Search Results (1,321)

This study proposes a smart energy management framework for a university campus microgrid aimed at reducing dependence on the main power grid and increasing the utilization of photovoltaic (PV) generation under dynamic load and environmental conditions. The core contribution is a two-stage approach that combines a genetic algorithm (GA) for static day-ahead optimization with a soft actor-critic (SAC) reinforcement learning (RL) agent performing adaptive supervisory management of microgrid active and reactive power flows via battery control. The GA provides an optimal reference schedule under forecasted conditions, while the SAC agent is trained on eight representative scenarios derived from measured PV generation and campus load data to adapt battery operation and grid exchange under uncertainty. The results show that the benefit of RL does not lie in reproducing the static GA solution, but in learning economically rationally adaptive behavior. In particular, the SAC agent exploits low-tariff periods and hedges against adverse PV conditions by proactively adjusting battery charging strategies in real time. This adaptive behavior addresses a key limitation of static optimization, which cannot respond to deviations from forecasted operation, and represents the main added value of the proposed framework. From a practical perspective, the GA-SAC architecture operates at a supervisory level with low computational requirements, making it suitable for scalable deployment in smart campus and smart city energy management systems. Full article

Urban sustainability has become a defining governance challenge as smart cities increasingly integrate artificial intelligence (AI) into public service delivery and decision-making. While AI-enabled systems promise efficiency and responsiveness, growing concerns regarding trust, legitimacy, and citizen engagement suggest that technological adoption alone does not guarantee sustainable urban outcomes. Existing studies have largely emphasized technological performance or individual adoption, paying limited attention to the governance mechanisms through which AI acceptance translates into sustainability co-creation. To address this gap, this study develops and empirically examines the AI–Urban Citizen Sustainability Co-Creation Framework (AI–CSCF) within the context of smart cities in Thailand. A quantitative survey was conducted with 1002 citizens across three smart city settings, and structural equation modeling (SEM) was employed to examine the relationships among AI acceptance, trust in AI, citizen adaptability, and sustainability co-creation. The results indicate that AI acceptance functions as a foundational condition shaping trust in AI and citizen adaptability, through which its influence on sustainability co-creation is indirectly transmitted. Trust in AI emerges as a key mediating mechanism linking AI-enabled governance to participatory sustainability outcomes. These findings underscore the importance of human-centered and trustworthy AI governance that strengthens citizen trust, enhances adaptive capacities, and positions citizens as active co-creators of sustainable urban development aligned with SDG 11. Full article

This paper presents the design and optimisation of a low-power embedded sensor-node architecture for real-time environmental monitoring with on-board machine-learning inference. The proposed system integrates heterogeneous sensing elements for air quality and ambient parameters (temperature, humidity, gas concentration, and particulate matter) into a modular embedded platform based on a low-power microcontroller coupled with an energy-efficient neural inference accelerator. The design emphasises end-to-end energy optimisation through adaptive duty-cycling, hierarchical power domains, and edge-level data reduction. The embedded machine-learning layer performs lightweight event/anomaly detection via on-device multi-class classification (normal/anomalous/critical) using quantised neural models in fixed-point arithmetic. A comprehensive system-level analysis, performed via MATLAB Simulink simulations, evaluates inference accuracy, latency, and energy consumption under realistic environmental conditions. Results indicate that the proposed node achieves 94% inference accuracy, 0.87 ms latency, and an average power consumption of approximately 2.9 mWh, enabling energy-autonomous operation with hybrid solar–battery harvesting. The adaptive LoRaWAN communication strategy further reduces data transmissions by ≈88% relative to periodic reporting. The results indicate that on-device inference can reduce network traffic while maintaining reliable event detection under the evaluated operating conditions. The proposed architecture is intended to support energy-efficient environmental sensing deployments in smart-city and climate-monitoring contexts. Full article

