Search Results (948)

The rapid digitalization of energy systems and the increasing integration of distributed energy resources, renewable energy technologies, and prosumer-oriented infrastructures have accelerated the development of IoT-enabled Smart Grids as a foundation for intelligent and adaptive energy management. Modern Smart Grids increasingly depend on the coordinated interaction of IoT architectures, artificial intelligence, distributed analytics, and decentralized control mechanisms to ensure reliability, scalability, and real-time operational flexibility. Despite extensive research activity, existing studies remain predominantly technology-centric, focusing on isolated architectural layers or individual intelligent methods without providing a unified system-level perspective on their coordinated operation and interoperability. This article presents a system-level integrative review and challenge-oriented comparative synthesis of intelligent operational and architectural foundations of IoT-enabled Smart Grids. The study analyzes data-driven, model-driven, knowledge-driven, agent-based, and hybrid-oriented intelligent paradigms within multi-layer IoT energy infrastructures. In addition, the research establishes a cross-layer mapping between Smart Grid operational challenges, enabling technologies, and corresponding analytical approaches while identifying interoperability constraints, scalability limitations, and coordination challenges associated with decentralized energy ecosystems. The conducted synthesis demonstrates that hybrid-oriented intelligent approaches represent the most promising direction for future Smart Grid evolution due to their ability to integrate AI, ML, digital twins, semantic reasoning, and decentralized multi-agent coordination within unified IoT architectures. The conducted comparative synthesis identifies the ongoing transition from isolated intelligent solutions toward integrated hybrid cyber–physical energy ecosystems and highlights key characteristics of future adaptive, interoperable, scalable, and explainable Smart Grid architectures. Full article

The construction sector is undergoing a rapid transition toward more resilient, sustainable, and digitally connected systems, creating increasing demand for materials capable of providing functions beyond conventional structural performance. In this context, smart materials have emerged as promising solutions due to their ability to respond to mechanical, thermal, chemical, or electromagnetic stimuli through adaptive behaviors such as self-healing, structural sensing, energy regulation, vibration control, and reversible deformation. Despite growing scientific interest, available knowledge remains fragmented across specific material families and isolated application domains. Therefore, this study presents a PRISMA-based systematic review of smart materials in construction using peer-reviewed journal literature indexed in Scopus during the 2021–2026 period. The review examines the principal smart material families currently applied in construction, including self-healing concretes, self-sensing cementitious systems, Shape Memory Alloys (SMA), piezoelectric materials, phase change materials, adaptive coatings, conductive nanocomposites, and multifunctional geopolymers. Their engineering functions, structural and architectural applications, reported performance characteristics, sustainability contributions, digital integration potential, and implementation barriers are comparatively discussed and qualitatively synthesized based on the reviewed literature. The findings indicate that smart materials can improve durability, structural health monitoring, seismic resilience, thermal efficiency, lifecycle performance, and carbon reduction when properly integrated into buildings and infrastructure. However, large-scale adoption remains constrained by high initial costs, manufacturing scalability, regulatory uncertainty, long-term durability validation, and limited market confidence. The review further shows that the greatest future potential lies in combining material intelligence with IoT platforms, artificial intelligence, BIM environments, and digital twins. Overall, smart materials are positioned as strategic enablers of next-generation low-carbon, adaptive, and intelligent construction systems. Full article

Precision agriculture increasingly relies on machine learning applied to high-resolution data acquired by unmanned aerial vehicles (UAVs) to support crop monitoring, stress detection, and yield forecasting. This survey presents a structured review of machine learning methods for UAV-enabled precision agriculture and organizes over 100 peer-reviewed studies within a unified four-dimensional taxonomy defined by sensing modality, data type, model family, and analytical task. The taxonomy enables systematic comparison across RGB, multispectral, hyperspectral, LiDAR, and IoT data sources and across classical machine learning, deep learning, hybrid sequential models, and emerging transformer-based architectures. We analyze how modeling choices interact with data characteristics to influence robustness, cross-environment generalization, computational efficiency, and deployment feasibility on UAV and edge platforms. Recurring challenges include limited labeled data, domain shift across seasons and fields, multimodal heterogeneity, occlusion, and real-time processing constraints. We identify emerging research directions, including data-efficient learning, representation-level multimodal fusion, domain adaptation, lightweight architectures for embedded deployment, and uncertainty aware decision support. By formalizing the landscape through a unified taxonomy, this survey provides a foundation for designing scalable, robust, and deployable machine learning systems for next-generation precision agriculture. Full article

