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Healthcare 5.0 represents the next evolution in intelligent and interconnected healthcare systems, leveraging emerging technologies such as Artificial Intelligence (AI) and the Internet of Medical Things (IoMT) to enhance patient care and automation. While Intrusion Detection Systems (IDSs) are a critical component for securing these environments, the current literature lacks a systematic analysis that jointly evaluates the effectiveness of AI models, the suitability of datasets, and the role of Explainable Artificial Intelligence (XAI) in the Healthcare 5.0 landscape. To fill this gap, this survey provides a comprehensive review of IDSs for Healthcare 5.0, analyzing state-of-the-art approaches and available datasets. Furthermore, a practical case study is presented, demonstrating that the fusion of network and biomedical features significantly improves threat detection, with physiological signals proving crucial for identifying complex attacks like spoofing. The primary contribution is therefore an integrated analysis that bridges the gap between cybersecurity theory and clinical practice, offering a guide for researchers and practitioners aiming to develop more secure, transparent, and patient-centric systems. Full article

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

This study investigated the transformative potential of Compressive Sensing (CS) for optimizing multimodal biomedical signal fusion in Wireless Body Sensor Networks (WBSN), specifically targeting challenges in data storage, power consumption, and transmission bandwidth. Through a Systematic Mapping Study (SMS) and Systematic Literature Review (SLR) following the PRISMA protocol, significant advancements in adaptive CS algorithms and multimodal fusion have been achieved. However, this research also identified crucial gaps in computational efficiency, hardware scalability (particularly concerning the complex and often costly adaptive sensing hardware required for dynamic CS applications), and noise robustness for one-dimensional biomedical signals (e.g., ECG, EEG, PPG, and SCG). The findings strongly emphasize the potential of integrating CS with deep reinforcement learning and edge computing to develop energy-efficient, real-time healthcare monitoring systems, paving the way for future innovations in Internet of Medical Things (IoMT) applications. Full article

The Internet of Medical Things (IoMT) is the latest advancement in the Internet of Things (IoT). Researchers are increasingly drawn to its vast potential applications in secure healthcare systems. The growing use of internet-connected medical device sensors has significantly transformed healthcare, necessitating the development of robust methodologies to assess their integrity. As access to computer networks continues to expand, these sensors have become vulnerable to a wide range of security threats, thereby compromising their integrity. To prevent such lapses, it is essential to understand the complexities of the operational environment and to systematically identify technical vulnerabilities. This paper proposes a unified hesitant fuzzy-based healthcare system for assessing IoMT sensor integrity. The approach integrates the hesitant fuzzy Analytic Network Process (ANP) and the hesitant fuzzy Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). In this study, a hesitant fuzzy ANP is employed to construct a comprehensive network that illustrates the interrelationships among various integrity criteria. This network incorporates expert input and accounts for inherent uncertainties. The research also offers sensitivity analysis and comparative evaluations to show that the suggested method can analyse many medical device sensors. The unified hesitant fuzzy-based healthcare system presented here offers a systematic and valuable tool for informed decision-making in healthcare. It strengthens both the integrity and security of healthcare systems amid the rapidly evolving landscape of medical technology. Healthcare stakeholders and beyond can significantly benefit from adopting this integrated fuzzy-based approach as they navigate the challenges of modern healthcare. Full article

The burgeoning Internet of Medical Things (IoMT) offers unprecedented opportunities for real-time patient monitoring and predictive diagnostics, yet the current systems struggle with scalability, data confidentiality against quantum threats, and real-time privacy-preserving intelligence. This paper introduces Med-Q Ledger, a novel, multi-layered framework designed to overcome these critical limitations in the Medical IoT domain. Med-Q Ledger integrates a permissioned Hyperledger Fabric for transactional integrity with a scalable Holochain Distributed Hash Table for high-volume telemetry, achieving horizontal scalability and sub-second commit times. To fortify long-term data security, the framework incorporates post-quantum cryptography (PQC), specifically CRYSTALS-Di lithium signatures and Kyber Key Encapsulation Mechanisms. Real-time, privacy-preserving intelligence is delivered through an edge-based federated learning (FL) model, utilizing lightweight autoencoders for anomaly detection on encrypted gradients. We validate Med-Q Ledger’s efficacy through a critical application: the prediction of intestinal complications like necrotizing enterocolitis (NEC) in preterm infants, a condition frequently necessitating emergency colostomy. By processing physiological data from maternal wearable sensors and infant intestinal images, our integrated Random Forest model demonstrates superior performance in predicting colostomy necessity. Experimental evaluations reveal a throughput of approximately 3400 transactions per second (TPS) with ~180 ms end-to-end latency, a >95% anomaly detection rate with <2% false positives, and an 11% computational overhead for PQC on resource-constrained devices. Furthermore, our results show a 0.90 F1-score for colostomy prediction, a 25% reduction in emergency surgeries, and 31% lower energy consumption compared to MQTT baselines. Med-Q Ledger sets a new benchmark for secure, high-performance, and privacy-preserving IoMT analytics, offering a robust blueprint for next-generation healthcare deployments. Full article