Smart city initiatives increasingly rely on interoperable and high-quality urban data, yet many cities lack systematic methods for prioritizing which datasets should be developed first. This study proposes an evidence-based framework for smart city data prioritization that integrates data need, data availability, and policy urgency into a unified decision-support model. Using standardized data elements across seven nationally defined smart city domains, the framework was applied to thirty-six certified smart cities in Thailand. Data were collected from municipal authorities and national platforms and structured using ISO-based data element and metadata principles. For each data element, a Need Priority Index, Coverage score, and Policy Readiness indicator were computed to assess governance-relevant data readiness. The results reveal a persistent imbalance between high data demand and low data availability across all domains, with Smart Mobility, Smart Living, Smart Energy, and Smart Economy showing the highest urgency. A Core Common Data Set representing 6.7% of assessed properties was identified, centered on population data, geospatial infrastructure, and plans and performance indicators. The framework provides a scalable approach for guiding investments in interoperable smart city data systems. Full article

The increasingly frequent and severe natural disasters have posed significant challenges to the resilience of power systems worldwide, creating an urgent need to investigate the security issues associated with these extreme events and to develop effective risk mitigation strategies. Meanwhile, as one of the leading topics in current research, artificial intelligence (AI) has demonstrated outstanding performance across various domains, such as AI-driven smart grids and smart cities. In particular, its efficiency in processing big data and solving complex computational problems has made AI a powerful tool for supporting decision-making in complex scenarios. This article presents a focused overview of power system resilience against natural disasters, highlighting recent advancements in AI-based approaches aimed at enhancing system security and response capabilities. It begins by introducing various types of natural disasters and their corresponding impacts on power systems. Then, a systematic overview of AI applications in power systems under disaster scenarios is provided, with a classification based on the task categories, i.e., predictive, descriptive and prescriptive tasks. Following this, this article analyzes current research trends and finds a growing shift from knowledge-based models towards data-driven models. Furthermore, this paper discusses the major challenges in this research field, including data processing, data management, and data analytics; the challenges introduced by large language models in power systems; and the limitations related to AI model interpretability and generalization capability. Finally, this article outlines several potential future research directions. Full article

AI technologies are increasingly applied to optimize operations in both port and urban logistics systems, yet integration across the full maritime city chain remains limited. The objective of this study is to assess, using a simulation-based experiment, the impact of an AI-orchestrated control policy on the performance of port–city logistics relative to a baseline scheduler. The study proposes an AI-orchestrated approach that connects autonomous ships, smart ports, central warehouses, and multimodal urban networks via a shared cloud control layer. This approach is designed to enable real-time, cross-domain coordination using federated sensing and adaptive control policies. To evaluate its impact, a simulation-based experiment was conducted comparing a traditional scheduler with an AI-orchestrated policy across 20 paired runs under identical conditions. The orchestrator dynamically coordinated container dispatching, vehicle assignment, and gate operations based on capacity-aware logic. Results show that the AI policy substantially reduced the total completion time, lowered truck idle time and estimated emissions, and improved system throughput and predictability without modifying physical resources. These findings support the expectation that integrated, data-driven decision-making can significantly enhance logistics performance and sustainability in port–city contexts. The study provides a replicable pathway from conceptual architecture to quantifiable evidence and lays the groundwork for future extensions involving learning controllers, richer environmental modeling, and real-world deployment in digitally connected logistics corridors. Full article

