Search Results (13,665)

The growing need for sustainable energy practices necessitates technology-driven interventions that can effectively bridge the disparity between consumer intentions and actual behavior. This paper formulates and empirically substantiates an IoT-enabled digital nudge architecture designed to promote sustainable energy behavior. The architecture provides goal-setting, social comparison, feedback, and informational nudges across multiple digital channels, utilizing linked devices, data processing layers, and a rule-based nudge engine. An 815-responder survey was analyzed using structural equation modeling with partial least squares (SEM-PLS) to identify the drivers of sustainable energy behavior and explore technology readiness as a moderating factor. The results show that nudges utilizing the Internet of Things (IoT) significantly enhance the alignment between intention and behavior. Goal-setting and feedback mechanisms have the highest effects. The findings also demonstrate that being ready for new technology improves nudge response, highlighting the importance of user-centered system design. This paper presents a scalable infrastructure for integrating IoT into sustainability projects, as well as theoretical contributions to technology adoption and behavioral intervention research. The study enhances the dialogue on environmental technology by illustrating the implementation of digital nudges through IoT infrastructures to expedite progress toward the Sustainable Development Goals (SDGs). Full article

The increasing global demand for electricity has heightened the need for stable and reliable power distribution systems. Disruptions in power distribution can cause substantial economic losses and societal impact, underscoring the importance of accurate, timely, and scalable monitoring. The integration of Internet of Things (IoT) technologies into smart grids offers promising capabilities for real-time data collection and intelligent control. However, the application of IoT has created new challenges such as high communication overhead and insufficient user privacy protection due to the continuous exchange of sensitive data. In this paper, we propose a method for power distribution analytics in smart grids based on IoT called PSDA. PSDA collects real-time power usage data from IoT sensor nodes distributed across different grid regions. The collected data is spatially organized using Hilbert curves to preserve locality and enable efficient encoding for subsequent processing. Meanwhile, we adopt a dual-server architecture and distributed point functions (DPF) to ensure efficient data transmission and privacy protection for power usage data. Experimental results indicate that the proposed approach is capable of accurately analyzing power distribution, thereby facilitating prompt responses within smart grid management systems. Compared with traditional methods, our scheme offers significant advantages in privacy protection and real-time processing, providing an innovative IoT-integrated solution for the secure and efficient operation of smart grids. Full article

Prehabilitation programs for abdominal pre-operative patients are increasingly recognized for improving surgical outcomes, reducing post-operative complications, and enhancing recovery. Internet of Things (IoT)-enabled human movement monitoring systems offer promising support in mixed-mode settings that combine clinical supervision with home-based independence. These systems enhance accessibility, reduce pressure on healthcare infrastructure, and address geographical isolation. However, current implementations often lack personalized movement analysis, adaptive intervention mechanisms, and real-time clinical integration, frequently requiring manual oversight and limiting functional outcomes. This review-based paper proposes a conceptual framework informed by the existing literature, integrating Digital Twin (DT) technology, and machine learning/Artificial Intelligence (ML/AI) to enhance IoT-based mixed-mode prehabilitation programs. The framework employs inertial sensors embedded in wearable devices and smartphones to continuously collect movement data during prehabilitation exercises for pre-operative patients. These data are processed at the edge or in the cloud. Advanced ML/AI algorithms classify activity types and intensities with high precision, overcoming limitations of traditional Fast Fourier Transform (FFT)-based recognition methods, such as frequency overlap and amplitude distortion. The Digital Twin continuously monitors IoT behavior and provides timely interventions to fine-tune personalized patient monitoring. It simulates patient-specific movement profiles and supports dynamic, automated adjustments based on real-time analysis. This facilitates adaptive interventions and fosters bidirectional communication between patients and clinicians, enabling dynamic and remote supervision. By combining IoT, Digital Twin, and ML/AI technologies, the proposed framework offers a novel, scalable approach to personalized pre-operative care, addressing current limitations and enhancing outcomes. Full article

This study lays the foundation for a multidimensional framework aimed at facilitating the effective integration of Internet of Medical Things (IoMT) technologies into real-world health management systems. It critically examines the technological, organizational, and societal barriers that hinder this transition and identifies key enabling conditions, such as interoperability, user co-design, and ethical design principles, that promote sustainability, inclusiveness, and trust. By proposing a structured approach to integration, this paper aims to bridge the gap between innovation and long-term, reliable adoption across diverse healthcare contexts. Full article

