Search Results (3,448)

Smart-building structural health monitoring (SHM) requires a unified digital representation capable of integrating heterogeneous sensing devices, continuous structural states, and burst-oriented post-event assessment without embedding device-specific logic throughout the software stack. This study proposes a semantic digital twin architecture in which SensorType, DeviceProfile, and site metadata form a semantic single source of truth and generate W3C Web of Things Thing Descriptions at runtime. The resulting WoT-driven contract governs field mapping, schema-on-write persistence, generic API access, state visualization, and engineering-threshold evaluation. To accommodate heterogeneous temporal behavior, event-driven seismic assessment and state-driven construction tilt monitoring are orchestrated as distinct workflows that share persistence, notification, and observability services while retaining separate timing contracts. Controlled extension experiments required no manual data-layer, backend, ingestion, or frontend modification, with a runtime source-hash difference of zero. Under a ten-building seismic-event burst, continuous write-lag p95 changed by $-20$ ms from a 969 ms baseline while all event jobs completed without restart or out-of-memory conditions. The ingestion path further sustained 71,040 points/s at 300 sensors with no dropped points. These results demonstrate that WoT-driven semantic interoperability and event–state workflow orchestration can provide an extensible integration foundation for smart-building SHM within a clearly defined configuration boundary. Full article

Grid-impact studies treat battery electric vehicles as loads, and ask when network capacity will be exhausted. This paper reverses the question: how much of the fleet must operate bidirectionally, and with what probability will an achievable participation rate suffice, for the network to remain within its limits? A conceptual framework adds a vehicle-to-grid and vehicle-to-building flexibility term to the balance between available and required power, nests the authors’ earlier deterministic model for twenty municipalities in Northern Portugal as its zero-flexibility special case, derives a closed-form break-even participation rate per municipality and year, and keeps the simultaneity assumption of that model explicit as a coincidence factor. Participation, location, plug-in and export parameters follow beta-PERT distributions calibrated on published trials and surveys, propagated by Monte Carlo simulation without new field data. The framework is an apparent-power balance per municipality, so its outputs are an upper bound on usable flexibility, not a feeder-level feasibility check. An enrolled vehicle provides about 11 kVA of peak relief, over nine tenths from not charging rather than exporting. Under worst-case simultaneity, observed participation rates, if in place from the outset, halve the 2028 shortfall probability but cannot prevent shortfall by 2030; under realistic coincidence the regional network is not constrained and only eight of twenty municipalities remain critical. The network balance is replicable wherever municipal substation data exist; behavioural parameters require local calibration. Full article

Accurate forecasting of energy consumption, renewable power output and utility expenditure is essential for sustainable planning of residential buildings and improving smart grid integration. This study presents several techniques such as random forest, gradient boosting regression, extreme gradient boosting, deep belief networks, random vector functional link, multi-layer perceptron and hybrid ensemble for forecasting of residential load demand, electricity bills, solar energy generation and solar irradiance. Electricity bills under Time-of-Use tariffs are introduced in the paper to accomplish realistic evaluation of economic implications and facilitation of optimized energy usage and cost savings using real-time residential energy data collected from Durban, South Africa. The performance of the forecasting model is assessed by root mean square error (RMSE), mean absolute error (MAE), mean squared error (MSE), coefficient of determination (R²) and mean absolute scaled error (MASE). The outcomes of the study show that the hybrid ensemble model accomplished the highest forecasting accuracy of the electricity bill with MAE, RMSE, MSE, MASE and R² of 0.018126, 0.022961, 0.00052719, 0.30006 and 0.97978 when compared to other models. The findings of the research can be used as potential benchmarks for intelligent tariff forecasting, demand response planning, smart energy management and renewable energy integration in residential buildings. Full article

