Search Results (868)

Compact wireless electronic devices require charging interfaces that can support different receiver positions and orientations within limited spaces. In this context, a double-sided mixed-coupling structure can provide independent magnetic- and electric-field power-transfer paths by combining coil-based and plate-based coupling mechanisms. This paper proposes a double-sided mixed-coupling wireless power transfer (DMPT) coupler for compact wireless electronic devices related to the Internet of Things (IoT) and the Internet of Drones (IoD). The proposed coupler integrates an upper coil-based magnetic-field coupling path and a lower stacked-plate-based electric-field coupling path within a single transmitter structure. Through this configuration, inductive wireless power transfer (IPT) and capacitive wireless power transfer (CPT) are implemented as independent double-sided power-transfer paths. To analyze the resonant behavior, a three-port equivalent circuit including mutual inductance and mutual capacitance is developed, and the resonance splitting under the uncompensated condition is investigated using even/odd mode decomposition. The predicted resonant frequencies agree with the ANSYS HFSS results with errors of 0.16% and 1.12%. After series-L compensation, the 60 × 60 × 7.31 mm³ coupler operates at the 6.78 MHz industrial, scientific, and medical band, showing S₁₁ ≈ 0.042, S₂₁ ≈ 0.68, and S₃₁ ≈ 0.64 under the double-sided aligned condition. Field and transient waveform analyses further verify that the upper H-coupling region and lower E-coupling region operate simultaneously while being spatially separated. The proposed DMPT coupler provides a coupler-level design framework for implementing IPT and CPT as independent double-sided coupling paths. Full article

The increasing connectivity of Internet of Things (IoT)-oriented environments has made them more vulnerable to cyberattacks, requiring intrusion-detection systems (IDSs) to ensure their secure and reliable operation. The feature selection (FS) process of an IDS affects its performance, as effective FS can enhance detection accuracy and reduce the computational cost and model complexity. This paper presents Adaptive Hybrid Genetic Algorithm-Simulated Annealing (AHGA-SA) as an FS framework that integrates the global search ability of a genetic algorithm and the local exploitation ability of simulated annealing. AHGA-SA aims to find compact, informative feature subsets in high-dimensional intrusion-detection datasets at an acceptable computational cost while maintaining detection performance. The experimental results on three recent benchmarks demonstrate feature-space reduction, with classification accuracies of 99.04% on IoTID20 (using 12 features), 98.25% on WUSTL-EHMS (using seven features), and 99.18% on Edge-IIoTset (using nine features). The results also demonstrate reduced training and testing times, central processing unit usage, resident set size overhead, and subset size compared to the baseline. Furthermore, Shapley additive explanations, as an explainable artificial intelligence technique, are applied to explain the model’s predictions and to show the contribution of the selected features to the IDS decision-making process. Full article

Healthcare and urban infrastructure are increasingly supported by Internet of Things-based sensing systems, in which heterogeneous physiological, environmental, and transmission-level data require reliable, low-latency processing. Existing works typically treat medical IoT sensing, smart-city anomaly detection, or edge-cloud offloading as isolated problems, thereby failing to support integrated sensing scenarios in shared smart environments. This paper introduces a Hybrid Edge–Cloud Intelligence Framework (HECIF) for reliable sensing and data fusion in smart healthcare and urban IoT environments. HECIF introduces modality-specific feature extraction, adaptive offloading to the edge cloud, an attention mechanism for multimodal fusion, and a reliability-weighted decision layer that incorporates sensor quality and transmission delay. The framework was tested on three publicly available datasets: the Multi-Sensor Medical IoT dataset for physiological signal classification, the UrbanIoT Anomaly dataset for urban anomaly detection, and the IoT Sensor Cloud Data Transmission dataset for offloading decision modeling, all from Kaggle. It achieved a 92.1% accuracy, 91.3% F1-score, 93.8% AUC, and 0.821 Matthews correlation coefficient in a simulated edge cloud environment, outperforming the baselines (logistic regression, random forest, XGBoost, MLP, CNN/LSTM). The framework also reduced the mean inference time to 29 ms, down from 142 ms in the cloud-only configuration, while achieving a throughput of 1150 samples per second. The results show that reliability-aware edge cloud fusion is feasible for cross-domain IoT sensing with a simulated edge cloud. However, physical device validation and real-world IoT network validation are still required before practical deployment. Full article

