Search Results (626)

Hospital in the Home (HITH) programs are emerging as a key pillar of smart city healthcare infrastructure, leveraging technology to extend care beyond traditional hospital walls. The global healthcare sector has been conceptualizing the notion of a care without walls hospital, also called HITH, where virtual care takes precedence to address the multifaceted needs of an increasingly aging population grappling with a substantial burden of chronic disease. HITH programs have the potential to significantly reduce hospital bed occupancy, enabling hospitals to better manage the ever-increasing demand for inpatient care. Although many health providers and hospitals have established their own HITH programs, there is a lack of research that provides healthcare executives and HITH program managers with management models and frameworks for such initiatives. There is also a lack of research that provides strategies for improving HITH management in the health sector. To fill this gap, the current study ran a systematic literature review to explore state-of-the-art with regard to this topic. Out of 2631 articles in the pool of this systematic review, 20 articles were deemed to meet the eligibility criteria for the study. After analyzing these studies, nine management models were extracted, which were then categorized into three categories, namely, governance models, general models, and virtual models. Moreover, this study found 23 strategies and categorized them into five groups, namely, referral support, external support, care model support, technical support, and clinical team support. Finally, implications of findings for practitioners are carefully provided. These findings provide healthcare executives and HITH managers with practical frameworks for selecting appropriate management models and implementing evidence-based strategies to optimize program effectiveness, reduce costs, and improve patient outcomes while addressing the growing demand for home-based care. Full article

This study explores the migration intentions of university students—representing the potential creative class—in Erzurum, a medium-sized city in eastern Turkey experiencing shrinkage. Within the theoretical framework of shrinking cities, it investigates how economic, social, physical, and personal factors influence students’ post-graduation stay or leave decisions. Survey data from 742 Architecture and Fine Arts students at Atatürk University were analyzed using factor analysis, logistic regression, and correlation to identify key migration drivers. Findings reveal that, in addition to economic concerns such as limited job opportunities and low income, personal development opportunities and social engagement also play a decisive role. In particular, the perception of limited chances for skill enhancement and the belief that Erzurum is not a good place to meet people emerged as the strongest predictors of migration intentions. These results suggest that members of the creative class are influenced not only by economic incentives but also by broader urban experiences related to self-growth and social connectivity. This study highlights spatial inequalities in access to cultural, educational, and social infrastructure, raising important questions about spatial justice in shrinking urban contexts. This paper contributes to the literature on shrinking cities by highlighting creative youth in mid-sized Global South cities. It suggests smart shrinkage strategies focused on creative sector development, improved quality of life, and inclusive planning to retain young talent and support sustainable urban revitalization. Full article

This study proposes the SMR Smart Net-Zero City (SSNC) framework—a scalable model for achieving carbon neutrality by integrating Small Modular Reactors (SMRs), renewable energy sources, and sector coupling within a microgrid architecture. As deploying renewables alone would require economically and technically impractical energy storage systems, SMRs provide a reliable and flexible baseload power source. Sector coupling systems—such as hydrogen production and heat generation—enhance grid stability by absorbing surplus energy and supporting the decarbonization of non-electric sectors. The core contribution of this study lies in its real-time data emulation framework, which overcomes a critical limitation in the current energy landscape: the absence of operational data for future technologies such as SMRs and their coupled hydrogen production systems. As these technologies are still in the pre-commercial stage, direct physical integration and validation are not yet feasible. To address this, the researchers leveraged real-time data from an existing commercial microgrid, specifically focusing on the import of grid electricity during energy shortfalls and export during solar surpluses. These patterns were repurposed to simulate the real-time operational behavior of future SMRs (ProxySMR) and sector coupling loads. This physically grounded simulation approach enables high-fidelity approximation of unavailable technologies and introduces a novel methodology to characterize their dynamic response within operational contexts. A key element of the SSNC control logic is a day–night strategy: maximum SMR output and minimal hydrogen production at night, and minimal SMR output with maximum hydrogen production during the day—balancing supply and demand while maintaining high SMR utilization for economic efficiency. The SSNC testbed was validated through a seven-day continuous operation in Busan, demonstrating stable performance and approximately 75% SMR utilization, thereby supporting the feasibility of this proxy-based method. Importantly, to the best of our knowledge, this study represents the first publicly reported attempt to emulate the real-time dynamics of a net-zero city concept based on not-yet-commercial SMRs and sector coupling systems using live operational data. This simulation-based framework offers a forward-looking, data-driven pathway to inform the development and control of next-generation carbon-neutral energy systems. Full article

