Search Results (9,527)

As global populations grow and technology advances, daily life is increasingly shaped by digital systems such as computers and smart devices. However, prolonged device use has contributed to increasing physical and mental health concerns, particularly those associated with poor sitting posture. Posture-related strain is frequently overlooked and contributes to musculoskeletal discomfort, including back, neck, shoulder, and wrist pain, and may also be associated with sleep disturbances and elevated stress levels. To the best of our knowledge and based on the existing literature, this is the first study to introduce a machine learning-based framework for advanced muscle strain severity classification using Internet of Things (IoT) devices that integrates posture monitoring and muscle strain detection into a unified low-cost framework ($23 hardware cost). The primary objective of this work is accurate classification of muscle strain severity, while real-time alerts serve as a secondary ergonomic feedback mechanism. Specifically, this study makes four major contributions. First, we created a novel dataset through real-time acquisition of electromyography (EMG) and posture signals from participants in hospital and industrial environments, capturing diverse muscle strain patterns validated against clinical assessment procedures. Second, we designed a two-part hardware architecture consisting of posture detection (PD) and strain detection (SD) modules using a NodeMCU ESP8266, HC-SR04 ultrasonic sensor, EMG sensor, and buzzer for real-time physiological monitoring, incorporating EMG-specific preprocessing including band-pass filtering, rectification, and RMS smoothing. Third, we proposed and evaluated a hybrid machine learning framework integrating Vision Transformer (ViT) and XGBoost to classify strain severity into three study-specific categories: baseline (EMG RMS < 40 µV), compensatory strain (40–59 µV), and overload (≥60 µV). These categories were used as reproducible severity proxies for machine learning annotation and should not be interpreted as universal biomarkers of structural tissue damage. Finally, the proposed framework achieved a classification accuracy of 99.0% (95% CI: 98.5–99.5%) with an inference latency of 15.2 ms. Full article

The application of Internet of Things-based ecosystems is growing rapidly. Cyber attacks are also increasing at a similar pace. Intrusion detection using deep learning is getting harder as these devices lack enough resources for a large Intrusion Detection System. A compact deep learning-based Intrusion Detection System for IoT, called CID, has been proposed to reduce computational complexity. The proposed CID framework uses MobileNet v1 as the main classification model, and the Binary Greylag Goose Optimization technique is used for feature selection to improve detection while minimizing processing time. On comparing the experimental results, it has been found that the proposed method works better than the baseline methods. Full article

Hybrid RF-FSO-THz communication systems are promising candidates for future Internet of Things (IoT) and 6G networks because they combine the robustness of radio frequency links, the high-capacity potential of Free-Space Optical communications, and the ultra-wideband capabilities of terahertz transmission. Adaptive channel selection in such systems depends on multiple correlated environmental and physical-layer variables, including distance, rain intensity, humidity, visibility, turbulence strength, signal-to-noise ratio, channel capacity, and energy-efficiency metrics. This paper presents a physics-supported benchmark framework for supervised adaptive channel selection in hybrid RF-FSO-THz systems and systematically investigates the impact of linear and nonlinear dimensionality-reduction techniques on predictive performance, statistical robustness, computational complexity, and physical interpretability. A multi-scenario dataset comprising 5000 samples was generated using calibrated RF, FSO, and THz propagation models under clear, rain, fog, and worst-case environmental conditions. Principal Component Analysis (PCA) and Kernel PCA were evaluated together with Random Forest, Support Vector Machines (SVMs), XGBoost, Gradient Boosting (GB), Multi-Layer Perceptron (MLP), Logistic Regression, and Decision Trees. The results demonstrate that PCA preserves nearly all predictive capabilities while reducing the original 33-dimensional feature space by approximately 81.8%, maintaining accuracies close to 97–98% with the best-performing classifiers. Statistical significance analysis confirms that PCA introduces only modest degradations, whereas Kernel PCA consistently reduces the predictive performance while increasing memory requirements and inference latency. Additional environmental-only validation experiments indicate that adaptive channel selection remains highly learnable even when only pre-selection environmental descriptors are available, partially mitigating concerns regarding self-consistency bias. Overall, the results suggest that PCA provides an advantageous compromise among predictive accuracy, computational efficiency, statistical robustness, and physical interpretability for supervised adaptive channel selection in physics-supported hybrid wireless communication systems. Full article

Energy consumption in hotels is strongly influenced by HVAC operation, lighting systems, and highly variable occupancy patterns. Internet of Things (IOT) technologies have been widely proposed to improve energy efficiency in building interiors; however, the maturity and practical applicability of this research remain unclear. This study presents a PRISMA-based systematic literature review of IOT-driven energy efficiency research in hospitality environments. A total of 1709 records were initially identified across Web of Science, Scopus, and Google Scholar, from which 60 peer-reviewed articles were selected for detailed analysis. Each study was evaluated using a three-dimensional research maturity assessment framework and a four-level ordinal scoring scale. The results indicated a moderate research maturity (average score 2.65/4), limited real-world implementation, and insufficient reporting of technological architectures and operational details required for replicability. These findings highlight the need for more rigorous empirical validation and clearer reporting standards to enable scalable adoption of IOT-based energy management in hospitality. Full article