Background/Objectives: Mucinous adenocarcinoma (MAC) is a rare and clinically problematic subtype of rectal cancer, tending to present at an advanced stage and to respond poorly to neoadjuvant therapy. The consistently worse prognosis than that of not-otherwise-specified adenocarcinoma (NOS-AC) is not fully understood, potentially owing to intrinsically more aggressive biology or specific immune evasion mechanisms. We used the IMMUNOREACT multicentre cohort, with external validation in TCGA, to investigate the clinical and immunological features of rectal MAC in detail. Methods: Two hundred patients with rectal adenocarcinoma (16 MAC, 184 NOS-AC) from the IMMUNOREACT 1 (NCT04915326) and IMMUNOREACT 2 (NCT04917263) prospective cohorts were included. To account for the imbalance in baseline characteristics, propensity score matching (PSM) was performed on age, sex, neoadjuvant treatment and TNM stage. The immune microenvironment was characterised using immunohistochemistry (CD3, CD4, CD8, CD8β, Tbet, FoxP3, PD-L1, MSH6, PMS2, CD80), flow cytometry and NanoString PanCancer IO 360™ transcriptomics of adjacent healthy mucosa. Findings were externally validated against TCGA rectal and colon adenocarcinoma datasets. Results: MAC presented at significantly more advanced stage than NOS-AC across all TNM parameters: higher T stage (p = 0.006), N stage (p < 0.001), M stage (p = 0.039) and overall TNM stage (p < 0.001). In the unmatched cohort, MAC was associated with worse overall survival (HR 2.53; 95% CI 1.03–6.23; p = 0.043) and disease-free survival (HR 2.86; 95% CI 1.25–6.55; p = 0.013), but both differences became non-significant after PSM. MAC patients had higher haemoglobin after adjusting for confounders (mean difference [MD] 1.26 g/dL, 95% CI 0.30–2.31, p = 0.012), consistent with a hypothesis of reduced chronic rectal bleeding as a possible mechanism for late presentation. Transcriptomically, MAC showed suppression of HLA class II antigen presentation genes (HLA-DQA1, HLA-DQB1, HLA-DRB1) and myeloid activation genes (S100A8/A9/A12) in adjacent healthy mucosa. Loss of MMR proteins MSH6 and PMS2 in histologically normal mucosa was significantly more frequent in MAC. These findings were replicated in the TCGA cohort, which also showed lower tumour mutational burden and a distinct mucin-associated transcriptomic profile in MAC. Conclusions: The worse outcomes of rectal MAC appear to be driven largely by late-stage presentation, possibly owing to later diagnosis. MAC nonetheless carries a distinct immune phenotype, detectable even in histologically normal surrounding mucosa, that likely contributes to its treatment resistance. These observations provide a basis for developing histotype-specific approaches to both early detection and treatment in this uncommon but clinically challenging tumour subtype. Full article

