Search Results (8,609)

This research presents a novel conceptual framework for inclusive education by integrating Internet of Things (IoT)-driven real-time environmental and behavioral monitoring with adaptive teaching strategies. Unlike traditional methods, our model leverages sensor-based data collection to analyze classroom conditions, teacher mobility, and student interactions, enabling dynamic adjustments that aim to enhance engagement and inclusivity. While the framework is theoretical and has not yet undergone experimental validation, we discuss how optimizing spatial configurations, voice dynamics, and movement patterns could support student participation, particularly for learners with diverse needs. Pilot implementations and empirical testing are planned for future research. By merging data-driven insights with educators’ expertise, our approach offers a scalable vision for creating responsive, inclusive learning environments that proactively address barriers to education. Full article

The evolution of Industrial Internet of Things (IIoT) systems demands flexible and intelligent architectures capable of addressing low-latency requirements, real-time analytics, and adaptive resource management. In this context, softwarized edge computing emerges as a key enabler, supporting advanced IoT deployments through programmable infrastructures, distributed intelligence, and seamless integration with cloud environments. This paper presents an extended and publicly available proof of concept (PoC) for a softwarized, data-driven architecture designed to operate across the cloud/edge/IoT continuum. The proposed architecture incorporates containerized microservices, open standards, and ML-based inference services to enable runtime decision-making and on-the-fly network reconfiguration based on real-time telemetry from IIoT nodes. Unlike traditional solutions, our approach leverages a modular control plane capable of triggering dynamic adaptations in the system through RESTful communication with a cloud-hosted inference engine, thus enhancing responsiveness and autonomy. We evaluate the system in representative IIoT scenarios involving multi-agent collaboration, showcasing its ability to process data at the edge, minimize latency, and support real-time decision-making. This work contributes to the ongoing efforts toward building advanced IoT ecosystems by bridging conceptual designs and practical implementations, offering a robust foundation for future research and deployment in intelligent, software-defined industrial environments. Full article

The Internet of Things (IoT) has transformed multiple industries, providing significant potential for automation, efficiency, and enhanced decision-making. The incorporation of IoT and data analytics in smart grid represents a groundbreaking opportunity for the energy sector, delivering substantial advantages in efficiency, sustainability, and customer empowerment. This integration enables smart grids to autonomously monitor energy flows and adjust to fluctuations in energy demand and supply in a flexible and real-time fashion. Statistical analytics, as a fundamental component of data analytics, provides the necessary tools and techniques to uncover patterns, trends, and insights within datasets. Nevertheless, it is crucial to address privacy and security issues to fully maximize the potential of data analytics in smart grids. This paper makes several significant contributions to the literature on secure, privacy-aware aggregation schemes in smart grids. First, we introduce a Fog-enabled Secure Data Analytics Operations (FESDAO) scheme which offers a distributed architecture incorporating robust security features such as secure aggregation, authentication, fault tolerance and resilience against insider threats. The scheme achieves privacy during data aggregation through a modified Boneh-Goh-Nissim cryptographic scheme along with other mechanisms. Second, FESDAO also supports statistical analytics on metering data at the cloud control center and fog node levels. FESDAO ensures reliable aggregation and accurate data analytical results, even in scenarios where smart meters fail to report data, thereby preserving both analytical operation computation accuracy and latency. We further provide comprehensive security analyses to demonstrate that the proposed approach effectively supports data privacy, source authentication, fault tolerance, and resilience against false data injection and replay attacks. Lastly, we offer thorough performance evaluations to illustrate the efficiency of the suggested scheme in comparison to current state-of-the-art schemes, considering encryption, computation, aggregation, decryption, and communication costs. Moreover, a detailed security analysis has been conducted to verify the scheme’s resistance against insider collusion attacks, replay attack, and false data injection (FDI) attack. Full article

This study presents a novel integrative review of 329 review articles on sustainable buildings from 2015 to 2025, combining quantitative bibliometrics with qualitative insights to map the field’s evolution and pinpoint critical future pathways. Seven core research themes were identified in this study: (1) material and advanced construction technologies, (2) energy efficiency and renewable energy systems, (3) digitalization and smart technologies, (4) policy, standards, and certification, (5) sustainable design and optimization, (6) stakeholder and socio-economic factors, (7) other (cross-cutting) topics. Key findings reveal a surge in publications post-2020, driven by global net-zero commitments, with China, Australia, and Hong Kong leading research output. Innovations in low-carbon materials (e.g., hemp concrete, geopolymers), artificial intelligent (AI)-driven energy optimization, and digital tools (e.g., building information modeling (BIM), internet of things (IoT)) dominate recent advancements. However, challenges persist, including policy fragmentation, scalability barriers for sustainable materials, and socio-economic disparities in green building adoption. The study proposes a unique future research framework emphasizing nanotechnology-enhanced materials, interpretable AI models, harmonized global standards, and inclusive stakeholder engagement. This review provides actionable recommendations to bridge gaps between technological innovation, policy frameworks, and practical implementation in sustainable construction. Full article