This study proposes an intelligent patrol planning framework that leverages reinforcement learning, spatiotemporal crime forecasting, and simulated sensor telemetry to optimize autonomous vehicle (AV) navigation in urban environments. Crime incidents from Washington DC (2024–2025) and Seattle (2008–2024) are modeled as a dynamic spatiotemporal graph, capturing the evolving intensity and distribution of criminal activity across neighborhoods and time windows. The agent’s state space incorporates synthetic AV sensor inputs—including fuel level, visual anomaly detection, and threat signals—to reflect real-world operational constraints. We evaluate and compare three learning strategies: Deep Q-Network (DQN), Double Deep Q-Network (DDQN), and Proximal Policy Optimization (PPO). Experimental results show that DDQN outperforms DQN in convergence speed and reward accumulation, while PPO demonstrates greater adaptability in sensor-rich, high-noise conditions. Real-map simulations and hourly risk heatmaps validate the effectiveness of our approach, highlighting its potential to inform scalable, data-driven patrol strategies in next-generation smart cities. Full article

One of the fundamental goals of contemporary mobility is to optimize transport processes in urban areas. The solution in this area seems to be the implementation of the idea of sustainable transport systems based on the Smart City concept. The article presents a case study—an assessment of the possibilities of changing mobility habits based on the idea of sustainable urban transport, taking into account the criterion of energy consumption of individual means of transport. The analyses are based on a comparison of selected means of transport occurring in the urban environment according to several key parameters for the optimization and efficiency of transport processes, i.e., cost, time, travel comfort, and impact on the natural environment, while simultaneously linking them to the criterion of energy consumption of individual means of transport. The analyzed parameters currently constitute the most important group of challenges in the area of shaping and planning optimal and sustainable urban transport. The presented research was used to indicate the connections between various areas of optimization of the transport process and the energy efficiency of individual modes of transport. Analyses have shown that the least time-consuming process of urban mobility is associated with the highest level of CO₂ emissions and, at the same time, the highest level of energy efficiency. However, combining public transport with other means of transport can meet most of the transport expectations of city residents, also in terms of energy optimization. The research results presented in the article can contribute to the creation of a strategy for the development of the transport network based on the postulates of increasing the optimization and efficiency of individual means of transport in urban areas. At the same time, recognizing the criterion of energy intensity of means of transport as leading in the development of sustainable urban mobility. Thus, confirming the important role of existing transport systems in the process of shaping and planning sustainable urban mobility in accordance with the idea of Smart City. Full article

Urban transport is a key driver of greenhouse gas emissions in Europe, making its decarbonization essential to achieving EU climate neutrality targets. This study examines how European strategies, such as the Green Deal, the Sustainable and Smart Mobility Strategy, and the Fit for 55 package, are reflected in Romania’s transport policies, with a focus on implementation challenges and urban outcomes in Bucharest. By combining policy analysis, stakeholder mapping, and comparative mobility indicators, the paper critically assesses Bucharest’s current reliance on private vehicles, underperforming public transport satisfaction, and limited progress on active mobility. The study develops a context-sensitive reform framework for the Romanian capital, grounded in transferable lessons from Western and Central European cities. It emphasizes coordinated metropolitan governance, public trust-building, phased car-restraint measures, and investment alignment as key levers. Rather than merely cataloguing policy intentions, the paper offers practical recommendations informed by systemic governance barriers and public attitudes. The findings will contribute to academic debates on urban mobility transitions in post-socialist cities and provide actionable insights for policymakers seeking to operationalize EU decarbonization goals at the metropolitan scale. Full article