Distributed seismic sensor networks integrated into the Internet of Things (IoT) infrastructure enable continuous condition monitoring of large-scale engineering structures. During long-term operation, however, measurement channels are subject to sensitivity drift, increased noise, and pulse artifacts that statistically mimic real vibration events. Related deep-learning techniques for noisy and ill-posed inverse problems have demonstrated the value of combining principled physical priors with deep models. Although the application domain differs, the underlying methodological insight—that constrained, physics-aware feature mappings can stabilize learning under noisy and partially observed conditions—directly motivates the use of a parameterized quantum circuit as a nonlinear feature transformer in the present work, where Hilbert space mapping serves as an analogous structural prior for the latent representation. Three principal fault modes are considered in this work, corresponding to the dominant degradation mechanisms observed in long-term seismic instrumentation: sensor drift, increased noise, and sensor failure. Each fault mode produces a distinct signature in the windowed feature space; the proposed model is trained to discriminate between them based on the latent CNN-LSTM-VQC representation. We propose a hybrid quantum-inspired deep-learning model (QC-DL) for the detection and diagnosis of channel-degradation anomalies. The architecture combines a 1D-CNN+LSTM feature extractor with a parameterized variational quantum circuit (VQC) used as a nonlinear feature transformer. All quantum experiments were performed on the QPanda3 CPUQVM simulator. The data were split chronologically prior to windowing to avoid information leakage. On real-world labeled accelerometric data with four operating modes (normal/drift/high-noise/failure), the QC-DL model achieved a macro-averaged F1 score of approximately 0.69 and per-class AUC values in the range 0.88–0.99. The mean early-detection latency was 1.6 s versus 2.1 s for the CNN-LSTM baseline (~24% reduction). An ablation study against a parameter-matched classical MLP showed that the gain is modest and not solely attributable to additional nonlinearity. The reported p-values (p = 0.70, p = 0.29) do not establish statistical significance. The results support the feasibility of hybrid quantum-inspired deep learning for sensor-channel verification, while highlighting the need for evaluation on real NISQ hardware. This paper proposes a hybrid quantum-inspired approach for detecting and diagnosing such anomalies in the time series of distributed seismic networks. The architecture combines a classical temporal feature extraction module based on one-dimensional convolutional layers and a recurrent long short-term memory (LSTM) network, which generates a latent window representation of the signal, with a parameterized variational quantum circuit used as a nonlinear feature processor in a hybrid computational circuit. Experimental validation was performed on real-world labeled data with multiple sensor degradation modes. The evaluation was organized in a scoring framework aligned with autonomous operation through window ranking and threshold alarm generation. In the experiments, the proposed model provided a macro-averaged F1 score of approximately 0.69 and area under the receiver operating characteristic (AUC) curve values in the range of 0.88–0.99 across classes, outperforming baseline deep models. The average early detection latency was 1.6 s versus 2.1 s for the baseline recurrent model (a 24% reduction). An ablative comparison with a control model based on a classical multilayer perceptron of comparable dimension confirmed that the improvement is not limited to the addition of additional nonlinearity. The obtained results indicate the potential of quantum-supported deep learning for improving the reliability of long-term vibration monitoring and verifying the correctness of sensor channels in distributed seismic networks. Full article

This paper proposes a lightweight and adaptive anomaly detection framework for Internet of Things (IoT) time-series data that progressively combines statistical filtering with reinforcement learning (RL)-based decision mechanisms. Three classical statistical filters, Hampel, interquartile range (IQR), and Z-score, are initially evaluated under controlled IoT anomaly scenarios. While fixed-parameter configurations perform well under specific conditions, their performance degrades in non-stationary and heterogeneous environments. To address this limitation, a tabular Q-learning agent is introduced to dynamically select both filtering methods and their associated parameters according to scenario-specific signal characteristics. By extending the action space to include joint filter and parameter selection, the framework improves adaptability while reducing the need for manual tuning. A multi-agent reinforcement learning (MARL) formulation is further introduced to support distributed learning across heterogeneous IoT environments. The framework is additionally evaluated using real-world IoT temperature data augmented with controlled anomaly injection, enabling reproducible benchmarking under partially realistic sensing conditions. Experimental results show that both RL and MARL maintain stable detection performance across heterogeneous sensor streams. While MARL does not systematically outperform the single-agent approach in detection accuracy, it improves scalability and supports scenario-specific policy specialization, which is particularly relevant for distributed IoT deployments. Overall, the proposed approach provides a lightweight, interpretable, and computationally efficient solution for adaptive anomaly detection in resource-constrained IoT systems. Full article

Microwave wireless power transfer (MWPT) is a promising technology for powering dedicated industrial Internet of Things (IoT) devices, enabling battery-free operation. However, in realistic MWPT deployments, the received RF signals fluctuate drastically due to varying transmission distances and multipath fading. Additionally, the equivalent impedance of sensor nodes varies significantly during duty cycles, shifting between a low-resistance active state and a high-resistance sleep state. Consequently, maintaining high rectification efficiency under these dynamic conditions remains a critical challenge. This paper proposes a high-efficiency rectifier with a wide input power and load range based on the suppression of second and third harmonics. The rectifier adopts a dual-diode parallel configuration. By leveraging the impedance compensation characteristics of two short-circuited stubs with distinct electrical lengths, it simultaneously achieves fundamental-frequency impedance matching and harmonic suppression without the need for an additional matching network. Validated through theoretical derivation, simulation analysis, and physical prototype testing, the proposed 2.45 GHz rectifier realizes high-efficiency rectification over a wide dynamic range. Experimental results demonstrate that the power dynamic range reaches 10 dB when the rectification efficiency exceeds 70%, and extends to 17 dB when the efficiency is above 60%. Furthermore, the rectification efficiency is insensitive to load variations (100–1200 Ω), making it highly suitable for powering wireless sensor nodes with varying operating modes in complex electromagnetic environments. Full article

