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Embedded devices are used in many domains, including healthcare, industries, and home automation, all of which entail significant workloads. As a direct consequence, the embedded devices require retrieval and processing of data of large volume, which occupy large memory space in the embedded devices. Compression along with workload characterization is an effective technique for minimizing memory usage and to the improvement of endurance of memory in such devices. This paper investigates the embedded workload characterization using Extreme Learning Machine (ELM) that is particularly suitable for large-scale datasets and real-time applications. Though ELM is single-layer feedforward network, its input weight randomization has a considerable effect on the accuracy of the classification. In this paper, the authors have proposed a hybrid algorithm for the optimization of the randomization of ELM. The Particle Swarm Optimizer (PSO), the Genetic Algorithm (GA), the Ant Colony Optimizer (ACO), and the Whale Optimization Algorithm (WOA) were used in the optimization of the classification process in ELM to increase the accuracy of the results. The input data must be categorized based on the energy consumed by each workload to proceed with further processing according to the system requirements. This paper explores and analyses the performance of hybridized ELM-Genetic algorithm (ELM-GA), ELM-Particle Swarm Optimization (PSO), ELM-Ant Colony Optimizer (ELM-ACO), and ELM- Whale Optimization Algorithm (ELM-WOA) optimization algorithms. The embedded benchmarks Internet of Medical Things (IoMT), Mi Benchmark (MiBench), and Embedded Microprocessor Benchmark Consortium (EEMBC) have been used as a dataset for classification in this paper. The extensive experimental study shows that the hybridized ELM-WOA provides a 98.5 % classification accuracy, 98.1 % specificity, and 98 % sensitivity compared to the other optimization methods discussed in this paper.

Abstract This study explores integrating blockchain technology into the Internet of Medical Things (IoMT) to address security and privacy challenges. Blockchain’s transparency, confidentiality, and decentralization offer significant potential benefits in the healthcare domain. The research examines various blockchain components, layers, and protocols, highlighting their role in IoMT. It also explores IoMT applications, security challenges, and methods for integrating blockchain to enhance security. Blockchain integration can be vital in securing and managing this data while preserving patient privacy. It also opens up new possibilities in healthcare, medical research, and data management. The results provide a practical approach to handling a large amount of data from IoMT devices. This strategy makes effective use of data resource fragmentation and encryption techniques. It is essential to have well-defined standards and norms, especially in the healthcare sector, where upholding safety and protecting the confidentiality of information are critical. These results illustrate that it is essential to follow standards like HIPAA, and blockchain technology can help ensure these criteria are met. Furthermore, the study explores the potential benefits of blockchain technology for enhancing inter-system communication in the healthcare industry while maintaining patient privacy protection. The results highlight the effectiveness of blockchain’s consistency and cryptographic techniques in combining identity management and healthcare data protection, protecting patient privacy and data integrity. Blockchain is an unchangeable distributed ledger system. In short, the paper provides important insights into how blockchain technology may transform the healthcare industry by effectively addressing significant challenges and generating legal, safe, and interoperable solutions. Researchers, doctors, and graduate students are the audience for our paper.

The healthcare sector has undergone a profound transformation, owing to the influential role played by Internet of Medical Things (IoMT) technology. However, there are substantial concerns over these devices’ security and privacy-preserving mechanisms. The current literature on IoMT tends to focus on specific security features, rather than wholistic security concerning Confidentiality, Integrity, and Availability (CIA Triad), and the solutions are generally simulated and not tested in a real-world network. The proposed innovative solution is known as Secure-by-Design Real-Time IoMT Architecture for e-Health Population Monitoring (RTPM) and it can manage keys at both ends (IoMT device and IoMT server) to maintain high privacy standards and trust during the monitoring process and enable the IoMT devices to run safely and independently even if the server is compromised. However, the session keys are controlled by the trusted IoMT server to lighten the IoMT devices’ overheads, and the session keys are securely exchanged between the client system and the monitoring server. The proposed RTPM focuses on addressing the major security requirements for an IoMT system, i.e., the CIA Triad, and conducts device authentication, protects from Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks, and prevents non-repudiation attacks in real time. A self-healing solution during the network failure of live e-health monitoring is also incorporated in RTPM. The robustness and stress of the system are tested with different data types and by capturing live network traffic. The system’s performance is analysed using different security algorithms with different key sizes of RSA (1024 to 8192 bits), AES (128 to 256 bits), and SHA (256 bits) to support a resource-constraint-powered system when integrating with resource-demanding secure parameters and features. In the future, other security features like intrusion detection and prevention and the user’s experience and trust level of such a system will be tested.

The consistency and duration of the menstrual cycle exhibit significant associations with specific psychiatric conditions throughout an individual’s lifespan. The proposed methodology surveys the relationship between psychiatric disorders and the length or regularity of the menstrual cycle and analyzes the difficulties undergone by the women. A comprehensive dataset is generated and a mathematical model using an exploratory data analytics approach is developed, in order to establish a correlation between these variables. It utilizes a cyclic methodology, leveraging shared menstrual data and a predictive model derived from vehicles to enhance network learning. A decentralized secure learning procedure is implemented to ensure data privacy and security. The transfer learning techniques helps to enhance the ability to learn from diverse data distributions in IoMT (Internet of Medical Things) networks, improve the robustness of the learning process. This approach presents a practical and effective solution for IoMT network learning, allowing each participant to contribute their individual features to collectively extract valuable insights from the data. The decentralization facilitates end-users in accessing their personal medical records while ensuring privacy, irrespective of their location and time. This system also achieves a minimal delay sensitivity of 3.2%, by providing timely access to the required information.

The Internet of Medical Things (IoMT) has revolutionized healthcare with remote patient monitoring and real-time diagnosis, but securing patient data remains a critical challenge due to sophisticated cyber threats and the sensitivity of medical information. Traditional machine learning methods struggle to capture the complex patterns in IoMT data, and conventional intrusion detection systems often fail to identify unknown attacks, leading to high false positive rates and compromised patient data security. To address these issues, we propose RCLNet, an effective Anomaly-based Intrusion Detection System (A-IDS) for IoMT. RCLNet employs a multi-faceted approach, including Random Forest (RF) for feature selection, the integration of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models to enhance pattern recognition, and a Self-Adaptive Attention Layer Mechanism (SAALM) designed specifically for the unique challenges of IoMT. Additionally, RCLNet utilizes focal loss (FL) to manage imbalanced data distributions, a common challenge in IoMT datasets. Evaluation using the WUSTL-EHMS-2020 healthcare dataset demonstrates that RCLNet outperforms recent state-of-the-art methods, achieving a remarkable accuracy of 99.78%, highlighting its potential to significantly improve the security and confidentiality of patient data in IoMT healthcare systems.