With the rapid proliferation of Internet of Things (IoT) devices and mobile applications and the growing demand for low-latency services, edge computing has emerged as a transformative paradigm that brings computation and storage closer to end users. However, the dynamic nature and limited resources of edge networks bring challenges such as load imbalance and high latency while satisfying user requests. Service migration, the dynamic redeployment of service instances across distributed edge nodes, has become a key enabler for solving these challenges and optimizing edge network characteristics. Moreover, the low-latency nature of edge computing requires that service migration strategies must be in real time in order to ensure latency requirements. Thus, this paper presents a systematic survey of real-time service migration in edge networks. Specifically, we first introduce four network architectures and four basic models for real-time service migration. We then summarize four research motivations for real-time service migration and the real-time guarantee introduced during the implementation of migration strategies. To support these motivations, we present key techniques for solving the task of real-time service migration and how these algorithms and models facilitate the real-time performance of migration. We also explore latency-sensitive application scenarios, such as smart cities, smart homes, and smart manufacturing, where real-time service migration plays a critical role in sustaining performance and adaptability under dynamic conditions. Finally, we summarize the key challenges and outline promising future research directions for real-time service migration. This survey aims to provide a structured and in-depth theoretical foundation to guide future research on real-time service migration in edge networks. Full article

Rapid urbanization and escalating disaster risks necessitate resilient urban governance systems. Smart city initiatives that leverage digital technologies—such as the internet of things (IoT), big data analytics, and artificial intelligence (AI)—demonstrate transformative potential in enhancing emergency management capabilities. However, empirical evidence regarding their causal impact and underlying mechanisms remains limited, particularly in developing economies. Drawing on panel data from 275 Chinese prefecture-level cities over the period 2006–2021 and using China’s smart city pilot policy as a quasi-natural experiment, this study applies a multi-period difference-in-differences (DID) approach to rigorously assess the effects of smart city construction on emergency management capabilities. Results reveal that smart city construction produced a statistically significant improvement in emergency management capabilities, which remained robust after conducting multiple sensitivity checks and controlling for potential confounding policies. The benefits exhibit notable heterogeneity: emergency management capability improvements are most pronounced in central China and in cities at the extremes of population size—megacities (>10 million residents) and small cities (<1 million residents)—while effects remain marginal in medium-sized and eastern cities. Crucially, mechanism analysis reveals that digital technology application fully mediates 86.7% of the total effect, whereas factor allocation efficiency exerts only a direct, non-mediating influence. These findings suggest that smart cities primarily enhance emergency management capabilities through digital enablers, with effectiveness contingent upon regional infrastructure development and urban scale. Policy priorities should therefore emphasize investments in digital infrastructure, interagency data integration, and targeted capacity-building strategies tailored to central and western regions as well as smaller cities. Full article

The widespread integration of electric vehicles (EVs) into smart grid infrastructures necessitates intelligent and robust battery health diagnostics to ensure system resilience and performance longevity. While numerous studies have addressed the estimation of State of Health (SOH) and the prediction of remaining useful life (RUL) using machine and deep learning, most existing models fail to capture both short-term degradation trends and long-range contextual dependencies jointly. In this study, we introduce V2G-HealthNet, a novel hybrid deep learning framework that uniquely combines Long Short-Term Memory (LSTM) networks with Transformer-based attention mechanisms to model battery degradation under dynamic vehicle-to-grid (V2G) scenarios. Unlike prior approaches that treat SOH estimation in isolation, our method directly links health prediction to operational decisions by enabling SOH-informed adaptive load scheduling and predictive maintenance across EV fleets. Trained on over 3400 proxy charge-discharge cycles derived from 1 million telemetry samples, V2G-HealthNet achieved state-of-the-art performance (SOH RMSE: 0.015, MAE: 0.012, $R^2$: 0.97), outperforming leading baselines including XGBoost and Random Forest. For RUL prediction, the model maintained an MAE of 0.42 cycles over a five-cycle horizon. Importantly, deployment simulations revealed that V2G-HealthNet triggered maintenance alerts at least three cycles ahead of critical degradation thresholds and redistributed high-load tasks away from ageing batteries—capabilities not demonstrated in previous works. These findings establish V2G-HealthNet as a deployable, health-aware control layer for smart city electrification strategies. Full article

