. Full article

Background: The increasing deployment of unmanned aerial vehicles (UAVs) for logistics in smart cities presents pressing challenges related to identity spoofing, unauthorized payload transport, and airspace security. Existing drone defense systems (DDSs) struggle to verify both drone identity and payload authenticity in real time, while blockchain-assisted solutions are often hindered by high latency and limited scalability. Methods: To address these challenges, we propose iBANDA, a blockchain- and AI-assisted DDS framework. The system integrates a lightweight You Only Look Once 5 small (YOLOv5s) object detection model with a Snowball-based Proof-of-Stake consensus mechanism to enable dual-layer authentication of drones and their attached payloads. Authentication processes are coordinated through an edge-deployable decentralized application (DApp). Results: The experimental evaluation demonstrates that iBANDA achieves a mean average precision of 99.5%, recall of 100%, and an F1-score of 99.8% at an inference time of 0.021 s, validating its suitability for edge devices. Blockchain integration achieved an average network latency of 97.7 ms and an end-to-end transaction latency of 1.6 s, outperforming Goerli, Sepolia, and Polygon Mumbai testnets in scalability and throughput. Adversarial testing further confirmed resilience to Sybil attacks and GPS spoofing, maintaining a false acceptance rate below 2.5% and continuity above 96%. Conclusions: iBANDA demonstrates that combining AI-based visual detection with blockchain consensus provides a secure, low-latency, and scalable authentication mechanism for UAV-based logistics. Future work will explore large-scale deployment in heterogeneous UAV networks and formal verification of smart contracts to strengthen resilience in safety-critical environments. Full article

For addressing the technical challenges of photon denoising and water level extraction in ICESat-2 satellite-based water monitoring applications, this paper proposes an innovative solution integrating Gaussian function fitting with Hough transform. The method first employs histogram Gaussian fitting to achieve coarse denoising of water body regions. Subsequently, a probability attenuation model based on elevation differences between adjacent photons is constructed to accomplish refined denoising through iterative optimization of adaptive thresholds. Building upon this foundation, the Hough transform technique from image processing is introduced into photon cloud processing, enabling robust water level extraction from ICESat-2 data. Through rasterization, discrete photon distributions are converted into image space, where straight lines conforming to the photon distribution are then mapped as intersection points of sinusoidal curves in Hough space. Leveraging the noise-resistant characteristics of the Hough space accumulator, the interference from residual noise photons is effectively eliminated, thereby achieving high-precision water level line extraction. Experiments were conducted across five typical water bodies (Qinghai Lake, Long Land, Ganquan Island, Qilian Yu Islands, and Miyun Reservoir). The results demonstrate that the proposed denoising method outperforms DBSCAN and OPTICS algorithms in terms of accuracy, precision, recall, F1-score, and computational efficiency. In water level estimation, the absolute error of the Hough transform-based line detection method remains below 2 cm, significantly surpassing the performance of mean value, median value, and RANSAC algorithms. This study provides a novel technical framework for effective global water level monitoring. Full article

As smart cities continue to generate vast quantities of data, data integrity is increasingly threatened by instances of fraud. Anomalous or fake data deteriorate the process and have impacts on decision-making systems and predictive analytics. Hence, an effective and intelligent fake data detection model was designed by combining an advanced feature selection method with a robust ensemble classification framework. Initially, the raw data are eliminated by performing normalization, feature transformation, and noise filtering that enhances the reliability of the model. The dimensionality issues are mitigated by eliminating redundant features via the proposed Elite Tuning Strategy-Enhanced Polar Bear Optimization algorithm. It simulates the hunting behavior of polar bears, balancing exploration and exploitation features. The proposed Stacking Ensemble-based Random AdaBoost Quadratic Discriminant model leverages the merits of diverse base learners, including AdaBoost, Quadratic Discriminant Analysis, and Random Forest, that classify the feature subset and the integration of prediction processes with a meta-feature vector-processed meta-classifier such as a multilayer perceptron or logistic regression model that predicts the final outcome. This hierarchical architecture validates resilience against noise and improves generalization and prediction accuracy. Thus, the experimental results show that the proposed method outperforms existing approaches in terms of accuracy, precision, and latency, yielding values of 98.78%, 98.75%, and 16 ms, respectively, using the UNSW-NB15 dataset. Full article

Wireless sensor networks (WSNs) have gained significant attention across various industries and scientific fields. Localization, a crucial aspect of WSNs, involves accurately determining node positions to track events and execute actions. Despite the development of numerous localization algorithms, real-world environments pose challenges such as anisotropy, noise, and faults. To improve accuracy amidst these complexities, researchers are increasingly adopting advanced methodologies, including soft computing, software-defined networking, maximum likelihood estimation, and optimization techniques. Our comprehensive review from 2020 to 2024 reveals that approximately 29% of localization solutions employ optimization techniques, 48% of which utilize nature-inspired swarm-based algorithms. These algorithms have proven effective for node localization in a variety of applications, including smart cities, seismic exploration, oil and gas reservoir monitoring, assisted living environments, forest monitoring, and battlefield surveillance. This underscores the importance of swarm intelligence algorithms in sensor node localization, prompting a detailed investigation in our study. Additionally, we provide a comparative analysis to elucidate the applicability of these algorithms to various localization challenges. This examination not only helps researchers understand current localization issues within WSNs but also paves the way for enhanced localization precision in the future. Full article

This study investigates the potential of fine-tuned large language models (LLMs) to enhance geospatial intelligence by translating natural language queries into executable Python code. Traditional GIS workflows, while effective, often lack usability and scalability for non-technical users. LLMs offer a new approach by enabling conversational interaction with spatial data. We evaluate OpenAI’s GPT-4o-mini model in two forms: an “As-Is” baseline and a fine-tuned version trained on 600+ prompt–response pairs related to geospatial Python scripting in Virginia. Using U.S. Census shapefiles and hospital data, we tested both models across six types of spatial queries. The fine-tuned model achieved 89.7%, a 49.2 percentage point improvement over the baseline’s 40.5%. It also demonstrated substantial reductions in execution errors and token usage. Key innovations include the integration of spatial reasoning, modular external function calls, and fuzzy geographic input correction. These findings suggest that fine-tuned LLMs can improve the accuracy, efficiency, and usability of geospatial dashboards when they are powered by LLMs. Our results further imply a scalable and replicable approach for future domain-specific AI applications in geospatial science and smart cities studies. Full article