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT), known as Artificial Intelligence of Things (AIoT), has emerged as a transformative paradigm for enabling intelligent, data-driven, and context-aware decision-making in urban environments to reduce the carbon footprint of mobility and industry. This review examines the conceptual foundations, and state-of-the-art developments of AIoT, with a particular emphasis on its applications in smart cities and its relevance to climate change management. AIoT integrates sensing, connectivity, and intelligent analytics to provide optimized solutions in transportation systems, energy management, waste collection, and environmental monitoring, directly influencing urban sustainability. Beyond urban efficiency, AIoT can play a critical role in addressing the global challenges and management of climate change by (a) precise measurements and autonomously remote monitoring; (b) real-time optimization in renewable energy distribution; and (c) developing prediction models for early warning of climate disasters. This paper performs a literature review and bibliometric analysis to identify the current landscape of AIoT research in smart city contexts. Over 1885 articles from Web of Sciences and over 1854 from Scopus databases, published between 1993 and January 2026, were analyzed. The results reveal a strong and accelerating growth in research activity, with publication output doubling in the most recent two years compared to 2023. Waste management and air quality monitoring have emerged as leading application domains, where AIoT-based optimization and predictive models demonstrate measurable improvements in operational efficiency and environmental impact. Altogether, these support faster and more effective decisions for reducing greenhouse gas emissions and ensuring the sustainable use of resources. The reviewed studies reveal rapid advancements in edge intelligence, federated learning, and secure data sharing through the integration of AIoT with blockchain technologies. However, significant challenges remain regarding scalability, interoperability, privacy, ethical governance, and the effective translation of research outcomes into policy and citizen-oriented tools such as climate applications, insurance models, and disaster alert systems. By synthesizing current research trends, this article highlights the potential of AIoT to support sustainable, resilient, and citizen-centric smart city ecosystems while identifying both critical gaps and promising directions for future investigations. Full article

With the fast development of the 5G technology and IoV, a vehicle has become a smart device with communication, computing, and storage capabilities. However, the limited on-board storage and computing resources often cause large latency for task processing and result in degradation of system QoS as well as user QoE. In the meantime, to build the environmentally harmonious transportation system and green city, the energy consumption of data processing has become a new concern in vehicles. Moreover, due to the fast movement of IoV, traditional GSI-based methods face the dilemma of information uncertainty and are no longer applicable. To address these challenges, we propose a T2VC model. To deal with information uncertainty and dynamic offloading due to the mobility of vehicles, we propose a MAB-based QEVA-UCB solution to minimize the system cost expressed as the sum of weighted latency and power consumption. QEVA-UCB takes into account several related factors such as the task property, task arrival queue, offloading decision as well as the vehicle mobility, and selects the optimal location for offloading tasks to minimize the system cost with latency energy awareness and conflict awareness. Extensive simulations verify that, compared with other benchmark methods, our approach can learn and make the task offloading decision faster and more accurately for both latency-sensitive and energy-sensitive vehicle users. Moreover, it has superior performance in terms of system cost and learning regret. Full article

The goal of this scientific study is to clarify whether and how digital–intelligent integration contributes to urban carbon efficiency and to identify the conditions under which this contribution becomes nonlinear and policy-relevant. Focusing on 39 prefecture-level cities in the middle reaches of the Yellow River Basin during 2011–2022, we adopt an integrated measurement–modelling approach that combines efficiency evaluation, machine-learning interpretation, and dynamic–spatial validation. Specifically, we construct two super-efficiency DEA indicators: an undesirable-output SBM incorporating CO₂ emissions and a conventional super-efficiency CCR index. We then estimate nonlinear city-level relationships using XGBoost and interpret the marginal effects with SHAP, while panel vector autoregression (PVAR) and spatial diagnostics are employed to validate the dynamic responses and spatial dependence. The results show that digital–intelligent integration is positively associated with both carbon-related and conventional efficiency, but its marginal contribution is strongly conditioned by human capital, urbanisation, and environmental regulation, exhibiting threshold-type behaviour and diminishing returns at higher digitalisation levels. Green efficiency reacts more strongly to short-run shocks, whereas conventional efficiency follows a steadier improvement trajectory. Heterogeneity across urban agglomerations and evidence of spatial clustering further suggest that uniform policy packages are unlikely to perform well. These findings highlight the importance of sequencing and policy complementarity: investments in digital infrastructure should be coordinated with institutional and structural measures such as green finance, environmental standards, and industrial upgrading and place-based pilots can help scale effective digital applications toward China’s dual-carbon objectives. The proposed framework is transferable to other regions where the digital–climate nexus is central to smart and sustainable urban development. Full article