The integration of blockchain technology into the Internet of Things (IoT) offers a decentralized paradigm for data integrity. However, the emergence of 51% attacks—driven by hashrate concentration—threatens the foundational trust of these resource-constrained networks. In resource-constrained IoT-enabled blockchain environments, mining-power asymmetry and limited governance capability may amplify the impact of strategic attacks. These characteristics motivate the need to analyze long-term adversarial behavior and governance effectiveness under repeated interactions. This paper develops a threshold-based analytical framework that integrates a single-stage decision model and a multi-stage discounted decision model to analyze 51% attack decisions and governance effects in asymmetric blockchain mining environments. We characterize the interaction between competing mining pools as a multi-stage game, integrating key parameters such as the discount factor of future utility and recovery penalty cycles. Our analysis demonstrates that a multi-stage framework creates a “long-term deterrent effect” where the net present value of potential future losses outweighs the immediate gains of hashrate abuse. analytical results indicate that the strategic threshold for launching an attack is highly sensitive to the duration of punitive measures and the accuracy of IoT-based anomaly detection. The results provide useful insights into the design of governance and incentive mechanisms for blockchain systems deployed in resource-constrained and heterogeneous environments. Full article

This study examines elementary school parents’ perceptions, expectations, concerns, and preferences regarding the use of Internet of Things (IoT) systems from an administrative perspective. Using survey data from 453 parents with at least one child attending an elementary school, descriptive statistics, independent samples t-tests, and one-way analyses of variance were conducted to examine differences according to parental characteristics. Overall, parents reported relatively low-to-moderate levels of familiarity with IoT systems. Parental educational background and child grade level significantly influenced perceptions, with more highly educated parents and parents of younger children reporting more favorable views. Younger parents expressed more positive expectations regarding IoT systems, but also greater concern about personal information leakage. Parents showed strong demand for IoT applications related to emergency detection, environmental management, nutrition, and safety and preferred shared funding arrangements between schools and parents within clearly defined affordability thresholds. These findings suggest that parental acceptance of school-based IoT systems is conditional and shaped by perceptions of administrative relevance, governance quality, privacy safeguards, and cost fairness. Full article

This study presents a short-term, IoT-enabled sensor-based assessment of treated municipal water and decentralized groundwater in Bragança, northeastern Portugal. Two drinking-water supply contexts were compared: treated surface-water-derived municipal water from the public supply system and groundwater from a decentralized supply system serving part of a higher education campus. Five sampling points were monitored during three campaigns between January and March 2026. At each point, pH, electrical conductivity, temperature, oxidation–reduction potential, and total dissolved solids were recorded at 10 s intervals over approximately 10 min monitoring windows using a multiparameter probe integrated into an IoT-enabled data acquisition workflow. Microbiological analyses were performed on groundwater samples as complementary information. Treated municipal water showed lower mineralization, narrower parameter ranges, and higher oxidation–reduction potential, reflecting source-water characteristics, treatment, and operational control. Groundwater showed higher mineralization, lower oxidation–reduction potential, and greater variability among sampling points and campaigns, consistent with stronger local hydrogeochemical and operational influences. The repeated short-interval readings provided more detailed physicochemical profiles than isolated spot measurements, although the short monitoring windows do not represent continuous long-term high-frequency monitoring. Overall, the results support standardized IoT-enabled sensor-based monitoring as a complementary tool for short-term water-quality assessment and indicate the need for longer seasonal datasets and laboratory confirmation. Full article