The Industrial Internet of Things (IIoT) is being used a lot in important areas like advanced manufacturing, smart energy systems, and intelligent cities. Watching for intrusion detection is very important for the safety of the IIoT. Nevertheless, multivariate sensor sequences frequently demonstrate pronounced physical coupling, non-stationarity, and periodicity concurrently, rendering it prone for detection models grounded in statistical correlation to erroneously classify normal collaborative variations as anomalies. Moreover, prevailing methods predominantly concentrate on single-domain representations within either the time or frequency domain, posing challenges in addressing both burst and periodic attacks concurrently. To address these challenges, this article introduces the Time–Frequency Dual-Branch Fusion and PCMCI-Based Association-Guided Network (TFPAG-Net) for IIoT Intrusion Detection. This model initially constructs a lightweight temporal convolutional network backbone employing depthwise separable convolutions. Subsequently, parallel branches in the time and frequency domains are established to respectively model local abrupt changes, long-range dependencies, and periodic spectral structures, with time–frequency feature fusion facilitated through a sample-dependent gating mechanism. Building on this, multi-scale temporal pyramids are employed to amalgamate fine, intermediate, and coarse-scale information. Furthermore, as an auxiliary refinement, a lagged conditional-dependence prior estimated from the training data via PCMCI is projected into a bounded attention bias to provide supplementary guidance for channel feature reweighting. Evaluations on Edge-IIoTset, X-IIoTID, and SWaT yield mean Macro-F1 scores of 0.9886, 0.9466, and 0.9607, respectively, over five predefined random seeds. Under the unified training protocol, TFPAG-Net ranks second on Edge-IIoTset and achieves the highest mean Macro-F1 on X-IIoTID and SWaT. Ablation experiments show dataset-dependent effects of the proposed components. On Edge-IIoTset and X-IIoTID, the final attention-stage improvement reflects the joint effect of SE-based modulation and the PCMCI-derived association prior. Accordingly, PCMCI is treated as an auxiliary association refinement rather than a principal contribution of TFPAG-Net. Additionally, TFPAG-Net maintains a moderate computational footprint, with approximately 0.29 M parameters, providing a favorable balance between model complexity and intrusion detection performance. Full article

Standardized urban indicators are essential for assessing smart and sustainable cities, yet their application in developing countries remains limited. This study addresses the critical gap regarding the availability of ISO 3712x standard indicators among local governments in Brazil—a nation marked by territorial and socioeconomic heterogeneity. Urban indicators based on open government data across all 5570 Brazilian municipalities were analyzed in this study using the ISO 37120, 37122, 37123, and 37125 standards. A synthetic smart and sustainable cities index was drawn up, supported by Factor Analysis and Principal Component Analysis (PCA). Considering the Local Indicator of Spatial Association (LISA), clustering patterns of municipal sustainability and smartness performance were identified: LISA analysis identified pronounced spatial clusters, with high-performance municipalities concentrated in the Southeast and South-Central regions, while low-performance clusters spread across the North and Northeast of Brazil. Despite the diversity of characteristics among Brazilian cities, 22 of the 302 ISO 3712x indicators (7.3%) could be collected with complete national data for all 5570 Brazilian municipalities; data for 59 indicators were available from open sources, though with gaps. Since the ISO 3712x standards offer a voluntary portfolio of indicators rather than a mandatory set, partial data coverage does not make the framework inapplicable; rather, they delimit the scope of the synthetic index presented here and highlight where open-data infrastructure must be strengthened. Municipal smartness and sustainability result from the multidimensional integration of technological innovation, human capital, social protection, and responsible environmental management. Despite the availability of ISO indicators, observations based on the authors’ institutional experience as members of ABNT/CEE-268—Brazilian mirror committee of ISO/TC 268—“Sustainable cities and communities”—reveal that local governments face challenges in systematically integrating them into strategic planning, presenting a significant opportunity for capacity building and evidence-based governance. In addition to the innovative aspect of this research—which involves using ISO-standardized indicators and collecting numerical data from official open data sources—this article presents a comprehensive index calculation for sustainable and smart cities based on these indicators, followed by the identification of its spatial clusters. Full article