The trend of the world to electric mobility and the inclusion of renewable energy requires complex control and predictive models of Smart Electric Vehicle Charging Stations (SEVCSs). The paper describes an experimental artificial intelligence (AI) model that can be used to optimize EV charging in New York City based on ten years of historical load and weather information. Nonlinear environmental relationships with urban energy demand and the use of Neural Fitting and Regression Learner models in MATLAB were used to explore the nonlinear relationships between the environment and energy demand. The quality of the input data was maintained with a lot of preprocessing, such as outlier removal, smoothing, and time alignment. The performance measurements showed that there was a Mean Absolute Percentage Error (MAPE) of 4.9, and a coefficient of determination (R²) of 0.93, meaning that there was a high level of concordance between the predicted and measured load profiles. Such findings indicate that AI-based models can be used to replicate load dynamics during renewable energy variability. The research combines the findings of long-term and multi-source data with the short-term forecasting to address the research gaps of past studies that were limited to a few small datasets or single-variable-based time series, which will provide a replicable base to develop energy-efficient and intelligent EV charging networks in line with future grid decarbonization goals. The proposed neural network had an R² = 0.93 and RMSE = 36.4 MW. The Neural Fitting model led to less RMSE than linear regression and lower MAPE than the persistence method by a factor of about 15 and 22 percent, respectively. Full article

The fusion of Artificial Intelligence and the Internet of Things (AI-IoT, also widely referred to as AIoT) offers transformative potential for smart cities, yet presents a critical challenge: how to process heterogeneous data streams from intelligent sensing—particularly crowd sensing data derived from citizen interactions like text, voice, and system logs—into reliable intelligence for sustainable urban governance. To address this challenge, we introduce the Intelligent Multimodal Ticket Processing System (IMTPS), a novel AI-IoT smart system. Unlike ad hoc solutions, the novelty of IMTPS resides in its theoretically grounded architecture, which orchestrates Information Theory and Game Theory for efficient, verifiable extraction, and employs Causal Inference and Meta-Learning for robust reasoning, thereby synergistically converting noisy, heterogeneous data streams into reliable governance intelligence. This principled design endows IMTPS with four foundational capabilities essential for modern smart city applications: Sustainable and Efficient AI-IoT Operations: Guided by Information Theory, the IMTPS compression module achieves provably efficient semantic-preserving compression, drastically reducing data storage and energy costs. Trustworthy Data Extraction: A Game Theory-based adversarial verification network ensures high reliability in extracting critical information, mitigating the risk of model hallucination in high-stakes citizen services. Robust Multimodal Fusion: The fusion engine leverages Causal Inference to distinguish true causality from spurious correlations, enabling trustworthy integration of complex, multi-source urban data. Adaptive Intelligent System: A Meta-Learning-based retrieval mechanism allows the system to rapidly adapt to new and evolving query patterns, ensuring long-term effectiveness in dynamic urban environments. We validate IMTPS on a large-scale, publicly released benchmark dataset of 14,230 multimodal records. IMTPS demonstrates state-of-the-art performance, achieving a 96.9% reduction in storage footprint and a 47% decrease in critical data extraction errors. By open-sourcing our implementation, we aim to provide a replicable blueprint for building the next generation of trustworthy and sustainable AI-IoT systems for citizen-centric smart cities. Full article

Smart cities aim to integrate technological, infrastructural, and socio-environmental systems in order to improve urban sustainability and quality of life. To qualify as both smart and sustainable, a city is generally expected to pursue self-sufficiency through the adoption of sustainable practices in energy production, water supply, and food systems. Such cities also seek to reduce operational costs for both private operators and municipalities, while aiming to enhance the quality of life of their residents. Within this context, the relevance of a web-based application becomes particularly apparent. An application equipped with predefined indicators can provide a structured and measurable framework for assessing the current status of a city or town in relation to smart and sustainable development. This framework allows for the evaluation of the extent to which a city aligns with established criteria associated with smart and sustainable urban models. This paper introduces a Python-based web application, developed using Python version 3.10, designed to assess or support the self-assessment of a city’s alignment with identified smart and sustainable development indicators. This study does not claim empirical validation or benchmarking performance; the proposed system is presented as a proof-of-concept framework. The work does not propose new smart city indicators. Rather, it presents an integrative system that seeks to operationalise existing smart and sustainable city indicators within a unified and modular web-based assessment framework, designed to support cross-domain evaluation and citizen-accessible self-assessment. Full article