The deployment of deep learning models in Internet of Things (IoT) systems is increasingly threatened by adversarial attacks. To address the challenge of effectively detecting adversarial examples generated by Generative Adversarial Networks (AdvGANs), this paper proposes an unsupervised detection method that integrates spatial statistical features and multidimensional distribution characteristics. First, a collection of adversarial examples under four different attack intensities was constructed on the CIFAR-10 dataset. Then, based on the VGG16 and ResNet50 classification models, a dual-module collaborative architecture was designed: Module A extracted spatial statistics from convolutional layers and constructed category prototypes to calculate similarity, while Module B extracted multidimensional statistical features and characterized distribution anomalies using the Mahalanobis distance. Experimental results showed that the proposed method achieved a maximum AUROC of 0.9937 for detecting AdvGAN attacks on ResNet50 and 0.9753 on VGG16. Furthermore, it achieved AUROC scores exceeding 0.95 against traditional attacks such as FGSM and PGD, demonstrating its cross-attack generalization capability. Cross-dataset evaluation on Fashion-MNIST confirms its robust generalization across data domains. This study presents an effective solution for unsupervised adversarial example detection, without requiring adversarial samples for training, making it suitable for a wide range of attack scenarios. These findings highlight the potential of the proposed method for enhancing the robustness of IoT systems in security-critical applications. Full article

Cloud monitoring systems combine physical sensors with cloud computing capabilities. Modern manufacturing techniques and smart factories under Industry 4.0 and Industry 5.0 call for the integration of monitoring systems as part of the broader digitization process. Digitization typically occurs by integrating external sensors onto existing legacy machines. Data obtained can be utilized in digital twins, simulations, machine learning models, and Industrial Internet Of Things (IIoT) applications. The adaptation of these new technologies usually stalls due to the reluctance of end users to make modifications to already existing equipment, the legacy equipment that is in use and does not provide the information needed, and the substantial costs of integrating new measuring systems that typically require additional IT infrastructure. Having identified the need for easily scalable affordable measurement systems, new disseminated systems that utilize cloud solutions and use 5G as an enabler for real-time communication are on the rise. This publication proposes a methodology, and tests and demonstrates a relevant manufacturing use case for integrating a non-invasive-to-IT-infrastructure, cloud-based and artificial intelligence-powered monitoring system focused on high performance applications. The proposed methodology has been evaluated in a real industrial environment. Full article

Forest height is a key structural parameter for evaluating ecological functions, biodiversity, and carbon dynamics. While LiDAR and Synthetic Aperture Radar (SAR) provide vertical structure information, their large-scale use is restricted by sparse sampling (LiDAR) and temporal decorrelation (SAR). Optical remote sensing offers complementary spectral information but lacks direct height retrieval. To address these limitations, we developed a multi-modal framework integrating GEDI waveform LiDAR, Sentinel-1 SAR (InSAR and PolSAR), and Sentinel-2 multispectral data, combined with machine learning, to estimate forest canopy height across Jiangsu Province, China. GEDI L2A footprints were used as training labels, and a suite of structural and spectral features was extracted from SAR, GEDI, and Sentinel-2 data as input variables for canopy height estimation. The performance of two ensemble algorithms, Random Forest (RF) and Gradient Tree Boosting (GTB) for canopy height estimation, was evaluated through stratified five-fold cross-validation. RF consistently outperformed GTB, with the integration of SAR, GEDI, and optical features achieving the best accuracy (R² = 0.708, RMSE = 2.564 m). The results demonstrate that InSAR features substantially enhance sensitivity to vertical heterogeneity, improving forest height estimation accuracy. These findings highlight the advantage of incorporating SAR, particularly InSAR with optical data, in enhancing sensitivity to vertical heterogeneity and improving the performance of RF and GTB in estimating forest height. The framework we proposed is scalable to other regions and has the potential to contribute to global sustainable forest monitoring initiatives. Full article