As the Industrial Internet of Things becomes increasingly interconnected with critical infrastructure, intrusion traffic exhibits characteristics such as high-dimensional redundancy, class imbalance, and temporal correlation, posing challenges for detection systems in terms of feature representation, model complexity control, and real-time performance. To address the aforementioned issues, this paper proposes an intrusion detection algorithm based on feature optimization and asynchronous advantage actor-critic learning (APA3CID). First, the raw dataset was preprocessed using methods such as label encoding and normalization. Feature selection was performed using the improved Whale Optimization Algorithm (WOA) to reduce data redundancy and eliminate irrelevant features. The samples were then serialized based on the order in which they were collected. Second, we model the detection process as a Markov decision process, use a sliding window to construct states that capture recent temporal features, and, building upon the Asynchronous Advantage Actor-Critic (A3C) framework, we incorporate an adaptive exploration mechanism to address the issues of insufficient exploration in the early training phase and unstable convergence in the later phase. Additionally, we introduce an asynchronous lag correction strategy that utilizes truncated importance weights to mitigate the bias caused by policy lag in asynchronous parallel training, thereby enhancing the stability and robustness of policy updates. Finally, experimental results show that on the X-IIoTID dataset, APA3CID achieves a 3.51% increase in detection rate and a 4.26% increase in F1-score compared to the traditional A3C algorithm. On the WUSTL-IIoT-2021 dataset, single-sample prediction takes as little as 11.56 microseconds, with Acc, DR, and F1-score all exceeding 90%. This outperforms comparison models such as LR, XGBoost, CNN, and the baseline A3C, meeting the requirements of industrial IoT scenarios for low false-negative rates and high real-time performance. Full article

The growth of Internet of Things (IoT) environments has expanded the attack surface of modern systems. Trojan attacks are a major challenge as they evade conventional detection mechanisms and operate silently within legitimate processes. This paper presents an automated Trojan detection framework for Windows-based IoT gateways. The framework combines custom dataset generation informative feature engineering and deep learning-driven analysis. A dataset of 3000 real world executable samples was created through controlled sandbox execution and forensic monitoring. The process captured behavioral static and network-level characteristics. An initial set contained 146 extracted features. A multi-stage feature selection process identified 33 informative attributes. This step allowed efficient learning and preserved discriminative power. A custom deep neural network model named TrDNN was developed using these features. The model captures complex nonlinear patterns linked to Trojan activity. The framework was evaluated against five classical machine learning models. It was also compared with five deep learning baselines. Results show that TrDNN achieves strong detection performance. The accuracy is 0.975. The precision is 0.972. The recall is 0.969. The F1 score is 0.970. The study also examines inference time and energy consumption. The model shows a balance between detection effectiveness, computational cost and energy efficiency. This makes it suitable for resource-constrained IoT gateway deployment. The detection model was integrated into an automated real-time monitoring pipeline. The system enables continuous process surveillance through Windows command line automation with minimal operational overhead. Statistical validation used paired t tests, Wilcoxon signed rank tests and McNemar chi-square test. The performance gains are statistically significant and do not indicate overfitting. The framework provides a reliable, efficient and deployable solution for Trojan detection in modern IoT systems. Full article

Attributed graphs have recently emerged as a powerful tool for representing diverse data in numerous real-world sensors. Among various applications, unsupervised graph anomaly detection (UGAD) aims to identify abnormal data that significantly deviate from the majority of normal nodes without label annotations. Hence, UGAD can provide crucial assistance in enhancing the reliability of IoT, intelligent sensors and so on. Under the class-imbalanced reality caused by anomaly scarcity, the common paradigm of UGAD focuses on learning a model that primarily captures normal patterns. However, the traditional Graph Neural Network (GNN) paradigm suffers from local-aggregation limitations and over-smoothing, constraining their discrimination capacity. To address these issues, we introduce Graph Transformers (GTs) into UGAD task, termed as unsupervised attributed graph Anomaly detectioN wIth Masked grAph TransformErs (ANIMATE). Leveraging the global receptive field of Transformers, we can capture graph information that preserves the distinguishable characteristics of abnormalities from a global perspective. Furthermore, we employ masked auto-encoders to reconstruct node features and prompt our model to focus more on learning normal patterns. Additionally, we enhance the performance through a self-paced enhancement scheme specifically for UGAD tasks. Experiments conducted on various real-world benchmark datasets with organic anomalies validate the effectiveness of our proposed method compared to state-of-the-art competitors. Full article