The rapid growth of the green bond market has intensified the need for transparent and reliable monitoring systems, particularly in circular-economy environments characterized by complex, multi-stakeholder, and dynamic interactions. However, existing monitoring approaches still rely heavily on static, issuer-driven disclosures, which sustain information asymmetry and increase the risk of greenwashing. This study systematically reviews the role of digital technologies in enhancing green bond monitoring within circular economy systems. A systematic literature review (SLR) was conducted using the Scopus database, covering publications from 2022 to 2026 and yielding 56 eligible studies. A bibliometric analysis using VOSviewer identified major research trends, thematic clusters, and collaboration patterns within the field. The findings reveal four dominant technological pillars—blockchain, artificial intelligence (AI), Internet of Things (IoT), and digital twin—that support data verification, automated analytics, real-time environmental monitoring, and system-wide integration. Although these technologies show significant potential, the literature remains fragmented and lacks comprehensive monitoring architectures that integrate technological, governance, and regulatory dimensions. This study contributes to the literature by synthesizing these technologies through a business informatics perspective and highlighting digital twin architectures as a promising foundation for integrated green bond monitoring. The findings provide practical insights for regulators, issuers, and investors seeking interoperable, transparent, and trustworthy monitoring ecosystems that strengthen accountability and credibility in sustainable finance. Full article

Sleep is crucial to infant development, and excessive sleep disturbances are associated with adverse outcomes for both infants and their caregivers. There is limited information on the longitudinal development of sleep (e.g., duration, fragmentation, etc.) from birth to 6 months of age. New technologies, which include real-time environmental sensing and responses, have the potential to overcome many of the traditional limitations on infant sleep monitoring. In this study, we demonstrate the feasibility of utilizing aggregated activity logs from a commercially available IoT (Internet of Things) bassinet to derive traditional sleep metrics (longest sleep stretch, total night sleep, and sleep efficiency), as well as novel metrics related to infant fussing and impacts of the bed’s ability to deliver responsive motion and sound. A total of 26,187 infants (1000–8000 per night) were included in this analysis. A data-driven approach was utilized to define the temporal boundaries of each night, divide each night into periods of sleep and fussing, and identify appropriate nights for inclusion. The derived data provide, in unprecedented resolution, a detailed longitudinal view of infant sleep in this specific population. Our results generally align with previous studies of traditional sleep metrics; however, they also demonstrate a methodological framework for descriptive or comparative monitoring of sleep and soothing, and uniquely characterize dyadic interactions that are not well-captured by traditional metrics. For example, the bassinet’s activity logs indicate not only the proportion of fussing episodes that are resolved without caregiver intervention (e.g., removal), but also reflect the delay between fussing and the need for caregiver intervention. Further evaluation of this sensor-enabled, responsive technology in relation to sleep and fussing is merited. Full article

Buildings account for 40% of global energy consumption and one-third of greenhouse gas emissions. Renewable energy systems (RESs), such as solar photovoltaic (PV) and geothermal heat pumps, are critical technological solutions for decarbonization. Despite the growing literature, existing reviews lack a comprehensive synthesis integrating machine learning (ML), Internet of Things (IoT), and Building Information Modeling (BIM). Following the PRISMA protocol, this paper presents a systematic review of 41 studies published between 2012 and 2025. The review evaluates four primary domains: RES performance, building energy prediction, HVAC optimization, and occupancy-aware management. Quantitative findings reveal that solar PV-integrated buildings achieve electricity cost reductions of 35–64%, while ML-enhanced energy prediction models attain accuracies up to R² = 0.989. Critical research gaps are identified, including the scarcity of real-time sensor integration and geographically inclusive multi-climate datasets. Ultimately, this review contributes a structured synthesis of effective technologies, a comparative analysis of methodological approaches (ML, simulation, hybrid), and actionable future directions. It provides practical guidance for researchers and policymakers toward achieving net-zero energy buildings. This study serves as a definitive reference for the development of sustainable, low-energy built environments. Full article