IoT technology is transforming the healthcare industry through what is known as the Internet of Medical Things (IoMT), enhancing patient care while simultaneously reducing costs. Nevertheless, it introduces critical security challenges, particularly the risk of jeopardizing patient safety. Existing IoMT security solutions, often limited to proprietary platforms or generic IoT firewalls, frequently lack transparency, scalability, and awareness with clinical regulations. To address these gaps, we present MEDIotWALL, a customized two-tier security architecture tailored for healthcare environments. The system integrates distributed real-time traffic monitoring with AI-driven rule generation, delivering a non-intrusive, centralized, human-supervised, and regulation-aware security framework. Experimental results demonstrate the cost-efficiency and effectiveness of the approach, ensuring robust medical protection while preserving authentication, confidentiality, and integrity of the environment. Full article

Visible Light Positioning (VLP) has emerged as a pivotal technology for industrial Internet of Things (IoT) and smart logistics, offering high accuracy, immunity to electromagnetic interference, and cost-effectiveness. However, fluctuations in signal gain caused by target motion significantly degrade the positioning accuracy of current VLP systems. Conventional approaches face intrinsic limitations: propagation-model-based techniques rely on static assumptions, fingerprint-based approaches are highly sensitive to dynamic parameter variations, and although CNN/LSTM-based models achieve high accuracy under static conditions, their inability to capture long-term temporal dependencies leads to unstable performance in dynamic scenarios. To overcome these challenges, we propose a novel dynamic VLP algorithm that incorporates a Spatio-Temporal Feature Information Network (STFI-Net) for joint localization and orientation estimation of moving targets. The proposed method integrates a two-layer convolutional block for spatial feature extraction and employs modern Temporal Convolutional Networks (TCNs) with dilated convolutions to capture multi-scale temporal dependencies in dynamic environments. Experimental results demonstrate that the STFI-Net-based system enhances positioning accuracy by over 26% compared to state-of-the-art methods while maintaining robustness in the face of complex motion patterns and environmental variations. This work introduces a novel framework for deep learning-enabled dynamic VLP systems, providing more efficient, accurate, and scalable solutions for indoor positioning. Full article

One of the main challenges faced by DC microgrid (DCMG) is their low inertia, which leads to rapid and significant voltage fluctuations during load or generation changes. These fluctuations can negatively impact sensitive loads and protection devices. Previous studies have addressed this by enabling battery converters to mimic the behavior of synchronous generators (SGs), but this approach becomes ineffective when the converters or batteries reach their current or energy limits, leading to a loss of inertia and potential system instability. In interconnected multi-microgrid (MMG) systems, the presence of multiple batteries offers the potential to enhance system inertia, provided there is a coordinated control strategy. This research introduces a hierarchical control method that combines decentralized and centralized approaches. Decentralized control allows individual converters to emulate SG behavior, while the centralized control uses Internet of Things (IoT) technology to enable real-time coordination among all Energy Storage Units (ESUs). This coordination improves inertia across the DCMMG system, enhances energy management, and strengthens overall system stability. IoT integration ensures real-time data exchange, monitoring, and collaborative decision-making. The proposed scheme is validated through MATLAB simulations, with results confirming its effectiveness in improving inertial response and supporting the integration of renewable energy sources within DCMMGs. Full article

Recent advancements in the Internet of Things (IoT) and artificial intelligence (AI) are unlocking transformative opportunities across society. One of the most critical challenges addressed by these technologies is the ageing population, which presents mounting concerns for healthcare systems and quality of life worldwide. By supporting continuous monitoring, personal care, and data-driven decision-making, IoT and AI are shifting healthcare delivery from a reactive approach to a proactive one. This paper presents a comprehensive overview of IoT-based systems with a particular focus on the Internet of Healthcare Things (IoHT) and their integration with AI, referred to as the Artificial Intelligence of Things (AIoT). We illustrate the operating procedures of IoHT systems in detail. We highlight their applications in disease management, health promotion, and active ageing. Key enabling technologies, including cloud computing, edge computing architectures, machine learning, and smart sensors, are examined in relation to continuous health monitoring, personalized interventions, and predictive decision support. This paper also indicates potential challenges that IoHT systems face, including data privacy, ethical concerns, and technology transition and aversion, and it reviews corresponding defense mechanisms from perception, policy, and technology levels. Future research directions are discussed, including explainable AI, digital twins, metaverse applications, and multimodal sensor fusion. By integrating IoT and AI, these systems offer the potential to support more adaptive and human-centered healthcare delivery, ultimately improving treatment outcomes and supporting healthy ageing. Full article