Handover (HO) management plays a key role in ensuring uninterrupted connectivity across evolving wireless networks. While previous generations such as 4G and 5G have introduced several HO strategies, these techniques are insufficient to meet the rigorous demands of sixth-generation (6G) networks in ultra-dense, heterogeneous smart city environments. Existing studies often fail to provide integrated HO solutions that consider key concerns such as energy efficiency, security vulnerabilities, and interoperability across diverse network domains, including terrestrial, aerial, and satellite systems. Moreover, the dynamic and high-mobility nature of smart city ecosystems further complicate real-time HO decision-making. This survey aims to highlight these critical gaps by systematically categorizing state-of-the-art HO approaches into AI-based, fuzzy logic-based, and hybrid frameworks, while evaluating their performance against emerging 6G requirements. Future research directions are also outlined, emphasizing the development of lightweight AI–fuzzy hybrid models for real-time decision-making, the implementation of decentralized security mechanisms using blockchain, and the need for global standardization to enable seamless handovers across multi-domain networks. The key outcome of this review is a structured and in-depth synthesis of current advancements, which serves as a foundational reference for researchers and engineers aiming to design intelligent, scalable, and secure HO mechanisms that can support the operational complexity of next-generation smart cities. Full article

The aging water distribution system in the United States, constructed mainly during the 1970s with some pipes dating back 125 years, is experiencing significant deterioration leading to substantial water losses. Along with the potential for water loss savings, improvements in the distribution system by using leak detection technologies can create net energy and cost savings. In this work, a new framework has been presented to calculate the economic level of leakage within water supply and distribution systems for two primary leak detection technologies (acoustic vs. satellite). In this work, a new framework is presented to calculate the economic level of leakage (ELL) within water supply and distribution systems to support smart infrastructure in smart cities. A case study focused using water audit data from Atlanta, Georgia, compared the costs of two leak mitigation technologies: conventional acoustic leak detection and artificial intelligence–assisted satellite leak detection technology, which employs machine learning algorithms to identify potential leak signatures from satellite imagery. The ELL results revealed that conducting one survey would be optimum for an acoustic survey, whereas the method suggested that it would be expensive to utilize satellite-based leak detection technology. However, results for cumulative financial analysis over a 3-year period for both technologies revealed both to be economically favorable with conventional acoustic leak detection technology generating higher net economic benefits of USD 2.4 million, surpassing satellite detection by 50%. A broader national analysis was conducted to explore the potential benefits of US water infrastructure mirroring the exemplary conditions of Germany and The Netherlands. Achieving similar infrastructure leakage index (ILI) values could result in annual cost savings of $4–$4.8 billion and primary energy savings of 1.6–1.9 TWh. These results demonstrate the value of combining economic modeling with advanced leak detection technologies to support sustainable, cost-efficient water infrastructure strategies in urban environments, contributing to more sustainable smart living outcomes. Full article

As climate change intensifies the frequency and severity of extreme weather events, the urgency for resilient and sustainable urban development becomes increasingly critical. This study investigates the role of digital twins in advancing climate-responsive urban strategies, with a focus on their integration into zero-energy buildings (ZEBs) and smart city frameworks. A systematic literature review was conducted following PRISMA guidelines, covering 1000 articles initially retrieved from Scopus and Web of Science between 2014 and 2024. After applying inclusion and exclusion criteria, 70 full-text articles were analyzed. Bibliometric analysis using VOSviewer revealed five key application areas of digital twins: energy efficiency optimization, renewable energy integration, design and retrofitting, real-time monitoring and control, and predictive maintenance. The findings suggest that digital twins can contribute to up to 30–40% improvement in building energy efficiency through enhanced performance monitoring and predictive modeling. This review synthesizes trends, identifies research gaps, and contextualizes the findings within the Middle Eastern urban landscape, where climate action and smart infrastructure development are strategic priorities. While offering strategic guidance for urban planners and policymakers, the study also acknowledges limitations, including the regional focus, lack of primary field data, and potential publication bias. Overall, this work contributes to advancing digital twin applications in climate-resilient, zero-energy urban development. Full article