The rapid proliferation of Internet of Things (IoT) devices in cloud-based e-learning platforms has posed significant security risks, particularly in protecting learner information, authentication of devices, and safe communication in the highly heterogeneous learning settings. Current cryptographic solutions are largely based on classical public-key infrastructure (PKI) protocols such as RSA and ECC, which will become vulnerable with the advent of large-scale quantum computers capable of executing Shor’s algorithm. In addition, traditional perimeter-based security models are inadequate for handling the dynamics, scattered, and resource-limited characteristics of IoT-enabled educational systems. As a solution to these problems, this paper introduces ZeroTrustEdu, a scalable zero-trust cryptographic solution that combines lightweight post-quantum key management with adaptive trust scoring of cloud-connected IoT e-learning infrastructure. The proposed framework makes three fundamental contributions namely: (1) a hierarchical zero-trust security model with no implicit trust, operating across device, edge, and cloud layers; (2) a lightweight key distribution protocol based on the Module-Lattice Key Encapsulation Mechanism (ML-KEM) compliant with NIST FIPS 203 standards and (3) an adaptive behavioral trust scoring engine that dynamically adjusts device and user trust levels based on real-time interaction analytics. The architecture is evaluated using extensive NS-3 network simulations with up to 100,000 concurrent IoT nodes with formal security analysis under Chosen Plaintext Attack (CPA) and Chosen Ciphertext Attack (CCA) threat models. Comparative evaluation against RSA-2048, ECC-P256, and AES-256 baselines demonstrates that, ZeroTrustEdu delivers a 62% ± 3% (95% CI, 10 independent runs) reduction in ML-KEM encapsulation latency (12.8 ms for key encapsulation/decapsulation, contributing to a complete device authentication latency of 47.3 ms including ML-DSA signature operations), 45% reduced communication overheads, and 38% reduction in energy consumption on ARM Cortex-M4 constrained devices compared to RSA-2048 and achieves provable post-quantum security reducible to the hardness of the Module Learning With Errors (MLWE) problem. These findings demonstrate that the proposed architecture provides a viable, scalable, and quantum-resilient security solution for next-generation IoT-enabled e-learning environments. The cryptographic security of ZeroTrustEdu is guaranteed at the primitive level through NIST-standardized ML-KEM (FIPS 203) and ML-DSA (FIPS 204), with IND-CCA2 and EUF-CMA security formally proven in the respective standards; full protocol-level formal verification using automated theorem provers (ProVerif, Tamarin) is identified as valuable future work to rule out protocol-composition vulnerabilities beyond primitive-level guarantees. Full article

Asymmetry is a prevalent phenomenon in low-power and lossy networks (LLNs) due to resource constraints and unstable links. The routing protocol for the low power and lossy network (RPL), standardized by the Internet Engineering Task Force (IETF), is specifically designed for LLNs with characteristics of resource constraints, lossy links, and complex communication environments. However, its performance is fundamentally limited by node capabilities and unstable links, a contradiction exacerbated by the stringent QoS demands of emerging applications like IIoT or precision agriculture. Consequently, new RPL routing technologies based on the digital twin-enabled computing power network, called RPL-DTCP, were designed to improve network QoS and support practical applications. First, a low-power and lossy network architecture based on twin-enabled computing network was proposed, considering LLN requirements and computing twin services. Second, in response to the requirements of the digital twin, computing power network and LLNs for low synchronization latency, high data accuracy, efficient computing resource utilization, and energy conservation, several routing metrics were designed, including the data processing model, model deployment rate, end-to-end delay, node remaining energy, and ETX. Then an initial matrix and a comprehensive objective function were formulated to comprehensively evaluate these metrics. Third, to solve the multi-objective optimization problem, an enhanced whale optimization algorithm (E-WOA) was developed. E-WOA improved upon the standard version by using improved Tent chaotic mapping for population initialization, nonlinear adaptive convergence factor, and Cauchy variation mutation operator for solution perturbation, thereby enhancing its global search capability and convergence speed, enabling it to effectively identify the optimal routing path. Simulations confirmed that RPL-DTCP outperforms benchmark algorithms, achieving significant reductions in end-to-end delay, higher packet delivery ratios, extended network lifetime, etc. These findings demonstrate that RPL-DTCP effectively addresses the resource-performance contradiction in LLNs, providing a reliable and efficient routing framework for emerging compute-intensive IoT applications. Full article