The Internet of Things (IoT) revolutionizes connectivity, enabling innovative applications across healthcare, industry, and smart cities but also introducing significant cybersecurity challenges due to its expanded attack surface. Intrusion Detection Systems (IDSs) play a pivotal role in addressing these challenges, offering tailored solutions to detect and mitigate threats in dynamic and resource-constrained IoT environments. Through a rigorous analysis, this study classifies IDS research based on methodologies, performance metrics, and application domains, providing a comprehensive synthesis of the field. Key findings reveal a paradigm shift towards integrating artificial intelligence (AI) and hybrid approaches, surpassing the limitations of traditional, static methods. These advancements highlight the potential for IDSs to enhance scalability, adaptability, and detection accuracy. However, unresolved challenges, such as resource efficiency and real-world applicability, underline the need for further research. By contextualizing these findings within the broader landscape of IoT security, this work emphasizes the critical importance of developing IDS solutions that ensure the reliability, privacy, and security of interconnected systems, contributing to the sustainable evolution of IoT ecosystems. Full article

Radon exposure is the second leading cause of lung cancer worldwide, yet monitoring strategies remain limited, expensive, and unevenly applied. Recent advances in the Internet of Things (IoT) offer the potential to change radon surveillance through low-cost, real-time, distributed sensing networks. This review consolidates emerging research on IoT-based radon monitoring, drawing from both primary radon studies and analogous applications in environmental IoT. A search across six major databases and relevant grey literature yielded only five radon-specific IoT studies, underscoring how new this research field is rather than reflecting a shortcoming of the review. To enhance the analysis, we delve into sensor physics, embedded system design, wireless protocols, and calibration techniques, incorporating lessons from established IoT sectors like indoor air quality, industrial safety, and volcanic gas monitoring. This interdisciplinary approach reveals that many technical and logistical challenges, such as calibration drift, power autonomy, connectivity, and scalability, have been addressed in related fields and can be adapted for radon monitoring. By uniting pioneering efforts within the broader context of IoT-enabled environmental sensing, this review provides a reference point and a future roadmap. It outlines key research priorities, including large-scale validation, standardized calibration methods, AI-driven analytics integration, and equitable deployment strategies. Although radon-focused IoT research is still at an early stage, current progress suggests it could make continuous exposure assessment more reliable, affordable, and widely accessible with clear public health benefits. Full article

The rapid growth of urban populations intensifies congestion, air pollution, and energy demand. Green mobility is central to sustainable smart cities, and the Internet of Things (IoT) offers a means to monitor, coordinate, and optimize transport systems in real time. This paper presents an Internet of Things (IoT)-based architecture integrating heterogeneous sensing with edge–cloud orchestration and AI-driven control for green routing and coordinated Electric Vehicle (EV) charging. The framework supports adaptive traffic management, energy-aware charging, and multimodal integration through standards-aware interfaces and auditable Key Performance Indicators (KPIs). We hypothesize that, relative to a static shortest-path baseline, the integrated green routing and EV-charging coordination reduce (H1) mean travel time per trip by ≥7%, (H2) CO₂ intensity (g/km) by ≥6%, and (H3) station peak load by ≥20% under moderate-to-high demand conditions. These hypotheses are tested in Simulation of Urban MObility (SUMO) with Handbook Emission Factors for Road Transport (HBEFA) emission classes, using 10 independent random seeds and reporting means with 95% confidence intervals and formal significance testing. The results confirm the hypotheses: average travel time decreases by approximately 9.8%, CO₂ intensity by approximately 8%, and peak load by approximately 25% under demand multipliers ≥1.2 and EV shares ≥20%. Gains are attenuated under light demand, where congestion effects are weaker. We further discuss scalability, interoperability, privacy/security, and the simulation-to-deployment gap, and outline priorities for reproducible field pilots. In summary, a pragmatic edge–cloud IoT stack has the potential to lower congestion, reduce per-kilometer emissions, and smooth charging demand, provided it is supported by reliable data integration, resilient edge services, and standards-compliant interoperability, thereby contributing to sustainable urban mobility in line with the objectives of SDG 11 (Sustainable Cities and Communities). Full article

