Search Results (150)

Road infrastructure underpins Nigeria’s economic competitiveness, yet Public–Private Partnership (PPP) performance is constrained not by inadequate legislation but by persistent weaknesses in enforcement and governance. Transparency deficits across procurement, design management, certification, and toll-revenue reporting have produced chronic delays, cost overruns, and declining public trust. This study offers the first empirical investigation of blockchain–Building Information Modelling (BIM) integration as a transparency-enhancing mechanism within Nigeria’s PPP road sector, focusing on Lagos State. Using a qualitative design, ten semi-structured interviews with stakeholders across the PPP lifecycle were thematically analysed to diagnose systemic governance weaknesses and assess the contextual feasibility of digital innovations. Findings reveal entrenched opacity rooted in weak enforcement, discretionary decision-making, and informal communication practices—including biased bidder evaluations, undocumented design alterations, manipulated certifications, and toll-revenue inconsistencies. While respondents recognised BIM’s potential to centralise project information and blockchain’s capacity for immutable records and smart-contract automation, they consistently emphasised that technological benefits cannot be realised absent credible institutional foundations. The study advances an original theoretical contribution: the Codify–Condition–Capacitate framework, which explains the institutional preconditions under which digital governance tools can improve transparency. This framework argues that effectiveness depends on: codifying digital standards and legal recognition; conditioning enforcement mechanisms to reduce discretionary authority; and capacitating institutions through targeted training and phased pilots. The research generates significant practical implications for policymakers in Nigeria and comparable developing contexts seeking institution-aligned digital transformation. Methodological rigour was ensured through purposive sampling, thematic saturation assessment, and documented analytical trails. Full article

We present an inverse designed metalens for long-wave infrared (LWIR) imaging tailored to consumer and Internet of Things (IoT) platforms. Conventional LWIR optics either rely on costly specialty materials or suffer from low efficiency and narrow fields of view (FoV), limiting scalability. Our approach integrates adjoint-based inverse design with fabrication-aware constraints and a cone-shaped source model that efficiently captures oblique incidence during optimization. The resulting multi-level metalens achieves a wide FoV up to 100° while maintaining robust focusing efficiency and stable angle-to-position mapping on low-power $4\times 4$ microbolometer arrays representative of edge devices. We further demonstrate application-level imaging on $4\times 4$ microbolometer arrays, showing that the proposed metalens delivers a substantially wider FoV than a commercial narrow FoV lens while meeting low-resolution, low-cost, and low-power constraints for edge LWIR modules. By eliminating bulky multi-element stacks and reducing cost and form factor, the proposed design provides a practical pathway to compact, energy-efficient LWIR modules for consumer applications such as occupancy analytics, smart-building automation, mobile sensing, and outdoor fire surveillance. Full article

This paper presents a computationally efficient two-stage analytical framework for predicting daylight performance in buildings. It is designed to support real-time applications in smart lighting and intelligent building management systems. This approach combines a facade lighting model—driven by solar geometry and atmospheric transmittance—with an interior light distribution module that represents the window as a discretized light source. This formulation provides a lightweight alternative to computationally intensive ray tracing methods. It allows rapid estimation of spatial lighting patterns with minimal input data. The framework is validated using a one-year measurement campaign with class A photometric sensors in three facade orientations. The facade module achieved an average relative error below 15%, while the interior lighting model yielded an RMSE of 83 lx (≈10% error). The integrated system demonstrated an overall average deviation of 18.6% under different sky and season conditions. Owing to its low computational complexity and physically transparent formulation, the proposed method is suitable for deployment in smart building platforms, including daylight-responsive lighting control, embedded energy management systems, and digital twins requiring fast and continuous simulation of daylight availability. Full article

The rapid development of the Internet of Things (IoT), a network of interconnected devices and sensors, has improved operational efficiency, comfort, and sustainability in smart buildings. However, relying on interconnected systems also introduces cybersecurity vulnerabilities. For instance, attackers can exploit zero-day vulnerabilities (previously unknown security flaws), launch Distributed Denial of Service (DDoS) attacks (overwhelming network resources with traffic), or access sensitive Building Management Systems (BMS, centralized platforms for controlling building operations). By targeting critical assets such as Heating, Ventilation, and Air Conditioning (HVAC) systems, security cameras, and access control networks, they may compromise the safety and functionality of the entire building. To address these threats, this paper presents a cybersecure intelligent sensor framework to protect smart buildings from various IoT-related cyberattacks. The main component is an automated Intrusion Detection System (IDS, software that monitors network activity for suspicious actions), which uses machine learning algorithms to rapidly identify, classify, and respond to potential threats. Furthermore, the framework integrates intelligent sensor networks with AI-based analytics, enabling continuous monitoring of environmental and system data for behaviors that might indicate security breaches. By using predictive modeling (forecasting attacks based on prior data) and automated responses, the proposed system enhances resilience against attacks such as denial of service, unauthorized access, and data manipulation. Simulation and testing results show high detection rates, low false alarm frequencies, and fast response times, thereby supporting the cybersecurity of smart building infrastructures and minimizing downtime. Overall, the findings suggest that AI-enhanced cybersecurity systems offer promise for IoT-based smart building security. Full article