The Internet of Medical Things (IoMT) is revolutionizing healthcare by providing remarkable possibilities for remote patient monitoring, instantaneous data analysis, and customized healthcare delivery. However, the widespread use of interconnected medical devices has exposed vulnerabilities to cyber threats, posing significant challenges to the security, privacy, and accessibility of healthcare data and services. The CICIoMT2024 dataset is a crucial resource in IoMT security, offering a wide range of cyber-attacks targeting IoMT devices. This paper uses data balancing techniques like SMOTE and advanced machine learning (ML) models to analyze cyber threats on IoMT devices, aiming to improve healthcare system safety by identifying and mitigating cyberattacks. By conducting extensive experiments, the paper has determined the most effective ML models for three different levels of classification of the dataset: binary, multiclass, and multitype. Employing ML techniques like AdaBoost, Random Forest, kNN, and XGBoost proves to be extremely powerful in accurately categorizing various types of attacks. This study emphasizes the importance of proactive cybersecurity measures in IoMT ecosystems, as well as the effectiveness of ML techniques in protecting healthcare systems from evolving cyber threats.

Abstract Reinforcement of the Internet of Medical Things (IoMT) network security has become extremely significant as these networks enable both patients and healthcare providers to communicate with each other by exchanging medical signals, data, and vital reports in a safe way. To ensure the safe transmission of sensitive information, robust and secure access mechanisms are paramount. Vulnerabilities in these networks, particularly at the access points, could expose patients to significant risks. Among the possible security measures, biometric authentication is becoming a more feasible choice, with a focus on leveraging regularly-monitored biomedical signals like Electrocardiogram (ECG) signals due to their unique characteristics. A notable challenge within all biometric authentication systems is the risk of losing original biometric traits, if hackers successfully compromise the biometric template storage space. Current research endorses replacement of the original biometrics used in access control with cancellable templates. These are produced using encryption or non-invertible transformation, which improves security by enabling the biometric templates to be changed in case an unwanted access is detected. This study presents a comprehensive framework for ECG-based recognition with cancellable templates. This framework may be used for accessing IoMT networks. An innovative methodology is introduced through non-invertible modification of ECG signals using blind signal separation and lightweight encryption. The basic idea here depends on the assumption that if the ECG signal and an auxiliary audio signal for the same person are subjected to a separation algorithm, the algorithm will yield two uncorrelated components through the minimization of a correlation cost function. Hence, the obtained outputs from the separation algorithm will be distorted versions of the ECG as well as the audio signals. The distorted versions of the ECG signals can be treated with a lightweight encryption stage and used as cancellable templates. Security enhancement is achieved through the utilization of the lightweight encryption stage based on a user-specific pattern and XOR operation, thereby reducing the processing burden associated with conventional encryption methods. The proposed framework efficacy is demonstrated through its application on the ECG-ID and MIT-BIH datasets, yielding promising results. The experimental evaluation reveals an Equal Error Rate (EER) of 0.134 on the ECG-ID dataset and 0.4 on the MIT-BIH dataset, alongside an exceptionally large Area under the Receiver Operating Characteristic curve (AROC) of 99.96% for both datasets. These results underscore the framework potential in securing IoMT networks through cancellable biometrics, offering a hybrid security model that combines the strengths of non-invertible transformations and lightweight encryption.

Katerina Svandova,* Zdenek Smutny* Faculty of Informatics and Statistics, Prague University of Economics and Business, Prague, Czech Republic*These authors contributed equally to this workCorrespondence: Zdenek Smutny, Faculty of Informatics and Statistics, Prague University of Economics and Business, W. Churchill Sq. 1938/4, 130 67 Prague 3, Prague, Czech Republic, Email zdenek.smutny@vse.czBackground: The massive expansion of the Internet of medical things (IoMT) technology brings many opportunities for improving healthcare. At the same time, their use increases security risks, brings security and privacy concerns, and threatens the functioning of healthcare facilities or healthcare provision.Purpose: This scoping review aims to identify progress in designing risk assessment and management frameworks for IoMT security. The frameworks found are divided into two groups according to whether frameworks address the technological design of risk management or assess technological measures to ensure the security of the IoMT environment. Furthermore, the article intends to find out whether frameworks also include an assessment of organisational measures related to IoMT security.Methods: This review was prepared using PRISMA ScR guidelines. Relevant studies were searched in the citation databases Web of Science and Scopus. The search was limited to articles published in English between 2018 and 17 September 2023. The initial search yielded 1341 articles, of which 44 (3.3%) were included in the scoping review. A qualitative content analysis focused on selected security perspectives and progress in the given area was carried out.Results: Thirty-two articles describe the design of risk assessment and management frameworks. Twelve articles describe the design of frameworks for assessing the security of IoMT devices and possibly offer a comparison of different IoMT alternatives. A description of the included articles was prepared from the selected security perspectives.Conclusion: The review shows the need to create comprehensive or holistic frameworks for operational security and privacy risk management at all layers of the IoMT architecture. It includes the design of specific technological solutions and frameworks for continuously assessing the overall level of information security and privacy of the IoMT environment. Unfortunately, none of the found frameworks offer an assessment of organizational measures even though the importance of the organization measures was highlighted in articles. Another area of interest for researchers could be the design of a general risk management database for IoMT, which would include potential IoMT-related risks connected to a particular device.Keywords: cybersecurity, healthcare, information systems, IoMT, internet of things, IoT, threat, sensors

Abstract The management of medications is a crucial component of healthcare, and pharmaceutical errors can have detrimental effects on patients, healthcare professionals, and healthcare systems. By utilizing patient-specific data and cutting-edge technology like the Internet of Medical Things (IoMT) and machine learning, customized drug management systems have the potential to increase patient safety and healthcare effectiveness. In this study, we reviewed a large body of literature on the subject of medication management in healthcare and the potential advantages of personalized medication management. We then assessed how IoMT and machine learning might be used to enhance medication management in smart hospitals. Then, we created a framework for assessing how personalized medication management utilizing IoMT and machine learning affects patient safety and healthcare effectiveness. Our study's findings demonstrate that in smart hospitals, tailored medication management with IoMT and machine learning can drastically lower medication errors while also enhancing patient safety and healthcare effectiveness. Our findings have important ramifications for the future of medication administration in smart hospitals, and we advise healthcare professionals and policymakers to give priority to integrating cutting-edge technology like IoMT and machine learning for customized medication management.