Indoor air quality (IAQ) in healthcare environments is affected by dynamic interactions between occupancy, ventilation, operational activities, and indoor emission sources, which are not always captured through conventional punctual assessments. This study evaluated long-term IAQ dynamics in different hospital microenvironments using continuous low-cost sensor monitoring combined with targeted chemical characterization of volatile organic compounds (VOCs) and specific aldehydes. Continuous monitoring was conducted from June 2023 to December 2025, measuring CO₂, PM_2.5, PM₁₀, formaldehyde (CH₂O), temperature, relative humidity, and total VOCs (TVOC). A total of 1.79 million raw records were processed, generating 1.42 million indoor measurements and 264,311 hourly aggregated observations. Complementary VOC and aldehyde sampling campaigns supported the interpretation of pollutant specific temporal patterns. Results revealed differentiated and recurrent IAQ signatures across hospital areas. CO₂ dynamics were mainly associated with occupancy and ventilation demand, whereas formaldehyde showed more persistent and seasonally dependent sensor patterns, compatible with the influence of indoor emission sources and ventilation heterogeneity. Targeted chemical characterization further identified area specific pollutant profiles associated with cleaning activities, laboratory processes, materials, and operational conditions. Importantly, recurrent periods were identified in which acceptable occupancy related CO₂ conditions coincided with elevated chemical pollutant levels. These findings show how long-term multi-parameter monitoring can distinguish function and pollutant specific IAQ signatures that would remain obscured by aggregated or single parameter assessments, providing an evidence base for area specific monitoring strategies and future adaptive hospital IAQ management. Full article

Indoor service robots are increasingly deployed in indoor environments, yet their performance is influenced not only by robot specifications and control algorithms but also by built-environment conditions and interior finishes. This study proposes a performance indicator framework for service robots in robot-friendly built environments using an evidence-based design approach. A systematic literature review identified 21 sources of evidence related to robot–built-environment interactions and measurable performance outcomes. The evidence was synthesized according to environmental conditions, robot responses or failures, performance outcomes, and measurable outputs. Six indicators were derived: mean sensing-distance accuracy, travel-time delay coefficient, mapped-corner deviation, rated-speed reduction coefficient, path deviation, and cumulative travel-induced damage coefficient. The expert evaluation used a 30-item questionnaire comprising five evaluation criteria. Of the 31 respondents, 26 worked in architectural construction or design and five in robot-related fields. The overall mean score was 4.00 out of 5, and the positive-response rate was 73.8%; mean sensing-distance accuracy received the highest rating. These results show that the framework’s appropriateness and potential usefulness were positively evaluated within this sample. However, they do not demonstrate the empirical validity or field applicability of the performance indicators. Controlled robot experiments remain necessary to establish calculation methods, measurement units, application ranges, and performance thresholds. Full article

Reliable structural monitoring for smart buildings and construction sites requires more than sensor acquisition; it requires sensing and communication paths that remain traceable, recoverable, and compatible with platform-side data processing under heterogeneous field constraints. This study presents a dual-path IoT sensing and communication framework that deliberately separates high-data-rate vibration monitoring from low-data-rate inclination-status monitoring while maintaining common requirements for preservation of available time information, data-source identification, and backend interpretability. The smart-building path integrates an ADXL355 triaxial accelerometer, ESP32-S3, Power over Ethernet (PoE), and Message Queuing Telemetry Transport (MQTT) for 200 Hz vibration acquisition, together with a second-order 10 Hz low-pass filter, 40-record batching, and a Flash LittleFS-based store-and-recovery mechanism that interleaves live and replayed records after reconnection. The construction-site path combines an SCL3300-D01 inclinometer with LoRaWAN, baseline-referenced relative-angle estimation, and a hysteresis state machine with distinct alarm and recovery thresholds. In a 24 h validation, four vibration nodes delivered all 69,120,000 expected records, and four forced-outage trials recovered all offline records while live transmission continued. Frequency-domain analysis confirmed attenuation of high-frequency components while retaining the dominant low-frequency response. The inclination path demonstrated quantifiable angle accuracy, correct alarm/recovery transitions, continuous LoRaWAN frame delivery over the observed interval, and correct backend decoding. The results show that path-specific communication design, combined with a common traceability concept, supports prototype functionality under the reported test conditions, not immediate construction-site deployment. Full 3D visual synchronization, BIM/GIS asset mapping, and digital-twin platform interfacing were not implemented and remain future development tasks. Full article