The quick transition of the healthcare industry to digital during the era of the Internet of Medical Things and Artificial Intelligence has ignited the demand for frameworks for data sharing while retaining safety and patient privacy. Centralized learning models place potentially sensitive patient data at risk of leakage, regulatory violation, and cyber-attacks which undermine receptivity and responsible ownership of big medical data. Federated learning is a novel paradigm that allows patients from various healthcare entities to train machine learning models while maintaining the ability to leverage their data without sharing their direct data. This study proposes a systematic literature review of approaches of privacy-preserving federated learning frameworks in healthcare applications. Following PRISMA guidelines, searches were conducted across Web of Science, Scopus, IEEE Xplore, ScienceDirect, PubMed, and ACM Digital Library with predefined query strings, explicit inclusion/exclusion criteria, and quality appraisal procedures. A total of 80 peer-reviewed studies, published from January 2015 to December 2025, were included in this systematic review, which examined cryptographic, architectural and algorithmic methods including differential privacy, homomorphic encryption, and Secure Multi-Party Computation, along with integrations using blockchain to enhance trust and confidence in distributed healthcare systems. The findings indicate a gradual shift towards hybrid privacy-preserving federated learning architectures which combined multiple security mechanisms to improve trust, confidentiality and robustness. Although significant progress has been achieved, the real-world deployment of such systems is heavily affected due to the challenges in communication efficiency, non-IID data distribution, adversarial attacks, and regulatory requirements. This research highlights future research directions for scalable, explainable and interoperable federated architectures that strike an optimal balance of privacy, utility and system performance for next-gen health intelligence. Trial registration: PROSPERO (CRD420261401073). Full article

Implantable medical device (IMD) and Internet of Medical Things (IoMT) environments need intrusion detection systems that learn across distributed hospitals without centralizing sensitive data, while controlling admission, protecting shared model artifacts, filtering unreliable contributors, and supporting post-run auditability. However, many secure federated learning designs treat identity, privacy, robustness, and evidence verification as separate layers, leaving a gap between privacy-preserving execution and public accountability. This paper presents an implemented zero-trust hierarchical federated learning-based intrusion detection system (FL-IDS) framework for IMD/IoMT security analytics. Hospital clients train eXtreme Gradient Boosting (XGBoost) detectors; self-sovereign identity gates participation; contribution-level differential privacy (DP) perturbs exported booster leaf weights; country aggregators apply adaptive Krum-inspired selection; and the global server performs trust-weighted prediction-level fusion. The evidence layer binds artifacts using Module-Lattice-Based Digital Signature Algorithm signatures, canonical hashes, Merkle roots, decentralized publication, Ethereum Sepolia anchoring, and standalone auditor verification. The framework is evaluated on WUSTL-EHMS-2020, ECU-IoHT, and CICIoMT2024 under paired DP-disabled and DP-enabled modes. Under DP-enabled execution, CICIoMT2024 achieved an F1-score of 0.998789 and area under the receiver operating characteristic curve (AUROC) of 0.999814, ECU-IoHT achieved an AUROC of 0.999337, and WUSTL-EHMS-2020 remained DP-sensitive with an F1-score of 0.422880 and AUROC of 0.776685. All paired evidence runs passed standalone auditor verification, demonstrating that privacy-preserving learning and public accountability can be integrated within a single experimental FL-IDS pipeline. Full article

The Internet of Medical Things (IoMT) enables real-time monitoring and decision support systems in healthcare. However, due to their heterogeneous structure, limited resources, and high criticality, IoMT networks are vulnerable to cyberattacks. This situation increases the need for low-latency, high-accuracy, and generalizable attack detection systems. In this experimental study, the Multi-Scale Depthwise Channel Attention Network (MSCA-Net) model is proposed for multi-class attack detection in IoMT environments. The model consists of three core components: multi-scale depthwise separable convolutions to capture traffic patterns across different time scales, a squeeze-and-excitation-based channel attention mechanism that adaptively weights discriminative features, and a lightweight unidirectional LSTM layer that models temporal dependencies. This architecture enables effective representation learning with low parameter costs. The proposed model was evaluated on the WUSTL-EHMS-2020 and CICIoMT2024 datasets. On the CICIoMT2024 dataset, it achieved 99.75% accuracy and a weighted F1 score of 99.77% in a 6-class scenario. It has also demonstrated competitive results in 19-class fine-grained classification. Experimental comparisons show that MSCA-Net offers a better performance-to-cost trade-off compared to nine different baseline models. Furthermore, it demonstrates a speed advantage of up to two times in inference time. The results obtained at the conclusion of the experimental study demonstrate that the proposed approach effectively addresses the challenges of multi-scale feature extraction, class imbalance, and computational efficiency. Furthermore, the model appears to offer a viable solution for real-time attack detection in IoMT environments. Full article

