Search Results (3,253)

In today’s densely populated and technology-driven smart cities, natural and human-made disasters increasingly threaten the resilience of communication infrastructures, creating critical challenges for maintaining reliable connectivity. The failure of conventional networks during crises significantly hampers emergency response, coordination, and information dissemination. To address these challenges, this paper presents Weighted Average Algorithm-based Clustering and Routing (WAA-CR), a novel, secure, and adaptive UAV-based framework for disaster response and recovery. WAA-CR integrates three key components: shelters or Ground Control Stations (GCSs) as communication anchors and support hubs, survivable clustering and routing using a WAA-based metaheuristic optimizer, and secure and trustworthy drone communication enabled by a lightweight trust evaluation mechanism, and authentication model. The framework formulates a multi-objective optimization model that simultaneously minimizes the number of active UAVs and routing cost, while maximizing trust, communication reliability, and coverage. Cluster head (CH) election and routing decisions are guided by a composite fitness function that considers residual energy, link stability, mobility, and dynamic trust scores. Additionally, an adaptive maintenance mechanism enables dynamic reconfiguration to handle CH failures, trust degradation, or mobility-driven topology changes. Extensive simulations conducted in MATLAB R2020ademonstrate that WAA-CR significantly outperforms existing baseline FANET protocols in terms of energy efficiency, cluster stability, trust accuracy, and end-to-end delivery performance. These results validate the proposed framework’s effectiveness in building resilient, scalable, and secure UAV-based communication networks for post-disaster environments. Full article

The rapid development of smart energy infrastructures and renewable energy systems requires advanced sensing solutions that provide high accuracy, expandability, and stability under real operating conditions. However, conventional gas monitoring systems are predominantly based on resistive or voltage-output sensors, which require complex analog front-end circuits and analog-to-digital conversion, leading to increased system complexity, cost, and susceptibility to electromagnetic interference. This paper tackles this limitation by proposing a frequency-domain sensing approach for multichannel monitoring of biogas plant parameters. The objective of this study is to develop and experimentally validate an extendable sensing architecture based on autogenerator microelectronic gas transducers with direct gas concentration–frequency conversion and FPGA-based digital acquisition. The proposed method is grounded in a physical–mathematical model of the space-charge capacitance of gas-sensitive semiconductor structures derived from Poisson’s equation, facilitating analytical formulation of conversion and sensitivity functions. A multichannel FPGA-based measurement system is implemented to process frequency signals without analog conditioning or ADC stages. Experimental validation was performed for CH₄ (0–85%), CO₂ (0–60%), H₂, NH₃, and H₂S (1–20,000 ppm). The results demonstrate measurement uncertainty within 0.25–0.5%, with sensitivity reaching 350–748 Hz/ppm for H₂, 455–750 Hz/ppm for NH₃, and 253–375 Hz/ppm for H₂S, while methane and carbon dioxide sensitivities reach up to 112 kHz/% and 98.7 kHz/%, respectively. Spectral analysis in the LTE-1800 band confirms improved noise immunity (up to 4.5×) and extended transmission capabilities. A 12-channel FPGA-based monitoring system (RDM-BP-1) with a 1 s sampling interval, IP67 protection, and wireless connectivity is developed and validated. The proposed architecture eliminates analog signal conditioning, reduces hardware complexity, and provides an easily expandable and reliable sensing solution for smart buildings, renewable energy systems, and cloud-integrated energy infrastructures. Full article

Thermochromic materials (TCMs) have attracted increasing attention as passive adaptive materials for solar regulation in buildings because they can reversibly change their optical properties in response to temperature without external energy input. Owing to this temperature-triggered optical modulation, they have been widely investigated for smart windows, temperature indicators, anti-counterfeiting labels, and flexible devices. In recent years, representative systems such as VO₂-based materials, polymers, hydrogels, and organic–inorganic hybrids have shown steady progress, especially in transition-temperature tuning, spectral selectivity, and cycling stability. This review summarizes the main classes of TCMs as well as their color-changing mechanisms, preparation methods, and performance-regulation strategies, with an emphasis on building energy efficiency and passive solar regulation. Typical applications and current bottlenecks are also discussed, including response speed, durability, environmental compatibility, and large-scale manufacturing. Finally, several practical directions for future work are highlighted, particularly low-cost synthesis, multifunctional integration, and application-oriented material design. Full article