The Internet of Medical Things (IoMT) is an evolving field in healthcare that connects medical devices to the Internet to enable efficient data sharing and health information collection. The IoMT aims to improve the quality of healthcare, facilitate diagnosis and treatment, and enhance patient safety. Nonetheless, the IoMT networks are usually exposed to multiple security attacks. Also, recent studies indicate that security protocols contain flaws in protecting patient data. Thus, data must be protected by innovative security protocols. In our work, we propose a Medical Security Protocol (MedSecP) to support security in IoMT. The proposed protocol adopts the Twofish encryption, Naive Bayes (NB), and decision tree (DT) within the private blockchain (PBC) Fog Computing (FC) to build robust security procedures. The Twofish encryption algorithm is used to provide medical information concealment. In our proposed protocol, the type of data is first determined, and accurate and appropriate medical decisions are made based on the collected data using a decision tree algorithm, and then rapid classification of the patient data is done using the Naive Bayes algorithm. Confidential medical data is then encrypted using the Twofish algorithm to ensure the confidentiality of this data and prevent unauthorized access. Finally, this encrypted medical data is stored using blockchain technology. Twofish, NB, and DT are organized to work harmoniously with the PBC. The latter manages and distributes data peer-to-peer in IoMT. We leverage Fog Computing to speed up decision-making without resorting to the remote cloud. We analyzed our protocol in terms of security and performance. Our results indicate that MedSecP provides reliable security against attacks as the protocol demonstrated an average security attack response rate of 97.20%, demonstrating its resistance to external threats by keeping the encrypted medical data, classified and achieving appropriate medical decisions. In terms of performance, MedSecP has demonstrated an average security response time of around 50ms, providing fast and efficient performance. In MedSecP, the highest value for encryption is 0.000015 ms, and decryption is 0.000017 ms when applying the Twofish algorithm which is considered extremely suitable for implementing health systems operations compared to existing encryption algorithms. Consequently, MedSecP provides lightweight operations in support of complex security measures that qualify it to support healthcare institutions.

The Internet of Medical Things (IoMT) has transformed healthcare by connecting medical devices, sensors, and patients, significantly improving patient care. However, the sensitive data exchanged through IoMT is vulnerable to security attacks, raising serious privacy concerns. Traditional key sharing mechanisms are susceptible to compromise, posing risks to data integrity. This paper proposes a Timestamp-based Secret Key Generation (T-SKG) scheme for resource-constrained devices, generating a secret key at the patient's device and regenerating it at the doctor's device, thus eliminating direct key sharing and minimizing key compromise risks. Simulation results using MATLAB and Java demonstrate the T-SKG scheme's resilience against guessing, birthday, and brute force attacks. Specifically, there is only a 9 % chance of key compromise in a guessing attack if the attacker knows the key sequence pattern, while the scheme remains secure against brute force and birthday attacks within a specified timeframe. The T-SKG scheme is integrated into a healthcare framework to securely transmit health vitals collected using the MySignals sensor kit. For confidentiality, the Data Encryption Standard (DES) with various Cipher Block modes (ECB, CBC, CTR) is employed.

Major depressive disorder (MDD) is a mental ailment marked by a loss of interest in activities, persistent depression, and hopelessness. MDD has been on the rise in society in recent decades for varied reasons and has spurred suicidal tendencies among individuals. Early detection, continuous monitoring, and effective treatment are crucial for its impact on quality of life and society. EEG signal models the brain’s electrical activities and has emerged as a potential tool to assess the depression status of a person. Due to advancements in sensor technology, fast, convenient, and cost-effective EEG acquisition is now possible, resulting in many EEG-based healthcare monitoring applications in recent years. This work proposes an EEG-headset-based smart monitoring system for real-time diagnosis of MDD in the Internet of Medical Things (IoMT) framework. In this study, we proposed a novel Linear Graph Convolution Network-Transformer-based deep learning approach for categorizing MDD through a time-frequency analysis of EEG signals. The Stockwell transform (S-transform) is employed to exploit the spectro-temporal information from the EEG and the resulting 2D representation is then fed into customized Linear Graph Convolution Network for MDD detection. We have utilized the Weighted Focal Binary Hinge Loss function, specifically designed for customized Linear Graph Convolution Network, to improve learning and handle unbalanced input. Subsequently, a novel Transformer model is designed to refine the MDD classification further. The proposed methodology named SLiTRANet, blends spectral analysis with the S-transform, graph-based learning with Linear Graph Convolution Network, and the sequence modeling capability of the Transformer. The proposed SLiTRANet model can be further integrated within an IoMT framework for automated real-time MDD diagnosis using EEG signals. The proposed methodology is evaluated on two publicly available datasets, MODMA and HUSM datasets. The evaluation results demonstrate the superior performance of the proposed SLiTRANet framework against the existing pre-trained and hybrid deep learning models, achieving remarkable accuracy, sensitivity, specificity, and precision rates of 99.92%, 99.90%, 99.95%, and 99.97%, respectively on HUSM dataset followed by an equally good performance on MODMA dataset with an accuracy of 99.68%. The proposed comprehensive approach implemented on two varied datasets highlights significant advancements in depression detection by outperforming state-of-art approaches.