This paper presents the development and validation of an IoT-enabled temperature-sensing device for real-time monitoring of the thermal insulation properties of glass windows. The system integrates contact and non-contact temperature sensors into a compact PCB platform equipped with WiFi connectivity, enabling seamless integration into smart home and building management frameworks. By continuously assessing window insulation performance, the device addresses the challenge of energy loss in buildings, where glazing efficiency often degrades over time. The collected data can be transmitted to cloud-based services or local IoT infrastructures, allowing for advanced analytics, remote access, and adaptive control of heating, ventilation, and air-conditioning (HVAC) systems. Experimental results demonstrate the accuracy and reliability of the proposed system, confirming its potential to contribute to energy conservation and sustainable living practices. Beyond energy efficiency, the device provides a scalable approach to environmental monitoring within the broader future internet ecosystem, supporting the evolution of intelligent, connected, and human-centered living environments. Full article

Blockchain-based financial ecosystems generate unprecedented volumes of multi-temporal data streams requiring sophisticated analytical frameworks that leverage both on-chain transaction patterns and off-chain market microstructure dynamics. This study presents an empirical evaluation of a two-class confidence-threshold framework for cryptocurrency direction prediction, systematically integrating macro momentum indicators with microstructure dynamics through unified feature engineering. Building on established selective classification principles, the framework separates directional prediction from execution decisions through confidence-based thresholds, enabling explicit optimization of precision–recall trade-offs for decentralized financial applications. Unlike traditional three-class approaches that simultaneously learn direction and execution timing, our framework uses post-hoc confidence thresholds to separate these decisions. This enables systematic optimization of the accuracy-coverage trade-off for blockchain-integrated trading systems. We conduct comprehensive experiments across 11 major cryptocurrency pairs representing diverse blockchain protocols, evaluating prediction horizons from 10 to 600 min, deadband thresholds from 2 to 20 basis points, and confidence levels of 0.6 and 0.8. The experimental design employs rigorous temporal validation with symbol-wise splitting to prevent data leakage while maintaining realistic conditions for blockchain-integrated trading systems. High confidence regimes achieve peak profits of 167.64 basis points per trade with directional accuracies of 82–95% on executed trades, suggesting potential applicability for automated decentralized finance (DeFi) protocols and smart contract-based trading strategies on similar liquid cryptocurrency pairs. The systematic parameter optimization reveals fundamental trade-offs between trading frequency and signal quality in blockchain financial ecosystems, with high confidence strategies reducing median coverage while substantially improving per-trade profitability suitable for gas-optimized on-chain execution. Full article

This paper introduces SmartBuildSim, an open-source synthetic-twin framework that generates configurable and reproducible multi-sensor building streams using lightweight statistical models with tunable trend, seasonality, correlation, delays, and anomaly mechanisms. Deterministic seeding ensures experiment-level reproducibility, while modular pipelines support unified evaluation across forecasting, anomaly detection, and RL tasks. A comprehensive validation against an ASHRAE Great Energy Predictor III reference signal demonstrates that the synthetic data capture realistic magnitude and variability (KS ≈ 0.32; DTW ≈ 9.69), while preserving interpretable and controllable temporal structure. Benchmark results show that simple linear models achieve strong forecasting performance (RMSE ≈ 21.27), IsolationForest reliably outperforms LOF in anomaly detection (F1 ≈ 0.17 vs. 0.10), and Soft-Q Learning provides substantially more stable RL convergence than tabular Q-learning (variance reduced by >95%). Scenario-level analyses further illustrate reproducible daily cycles, zone-specific differences, and the scalability of model behaviour across building configurations. By combining declarative YAML configurations, deterministic randomness management, and an extensible scenario engine, SmartBuildSim provides a transparent and lightweight alternative to high-fidelity building simulators. The framework offers a practical, reproducible testbed for smart-building AI research, bridging the gap between simplistic synthetic datasets and complex physical digital twins. All code, tables, figures, and a Google Colab workflow are openly available to ensure full replicability. Full article