Smart systems, the Industrial Internet of Things, and cyber-physical networks increasingly make decisions on the devices where data is generated, on nodes short of memory, compute, energy, and bandwidth, and exposed to real adversaries. Two research currents have grown to meet this: one makes artificial intelligence small and distributed (quantization, pruning, distillation, TinyML, federated and split learning), the other makes it safe (defenses against poisoning, backdoors, inversion, and evasion). This review argues that the two are entangled rather than parallel. Operators that shrink a model or scatter it across nodes also redraw its attack surface, each carrying a security dividend and a security liability, and because a node’s resources are finite and shared, model capacity and defense strength compete for one multi-dimensional budget. We formalize this as an efficiency-decentralization-security (EDS) design tension, explicitly a tension and not an impossibility, and show with published measurements that the coupling is non-monotonic. Around this thesis we build three artifacts, following an explicit design-science research process: an evidence-graded scoring matrix that separates each operator’s security dividend from its liability across seven axes and reports the direction of every effect separately from the confidence in the evidence behind it; a resource-aware threat model that judges attack and defense feasibility against a tiered device, gateway, network, and server budget with stated units; and a co-design framework whose decision workflow terminates in a defense-selection program and a verification step under adaptive attack. We work the framework through an industrial predictive-maintenance scenario with the resource arithmetic computed line by line, and evaluate it retrospectively against six published edge-AI systems. The result is a decision-support guide for building edge AI that is efficient, decentralized, and secure at once. Full article

Heating, Ventilation, and Air Conditioning (HVAC) systems account for 60–70% of residential electricity consumption in Oman, where extreme desert climate, with temperatures regularly exceeding 45 °C create substantial cooling demands. Unlike general reviews of smart HVAC controls, this study specifically evaluates the applicability and performance of advanced control strategies for Omani residential buildings operating under extreme heat conditions, synthesizing evidence from international research, the Gulf Cooperation Council (GCC) region, and Oman. Based on a systematic review of peer-reviewed literature published between 2015 and 2025, this analysis examines Model Predictive Control (MPC), Deep Reinforcement Learning (DRL), Fuzzy Logic Control, and Internet of Things-based integrated approaches. International studies demonstrate that MPC strategies achieve energy savings of 16–40% compared to conventional thermostatic control by utilizing dynamic building thermal models to optimize control sequences over finite prediction horizons. DRL-based controllers achieve energy reductions of 17–23% through adaptive learning of optimal policies without requiring explicit system models, offering adaptability to dynamic occupancy patterns. Real-world implementation case studies from Oman and the GCC region—including the GUtech EcoHaus net-zero energy building and national-scale retrofit programs—demonstrate realized energy savings ranging from 25–75%, with higher savings achieved through comprehensive interventions that combine advanced controls with high-performance building envelopes. These findings suggest that substantial potential for reducing residential energy consumption while maintaining occupant thermal comfort under Oman’s extreme climatic conditions is achieved through the integration of advanced HVAC control strategies with high-performance building envelopes. Future research may address the development of occupant-centric adaptive comfort models calibrated for extreme heat conditions and context-specific control strategies that account for regional occupancy patterns and cultural preferences. Full article

Human presence detection is critical when building smart cities with use cases in sectors like smart homes, emergency evacuation, health-care monitoring and others. Existing human detection systems predominantly rely on camera-based imaging, raising privacy concerns. Moreover, conventional FMCW radar approaches are primarily motion-based, thus often failing to detect the presence of unconscious individuals, as in the case of search and rescue (SAR) operations. Some radar approaches use Doppler or spectral peak analysis to estimate respiration but fail to exploit phase coherence to resolve sub-millimeter chest displacement and higher-order physiological harmonics. This paper presents an interferometric radar framework that models multi-feature vital-sign signatures for human detection under controlled clinical settings using respiratory harmonic relationships, inter-harmonic consistency, chest-displacement spectral characteristics, and radar-derived cardiac mechanical signatures. Physiological relationships are used to establish the expected structure of the extracted features, while subject-to-subject variability and measurement uncertainty are used to determine practical acceptance regions from the training cohort. Experimental data from 30 healthy subjects were analyzed using a single interferometric radar sensor under controlled clinical conditions. The resulting signatures were subsequently evaluated using a machine-learning validation pipeline. With 243 test cases, the proposed framework achieved 89.71% accuracy, 95.26% precision, 94.15% F1-score, and 93.06% sensitivity. The study demonstrates that interferometric chest-displacement sensing can provide a privacy-preserving physiological feature space for human presence detection, while also identifying the limitations associated with unresolved multi-person signal superposition and hardware-induced phase uncertainty. Moreover, interferometric sensing by principle will work better than conventional radar approaches for SAR operations. Although validated in a controlled clinical environment, the framework establishes a foundational pathway towards future research for eventual deployment in next-generation smart systems. Full article