Low-illumination conditions significantly degrade the performance of vision-based garbage recognition systems in practical smart city applications. To address this issue, this paper presents a garbage recognition framework that combines low-light image enhancement with attention-guided feature learning. A multi-branch low-light enhancement network (MBLLEN) is employed as the enhancement backbone, and a Convolutional Block Attention Module (CBAM) is integrated to alleviate local over-enhancement and guide feature responses under uneven illumination. The enhanced images are then used as inputs for a deep learning-based garbage classification model. In addition, a lightweight encryption mechanism is considered at the system level to support secure data transmission in practical deployment scenarios. Experiments conducted on a self-collected low-light garbage dataset show that the proposed framework achieves improved image quality and recognition performance compared with baseline approaches. These results suggest that integrating low-light enhancement with attention-guided feature learning can be beneficial for garbage recognition tasks under challenging illumination conditions. Full article

This paper provides an empirical basis for a Computational Integration Framework (CIF), a systematic and scientifically supported implementation of artificial intelligence (AI) in smart city applications. This study is a methodological framework-with-validation study, where large-scale bibliometric analysis is used as a justification for design in the identification of strategically relevant urban areas rather than a single research study. This evidence determines urban mobility as the most mature and computationally optimal domain for empirical verification. The exploitation of CIF is realized using a DRL-driven traffic signal control system to show that bibliometrically informed domain selection can be put into application by way of an algorithm. The empirical results show that the most traditional control strategies accomplish significant performance gains, such as about 48% reduction in average wait time, over 30% increase in traffic efficiency, and considerable reductions in fuel consumption and CO₂ emissions. A federated DRL solution maintains around 96% of central performance while still maintaining data privacy, which suggests that deployment in real-world situations is feasible. The contribution of this study is threefold: evidence-based domain selection through bibliometric analyses; introduction of CIF as an AI decision support bridge between AI techniques and urban application domains; and computational verification of the feasibility of DRL for sustainable urban mobility. These findings reveal policy information relevant to goals governing global sustainability, including the European Green Deal (EGD) and the United Nations Sustainable Development Goals (SDGs), and thus, the paper is a methodological framework paper based on literature and validated through computational experimentation. Full article

Fog computing has revolutionized the world by providing its services close to the user premises, which results in reducing the communication latency for many real-time applications. This communication latency has been a major constraint in cloud computing and ultimately causes user dissatisfaction due to slow response time. Many real-time applications like smart transportation, smart healthcare systems, smart cities, smart farming, video surveillance, and virtual and augmented reality are delay-sensitive real-time applications and require quick response times. The response delay in certain critical healthcare applications might cause serious loss to health patients. Therefore, by leveraging fog computing, a substantial portion of healthcare-related computational tasks can be offloaded to nearby fog nodes. This localized processing significantly reduces latency and enhances system availability, making it particularly advantageous for time-sensitive and mission-critical healthcare applications. Due to close proximity to end users, fog computing is considered to be the most suitable computing platform for real-time applications. However, fog devices are resource constrained and require proper resource management techniques for efficient resource utilization. This study presents an optimized resource allocation and scheduling framework for delay-sensitive healthcare applications using a Modified Particle Swarm Optimization (MPSO) algorithm. Using the iFogSim toolkit, the proposed technique was evaluated for many extensive simulations to obtain the desired results in terms of system response time, cost of execution and execution time. Experimental results demonstrate that the MPSO-based method reduces makespan by up to 8% and execution cost by up to 3% compared to existing metaheuristic algorithms, highlighting its effectiveness in enhancing overall fog computing performance for healthcare systems. Full article