Wireless Body Area Networks (WBANs) have become an essential component of modern Internet of Medical Things (IoMT) healthcare systems, enabling continuous monitoring of patient physiological signals through wearable sensors. Despite their advantages, WBAN environments remain highly prone to cyber threats, privacy breaches, and single points of failure. To address these risks, this work proposes a Hybrid Multi-Metric Anomaly Detection (HM-MAD) framework deployed on the NodeMCU-32S platform with BLE 5.0 connectivity for secure continuous glucose monitoring (CGM) data transmission. The detection model simultaneously analyses physiological signals, system-level parameters, and network-level communication metrics, enabling the reliable identification of multiple cyberattacks. The proposed system focuses on securing data transmission against relay attacks, where attackers induce communication delay without modifying payloads, potentially leading to false glucose readings, improper insulin dosage delivery, unauthorized control or denial-of-service. The Convolutional Neural Network (CNN) and Bi-Directional Long Short Term Memory (BiLSTM) model classifies attack types including timing manipulation, replay attacks, power glitches, firmware tampering, and sensor spoofing. Experimental evaluation demonstrates that the proposed CNN + BiLSTM framework achieves 94.6% detection accuracy with an average inference latency of 15 ms, representing a 50% latency reduction compared to Transformer-based intrusion detection models (30 ms), while simultaneously reducing computational overhead by 28% in terms of floating-point operations and memory utilization. These results indicate that the HM-MAD framework provides an effective and scalable solution for protecting resource-constrained IoMT healthcare systems against emerging cyber threats. Full article

Medication dispensing errors remain a significant concern in healthcare systems, particularly in elderly care and long-term medication management, where incorrect medication delivery may compromise patient safety and treatment outcomes. This study presents the design and experimental validation of a cyber–physical medication dispensing platform integrating robotic manipulation, edge AI-based visual verification, distributed motion control, and cloud synchronization. The platform combines a rotary medication storage mechanism, vacuum-based pill handling, a Klipper-based control framework, and a YOLOv8 perception subsystem deployed on a Hailo AI accelerator for real-time edge inference. Experimental evaluation was conducted under controlled laboratory conditions. Using an environment-specific validation dataset, the perception subsystem achieved a precision of 0.627, recall of 0.739, and mAP@0.5 of 0.786. An adaptive verification strategy was subsequently evaluated to improve dispensing verification under varying pill occupancy conditions. End-to-end system testing comprising 80 dispensing trials achieved an overall dispensing success rate of 86.25%, with no incorrect dispensing events observed. The results demonstrate the feasibility of integrating edge AI verification, distributed control, and cloud connectivity within a cyber–physical medication dispensing platform. The presented system provides a foundation for future research on perception-assisted medication dispensing, long-term deployment, and clinical validation in smart healthcare environments. Full article

Intelligent sensors and Internet of Medical Things (IoMT) platforms are rapidly changing smart healthcare by enabling continuous capture of physiological, behavioral, and clinical events outside conventional hospital settings. Yet the value of connected sensing depends on more than signal acquisition alone. A practical remote-monitoring ecosystem must also convert sensor alerts, clinician-facing summaries, and historical electronic clinical records (ECRs) into ranked evidence that supports care decisions. This study reframes a large-AI clinical retrieval model as the intelligence layer of an edge–cloud IoMT architecture. The proposed framework combines Transformer-Based Sequence (TBS) encoding, BioBERT-driven representation learning, explicit retrieval, and domain-guided re-ranking to connect sensor-originated narratives, patient records, and clinician queries. The empirical evaluation is conducted on Medical Information Mart for Intensive Care III (MIMIC-III) and i2b2, two de-identified clinical text benchmarks that approximate the documentation layer of real-world remote patient monitoring. Compared with strong baselines, including DeepBio, UniT2T, Web4IR, A2A-API, CoLTiD, VLRG, ColBERT, DeepSDH, BiRex, and DL4BTM, the proposed model achieves the best overall performance, reaching F1/Pre/NDCG scores of 0.8399/0.8338/0.5235 on MIMIC-III and 0.8090/0.8100/0.5129 on i2b2. Ablation experiments confirm the importance of exploratory data adaptation, critical feature modeling, critical token learning, cross-disciplinary supervision, and data-driven regularization. Parameter sensitivity analysis shows stable behavior for beta values greater than or equal to 1, with the strongest results at beta = 5. The study concludes that large-AI retrieval can strengthen the clinical interpretation layer required for IoMT-enabled remote monitoring, while future work should validate the approach on live multimodal sensor streams and privacy-preserving deployments. Full article