Smart Healthcare has brought advantages to both healthcare professionals and individuals living in remote areas. However, deliberately or inadvertently altering medical records can result in an inaccurate diagnosis. As a result of telemedicine, patients can easily communicate with their healthcare providers, and the Internet of Medical Things (IoMT) devices enable remote consultations, allowing for timely diagnosis and treatment. Medical data can be protected by watermarking, a combination of steganography, and fingerprinting. This paper proposes a hash-based approach using the Discrete Cosine Integer Wavelet Transform (DCIWT) to provide secure watermarking for confidential medical data. To preserve critical Region of Interest (RoI) integrity, we use SHA-256 cryptographic hash algorithm. The hashed Region of Interest (hRoI) is embedded in the Region of Non-Interest (RoNI) by DCIWT. To enhance the segmentation process, we employ adaptive thresholding, which dynamically adjusts the threshold based on local image characteristics, ensuring accurate segmentation of RoI and RoNI across varying image qualities. The hidden data is significantly affected if the watermarked image is altered. As a result of comparing the original RoI with the extracted RoI, we determine if an image has been altered. Tampered regions are be recovered by extracting the RoI and using it for diagnosis. The proposed digital watermarking technique maintains RoI integrity using message digests and histograms. Through extensive experiment results, we demonstrate that the perceptual properties of the images are intact in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM).

Tele-rehabilitation has garnered significant interest among clinicians and researchers with its potential to transform cardiac rehabilitation, affecting millions of patients annually. A critical requirement of tele-cardiac rehabilitation is a fail-safe, highly interactive system with timely feedback to both the patient and the therapists or physicians. Current systems often assume ideal network conditions, neglecting the nuances of real-world deployment. We have designed, developed, and tested an end-to-end tele-cardiac rehabilitation system that seamlessly combines Internet of Medical Things (IoMT) devices and AI-powered abnormality and activity detection, providing a fail-safe and real-time actionable closed-feedback loop system for the patient and the doctor. A pilot study evaluates system performance across diverse mobile networks in varying conditions (stable or unstable). The RESNET-18 model for cardiac abnormality detection (0.71 F1-score) and the VGG-16 model for human activity classification (0.89 F1-score) demonstrate significant performance. Furthermore, we optimize these models for edge devices, demonstrating significant speed improvements compared to cloud servers (up to 33 times faster).

The rise of smart health technologies has offered benefits such as remote patient monitoring, personalized therapy, and early disease detection. However this advancement has also raised concerns about security and privacy of patient data. Authentication is essential for protecting patient data and smart health devices. The existing scheme is identified with security vulnerabilities in terms of stolen verifier, impersonation, and man in middle attacks. Motivated by this, authors have presented a secure, lightweight authentication technique for smart health environments for a remote monitoring scenario employing a Multiple Factor approach and a lightweight algorithm. The scheme is verified through formal and informal analysis. The scheme demonstrated enhanced authentication performance and security with processing overhead of (0.04 milliseconds) and transmission overhead of (704 bits), in comparison to the baseline scheme. This system has the potential to improve data security and privacy in a variety of smart health scenarios.

Goal: To establish Pulse2AI as a reproducible data preprocessing framework for pulsatile signals that generate high-quality machine-learning-ready datasets from raw wearable recordings. Methods: We proposed an end-to-end data preprocessing framework that adapts multiple pulsatile signal modalities and generates machine-learning-ready datasets agnostic to downstream medical tasks. Results: a dataset preprocessed by Pulse2AI improved systolic blood pressure estimation by 29.58%, from 11.41 to 8.03 mmHg in root-mean-square-error (RMSE) and its diastolic counterpart by 26.01%, from 7.93 to 5.87 mmHg in RMSE. For respiration rate (RR) estimation, Pulse2AI boosted performance by 19.69%, from 1.47 to 1.18 breaths per minute (BrPM) in mean-absolute-error (MAE). Conclusion: Pulse2AI turns pulsatile signals into machine learning (ML) ready datasets for arbitrary remote health monitoring tasks. We tested Pulse2AI on multiple pulsatile modalities and demonstrated its efficacy in two medical applications. This work bridges valuable assets in remote sensing and internet of medical things to ML-ready datasets for medical modeling.

The Internet of Things (IoT) has shown remarkable competence in uniting devices and internet connectivity to make modern life’s applications a reality. These networked devices, empowered with computing and communications, usually exhibit an exponential increment in network traffic to enhance the end-user quality of experience (QoE). The exponential increase in network traffic makes the traditional communication system congested. To reduce the burden of network traffic, the data could be offloaded to device-to-device (D2D)-based infrastructure. This offloading scheme results in an innovative, eco-friendly, and sustainable data dissemination approach known as social networking and is mostly done in opportunistic ways. In this paper, we propose a scheme to explore the performance of the existing routing protocols with the movements and spatial closeness relations among ‘things’, termed as Internet of Moving Things (IoMT). The spatial closeness known as traces will help to make optimal data offloading decisions while considering the mobility traces of humans in the social network. The proposed mechanism is designed to optimally utilize moving physical objects to be involved as a third party in the data communication process between the sources and the destinations rather than using the traditional infrastructure while considering the quality of service (QoS). The simulation results have corroborated the proposed scheme and showed the remarkable performance of routing algorithms while incorporating mobility traces.

Anomaly detection is a critical aspect of various applications, including security, healthcare, and network monitoring. In this study, we introduce FusionNet, an innovative ensemble model that combines the strengths of multiple machine learning algorithms, namely Random Forest, K-Nearest Neighbors, Support Vector Machine, and Multi-Layer Perceptron, for enhanced anomaly detection. FusionNet’s architecture leverages the diversity of these algorithms to achieve high accuracy and precision. We evaluate FusionNet’s performance on two distinct datasets, Dataset 1 and Dataset 2, and compare it with traditional machine learning models, including SVM, KNN, and RF. The results demonstrate that FusionNet consistently outperforms these models across both datasets in terms of accuracy, precision, recall, and F1 score. On Dataset 1, FusionNet achieves an accuracy of 98.5% and on Dataset 2, it attains an accuracy of 99.5%. FusionNet’s remarkable ability to detect anomalies with exceptional accuracy underscores its potential for real-world applications. This study highlights the significance of FusionNet as a robust model for anomaly detection and provides insights into its superior performance over traditional models. The results emphasize the promising prospects of FusionNet in security, healthcare, and other domains where accurate anomaly detection is crucial.