Forecasting power consumption is essential for intelligent power management in IoT-enabled smart environments, where heterogeneous behaviors appear from diverse building usages. University campuses are considered environments that share similarities with smart cities, making them suitable for power dynamics analysis. We build upon an IoT-based Advanced Metering Infrastructure (AMI) we deployed at our Engineering School’s Campus (INPT, Morocco), and an optimized XGBoost pipeline enhanced via Genetic Algorithms. Limited modeling granularity is addressed in heterogeneous consumption patterns. We propose and justify a cluster-aware approach partitioning data $\left(D\right)$ into K regimes such that $D={\bigcup }_{c=1}^K{C}_c$. Each cluster is treated as a homogeneous behavioral profile and modeled using a GA-XGBoost model, enabling an intermediate granularity between global and meter-level learning. Experiments on real-world campus AMI data show that our proposed GA-XGBoost model consistently outperforms SVR and LSTM baselines across all clusters. In addition, cluster-specific models further improve performance compared to a single GA-XGBoost model trained without clustering, achieving a 48.42% improvement in MASE. Overall, beyond improving forecasting accuracy, cross-cluster generalization shows performance degradation and distributional shift when models are transferred across clusters, while residual diagnostics reveal differences in variance, temporal dependence, and non-Gaussianity. Full article

Artificial Intelligence and blockchain are converging in ways that are quietly reshaping how financial systems operate. AI brings predictive analytics, automated decision-making, and fraud detection; blockchain contributes an immutable, decentralised record that can be independently verified. Taken together, applications such as AI-augmented smart contracts and blockchain-anchored data pipelines are already changing how fraud is detected, how compliance is handled, and how decentralised finance (DeFi) functions. But the same combination that makes these systems powerful also makes them harder to govern: technical, regulatory, and ethical obstacles still stand in the way of adoption at scale. This paper uses a targeted, purposive literature synthesis alongside exploratory case analysis of financial institutions and fintech platforms to examine how AI and blockchain are transforming finance together, what barriers and systemic risks accompany that transformation, and where research and regulation need to go next. Drawing on the Technology Acceptance Model, Diffusion of Innovation Theory, and the Socio-Technical Systems perspective, the paper builds a multi-level framework for thinking about how AI–blockchain convergence can be adopted responsibly across the financial industry. Full article

Large university campuses need timely, privacy-conscious information about how indoor spaces are used in order to improve space management, energy efficiency, and operational responsiveness. Yet many indoor positioning solutions still depend on additional hardware such as Bluetooth Low Energy beacons, ultra-wideband anchors, or camera-based sensing, which increases deployment cost and maintenance complexity. This paper presents a lightweight campus occupancy analysis and indoor localization framework that reuses an existing Cisco Wireless LAN Controller (WLC) infrastructure as a sensing layer. The system retrieves received signal strength indicator (RSSI) observations from access points over secure SSH sessions, converts these observations into approximate distance estimates through a calibrated log-distance path loss model, and computes user positions using weighted non-linear least-squares multilateration (Mlat). In addition to point localization, the framework generates occupancy heatmaps, cumulative reliability curves, and access-point-density sensitivity analyses based on a 50 m × 50 m evaluation scenario with 500 randomized samples. To support privacy-preserving deployment, the service layer exposes only zone-level occupancy information and omits personally identifiable network identifiers. The results indicate that Wi-Fi-based localization can provide cost-effective and scalable occupancy intelligence with sufficient accuracy for campus-wide density monitoring and smart building applications.$\times$ Full article

Public procurement tenders in Bulgaria are in the focus of public attention, despite the transparency of the process, which is conducted online through the Public Procurement Agency platform, participants require greater trust in the conduct of the procedures themselves, as they are not yet fully automated. Blockchain solutions are used in a wide range of businesses, as they offer transparency of transactions, trust between parties and data immutability and also seem suitable for conducting e-tenders for public procurement. This publication offers a solution based on a private blockchain HyperLedger Fabric, which in terms of structure and functionality builds on the existing solution. It ensures bidder identities are confidential from the unauthorized participants and transparency of the work of administrators in the platform and their actions when changing the status of a public procurement. The proposed blockchain based solution improves the evaluation process, reducing certain technical and human-error risks. The results show that it provides greater transparency and efficiency in the spending of public funds, achieving the set quantitative indicators with automated evaluation and qualitative factors defined in the smart contract. Full article