The Internet of Medical Things (IoMT) is a rapidly expanding network of medical devices, sensors, and software that exchange patient health data. While IoMT supports personalized care and operational efficiency, it also introduces significant privacy risks, especially when handling sensitive health information. Data Identification and Classification (DIC) are therefore critical for distinguishing which data attributes require stronger safeguards. Effective DIC contributes to privacy preservation, regulatory compliance, and more efficient data management. This study introduces SDAIPA (SDAIA-HIPAA), a standardized hybrid IoMT data classification framework that integrates principles from HIPAA and SDAIA with a dual risk perspective—uniqueness and harm potential—to systematically classify IoMT health data. The framework’s contribution lies in aligning regulatory guidance with a structured classification process, validated by domain experts, to provide a practical reference for sensitivity-aware IoMT data management. In practice, SDAIPA can assist healthcare providers in allocating encryption resources more effectively, ensuring stronger protection for high-risk attributes such as genomic or location data while minimizing overhead for lower-risk information. Policymakers may use the standardized IoMT data list as a reference point for refining privacy regulations and compliance requirements. Likewise, AI developers can leverage the framework to guide privacy-preserving training, selecting encryption parameters that balance security with performance. Collectively, these applications demonstrate how SDAIPA can support proportionate and regulation-aligned protection of health data in smart healthcare systems. Full article

The integration of edge computing (EC) and Internet of Medical Things (IoMT) technologies facilitates the development of adaptive healthcare systems that significantly improve the accessibility and monitoring of individuals with disabilities. By enabling real-time disease identification and reducing response times, this architecture supports personalized healthcare solutions for those with chronic conditions or mobility impairments. The inclusion of untrusted devices leads to communication delays and enhances the security risks for medical applications. Therefore, this research presents a Trust-Driven Disability-Detection Model Using Secured Random Forest Classification (TTDD-SRF) to address the issues while monitoring real-time health records. It also increases the detection of abnormal movement patterns to highlight the indication of disability using edge-driven communication. The TTDD-SRF model improves the classification accuracy of abnormal motion detection while ensuring data reliability through trust scores computed at the edge level. Such a paradigm decreases the ratio of false positives and enhances decision-making accuracy in coping with health-related applications, mainly the detection of patients’ disabilities. The experimental analysis of the proposed TTDD-SRF model indicates improved performance in terms of network throughput by 48%, system resilience by 42%, device integrity by 49%, and energy consumption by 45% while highlighting the potential of medical systems using edge technologies, advancing assistive technology for healthcare accessibility. Full article

The number of IoT devices has been increasing at a rapid rate, and the advent of information-intensive Internet of Multimedia Things (IoMT) applications has placed serious challenges on computing infrastructure, especially for latency, energy efficiency, and responsiveness to tasks. The legacy cloud-centric approach cannot meet such requirements because it suffers from local latency and central resource allocation. To overcome such limitations, fog computing proposes a decentralized model by reducing latency and bringing computation closer to data sources. However, effective scheduling of tasks within heterogeneous and resource-limited fog environments is still an NP-hard problem, especially in multi-criteria optimization and priority-sensitive situations. This research work proposes a new simulated annealing (SA)-based task scheduling framework to perform multi-objective optimization for fog computing environments. The proposed model minimizes makespan, energy consumption, and execution cost, and integrates a priority-aware penalty function to provide high responsiveness to high-priority tasks. The SA algorithm searches the scheduling solution space by accepting potentially sub-optimal configurations during the initial iterations and further improving towards optimality as the temperature decreases. Experimental analyses on benchmark datasets obtained from Google Cloud Job Workloads demonstrate that the proposed approach outperforms ACO, PSO, I-FASC and M2MPA approaches in terms of makespan, energy consumption, execution cost, and reliability at all task volume scales. These results confirm the proposed SA-based scheduler as a scalable and effective solution for smart task scheduling within fog-enabled IoT infrastructures. Full article