This paper presents the development of a vital sign monitoring system designed specifically for professional athletes, with a focus on runners. The system aims to enhance athletic performance and mitigate health risks associated with intense training regimens. It comprises a wearable glove that monitors key physiological parameters such as heart rate, blood oxygen saturation (SpO2), body temperature, and gyroscope data used to calculate linear speed, among other relevant metrics. Additionally, environmental variables, including ambient temperature, are tracked. To ensure accuracy, the system incorporates an onboard filtering algorithm to minimize false positives, allowing for timely intervention during instances of physiological abnormalities. The study demonstrates the system’s potential to optimize performance and protect athlete well-being by facilitating real-time adjustments to training intensity and duration. The experimental results show that the system adheres to the classical “220-age” formula for calculating maximum heart rate, responds promptly to predefined thresholds, and outperforms a moving average filter in noise reduction, with the Gaussian filter delivering superior performance.

The continuous evolution of the IoT paradigm has been extensively applied across various application domains, including air traffic control, education, healthcare, agriculture, transportation, smart home appliances, and others. Our primary focus revolves around exploring the applications of IoT, particularly within healthcare, where it assumes a pivotal role in facilitating secure and real-time remote patient-monitoring systems. This innovation aims to enhance the quality of service and ultimately improve people’s lives. A key component in this ecosystem is the Healthcare Monitoring System (HMS), a technology-based framework designed to continuously monitor and manage patient and healthcare provider data in real time. This system integrates various components, such as software, medical devices, and processes, aimed at improvi1g patient care and supporting healthcare providers in making well-informed decisions. This fosters proactive healthcare management and enables timely interventions when needed. However, data transmission in these systems poses significant security threats during the transfer process, as malicious actors may attempt to breach security protocols.This jeopardizes the integrity of the Internet of Medical Things (IoMT) and ultimately endangers patient safety. Two feature sets—biometric and network flow metric—have been incorporated to enhance detection in healthcare systems. Another major challenge lies in the scarcity of publicly available balanced datasets for analyzing diverse IoMT attack patterns. To address this, the Auxiliary Classifier Generative Adversarial Network (ACGAN) was employed to generate synthetic samples that resemble minority class samples. ACGAN operates with two objectives: the discriminator differentiates between real and synthetic samples while also predicting the correct class labels. This dual functionality ensures that the discriminator learns detailed features for both tasks. Meanwhile, the generator produces high-quality samples that are classified as real by the discriminator and correctly labeled by the auxiliary classifier. The performance of this approach, evaluated using the IoMT dataset, consistently outperforms the existing baseline model across key metrics, including accuracy, precision, recall, F1-score, area under curve (AUC), and confusion matrix results.

The Internet of Medical Things (IoMTs) is a network of connected medical equipment such as pacemakers, prosthetics, and smartwatches. Utilizing the IoMT-based system, a huge amount of data is generated, offering experts a valuable resource for tasks such as prediction, real-time monitoring, and diagnosis. To do so, the patient’s health data must be transferred to database storage for processing because of the limitations of the storage and computation capabilities of IoMT devices. Consequently, concerns regarding security and privacy can arise due to the limited control over the transmitted information and reliance on wireless transmission, which leaves the network vulnerable to several kinds of attacks. Motivated by this, in this study, we aim to build and improve an efficient intrusion detection system (IDS) for IoMT networks. The proposed IDS leverages tree-based machine learning classifiers combined with filter-based feature selection techniques to enhance detection accuracy and efficiency. The proposed model is used for monitoring and identifying unauthorized or malicious activities within medical devices and networks. To optimize performance and minimize computation costs, we utilize Mutual Information (MI) and XGBoost as filter-based feature selection methods. Then, to reduce the number of the chosen features selected, we apply a mathematical set (intersection) to extract the common features. The proposed method can detect intruders while data are being transferred, allowing for the accurate and efficient analysis of healthcare data at the network’s edge. The system’s performance is assessed using the CICIDS2017 dataset. We evaluate the proposed model in terms of accuracy, F1 score, recall, precision, true positive rate, and false positive rate. The proposed model achieves 98.79% accuracy and a low false alarm rate 0.007 FAR on the CICIDS2017 dataset according to the experimental results. While this study focuses on binary classification for intrusion detection, we are planning to build a multi-classification approach for future work which will be able to not only detect the attacks but also categorize them. Additionally, we will consider using our proposed feature selection technique for different ML classifiers and evaluate the model’s performance empirically in real-world IoMT scenarios.

The availability of drugs across the country is a direct measure for fairer public health. Several issues have been reported drastically related to various organizations that fail to provide quality medicines on time. There has been a consistent increase in cases where the treatment, as well as exempted drugs, were supplied due to the unavailability of proper traceability of the supply chain. Several parties are involved in the supply and have similar interests that may defer the adequate shareability of the drugs. The existing system for managing the drug supply chain suffers from several backlogs. The loss of information, unavailability of resources to track the proper medicinal storage, transparency of information sharing between various stakeholders and sequential access. The applicability of the decentralized model emerging from the blockchain can apply to one of the perfect solutions in this case. The drug traceability chain can be deployed to a Ledger-based blockchain that may result in decentralized information. Continuous supply from the Internet of Things (IoT) based devices might be handy as the middleware for providing a trustworthy, safe, and proper transaction-oriented system. The data integrity, along with the provenance resulting from the IoT-connected devices, is an effective solution towards managing the supply chain and drug traceability. This study presents a model that can provide a token-based blockchain that will help provide a cost-efficient and secure system for a reliable drug supply chain.