This study investigates data augmentation as a strategy for addressing dataset scarcity in Internet of Medical Things (IoMT) cybersecurity and improving intrusion-detection system performance. Four augmentation methods—Rule-Based, Tabular Variational Autoencoder (TVAE), Conditional Tabular Generative Adversarial Network (CTGAN), and Gaussian Copula—were applied to two publicly available IoMT datasets (ECU-IoHT and WUSTL-EHMS) to generate augmented training data with differing class distributions and feature characteristics. Eleven machine learning algorithms were evaluated using Matthews Correlation Coefficient (MCC), F1-score, accuracy, and error-based metrics. Results showed consistent performance improvements across all evaluated models relative to the baseline datasets. The Rule-Based method produced the strongest overall results, achieving the highest MCC (0.9757), F1-score (99.19%), and accuracy (99.18%) with LightGBM, alongside low false-positive and false-negative rates. Among the generative approaches, TVAE delivered the strongest overall practical performance (F1-score = 96.94%, accuracy = 96.92%), while CTGAN achieved a marginally higher MCC (0.9047) and also produced competitive results with balanced class representation. Gaussian Copula generated the weakest overall outcomes, primarily due to highly skewed class distributions. Traditional models, such as Logistic Regression and Naive Bayes, recorded the largest relative gains, indicating that augmentation can substantially improve simpler classifiers in data-scarce environments. Overall, the findings demonstrate that augmentation quality depends not only on dataset expansion, but also on preserving class balance, feature diversity, and realistic traffic relationships. These results provide practical guidance for strengthening IoMT intrusion-detection systems in healthcare environments. Full article

Knee Osteoarthritis (KOA) is a leading contributor to global physical disability, where delayed diagnosis often results in irreversible joint damage and socio-economic cost. Early diagnosis remains challenging due to subtle radiographic biomarkers and limited access to specialized expertise, particularly in distributed healthcare settings. Within the evolving landscape of the Future Internet, characterized by Internet of Medical Things (IoMT), edge–cloud computing, and intelligent digital health infrastructures, there is an increasing demand for scalable, low-latency, and explainable AI-driven diagnostic solutions. In this work, we propose a Dual-Stream Wavelet Fusion Network (DS-WFN) alongside a distributed edge-cloud architectural roadmap tailored for deployment in distributed and edge-enabled healthcare ecosystems. The framework integrates a spatial morphological stream with a spectral wavelet stream, augmented by an Adaptive Wavelet Selection Mechanism (AWSM). The AWSM dynamically selects optimal frequency bases (Haar, Symlet, Daubechies) to preserve fine-grained diagnostic features typically lost in conventional CNN architectures. An Adaptive Spatial Alignment (ASA) module further ensures efficient fusion of heterogeneous representations, enabling robust feature integration across computational nodes. Experimental results across a five-fold patient-isolated cross-validation protocol demonstrate that the DS-WFN achieves a mean classification accuracy of 76.3% (95% CI: 71.6–80.8%) and a macro-averaged F1-score of 0.747 (95% CI: 0.697–0.795), consistently outperforming single-stream baselines while preventing patient-level data leakage. Furthermore, Grad-CAM visualizations provide interpretable outputs aligned with clinical diagnostic criteria, supporting trustworthy AI integration into digital healthcare workflows. Furthermore, we disclose a methodological framework for edge-based implementation, highlighting how localized inference ensures data sovereignty and real-time clinical support. By combining multiscale signal processing with deep learning under a Future Internet paradigm, this work contributes a scalable, explainable, and edge-ready diagnostic framework for early KOA detection, enabling intelligent, connected, and resource-efficient healthcare services. Full article

Artificial Intelligence of Things (AIoT) is transforming Internet of Things (IoT) systems from cloud-centric data processing into distributed intelligence across device, edge, fog, and cloud tiers. However, existing reviews often emphasize specific computational layers, learning paradigms, or application domains rather than the cross-domain problem of Artificial Intelligence (AI) workload placement under real deployment constraints. This paper presents a structured integrative review of AI workload placement in AIoT, based on a multi-stage literature search, two-stage screening process, and thematic synthesis of 132 sources. The review does not propose a new physical architecture; instead, it develops a terminology-harmonized and AI-centric perspective for assessing where AI functions should reside according to latency, privacy, bandwidth, power, scalability, resilience, and model complexity. Evidence is synthesized across Industrial Internet of Things (IIoT), smart cities, Internet of Medical Things (IoMT), and smart agriculture. The findings show that placement drivers are domain-dependent: deterministic response and reliability dominate IIoT, interoperability and scale shape smart cities, privacy and human oversight constrain IoMT, and energy scarcity and intermittent connectivity define agriculture. The review concludes that robust AIoT requires hybrid multi-layer architectures combining Tiny Machine Learning (TinyML), edge/fog coordination, cloud-scale optimization, and Federated Learning (FL) where appropriate. Full article