Solar energy has emerged as a promising alternative to fossil fuels for mitigating climate change; however, efficient photovoltaic (PV) operation requires continuous monitoring and accurate energy forecasting. This study proposes an intelligent IoT-based photovoltaic monitoring and short-term energy prediction system integrating real-time sensing, solar tracking, and machine learning techniques. A small-scale experimental prototype based on a 10 W photovoltaic panel was implemented to collect real-time data, including voltage, current, temperature, humidity, ultraviolet radiation, and dust accumulation during a 30-day monitoring period under outdoor conditions. The acquired data were transmitted through an IoT architecture based on the Arduino Uno and ESP32, programmed using Arduino IDE, and integrated with the Blynk cloud platform for real-time monitoring and analysis. To evaluate predictive performance, Random Forest, XGBoost, and LSTM models were trained and compared for photovoltaic energy forecasting. Experimental results showed that XGBoost achieved the best predictive performance, obtaining the lowest error values (MAE = 0.00077, RMSE = 0.001103) and the highest coefficient of determination (R² = 0.918), outperforming the other evaluated models. In addition, the proposed system enabled effective remote monitoring and degradation analysis associated with environmental conditions. The results demonstrate the potential of integrating IoT and machine learning for accurate short-term photovoltaic energy forecasting in small-scale experimental environments. Nevertheless, further long-term and large-scale validation is required to evaluate system robustness under operating conditions. Full article

The digital transformation of agrifood systems demands an integrated infrastructure to ensure traceability, trust, and intelligent decision-making across complex and heterogeneous value chains. METROFOOD-IT, a large-scale national research infrastructure in food metrology aligned with the ESFRI METROFOOD-RI, addresses these challenges by combining advanced experimental facilities with a comprehensive digital ecosystem. This paper focuses on the IT kernel of METROFOOD-IT and presents an integrated architectural model that brings together four key technological paradigms: data acquisition through Internet of Things (IoT) and laboratory infrastructures, an Open Data Platform for interoperability and sharing, blockchain-based notarization for integrity and provenance, and Artificial Intelligence (AI) for knowledge extraction and decision support. Rather than describing these components in isolation, the paper abstracts from their implementation within the Italian National Recovery and Resilience Plan (NRRP) project METROFOOD-IT to distill a coherent and reusable architectural pattern in which data management, trust enforcement, and intelligent analytics are tightly coupled. Five explicit design principles are identified and articulated: federated data with centralized metadata, selective on-chain anchoring, user-unobtrusive trust infrastructure, explainability as a first-class architectural concern, and machine learning as the backbone of decision-making. Two empirical case studies—one centered on explainable AI for hyperspectral crop nitrogen assessment and the other on IoT-driven sustainable agriculture monitoring secured by distributed ledger technology—serve a dual role: they motivate and shape the architectural pattern, and they exemplify the operational regimes the resulting design supports. A reference deployment on the Ethereum Sepolia public test network, grounded on an IBM Power E1050 and IBM Storage Scale enterprise substrate, provides quantitative evidence for the proposed hybrid on-chain/off-chain pattern with streaming hash-only notarization. The architecture illustrates how research infrastructures can evolve into integrated digital platforms that enable transparent, verifiable, and scalable agrifood systems, and offers a foundation for generalizable design principles in data-intensive and trust-sensitive settings. Full article

Conventional signature-based defenses no longer protect the heterogeneous, large-scale infrastructures that the Internet of Things (IoT) now constitutes. Large language models (LLMs) and agentic artificial intelligence (AI)—systems that autonomously perceive, reason, plan, and act—open a path to self-defending IoT ecosystems, but the integrating literature remains fragmented. Within the IEEE Xplore, ACM Digital Library, and MDPI literature, this survey is, to the best of our knowledge, among the first systematic reviews of agentic AI and LLM-driven approaches for autonomous IoT cybersecurity. Following a PRISMA 2020 protocol, we analyze 153 peer-reviewed studies published between 2020 and 2026 in IEEE Xplore, the ACM Digital Library, and MDPI journals. We organize the corpus along a four-pillar taxonomy: agent architecture (single- vs. multi-agent), reasoning strategy (chain-of-thought, ReAct, plan-and-solve, tool use), action scope (detection, response, threat hunting, vulnerability discovery, deception), and deployment topology (edge, fog, cloud). We synthesize four flagship application domains, consolidate datasets and benchmarks, and analyze open challenges including hallucination, prompt-injection robustness, explainability, privacy, latency, and governance. A 2026 research roadmap identifies federated agentic learning, verifiable autonomous reasoning, trustworthy multi-agent collaboration, and resource-hardened edge agents as high-priority directions. A companion reproducibility kit—prompt templates, reference single- and multi-agent loops, and an Edge-IIoTset-style evaluation harness, released as illustrative scaffolding rather than a validated framework—is released publicly and archived on Zenodo (DOI 10.5281/zenodo.20726552). Full article