Abstract Background Melanoma is one of the deadliest tumors in the world. Early detection is critical for first-line therapy in this tumor pathology and it remains challenging due to the need for histological analysis to ensure correctness in diagnosis. Therefore, multiple computer-aided diagnosis (CAD) systems working on melanoma images were proposed to mitigate the need of a biopsy. However, although the high global accuracy is declared in literature results, the CAD systems for the health fields must focus on the lowest false negative rate (FNR) possible to qualify as a diagnosis support system. The final goal must be to avoid classification type 2 errors to prevent life-threatening situations. Another goal could be to create an easy-to-use system for both physicians and patients. Results To achieve the minimization of type 2 error, we performed a wide exploratory analysis of the principal convolutional neural network (CNN) architectures published for the multiple image classification problem; we adapted these networks to the melanoma clinical image binary classification problem (MCIBCP). We collected and analyzed performance data to identify the best CNN architecture, in terms of FNR, usable for solving the MCIBCP problem. Then, to provide a starting point for an easy-to-use CAD system, we used a clinical image dataset (MED-NODE) because clinical images are easier to access: they can be taken by a smartphone or other hand-size devices. Despite the lower resolution than dermoscopic images, the results in the literature would suggest that it would be possible to achieve high classification performance by using clinical images. In this work, we used MED-NODE, which consists of 170 clinical images (70 images of melanoma and 100 images of naevi). We optimized the following CNNs for the MCIBCP problem: Alexnet, DenseNet, GoogleNet Inception V3, GoogleNet, MobileNet, ShuffleNet, SqueezeNet, and VGG16. Conclusions The results suggest that a CNN built on the VGG or AlexNet structure can ensure the lowest FNR (0.07) and (0.13), respectively. In both cases, discrete global performance is ensured: 73% (accuracy), 82% (sensitivity) and 59% (specificity) for VGG; 89% (accuracy), 87% (sensitivity) and 90% (specificity) for AlexNet.

In recent years, the Internet of medical things (IoMT) has become a significant technological advancement in the healthcare sector. This systematic review aims to identify and summarize the various applications, key challenges, and proposed technical solutions within this domain, based on a comprehensive analysis of the existing literature. This review highlights diverse applications of the IoMT, including mobile health (mHealth) applications, remote biomarker detection, hybrid RFID-IoT solutions for scrub distribution in operating rooms, IoT-based disease prediction using machine learning, and the efficient sharing of personal health records through searchable symmetric encryption, blockchain, and IPFS. Other notable applications include remote healthcare management systems, non-invasive real-time blood glucose measurement devices, distributed ledger technology (DLT) platforms, ultra-wideband (UWB) radar systems, IoT-based pulse oximeters, accident and emergency informatics (A&EI), and integrated wearable smart patches. The key challenges identified include privacy protection, sustainable power sources, sensor intelligence, human adaptation to sensors, data speed, device reliability, and storage efficiency. The proposed mitigations encompass network control, cryptography, edge-fog computing, and blockchain, alongside rigorous risk planning. The review also identifies trends and advancements in the IoMT architecture, remote monitoring innovations, the integration of machine learning and AI, and enhanced security measures. This review makes several novel contributions compared to the existing literature, including (1) a comprehensive categorization of IoMT applications, extending beyond the traditional use cases to include emerging technologies such as UWB radar systems and DLT platforms; (2) an in-depth analysis of the integration of machine learning and AI in IoMT, highlighting innovative approaches in disease prediction and remote monitoring; (3) a detailed examination of privacy and security measures, proposing advanced cryptographic solutions and blockchain implementations to enhance data protection; and (4) the identification of future research directions, providing a roadmap for addressing current limitations and advancing the scientific understanding of IoMT in healthcare. By addressing current limitations and suggesting future research directions, this work aims to advance scientific understanding of the IoMT in healthcare.

The Internet of Medical Things (IoMT) represents a specialized domain within the Internet of Things, focusing on medical devices that require regulatory approval to ensure patient safety. Trusted composition of IoMT systems aims to ensure high assurance of the entire composed system, despite potential variability in the assurance levels of individual components. Achieving this trustworthiness in IoMT systems, especially when using less-assured, commercial, off-the-shelf networks like Ethernet and WiFi, presents a significant challenge. To address this challenge, this paper advocates a systematic approach that leverages the Architecture Analysis & Design Language (AADL) along with Behavior Language for Embedded Systems with Software (BLESS) specification and implementation. This approach aims to provide high assurance on critical components through formal verification, while using less-assured components in a manner that maintains overall system determinism and reliability. A clinical case study involving an automated opioid infusion monitoring IoMT system is presented to illustrate the application of the proposed approach. Through this case study, the effectiveness of the systemic approach in achieving trusted composition of heterogeneous medical devices over less-assured networks is demonstrated.

Abstract Security of Internet-of-Medical-Things (IoMT) networks has evolved as a vital issue in recent years. The IoMT networks are designed to link patients with caregivers. All reports, data, and medical signals are transferred over these networks. Hence, these networks require robust and secure access strategies for patients to send their vital data or reports. Hence, hacking of these networks may lead to harmful effects on patients. One of the vulnerable points to hacking in these networks is the access point. Access to these networks could be performed with biometrics. The popular biometric traits for this purpose are biomedical signals such as Electrocardiogram (ECG) signals, as they are continuously monitored and measured for patients. A common thread between all biometric authentication systems is the possibility of losing the biometric traits forever if hacking attempts manage to concur the biometric template storage. A new trend in the field of biometric authentication is to avoid the utilization of original biometrics in access control processes. A possible alternative is to use cancelable biometrics instead. Cancelable biometrics can be generated through encryption schemes or non-invertible transforms. This paper adopts both strategies in a unified framework for cancelable ECG signal recognition that can be used in the access step of IoMT networks. The proposed framework begins with applying a non-invertible transformation on the ECG signals through fuzzy logic to change the dynamic range of the signals. As this process is non-invertible in nature, it prevents the recovery of the original ECG signals from the processed versions, which is the main target of cancelable biometric systems. After that, lightweight encryption through XOR operation with user-specific patterns is implemented. Here, the high complexity of full encryption schemes that need a large processing burden is eliminated. The addition of the encryption stage enhances the security of cancelable biometric traits, allowing a hybrid nature of the proposed cancelable biometric framework through the merging of non-invertible transforms and encryption algorithms. Moreover, an FPGA hardware implementation is introduced for real implementation of the proposed ECG-based cancelable biometric recognition framework. This hardware can accompany the user to allow access of the IoMT network when requested. Experimental results show a promising performance of the proposed framework with a large Area under the Receiver Operating Characteristic curve (AROC) of 99.5% and an Equal Error Rate (EER) of 0.058%.