Driven by the profound convergence of the Internet of Things (IoT) and ubiquitous computing, wearable multifunctional sensors have emerged as a key technology for high-precision human activity recognition (HAR). Advancements in novel materials and flexible electronics have propelled the evolution of these sensors, enabling advances in decoupling heterogeneous signals, enhancing system robustness, and expanding environmental perception. This review systematically examines the frontier research on wearable multifunctional sensors for HAR. We provide an in-depth analysis of three core architectural design paradigms: architecture-level integration, which relies on physical spatial isolation for hardware-level signal decoupling; monolithic integration, which strives for extreme spatial compactness and spatiotemporal signal consistency; and the emerging intrinsically multifunctional design, which leverages novel stimuli-responsive materials for the intrinsic orthogonal discrimination of multidimensional signals. Furthermore, we delineate the diverse application scenarios of these highly integrated sensing platforms across medical rehabilitation, sports science, human–computer interaction (HCI), and daily behavior perception. Finally, this article discusses the critical challenges currently confronting this technology and outlines its future development prospects. Full article

Background: This study evaluates the translational relevance of free computer-assisted ADMET platforms in drug discovery by comparing SwissADME and FAF-Drugs4 predictions with regulatory and curated reference data for approved anticancer drugs. Methods: Fourteen approved anticancer agents representing diverse chemical classes were analyzed using SwissADME and FAF-Drugs4. We compared predicted physicochemical, pharmacokinetic, and toxicity-related properties with information extracted from FDA- and EMA-approved product information, DrugBank, and PubChem. We evaluated the concordance for oral absorption, permeability, metabolic stability, and toxicity-related trends. Results: The platforms showed good concordance for broad descriptor-driven properties, particularly oral suitability and physicochemical trends. Strong agreement was observed for intravenously administered taxanes, which displayed unfavorable oral drug characteristics, and for several orally active small molecules with generally compatible profiles. Partial concordance was observed for compounds such as temozolomide, whose clinical behavior is influenced by factors not fully captured by descriptor-based models. Toxicity outputs were informative as early warning signals, with vandetanib showing the clearest alignment between predicted elevated risk and documented safety concerns. Conclusions: Free computational ADMET tools are valuable computer-assisted drug discovery resources for early triage, predictive toxicology, and prioritization of repositioning candidates, but they should complement rigorous experimental and clinical evaluation. Full article

The convergence of artificial intelligence, biotechnology, and the Internet of Bio-Nano Things (IoBNT) is enabling the creation of a new generation of intelligent in-body medical devices for continuous diagnosis and monitoring. In this context, a compact, wideband, circularly polarized conformal microstrip antenna is proposed for capsule endoscopy applications. The antenna is integrated along the inner wall of a 10 mm-diameter capsule and achieves an impedance bandwidth of 2.06–5.39 GHz (89.39%), maintaining stable matching under varying biological tissue conditions. A 3 dB axial ratio bandwidth (ARBW) of 2.31–3.14 GHz (30.45%) ensures reliable circular polarization and robust wireless communication in lossy and dynamic in-body environments. To extend beyond conventional electromagnetic analysis, a physics-informed neural network (PINN) framework is introduced to model the thermal response of biological tissues based on the governing bioheat equation. This AI-driven approach enables fast and generalizable prediction of temperature rise under varying operational conditions without repeated numerical simulations. At 2.45 GHz, the antenna exhibits a maximum gain of $-31.1$ dBi with a radiation efficiency of approximately $-34$ dB, consistent with in-body propagation constraints. Simulation and experimental results in realistic tissue phantoms, including muscle, small intestine, large intestine, and stomach, confirm stable wideband and polarization performance. Specific absorption rate (SAR) analysis demonstrates compliance with IEEE C95.1-2019 safety limits, while link budget evaluation validates reliable telemetry over a 1–3 m communication range. The integration of advanced antenna design with physics-informed machine learning provides a scalable framework for intelligent, safe, and adaptive IoBNT-enabled capsule endoscopy systems. Full article