Developing-country construction sectors continue to record disproportionately high occupational accident rates, partly attributable to the slow adoption of digital safety technologies, including Building Information Modeling (BIM) and Internet of Things (IoT) systems. While prior empirical research has established the population-level factors that explain stakeholder adoption intention through survey-based frameworks, the ability to classify individual stakeholder readiness for targeted, pre-deployment intervention remains methodologically unaddressed. This study fills that gap by applying three supervised machine learning classifiers (Random Forest [RF], XGBoost (XGB), and Support Vector Machine (SVM)) to a dataset of 107 construction professionals purposively sampled from large-scale infrastructure projects in Pakistan, including China−Pakistan Economic Corridor (CPEC) packages and the Barakahu Bypass project. Five construct-level features derived from an integrated Technology Acceptance Model and Technology−Organization−Environment (TAM-TOE) survey instrument were used to classify stakeholders into High, Moderate, and Low readiness tiers. XGBoost achieved the best classification performance (accuracy = 93%, macro F1 = 0.93), followed by RF (91%, F1 = 0.91) and SVM (87%, F1 = 0.87). The convergent performance across three structurally different algorithm families indicates that the readiness signal reflects a consistent attitudinal pattern rather than an artifact of any single modeling assumption. Feature importance analysis consistently identified Perceived Benefits (32%) and Technology Awareness (25%) as the dominant predictive features, followed by Organizational Readiness (20%), Perceived Barriers (15%), and Respondent Profile (8%). Attitudinal readiness mapping classified 62% of stakeholders as High readiness, 28% as Moderate, and 10% as Low, providing an exploratory attitudinal segmentation framework to assist construction managers in prioritizing capacity-building investments, subject to longitudinal behavioral validation. The study also finds that awareness of digital technology consistently outpaces Organizational Readiness for implementation, a pattern consistent with findings from analogous developing-country construction contexts. Full article

Smart labels are emerging as a key enabling technology for product traceability, environmental monitoring, and user interaction within Internet of Things (IoT) ecosystems. This work presents the design and experimental validation of a low-power smart label platform integrating Bluetooth Low Energy (BLE) communication, temperature sensing, and dynamic e-paper visualization based on the HY0020 System-on-Chip (SoC). This platform was implemented on a custom Printed Circuit Board (PCB) designed around a 1.02-inch monochrome e-paper display and incorporates a TXS0108E interface to support reliable display communication. The developed prototype enables wireless user interaction, dynamic QR code rendering, and ambient temperature monitoring while maintaining low average power consumption. Experimental evaluation included BLE communication testing, display operation validation, temperature monitoring assessment using the integrated HY0020 sensor, and energy consumption characterization. Experimental results confirmed reliable BLE connectivity, stable temperature monitoring performance under normal environmental conditions, and an estimated battery lifetime of approximately 54 days under the evaluated operating profile. The presented platform demonstrates the feasibility of integrating sensing, wireless communication, and electrophoretic display technology within a compact battery-powered smart label device. The proposed architecture provides a practical proof-of-concept foundation for future applications involving product traceability, digital information management, and Digital Product Passport (DPP)-oriented services. Full article

Efficient task assignment for mobile robots is a crucial challenge in modern intralogistics. This paper presents an integrated cyber-physical framework combining predictive tree search on switching max-plus linear systems with a physical IoT-based dispatch interface. The scheduling problem is modelled as a discrete event system, where standard max-plus algebra captures robot synchronization, and a switching mechanism represents alternative resource assignments. To address real-world operational disturbances, the predictive model is enhanced with a fault-tolerant control (FTC) mechanism that dynamically estimates and adapts to non-stationary transport delays. The resulting decision space, which grows exponentially with the prediction horizon, is explored via a predictive tree search algorithm utilizing a quadratic cost function to penalize excessive and uneven transport times. The physical dispatch layer is realized using KIS.BOX IoT devices acting as operator-controlled stations, communicating with the central controller via a WebSocket/STOMP event stream and a lightweight REST API. Simulation results obtained in a Blender 3D environment demonstrate that the proposed FTC predictive strategy significantly reduces the variance of task completion times under fault conditions compared to a baseline First-In-First-Out approach. Furthermore, the IoT integration successfully simulates and validates the feasibility of human-in-the-loop task injection within a realistic, stochastic scenario. Full article

Blockchain-assisted authentication frameworks have been introduced to mitigate the single point-of-failure problem in centralized IoT collaboration environments. Recently, a lightweight trust management framework based on a permissioned blockchain was proposed for distributed authentication and interaction traceability. However, our analysis shows that this protocol is vulnerable to offline password guessing, terminal device impersonation, session-key disclosure, and user traceability attacks. It also fails to provide perfect forward secrecy. Accordingly, we propose a secure and lightweight authentication and key agreement protocol for blockchain-assisted IoT collaboration environments. The proposed scheme integrates Physically Unclonable Functions to improve resistance against physical capture and device cloning attacks. It also uses a fuzzy extractor to support biometric-based authentication and a dynamic pseudo-identity update mechanism managed through a consortium blockchain to protect user anonymity and untraceability. The proposed protocol is verified using the Real-or-Random model, BAN logic, and AVISPA simulations. Full article