Internet of Things (IoT) enables different smart environment objects to communicate without involving humans. Recently, IoT has started a new challenge within the healthcare sector called the Internet of Medical Things (IoMT). The huge amounts of data generated by IoMT entities need to be analyzed in real-time to improve the performance and quality of service of the IoMT applications. Mobile Edge Computing-enabled 5G system is shown as a successful paradigm to address such an obstacle. Numerous frameworks are introduced in literature based on this idea. This paper presents a thorough discussion of MEC-based IoMT healthcare systems.

Mounir El Khatib,1 Haitham M Alzoubi,2,3 Samer Hamidi,4 Muhammad Alshurideh,5,6 Ali Baydoun,7 Ahmed Al-Nakeeb1 1School of Business and Quality Management, Hamdan Bin Mohammed Smart University, Dubai, United Arab Emirates; 2School of Business, Skyline University College, Sharjah, United Arab Emirates; 3Applied Science Research Center, Applied Science Private University, Amman, Jordan; 4School of Health and Environmental Studies, Hamdan Bin Mohammed Smart University, Dubai, United Arab Emirates; 5College of Business, University of Sharjah, Sharjah, United Arab Emirates; 6Department of Marketing, School of Business, The University of Jordan, Amman, Jordan; 7School of Medicine, St. George’s University, Grenada, West IndiesCorrespondence: Samer Hamidi, School of Health and Environmental Studies, Hamdan Bin Mohammed Smart University, PO Box: 71400, Dubai, United Arab Emirates, Tel +971-4-424-1089, Email s.hamidi@hbmsu.ac.aeBackground: This paper explores the use of blockchain technology and smart contracts in the Internet of Medical Things (IoMT). It aims to identify the challenges and benefits of implementing smart contracts based on blockchain technology in the IoMT. It provides solutions and evaluates the IoMT uses in e-healthcare performance.Methods: A quantitative approach used an online survey from public and private hospital administrative departments in Dubai, United Arab Emirates (UAE). ANOVA, t-test, correlation, and regression analysis were performed to assess the e-healthcare performance with and without IoMT (smart contract based on blockchain).Patients and Methods: A mixed method was used in this research, a quantitative approach for data analysis utilizing online surveys from public and private hospitals’ administrative departments in Dubai, UAE. A correlation, regression through ANOVA, and independent two-sample t-test were performed to assess the e-healthcare performance with and without IoMT (smart contract based on blockchain).Results: Blockchain application in smart contracts has proven to be significant in the healthcare sector. Results highlight the importance of integrating smart contracts and blockchain technology in the IoMT infrastructure to improve efficiency, transparency, and security. The study provides empirical evidence to support the implementation of smart contracts in the e-healthcare sector and suggests improved e-healthcare performance through this transition.Conclusion: The emergence of e-healthcare systems with upgraded smart contracts and blockchain technology brings continuous health monitoring, time-effective operations, and cost-effectiveness to the healthcare sector.Keywords: internet of medical things, IoMT, e-healthcare, smart contracts, blockchain

Recently, the use of the Internet of Medical Things (IoMT) has gained popularity across various sections of the health sector. The historical security risks of IoMT devices themselves and the data flowing from them are major concerns. Deploying many devices, sensors, services, and networks that connect the IoMT systems is gaining popularity. This study focuses on identifying the use of blockchain in innovative healthcare units empowered by federated learning. A collective use of blockchain with intrusion detection management (IDM) is beneficial to detect and prevent malicious activity across the storage nodes. Data accumulated at a centralized storage node is analyzed with the help of machine learning algorithms to diagnose disease and allow appropriate medication to be prescribed by a medical healthcare professional. The model proposed in this study focuses on the effective use of such models for healthcare monitoring. The amalgamation of federated learning and the proposed model makes it possible to reach 93.89 percent accuracy for disease analysis and addiction. Further, intrusion detection ensures a success rate of 97.13 percent in this study.

Abstract Recently, with the fast development of IoT, Internet of medical things (IoMT) has drawn wide attention from both industry and academia. However, pressing challenges exist in practical implementation of IoMT, such as service provision with stringent latency. To address the challenges, fog computing is generally employed in IoMT systems. However, it raises additional concerns of trust and security. To tackle the issue, the authors introduce the security measure of trust into this work, and a superior heterogeneous stacking ensemble learning measure for trustworthiness prediction (SEM‐TP) of fog services is proposed. Besides, to reduce unnecessary time cost incurred by unimportant features, an efficient voting‐based feature selection (FS) strategy called voting‐based feature selection method is proposed to select significant features, which is based on diverse FS measures. Extensive experiments are conducted and the results show that the proposed framework outperforms commonly used single classifiers and competing stacking models in terms of Accuracy, Precision, Recall, F1‐score, Kappa coefficient, and Hamming distance under different conditions, validating the effectiveness, robustness, and superiority of the proposed trustworthiness prediction and FS methods.

This paper investigates the application of ensemble learning techniques, specifically meta-learning, in intrusion detection systems (IDS) for the Internet of Medical Things (IoMT). It underscores the existing challenges posed by the heterogeneous and dynamic nature of IoMT environments, which necessitate adaptive, robust security solutions. By harnessing meta-learning alongside various ensemble strategies such as stacking and bagging, the paper aims to refine IDS mechanisms to effectively counter evolving cyber threats. The study proposes a performance-driven weighted meta-learning technique for dynamic assignment of voting weights to classifiers based on accuracy, loss, and confidence levels. This approach significantly enhances the intrusion detection capabilities for the IoMT by dynamically optimizing ensemble IDS models. Extensive experiments demonstrate the proposed model’s superior performance in terms of accuracy, detection rate, F1 score, and false positive rate compared to existing models, particularly when analyzing various sizes of input features. The findings highlight the potential of integrating meta-learning in ensemble-based IDS to enhance the security and integrity of IoMT networks, suggesting avenues for future research to further advance IDS performance in protecting sensitive medical data and IoT infrastructures.