Federated learning (FL) offers a promising paradigm for distributed model training in Internet of Medical Things (IoMT) systems, where patient data privacy and device heterogeneity are critical concerns. However, conventional FL remains vulnerable to gradient leakage, model poisoning, and adversarial inference, particularly in privacy-sensitive and resource-constrained medical environments. To address these challenges, we propose SAFE-MED, a secure and adversarially robust framework for privacy-preserving FL tailored for IoMT deployments. SAFE-MED integrates neural encryption, adversarial co-training, anomaly-aware gradient filtering, and trust-weighted aggregation into a unified learning pipeline. The encryption and decryption components are jointly optimized with a simulated adversary under a minimax objective, ensuring high reconstruction fidelity while suppressing inference risk. To enhance robustness, the system dynamically adjusts client influence based on behavioral trust metrics and detects malicious updates using entropy-based anomaly scores. Comprehensive experiments are conducted on three representative medical datasets: Cleveland Heart Disease (tabular), MIT-BIH Arrhythmia (ECG time series), and PhysioNet Respiratory Signals. SAFE-MED achieves near-baseline accuracy with less than 2% degradation, while reducing gradient leakage by up to 85% compared to vanilla FedAvg and over 66% compared to recent neural cryptographic FL baselines. The framework maintains over 90% model accuracy under 20% poisoning attacks and reduces communication cost by 42% relative to homomorphic encryption-based methods. SAFE-MED demonstrates strong scalability, reliable convergence, and practical runtime efficiency across heterogeneous network conditions. These findings validate its potential as a secure, efficient, and deployable FL solution for next-generation medical AI applications. Full article

Electroencephalography (EEG) communication technology with ultra-low power consumption, high transmission data rates, and low latency plays a significant role in mHealth, telemedicine, and Internet of Medical Things (IoMT). In this paper, generalized frequency division multiplexing (GFDM)-based direct mapping (DM) multi-input—multi-output (MIMO) mobile EEG communication technology (MECT) is proposed for implementation with the above-mentioned applications. The (2000, 1000) low-density parity-check (LDPC) code, four-quadrature amplitude modulation (4-QAM), a power assignment mechanism, and the 3rd Generation Partnership Project (3GPP) cluster delay line (CDL) channel model D were integrated into the proposed EEGCT. The transmission bit error rates (BERs), mean square errors (MSEs), and Pearson-correlation coefficients (PCCs) of the original and received EEG signals were evaluated. Simulation results show that, with a signal to noise ratio (SNR) of 14.51 dB, with a channel estimation error (CEE) of 5%, the BER, MSE, and PCC of the original and received EEG signals were 9.9777 × 10⁻⁸, 1.440 × 10⁻⁵ and 0.999999998, respectively, whereas, with an SNR of 15.0004 dB and a CEE of 10%, they were 9.9777 × 10⁻⁸, 1.4368 × 10⁻⁵, and 0.999999997622151, respectively. As the BER value, and PS saving are 9.9777 × 10⁻⁸, and 40%, respectively. With the CEE changes from 0% to 5%, and 5% to 10%, the N₀ values of the proposed MECT decrease by approximately 0.0022 and 0.002, respectively. The MECT has excellent EEG signal transmission performance. Full article

Background: By leveraging advanced wireless technologies, Healthcare Industry 5.0 promotes the continuous monitoring of real-time medical acquisition from the physical environment. These systems help identify early diseases by collecting health records from patients’ bodies promptly using biosensors. The dynamic nature of medical devices not only enhances the data analysis in medical services and the prediction of chronic diseases, but also improves remote diagnostics with the latency-aware healthcare system. However, due to scalability and reliability limitations in data processing, most existing healthcare systems pose research challenges in the timely detection of personalized diseases, leading to inconsistent diagnoses, particularly when continuous monitoring is crucial. Methods: This work propose an adaptive and secure framework for disability identification using the Internet of Medical Things (IoMT), integrating edge computing and artificial intelligence. To achieve the shortest response time for medical decisions, the proposed framework explores lightweight edge computing processes that collect physiological and behavioral data using biosensors. Furthermore, it offers a trusted mechanism using decentralized strategies to protect big data analytics from malicious activities and increase authentic access to sensitive medical data. Lastly, it provides personalized healthcare interventions while monitoring healthcare applications using realistic health records, thereby enhancing the system’s ability to identify diseases associated with chronic conditions. Results: The proposed framework is tested using simulations, and the results indicate the high accuracy of the healthcare system in detecting disabilities at the edges, while enhancing the prompt response of the cloud server and guaranteeing the security of medical data through lightweight encryption methods and federated learning techniques. Conclusions: The proposed framework offers a secure and efficient solution for identifying disabilities in healthcare systems by leveraging IoMT, edge computing, and AI. It addresses critical challenges in real-time disease monitoring, enhancing diagnostic accuracy and ensuring the protection of sensitive medical data. Full article