The rapid development of contemporary artificial intelligence and Internet of Things (IoT) technologies has given rise to the emerging paradigm of the AIoT (Artificial Intelligence of Things), which is profoundly impacting human life and driving the digital transformation of industries and society. The AIoT not only enhances product functionality and convenience but also accelerates the achievement of the United Nations Sustainable Development Goals (SDGs). However, the widespread adoption of these technologies still poses challenges related to social inclusivity, particularly regarding insufficient accessibility for elderly users, which may exacerbate the digital divide and social inequality, contradicting SDG 10 (reducing inequality). This study integrates AIoT product development processes based on sustainability and universal design principles using methods such as Quality Function Deployment, the Analytic Hierarchy Process, the Scenario Method, the Entropy Weight Method, and Fuzzy Comprehensive Evaluation. The features of this process include ease of operation and high flexibility, making it suitable for cross-departmental product development while prioritizing the needs of diverse age groups throughout the development process. The research findings indicate that the AIoT product concepts proposed can meet the needs of diverse users, contributing to the realization of age-friendly products. This study provides a reference point for future AIoT product development, promoting the inclusive and sustainable development of smart technology. Full article

This study presents the design and validation of an automated skip loading system for vertical shaft hoisting operations, aimed at addressing inefficiencies in current manual systems that contribute to consistent underperformance in meeting daily production targets. Initial assessments revealed a task completion rate of 91.6%, largely due to delays and inaccuracies in manual ore loading and accounting. To resolve these challenges, an automated system was developed using a bin and conveyor mechanism integrated with a suite of industrial automation components, including a programmable logic controller (PLC), stepper motors, hydraulic cylinders, ultrasonic sensors, and limit switches. The system is designed to transport ore from the draw point, halt when one ton is detected, and activate the hoisting process automatically. Digital simulations demonstrated that the automated system reduced loading time by 12% and increased utilization by 16.6%, particularly by taking advantage of the 2 h post-blast idle period. Financial evaluation of the system revealed a positive Net Present Value (NPV) of $1,019,701, a return on investment (ROI) of 69.7% over four years, and a payback period of 2 years and 11 months. The study concludes that the proposed solution significantly improves operational efficiency and recommends further enhancements to the hoisting infrastructure to fully optimize performance. Full article

Management of smart greenhouses represents a transformative advancement in precision agriculture, enabling sustainable intensification of food production through the integration of multi-sensor networks, intelligent control, and sophisticated data filtering techniques. Unlike conventional greenhouses that rely on manual monitoring, smart greenhouses combine environmental sensors, Internet of Things (IoT) platforms, and artificial intelligence (AI)-driven decision making to optimize microclimates, improve yields, and enhance resource efficiency. This review systematically investigates three key technological pillars, multi-sensor monitoring, intelligent control, and data filtering techniques, for smart greenhouse environment management. A structured literature screening of 114 peer-reviewed studies was conducted across major databases to ensure methodological rigor. The analysis compared sensor technologies such as temperature, humidity, carbon dioxide (CO₂), light, and energy to evaluate the control strategies such as IoT-based automation, fuzzy logic, model predictive control, and reinforcement learning, along with filtering methods like time- and frequency-domain, Kalman, AI-based, and hybrid models. Major findings revealed that multi-sensor integration enhanced precision and resilience but faced changes in calibration and interoperability. Intelligent control improved energy and water efficiency yet required robust datasets and computational resources. Advanced filtering strengthens data integrity but raises concerns of scalability and computational cost. The distinct contribution of this review was an integrated synthesis by linking technical performance to implementation feasibility, highlighting pathways towards affordable, scalable, and resilient smart greenhouse systems. Full article

Rapid urbanization and the proliferation of heterogeneous urban data have intensified the challenges of semantic interoperability and integrated urban governance. To address this, we propose the Smart City Ontology Framework (SMOF), a standards-driven ontology that unifies Building Information Modeling (BIM), Geographic Information Systems (GIS), Internet of Things (IoT), and relational data. SMOF organizes five core modules and eleven major entity categories, with universal and extensible attributes and relations to support cross-domain data integration. SMOF was developed through competency questions, authoritative knowledge sources, and explicit design principles, ensuring methodological rigor and alignment with real governance needs. Its evaluation combined three complementary approaches against baseline models: quantitative metrics demonstrated higher attribute richness and balanced hierarchy; LLM as judge assessments confirmed conceptual completeness, consistency, and scalability; and expert scoring highlighted superior scenario fitness and clarity. Together, these results indicate that SMOF achieves both structural soundness and practical adaptability. Beyond structural evaluation, SMOF was validated in two representative urban service scenarios, demonstrating its capacity to integrate heterogeneous data, support graph-based querying and enable ontology-driven reasoning. In sum, SMOF offers a robust and scalable solution for semantic data integration, advancing smart city governance and decision-making efficiency. Full article