Permissionless blockchains have evolved beyond cryptocurrency into foundations for Web3 applications, decentralized finance (DeFi), and digital asset ownership, yet this rapid expansion has intensified privacy vulnerabilities. This study provides a comprehensive review of recent trends, emerging privacy threats, and mitigation strategies in permissionless blockchain ecosystems. We examine six developments reshaping the landscape: meme coin proliferation on high-throughput networks, real-world asset tokenization linking on-chain activity to regulated identities, perpetual derivatives exposing trading strategies, institutional adoption concentrating holdings under regulatory oversight, prediction markets creating permanent records of beliefs, and blockchain–AI integration enabling both privacy-preserving analytics and advanced deanonymization. Through this work and forensic analysis of documented incidents, we analyze seven critical privacy threats grounded in verifiable 2024–2025 transaction data: dust attacks, private key management failures, transaction linking, remote procedure call exposure, maximal extractable value extraction, signature hijacking, and smart contract vulnerabilities. Blockchain exploits reached $2.36 billion in 2024 and $2.47 billion in the first half of 2025, with over 80% attributed to compromised private keys and signature vulnerabilities. We evaluate privacy-enhancing technologies, including zero-knowledge proofs, ring signatures, and stealth addresses, identifying the gap between academic proposals and production deployment. We further propose a Secure Development Lifecycle framework incorporating measurable security controls validated against incident data. This work bridges the disconnect between privacy research and industrial practice by synthesizing current trends, providing insights, documenting real-world threats with forensic evidence, and providing actionable insights for both researchers advancing privacy-preserving techniques and developers building secure blockchain applications. Full article

The global drive toward sustainability and energy efficiency has accelerated the development of smart buildings integrating the Internet of Things (IoT) and Artificial Intelligence (AI). These technologies optimise energy use, enhance occupant comfort, and advance building management systems. This study examines the integration of IoT and AI in energy-efficient smart buildings, emphasising applications and challenges. A qualitative methodology, combining systematic literature review, case study analysis, and systems analysis, underpins the research. Findings indicate that IoT enables smart metering, real-time energy monitoring, automated lighting and HVAC, occupancy-based energy optimisation, and renewable energy integration. AI complements these functions through predictive maintenance, energy forecasting, demand-side management, intelligent climate control, indoor air quality automation, and behaviour-driven analytics. Together, they reduce carbon emissions, lower operational costs, and improve occupant well-being. However, challenges remain, including data security and privacy risks, interoperability gaps, scalability and cost constraints, and retrofitting difficulties. To address these, the paper proposes a systems thinking-enabled conceptual framework structured around three pillars: adopting IoT and AI as enabling technologies, overcoming integration barriers, and identifying application areas that advance sustainability in smart buildings. This framework supports strategic decision-making toward net-zero and resilient building design. Full article

Urban building change detection (UBCD) is essential for urban planning, land-use monitoring, and smart city analytics, yet bi-temporal optical methods remain limited by spectral confusion, occlusions, and weak sensitivity to structural change. To overcome these challenges, we propose DDDMNet, a lightweight deep learning framework that fuses multi-source inputs—including DSM, dnDSM, DOM, and NDVI—to jointly model geometric, spectral, and environmental cues. A core component of the network is the DSM Difference Normalization Module (DDDM), which explicitly normalizes elevation differences and directs the model to focus on height-related structural variations such as rooftop additions and demolition. Embedded into a TinyCD backbone, DDDMNet achieves efficient inference with low memory cost while preserving detail-level change fidelity. Across LEVIR-CD, WHU-CD, and DSIFN, DDDMNet achieves up to 93.32% F1-score, 89.05% Intersection over Union (IoU), and 99.61% Overall Accuracy (OA), demonstrating consistently strong performance across diverse benchmarks. Ablation analysis further shows that removing multi-source fusion, DDDM, dnDSM, or morphological refinement causes notable drops in performance—for example, removing DDDM reduces IoU from 88.12% to 74.62%, underscoring its critical role in geometric normalization. These results demonstrate that DDDMNet is not only accurate but also practically deployable, offering strong potential for scalable 3D city updates and long-term urban monitoring under diverse data conditions. Full article

The built environment contributes approximately 25% of the UK’s total greenhouse gas emissions, positioning it as a critical sector in the national net-zero strategy. This review investigates the enabling role of the domestic smart metering infrastructure combined with other IoT systems in accelerating the decarbonisation of residential buildings. Drawing from experience gained from governmental and commercially funded R&D projects, the article demonstrates how smart metering data can be leveraged to assess building energy performance, underpin cost-effective carbon reduction solutions, and enable energy flexibility services for maintaining grid stability. Unlike controlled laboratory studies, this review article focuses on real-world applications where data from publicly available infrastructure is accessed and utilised, enhancing scalability and policy relevance. The integration of smart meter data with complementary IoT data—such as indoor temperature, weather conditions, and occupancy—substantially improves built environment digital energy analytics. This capability was previously unattainable due to the absence of a nationwide digital energy infrastructure. The insights presented in this work highlight the untapped potential of the UK’s multibillion-pound investment in smart metering, offering a scalable pathway for low-carbon innovation for the built environment, thus supporting the broader transition to a net-zero future. Full article