Assessing a city’s level of smartness according to global indices is a relatively new area of investigation. It is useful in encouraging a rethinking of urban digital strategies, although the different approaches to global smart city rankings have been subject to criticism. This paper highlights the methodological features of constructing the Smart City Index (SCI) from the IMD (International Institute for Management Development) based on residents’ assessments, their satisfaction with electronic services, and their perception of the priority of urban infrastructure areas. The Central European cities of the Visegrad region (Prague/Czech Republic, Budapest/Hungary, Bratislava/Slovakia, Warsaw and Krakow/Poland) were chosen as the basis for an in-depth analysis. The architectonics, i.e., the internal system of constructing and calculating city rankings by SCI, is analyzed. A comparative analysis of the technology indicators (e-services) in five cities of the Visegrad region, presented in the SCI, showed the smart features of each city. The progressive and regressive trends in the dynamics of smartness in the cities in the Visegrad region were identified in five urban spheres indicated in the Index: Government, Activity, Health and Safety, Mobility, and Opportunities. This also made it possible to identify certain methodological gaps in the SCI in establishing interdependencies between the data on the residents’ perception of the priority of areas of life in a particular city and the residents’ level of satisfaction with electronic services. In particular, the structural indicators “Affordable housing” and “Green spaces” are not supported by e-services. This research aims to bridge this methodological gap by proposing a model for evaluating the e-service according to the degree of coverage of different spheres of life in the city. The application of the project, as well as cross-sectoral and systemic approaches, made it possible to develop basic models for assessing the value of e-services. These models can be implemented by municipalities to assess and monitor e-services, as well as to select IT projects and elaborate strategies for smart sustainable city development. Full article

This work presents a parallel agent-based framework designed to analyze the dynamics of vegetable trade within a metropolitan area. The system integrates agent-based and discrete event techniques to capture the complex interactions among farmers, vendors, and consumers in urban agricultural supply chains. Decision-making processes are modeled in detail: farmers select crops based on market trends and environmental risks, while vendors and consumers adapt their purchasing behavior according to seasonality, prices, and availability. To efficiently handle the computational demands of large-scale scenarios, we adopt an optimistic approximate parallel execution strategy. Furthermore, we introduce a credit-based load balancing mechanism that mitigates the effects of heterogeneous communication patterns and improves scalability. This framework enables detailed analysis of food distribution systems in urban contexts, offering insights relevant to smart cities and digital agriculture initiatives. Full article

Understanding automated vehicle (AV) behavior in complex road environments and user attitudes in such contexts is critical for their safe and effective integration into smart cities. Despite growing deployment, limited public data exist on AV performance in construction zones; highly dynamic settings marked by irregular lane markings, shifting detours, and unpredictable human presence. This study investigates AV behavior in these conditions through qualitative, video-based analysis of user-documented experiences on YouTube, focusing on Tesla’s supervised Full Self-Driving (FSD) and Waymo systems. Spoken narration, captions, and subtitles were examined to evaluate AV perception, decision-making, control, and interaction with humans. Findings reveal that while AVs excel in structured tasks such as obstacle detection, lane tracking, and cautious speed control, they face challenges in interpreting temporary infrastructure, responding to unpredictable human actions, and navigating low-visibility environments. These limitations not only impact performance but also influence user trust and acceptance. The study underscores the need for continued technological refinement, improved infrastructure design, and user-informed deployment strategies. By addressing current shortcomings, this research offers critical insights into AV readiness for real-world conditions and contributes to safer, more adaptive urban mobility systems. Full article