Global decarbonization and the rapid growth of distributed energy resources increase the need for information-centric mechanisms that can support secure, scalable, cross-border coordination under heterogeneous technical and regulatory conditions. This paper proposes a Global Virtual Prosumer (GVP) framework that integrates IoT sensing, multi-agent coordination, and permissioned blockchain smart contracts to operationalize cross-border energy services as auditable service commitments rather than physical power exchange. Building on prior work that validated MAS-based power management and blockchain-secured operation within individual Virtual Prosumers, the present contribution lies in the cross-border coordination layer and its associated contractual and evaluation mechanisms, not in the constituent technologies themselves. A layered IoT–AI–blockchain architecture is introduced, where off-chain optimization produces allocations and admissibility indicators and on-chain contracts enforce identity, feasibility guards, delegation and partner-assignment rules, oracle verification, and settlement time compliance outcomes. The contractual lifecycle is formalized through four smart-contract algorithms covering trade registration, conditional delegation, cooperative fulfillment, and cross-border settlement with explicit failure semantics and event-based audit trails. The framework is evaluated on a global case study with seven Virtual Prosumers and quantified using contract-centric KPIs that capture registration time rejections, settlement success versus non-compliance, oracle-driven failure attribution, and full lifecycle traceability. The results demonstrate internal consistency of the proposed lifecycle and the practical value of KPI-driven accountability for cross-border energy service coordination. At the same time, the evaluation is based on synthetic parameterization and an emulated contract environment; realistic deployment constraints—including consensus latency, cross-region communication reliability, and regulatory overlap—are discussed as explicit limitations and directions for future empirical validation. Full article

Thermally activated building systems offer significant potential for low-carbon building operation and energy flexibility by using building mass as distributed thermal storage. Recent advances in data-driven control, machine learning, and digital building infrastructure have expanded their technical capabilities. However, practical deployment remains limited. This paper addresses that gap through a scoping review of the literature on data-driven thermally activated building systems, with a focus on the conditions required for implementation, integration, and sustained operation in practice. The review examines publication patterns, realization stages, dominant realization pathways, and recurring enablers and barriers across the field. The results show that the literature is concentrated in conceptual, simulation, and pilot-stage studies, while evidence of long-term operation in occupied buildings remains scarce and evidence of scalable or transferable realization in the reviewed TABS literature remains limited. The paper proposes five realization conditions for deployment as an interpretive synthesis of the reviewed literature: operational observability, deployable model architecture, embedded digital integration, operational acceptability, and organizational handover capacity. The review reframes data-driven thermally activated building systems as a realization challenge rather than only a control problem and provides a structured analytical framework to support future research and deployment-oriented evaluation in energy informatics. Full article

This study addresses a critical methodological gap in evaluating building envelope performance in hot, arid climates, the overreliance on annual energy indicators, which fail to capture transient thermal behavior during peak-load periods. In such environments, instantaneous heat gains, their intensity, and temporal distribution are decisive factors for cooling demand, occupant comfort, and grid stability. To overcome this limitation, a dynamic evaluation framework—the Thermal Adaptation Rating (TAC) system—is proposed. TAC integrates three interrelated indices—peak temperature reduction (ΔT_peak), relative peak cooling load reduction (ΔP_peak, %), and peak thermal delay (Δt_delay), representing thermal damping, load intensity mitigation, and temporal redistribution, respectively. A typical residential building in Karbala was modeled in DesignBuilder using the EnergyPlus engine, with inputs documented and calibration performed against real consumption data following ASHRAE standards (MBE and CV(RMSE)) to ensure reliability. The study examined advanced envelope systems, including thermochromic glass (TG), phase-change materials (PCMs), aerogel materials (AMs), and hybrid combinations. Results revealed that while AM achieved the greatest annual energy savings, its impact on instantaneous cooling load was limited. PCM, by contrast, effectively mitigated and delayed peak loads, enhancing thermal comfort (PMV/PPD). Hybrid systems, particularly TG-PCM, delivered the most balanced performance, simultaneously reducing peak cooling load and shifting its occurrence to reshape the cooling demand curve during critical periods. These findings demonstrate that annual indices alone are insufficient for evaluating envelope performance in extreme climates. Peak-condition analysis, expressed in terms of instantaneous cooling load, as operationalized through TAC, provides a more accurate representation of thermal behavior and offers a practical tool to guide envelope design decisions in hot, dry regions. Full article