Continuous cardiovascular monitoring is essential for the early detection of cardiac events, but conventional electrode-based ECG systems cause skin irritation and are unsuitable for long-term wear. We propose an electrode-free ECG monitoring approach that leverages synchronized phonocardiogram (PCG) and seismocardiogram (SCG) signals captured by wireless mechano-acoustic sensors. PCG provides precise valvular event timings, while SCG provides mechanical context, enabling the robust identification of systolic/diastolic intervals and pathological patterns. A deep learning model reconstructs ECG waveforms by intelligently combining mechano-acoustic sensor data. Its architecture leverages specialized neural network components to identify and correlate key cardiac signatures from multimodal inputs. Experimental validation on an IoT sensor dataset yields a mean Pearson correlation of 0.96 and an RMSE of 0.49 mV compared to clinical ECGs. By eliminating skin-contact electrodes through PCG–SCG fusion, this system enables robust IoT-compatible daily-life cardiac monitoring. Full article

We present a detailed theoretical and experimental study of cascaded double resonance long period gratings (C DR LPGs) for fabricated sensing applications. The matrix description of cascaded LPGs is presented, and several important particular cases are considered related to the regular and turn around point (TAP) gratings. A pulsed CO₂ laser was used to fabricate ordinary and cascaded DR LPGs in a photosensitive optical fiber. The responses of the fabricated C DR LPGs to surrounding refractive index (SRI) temperature as well to longitudinal strain have been studied. A statistical comparison of the SRI sensitivities of ordinary and cascaded DR LPGs is presented to outline the capabilities and advantages of cascaded DR gratings. It was experimentally established that the temperature dependence of the wavelength split at the TAP follows a logarithmic dependence and the sensitivity to temperature is inversely proportional to the temperature itself. We evaluate the temperature stability needed for SRI-based sensing application and the importance of fine-tuning to the operational point slightly after the TAP to ensure maximum sensitivity of the sensor. Full article

The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling continuous monitoring and real-time data collection through interconnected medical devices such as wearable sensors and smart health monitors. These devices generate sensitive physiological data, including cardiac signals, glucose levels, and vital signs, that are integrated into electronic health records (EHRs). Machine Learning (ML) and Deep Learning (DL) techniques have shown significant potential for predictive diagnostics and decision support based on such data. However, traditional centralized ML approaches raise significant privacy concerns due to the transmission and aggregation of sensitive health information. Additionally, compliance with data protection regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR), restricts centralized data sharing and analytics. To address these challenges, this study introduces FED-EHR, a privacy-preserving Federated Learning (FL) framework that enables collaborative model training on distributed EHR datasets without transferring raw data from its source. The framework is implemented using Logistic Regression (LR) and Multi-Layer Perceptron (MLP) models and was evaluated using two publicly available clinical datasets: the UCI Breast Cancer Wisconsin (Diagnostic) dataset and the Pima Indians Diabetes dataset. The experimental results demonstrate that FED-EHR achieves a classification performance comparable to centralized learning, with ROC-AUC scores of 0.83 for the Diabetes dataset and 0.98 for the Breast Cancer dataset using MLP while preserving data privacy by ensuring data locality. These findings highlight the practical feasibility and effectiveness of applying the proposed FL approach in real-world IoMT scenarios, offering a secure, scalable, and regulation-compliant solution for intelligent healthcare analytics. Full article

This paper presents a cross-layer intrusion detection framework leveraging explainable artificial intelligence (XAI) and interpretability methods to enhance transparency and robustness in attack detection within the Internet of Medical Things (IoMT) domain. By addressing the dual challenges of compromised data integrity, which span both biosensor and network-layer data, this study combines advanced techniques to enhance interpretability, accuracy, and trust. Unlike conventional flow-based intrusion detection systems that primarily rely on transport-layer statistics, the proposed framework operates directly on raw packet-level features and application-layer semantics, including MQTT message types, payload entropy, and topic structures. The key contributions of this research include the application of K-Means clustering combined with the principal component analysis (PCA) algorthim for initial categorization of attack types, the use of SHapley Additive exPlanations (SHAP) for feature prioritization to identify the most influential factors in model predictions, and the employment of Partial Dependence Plots (PDP) and Accumulated Local Effects (ALE) to elucidate feature interactions across layers. These methods enhance the system’s interpretability, making data-driven decisions more accessible to nontechnical stakeholders. Evaluation on a realistic healthcare IoMT testbed demonstrates significant improvements in detection accuracy and decision-making transparency. Furthermore, the proposed approach highlights the effectiveness of explainable and cross-layer intrusion detection for secure and trustworthy medical IoT environments that are tailored for cybersecurity analysts and healthcare stakeholders. Full article