Hydroponics offers a promising alternative to soil-based agriculture, enabling higher yields, resource efficiency, and improved crop quality. This study compares traditional hydroponic setups with systems enhanced through the Internet of Things (IoT) framework using the Nutrient Film Technique and a proportional–integral controller, focusing on growth performance and environmental control. Systems incorporating Internet of Things technology achieved a growth rate of 0.94 cm/day versus 0.16 cm/day for conventional setups, due to precise water temperature control, optimized lighting, data acquisition, targeted nutrients, and reduced pest incidence. The integration of Industry 4.0 principles further enhances sustainable production and resource management. Statistical validation under diverse conditions is recommended. Future work will add environmental sensors, refine mechanical design, and explore machine learning for adaptive control, highlighting the potential of Internet of Things–based hydroponics to transform agriculture through intelligent, efficient, and eco-friendly cultivation. Full article

With the digital transformation of the construction industry toward intelligent construction, advanced digital technologies—including Artificial Intelligence (AI), Digital Twins (DTs), and Internet of Things (IoT)—increasingly support Human–Robot Collaboration (HRC), offering productivity gains while introducing new safety risks. This study presents a systematic review of digital technology applications and risk management practices in HRC scenarios within intelligent construction environments. Following the PRISMA protocol, this study retrieved 7640 publications from the Web of Science database. After screening, 70 high-quality studies were selected for in-depth analysis. This review identifies four core digital technologies central to current HRC research: multi-modal acquisition technology, artificial intelligence learning technology (AI learning technology), Digital Twins (DTs), and Augmented Reality (AR). Based on the findings, this study constructed a systematic framework for digital technology in HRC, consisting of data acquisition and perception, data transmission and storage, intelligent analysis and decision support, human–machine interaction and collaboration, and intelligent equipment and automation. The study highlights core challenges across risk management stages, including difficulties in multi-modal fusion (risk identification), lack of quantitative systems (risk assessment), real-time performance issues (risk response), and weak feedback loops in risk monitoring and continuous improvement. Moreover, future research directions are proposed, including trust in HRC, privacy and ethics, and closed-loop optimization. This research provides theoretical insights and practical recommendations for advancing digital safety systems and supporting the safe digital transformation of the construction industry. These research findings hold significant important implications for advancing the digital transformation of the construction industry and enabling efficient risk management. Full article

The proposed forest monitoring photo trap ecosystem integrates a cost-effective architecture for observation and transmission using Internet of Things (IoT) technologies and long-range digital radio systems such as LoRa (Chirp Spread Spectrum—CSS) and nRF24L01 (Gaussian Frequency Shift Keying—GFSK). To address low-bandwidth links, a novel approach based on the Monte Carlo sampling algorithm enables progressive, bandwidth-aware image transfer and its thumbnail’s reconstruction on edge devices. The system transmits only essential data, supports remote image deletion/retrieval, and minimizes site visits, promoting environmentally friendly practices. A key innovation is the integration of no-reference image quality assessment (NR IQA) to determine when thumbnails are ready for operator review. Due to the computational limitations of the Raspberry Pi 3, the PIQE indicator was adopted as the operational metric in the quality stabilization module, whereas deep learning-based metrics (e.g., HyperIQA, ARNIQA) are retained as offline benchmarks only. Although single-pass inference may meet initial timing thresholds, the cumulative time–energy cost in an online pipeline on Raspberry Pi 3 is too high; hence these metrics remain offline. The system was validated through real-world field tests, confirming its practical applicability and robustness in remote forest environments. Full article

The rapid advancement of 5G/6G networks and the Internet of Things has rendered mobile traffic patterns increasingly complex and dynamic, posing significant challenges to achieving precise cell-level traffic forecasting. Traditional deep learning models, such as LSTM and CNN, rely heavily on substantial datasets. When confronted with new base stations or scenarios with sparse data, they often exhibit insufficient generalisation capabilities due to overfitting and poor adaptability to heterogeneous traffic patterns. To overcome these limitations, this paper proposes a meta-learning framework—GMM-MCM-NF. This framework employs a Gaussian mixture model as a probabilistic meta-learner to capture the latent structure of traffic tasks in the frequency domain. It further introduces a multi-component synthesis mechanism for robust weight initialisation and a negative feedback mechanism for dynamic model correction, thereby significantly enhancing model performance in scenarios with small samples and non-stationary conditions. Extensive experiments on the Telecom Italia Milan dataset demonstrate that GMM-MCM-NF outperforms traditional methods and meta-learning baseline models in prediction accuracy, convergence speed, and generalisation capability. This framework exhibits substantial potential in practical applications such as energy-efficient base station management and resilient resource allocation, contributing to the advancement of mobile networks towards more sustainable and scalable operations. Full article