Existing Environmental, Social, and Governance (ESG) monitoring approaches rely primarily on static reports and dashboard-based interfaces, limiting real-time analysis and interactive exploration of sustainability data in complex built environments. In addition, current digital twin systems often lack integration with IoT-based sensing or depend on cloud-based rendering infrastructures, increasing deployment complexity and restricting accessibility. This study proposes a lightweight Web3D-based digital twin framework for real-time ESG monitoring in smart buildings. The system integrates an independently developed IoT sensor network with a browser-native 3D visualization platform, enabling real-time monitoring of ESG indicators—including electricity consumption—without requiring proprietary software or dedicated rendering hardware. ESG indicators are derived using a rule-based classification aligned with the WELL Building Standard v1. The framework was validated through a 12-month real-world deployment involving 60 IoT sensors. Results demonstrate stable performance, achieving 66 FPS rendering, 78 ms system latency, and 98% sensor data consistency based on cross-sensor agreement. The system also enabled timely detection of environmental anomalies, leading to measurable improvements in air quality and lighting conditions. Unlike prior digital twin systems, the proposed framework delivers a fully browser-native, lightweight architecture that integrates real-time IoT sensing, adaptive Web3D visualization, and structured ESG monitoring within a single deployable system. This approach provides a practical solution with potential for broader deployment in real-time sustainability monitoring for smart buildings. Full article

Rapid urbanization and the growth of data-driven planning have increased the need for tools that support integrated, transparent, and accountable urban governance. While Building Information Modeling (BIM) is well established in project delivery, its potential role in city-scale governance remains underexplored. This study conducts a structured qualitative evidence synthesis informed by PRISMA reporting principles and comparative case analysis to investigate how BIM, in combination with GIS, IoT, and AI, intersects with emerging digital governance practices. Through a synthesis of peer-reviewed research and documented case studies, the review evaluates how BIM supports data integration, interoperability, decision-making, regulatory compliance, collaborative governance, and sustainability. The findings suggest that BIM functions as a governance-support infrastructure when embedded within coordinated institutional frameworks, standardized data environments, and interoperable digital ecosystems. Based on these insights, the paper proposes a conceptual framework that organizes BIM governance into technical, institutional, social, and ethical–regulatory dimensions. The review suggests that BIM’s governance potential depends on institutional alignment, regulatory clarity, and sustained organizational capacity, rather than technological capability alone. Full article

Internal thermal load fluctuations and variations in occupant density affect the performance of Hybrid Geothermal Heat Pump (HGHP) systems. Traditional control strategies cannot provide the rapid adjustments needed to operate efficiently in real time and can be inefficient, leading to increased energy consumption and reduced thermal comfort. A data-driven fuzzy logic control framework is developed in this paper to dynamically adjust the performance of an HGHP system in real time as a function of occupancy and environmental conditions (e.g., temperature and humidity differences). The controller analyzes input data related to real-time outdoor ambient conditions like temperature, humidity and occupied spaces; a real-time flow sensor attached to the occupants of the building (a count of the number of occupants currently in each occupied space); and the coefficient of performance (COP) of the HGHP system, and uses the analysis to generate a “smart” control decision for the following device types: variable speed drive (VSD), fan number, operating modes, system control and valve positions. The controller also controls the overall system. The model was developed and simulated in MATLAB Simulink^®, with realistic system parameters, and validated and calibrated using operational data from an HGHP system at a university, based on operating conditions. The simulation results indicate that our fuzzy controller achieves higher energy efficiency for thermal comfort than traditional thermostat-based controls, with COP improvements ranging from 7.36% to 11.76% and power consumption reductions between 4.13% and 8.55% across various occupancy scenarios. The improved COP also demonstrates the device’s responsiveness and effectiveness, even under frequent changes in occupancy patterns (dynamic occupancy), making it suitable for use in automated climate control systems in modern buildings. Full article