Human Activity Recognition (HAR) is a key technology with applications in healthcare, security, smart environments, and sports analytics. Despite advances in deep learning, challenges remain in building models that are both efficient and generalizable due to the large scale and variability of video data. To address these issues, we propose a novel Dual-Teacher Knowledge Distillation (DTKD) framework tailored for HAR. The framework introduces three main contributions. First, we define static and dynamic activity classes in an objective and reproducible manner using optical-flow-based indicators, establishing a quantitative classification scheme based on motion characteristics. Second, we develop subset-specialized teacher models and design a hybrid scoring mechanism that combines teacher confidence with cross-entropy loss. This enables dynamic weighting of teacher contributions, allowing the student to adaptively balance knowledge transfer across heterogeneous activities. Third, we provide a comprehensive evaluation on the UCF101 and HMDB51 benchmarks. Experimental results show that DTKD consistently outperforms baseline models and achieves balanced improvements across both static and dynamic subsets. These findings validate the effectiveness of combining subset-aware teacher specialization with hybrid scoring. The proposed approach improves recognition accuracy and robustness, offering practical value for real-world HAR applications such as driver monitoring, healthcare, and surveillance. Full article

Smart and sustainable cities are often assessed using indicator-based models. However, most existing systems evaluate cities as a whole, offering limited support for project-level decision-making, particularly in small and medium-sized cities with scarce resources. This study aims to fill this gap by developing a comprehensive indicator framework tailored to the evaluation of smart city projects, designed to guide investment choices and support evidence-based planning. To build this framework, a systematic review of international indicator systems was conducted, compiling and refining over 1200 indicators into a unified taxonomy. The analysis revealed structural imbalances, with environmental and social dimensions prevailing over economic and governance aspects, and confirmed substantial redundancies, with nearly one-third of indicators overlapping. Using project actions as an analytical lens, gaps were detected and 73 evaluation areas defined. From these, anticipated impact indicators were developed and linked to corresponding performance metrics. Beyond consolidating fragmented systems, the framework provides a practical and balanced tool for multidimensional project assessment. An initial empirical pre-validation demonstrated its coverage and usability, reinforcing its potential to support planners and policymakers in comparing investment alternatives. Unlike traditional ranking or maturity models, it directly bridges the gap between abstract smart city strategies and tangible, project-level outcomes. Full article

State Departments of Transportation (DOTs) face challenges with traditional bridge inspections that are time-consuming, inconsistent, and paper-based. This study focused on an existing research gap regarding automated methods that streamline the bridge inspection process, prioritize maintenance effectively, and allocate resources efficiently. Thus, this paper introduces a digitalized bridge inspection framework by integrating Building Information Modeling (BIM) and Business Intelligence (BI) to enable near-real-time monitoring and digital documentation. This study adopts a Design Science Research (DSR) methodology, a recognized paradigm for developing and evaluating the innovative SmartBridge to address pressing bridge inspection problems. The method involved designing an Autodesk Revit-based plugin for data synchronization, element-specific comments, and interactive dashboards, demonstrated through an illustrative 3D bridge model. An illustrative example of the digitalized bridge inspection with the proposed framework is provided. The results show that SmartBridge streamlines data collection, reduces manual documentation, and enhances decision-making compared to conventional methods. This paper contributes to this body of knowledge by combining BIM and BI for digital visualization and predictive analytics in bridge inspections. The proposed framework has high potential for hybridizing digital technologies into bridge infrastructure engineering and management to assist transportation agencies in establishing a safer and efficient bridge inspection approach. Full article

The retrofitting of traditional cities into smart cities is crucial for addressing rapid urban development by integrating smart technology while respecting the human dimension to fulfill human needs. The primary objective of this paper is to establish practical guidelines and develop a strategic, human-centered, comprehensive, and conceptual framework model that integrates smart technology through a set of smart city performance indicators. This framework aims to inform human-centered technological strategies for adapting Erbil City, retrofitting the old city into a smart one. Therefore, the paper aims to develop a roadmap scenario and build a conceptual framework model for retrofitting the traditional city of Erbil into a smart city. It outlines the methods that can be used, taking into account contemporary technology and citizens’ needs. In this context, the traditional city of Erbil in the Kurdistan Region of Iraq has been selected as a case study, represented explicitly by the Buffer Zone area. The research employed a combination of qualitative and quantitative methods, including a literature review, questionnaires, space syntax analysis, and statistical analysis. The results and conclusions demonstrate that the human-centered approach plays a significant role in achieving smart cities. In collaboration with smart technology strategies, old and traditional cities can be retrofitted to become smart cities. Full article