Improving the energy efficiency of traditional production methods while preserving their cultural and economic value is a challenge aligned with the Sustainable Development Goals of the 2030 agenda. Refrigeration during cheese maturation is particularly energy-intensive, contributing significantly to greenhouse gas emissions and operating costs. An approach to make traditional cheese production more sustainable, through the development of a prototype ripening chamber with a natural refrigerant-based refrigeration system powered by renewable energy was studied. A dedicated system based on an Internet of Things architecture was developed using low-cost sensors, microcontroller units, and single-board computers to enable real-time measurement and monitoring of environmental variables and energy consumption throughout the ripening process. A comparative analysis was conducted using ewe’s milk cheese, produced and ripened with Protected Designation of Origin conditions, in both the prototype and the conventional chambers over four weeks, quantifying energy consumption and evaluating product quality. Results demonstrate the technical feasibility of energy efficient and sustainable refrigeration systems, as well as the possibility of retrofitting installed cheese ripening chambers with affordable IoT monitoring systems, while maintaining traditional cheese quality standards. Full article

Ammonia is a gas primarily produced for use in agriculture, refrigeration systems, chemical manufacturing, and power generation. Despite its benefits, improper management of ammonia poses significant risks to human health and the environment. Consequently, monitoring ammonia is essential for enhancing industrial safety and preventing leaks that can lead to environmental contamination. Given the abundance and diversity of studies on Internet of Things (IoT) systems for gas detection, the main objective of this paper is to systematically review the literature to identify emerging research trends and opportunities. This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, focusing on sensor technologies, microcontrollers, communication technologies, IoT platforms, and applications. The main findings indicate that most studies employed sensors from the MQ family (particularly the MQ-135 and MQ-137), microcontrollers based on the Xtensa architecture (ESP32 and ESP8266) and ARM Cortex-A processors (Raspberry Pi 3B+/4), with Wi-Fi as the predominant communication technology, and Blynk and ThingSpeak as the primary cloud-based IoT platforms. The most frequent applications were agriculture and environmental monitoring. These findings highlight the growing maturity of IoT technologies in ammonia sensing, while also addressing challenges like sensor reliability, energy efficiency, and development of integrated solutions with Artificial Intelligence. Full article

Particulate matter (PM) poses serious health risks, including respiratory and cardiovascular diseases, and is classified as a carcinogen by the World Health Organization and International Agency for Research on Cancer. Roadside air pollution, which is strongly affected by traffic emissions, is a major contributor to urban air quality deterioration. This study investigated the feasibility of establishing a low-cost, Internet of Things (IoT)-based, high-density monitoring network for roadside PM₁₀ and PM_2.5 to support safer and more sustainable road environments. We developed low-cost IoT sensing devices, deployed them at three urban roadside sites with different environmental conditions, and compared their performances with those of nearby public monitoring stations. One-minute resolution data were analyzed using Pearson correlation, cross-correlation, dynamic time warping, Z-score, and the roulette index. The IoT sensor data were strongly correlated with public station data, confirming its reliability as a complementary observation method. Notable site-specific patterns were sharp concentration increases with traffic at an intersection and distinct diurnal and weekly cycles at residential and rooftop sites. These findings demonstrate that low-cost IoT sensing can complement sparse public networks by providing microscale air quality information. This approach offers a practical foundation for smart city development and intelligent roadside environmental management. Full article

As the global demand for water rises and climate pressures intensify, projections indicate that water scarcity will impact nearly 40% of the world’s population by 2030 and a quarter of all children by 2040. This study reviews the current literature on residential water efficiency, highlighting the most effective strategies for reducing water waste. A systematic literature review—guided by transparent criteria and quality assessments using the Critical Appraisal Skills Program (CASP)—was conducted to extract insights into water distribution management strategies. This study examines current smart water management initiatives aimed at reducing waste, with a particular focus on the policy and regulatory drivers behind global water conservation efforts. Furthermore, it shows innovative smart solutions such as Artificial Intelligence (AI)-powered forecasting, Internet of Things (IoT)-based metering, and predictive leak detection, which have demonstrated reductions in residential water loss by up to 30%, particularly through real-time monitoring and adaptive consumption strategies. The study concludes that innovative technologies must be actively supported and implemented by governments, utilities, and global organizations to proactively reduce water waste, safeguard future generations, and enable data-driven, AI-powered policy and decision-making for improved water use efficiency. Full article