Renewable energy technologies are becoming more and more relevant in a variety of engineering fields as a result of the move toward low-carbon, sustainable energy systems. Although research has historically concentrated on power generation, it now covers a broad range of applications, including precision agriculture, smart grids, energy storage, healthcare devices, and sustainable buildings. However, existing review studies are often limited to single disciplines or specific technologies, lacking a unified cross-disciplinary perspective that captures the interconnected nature of modern renewable energy systems. This gap motivates the need for a comprehensive review that bridges multiple engineering domains. This review provides a comprehensive synthesis of literature on renewable energy applications in electrical and electronics, computer, environmental, biomedical, architectural, and agricultural engineering. In electrical and electronics engineering, the use of renewable energy sources is largely based on the efficient generation of electricity from natural resources such as solar, wind, and ocean energy. Computer engineering contributes through artificial intelligence (AI), Internet of Things (IoT) architectures, digital twins, and cybersecurity solutions, optimizing energy management. Environmental engineering emphasizes life cycle assessment, carbon footprint reduction, and circular economy strategies. In biomedical engineering, energy harvesting and self-powered devices illustrate micro-scale applications of renewable energy. Architectural engineering integrates renewable systems through building-integrated photovoltaics, net-zero energy designs, and smart building management, while agricultural engineering uses solar-powered irrigation, biomass utilization, agrivoltaic systems, and other sustainable practices. To support a low-carbon future with integrated and sustainable engineering solutions, this study not only highlights innovations within individual fields but also showcases how different disciplines can connect and work together. Overall, the review offers a novel cross-disciplinary framework that advances the understanding of renewable energy systems beyond isolated applications and provides direction for future integrative research. Full article

Equitable prioritization of public investments is increasingly critical as municipalities face constrained budgets, heterogeneous neighborhood needs, and demands for transparent decisions. This paper proposes a fairness-aware group multi-criteria decision-making (MCDM) framework for ranking municipal infrastructure investments when budgets are constrained, and neighborhood needs differ. Six alternatives are assessed in the Istanbul case study: flood risk mitigation, inclusive public realm and cooling, smart and energy-efficient municipal assets, walking and cycling infrastructure, healthcare access improvements, and seismic retrofitting of public buildings. The criteria system combines efficiency, implementability, socio-environmental performance, and equity-oriented priorities through five main dimensions and 23 sub-criteria. In addition to cost, feasibility, and service effectiveness, the framework incorporates fairness-related criteria such as baseline need and deficit severity, vulnerability-targeting effectiveness, minimum service guarantee for the worst-off, and priority for low-accessibility centers. Public acceptance and environmental performance are also included. Stakeholder panels provide expert judgments using intuitionistic fuzzy sets, capturing membership, non-membership, and hesitation to reflect uncertainty. Criteria weights are derived with Intuitionistic Fuzzy Step-wise Weight Assessment Ratio Analysis (IF-SWARA), enabling importance elicitation and group aggregation without forcing crisp consensus. Alternatives are then ranked using Intuitionistic Fuzzy Combined Compromise Solution (IF-CoCoSo), which blends additive and multiplicative compromise solutions to balance overall performance with equity objectives. Robustness is assessed through sensitivity analysis by varying the γ parameter within the IF-CoCoSo procedure. A municipal case study demonstrates that healthcare access improvements achieve the highest compromise performance, followed by flood risk mitigation and seismic retrofitting of public buildings, while smart and energy-efficient municipal assets rank last. The findings confirm that explicitly embedding fairness criteria can shift municipal priorities toward alternatives that more directly reduce deprivation, risk, and spatial inequality. The main contribution of this study is not merely empirical application, but the development of a fairness-aware group MCDM framework that operationalizes distributive justice in municipal investment prioritization through a structured set of criteria. Full article

The increasing risk of air pollution in closed areas like passenger vehicles requires smart and real-time air quality reading solutions. Gases such as carbon monoxide (CO)—which is colorless and odorless and is produced by exhaust systems—air conditioners, and combustion sources are very dangerous to health because they can cause respiratory distress and poisoning at high levels. Traditional in-vehicle CO monitoring systems use a single-point sensor and a fixed threshold, which are insufficient in a dynamic cabin environment subject to factors such as vehicle size, ventilation rate, number of occupants, and incoming traffic. To address these drawbacks, this paper proposes a new E-Nose system with Virtual Sensor-Augmented Embedded Intelligence to estimate the CO concentration in vehicle cabins in real time. The system combines data from cheap gas sensors and improves it using virtual sensor machine learning models trained to predict or enhance sensor responses in real time. Embedded intelligence, deployed locally on edge hardware, supports low-latency processing, dynamic calibration, and noise filtering to respond to fluctuating environmental conditions adaptively. This architecture enables more accurate, robust, and context-aware estimation of CO levels compared to traditional threshold-based methods. Experimental validation across varied vehicular scenarios demonstrates superior precision and responsiveness, providing timely warnings even under complex dispersion patterns. Classifier Gradient Boosting, which builds an ensemble of weak learners sequentially, matched the Random Forest with 99.94% training and 98.59% model accuracy, confirming its strong predictive capability. The system is designed to be cost-effective, scalable, and easily integrable into modern automotive platforms. This study also contributes to the field of smart ecological recording and demonstrates the effectiveness of the virtual sensor-enhanced embedded system as an effective way to improve passenger safety by providing pre-emptive on-board air quality monitoring. Full article

The aim of this paper is to conceptualize smart culture as an important yet under-researched dimension of smart cities, and to empirically demonstrate the extent to which cultural events in Vilnius’ public spaces align with the key principles of smart culture. The theoretical section of the article provides a definition of smart culture in a smart city, based on which four categories of analysis are identified: accessibility, the integration of technology into the cultural experience, engagement of the population, and promotion of community building. The methodology consists of an instrumental case study, analysis of secondary sources, and directed content analysis. The research findings reveal that Culture Night festival events in Vilnius not only reduce social and geographical barriers to culture but also create spaces for active participation of the population, fostering community and the application of technological solutions in cultural activities. Culture Night represents a clear example of smart culture, highlighting the importance of this dimension in smart city policies. The study shows that the identified characteristics of smart culture may support inclusive and sustainable urban development trends associated with SDG 11 (Sustainable Cities and Communities) and SDG 10 (Reduced Inequalities). Full article

Fine-grained energy management in smart-campus buildings requires accurate load forecasting together with reliable and interpretable anomaly diagnosis. This study presents an integrated forecasting–diagnosis framework for building energy systems. Hourly energy demand is modeled using a Transformer-based sequence-to-sequence architecture, in which a domain-aware attention mechanism is introduced to separately represent historical consumption dynamics, environmental influences, and temporal regularities commonly observed in building energy use. Anomaly diagnosis is conducted through a dual-scale strategy that supports both the timely detection of abrupt abnormal events and the identification of gradual performance degradation. Short-term anomalies are detected from forecasting residuals using adaptive thresholds, while long-term anomalies are identified by comparing current residual patterns with same-season historical baselines and validating multi-window trends over a 48 h horizon. The two detection streams are jointly used to distinguish point, pattern, and composite anomalies. To support practical operation and maintenance, SHAP-based explanations are provided to interpret both energy predictions and detected anomalies. Case studies on two educational buildings from the Building Data Genome Project 2 demonstrate that the proposed framework achieves the best overall forecasting performance against both conventional baselines and stronger recent Transformer-based models, with mean absolute percentage errors of approximately 3%. The results indicate that the proposed framework provides a practical solution for data-driven energy monitoring and decision support in smart buildings. Full article

Building-integrated photovoltaic systems are essential components of smart buildings and sustainable urban infrastructure, contributing to energy efficiency and carbon footprint reduction in smart cities. Mismatch loss, particularly under partial shading, is one of the concerns in photovoltaic (PV) systems, especially in urban environments where buildings, trees, and other structures create complex shading patterns. It leads to significant power loss and poor efficiency. Several methods, such as string converters, multi-string converters, central converters, and micro-inverters/power optimizers, have been widely employed to address this issue. These methods suffer from hardware complexity and are good in certain shading patterns only; they remain ineffective otherwise. Power optimizers lead in efficiency under all the shading patterns, whereas string converters lead in hardware simplicity. We propose a novel parallel-series converter to mitigate mismatch losses in smart building applications that is as efficient as power optimizers and as simple as converters. In the proposed parallel-series converter design, multiple PV modules are connected in parallel to a very simple converter, and many such converters are then connected in series to get the final output. The proposed converter is rigorously evaluated for various shading patterns using MATLAB/SIMULINK. A prototype system of $3\times 2$ PV panels is also developed for hardware evaluation. The simulation and hardware results show that the proposed parallel-series converter dominantly competes with power optimizers with much simpler hardware and outperforms the other converters, making it particularly suitable for smart building energy systems where cost-effectiveness and reliability are critical. Full article