The rapidly expanding Internet of Medical Things (IoMT) landscape fosters enormous opportunities for personalized healthcare, yet it also exposes patients and healthcare systems to diverse security threats. Heterogeneous IoMT devices present challenges that need comprehensive risk assessment due to their varying functionality, protocols, and vulnerabilities. Hence, to achieve the goal of having risk-free IoMT devices, the authors used a hybrid approach using fuzzy logic and the Fuzzy Analytical Hierarchy Process (FAHP) to evaluate risks, providing effective and useful results for developers and researchers. The presented approach specifies qualitative descriptors such as the frequency of occurrence, consequence severity, weight factor, and risk level. A case study with risk events in three different IoMT devices was carried out to illustrate the proposed method. We performed a Bluetooth Low Energy (BLE) attack on an oximeter, smartwatch, and smart peak flow meter to discover their vulnerabilities. Using the FAHP method, we calculated fuzzy weights and risk levels, which helped us to prioritize criteria and alternatives in decision-making. Smartwatches were found to have a risk level of 8.57 for injection attacks, which is of extreme importance and needs immediate attention. Conversely, jamming attacks registered the lowest risk level of 1, with 9 being the maximum risk level and 1 the minimum. Based on this risk assessment, appropriate security measures can be implemented to address the severity of potential threats. The findings will assist healthcare industry decision-makers in evaluating the relative importance of risk factors, aiding informed decisions through weight comparison.

The emergence of remote health monitoring and increased at-home care emphasizes the importance of patient adherence outside the clinical setting. This is particularly pertinent in the treatment of Attention Deficit Hyperactivity Disorder (ADHD) in pediatric patients, as the population inherently has difficulty remembering and initiating treatment tasks. Neurostimulation is an emerging treatment modality for pediatric ADHD and requires strict adherence to a treatment regimen to be followed in an at-home setting. Thus, to achieve the desired therapeutic effect, careful attention must be paid to design features that can passively promote and effectively monitor therapeutic adherence. This work describes instrumentation designed to support a clinical trial protocol that tests whether choice of color, or color itself, can statistically significantly increase adherence rates in pediatric ADHD patients in an extraclinical environment. This is made possible through the development and application of an internet-of-things approach in a remote adherence monitoring technology that can be implemented in forthcoming neurostimulation devices for pediatric patient use. This instrumentation requires minimal input from the user, is durable and resistant to physical damage, and provides accurate adherence data to parents and physicians, increasing assurance that neurostimulation devices are effective for at-home care.

The Internet of Things (IoT) is still a relatively new field of research, and its potential to be used in the healthcare and medical sectors is enormous. In the last five years, IoT has been a go-to option for various applications such as using sensors for different features, machine-to-machine communication, etc., but precisely in the medical sector, it is still lagging far behind compared to other sectors. Hence, this study emphasises IoT applications in medical fields, Medical IoT sensors and devices, IoT platforms for data visualisation, and artificial intelligence in medical applications. A systematic review considering PRISMA guidelines on research articles as well as the websites on IoMT sensors and devices has been carried out. After the year 2001, an integrated outcome of 986 articles was initially selected, and by applying the inclusion–exclusion criterion, a total of 597 articles were identified. 23 new studies have been finally found, including records from websites and citations. This review then analyses different sensor monitoring circuits in detail, considering an Intensive Care Unit (ICU) scenario, device applications, and the data management system, including IoT platforms for the patients. Lastly, detailed discussion and challenges have been outlined, and possible prospects have been presented.

Lactate, once merely regarded as an indicator of tissue hypoxia and muscular fatigue, has now gained prominence as a pivotal biomarker across various medical disciplines. Recent research has unveiled its critical role as a high-value prognostic marker in critical care medicine. The current practice of lactate detection involves periodic blood sampling. This approach is invasive and confined to measurements at six-hour intervals, leading to resource expenditure, time consumption, and patient discomfort. This review addresses non-invasive sensors that enable continuous monitoring of lactate in critical care patients. After the introduction, it discusses the iontophoresis system, followed by a description of the structural materials that are universally employed to create an interface between the integumentary system and the sensor. Subsequently, each method is detailed according to its physical principle, outlining its advantages, limitations, and pertinent aspects. The study concludes with a discussion and conclusions, aiming at the design of an intelligent sensor (Internet of Medical Things or IoMT) to facilitate continuous lactate monitoring and enhance the clinical decision-making support system in critical care medicine.

Advancements in networking technologies have enabled doctors to remotely diagnose and monitor patients using the Internet of Medical Things (IoMT), telemedicine, and edge-enabled healthcare. In e-healthcare, medical reports and patient records are typically outsourced to a server, which can make them vulnerable to unauthorized access and tampering. Therefore, it is crucial to ensure the authorization, security, confidentiality, and integrity of medical data. To address these challenges, this paper proposes a novel reversible watermarking approach with a high payload and low computational cost. First, the input medical image is divided into a Border region (BR) and a Non-Border region (NBR). The NBR region is upscaled using Neighbour Mean Interpolation (NMI) to ensure reversibility. The Electronic Patient Record (EPR) is encrypted using a pseudorandom key, which is generated adaptively from the host medical image and the Enigma machine. The encrypted EPR is then embedded in the medical image using NMI. Two levels of tamper detection (global and local) are performed at the receiver’s end for higher accuracy. A Global Integrity Code is generated and embedded in BR using LSB embedding technique for global tamper detection. The experimental results show that the visual quality and robustness are both high (Avg. PSNR = 41.03 dB and Avg. SSIM = 0.99, NC = 0.99, and BER = 0.0019 calculated for 100 images). The subjective and objective experimental analysis indicates that the proposed scheme is highly secure and the computational cost is also low. The average embedding and extraction time (including embedding, encryption and decryption, extraction process respectively) is 0.88 s and 0.83 s. It is resistant to various image processing attacks. A comparison with some of the most recent popular schemes confirms the scheme’s effectiveness.

Identifying human diseases remains a difficult process, even in the age of advanced information technology and the smart healthcare industry 5.0. In the smart healthcare industry 5.0, precise prediction of human diseases, particularly lethal cancer diseases, is critical for human well-being. The global Internet of Medical Things sector has advanced at a breakneck pace in recent years, from small wristwatches to large aircraft. The critical aspects of the Internet of Medical Things include security and privacy, owing to the massive scale and deployment of the Internet of Medical Things networks. Transfer learning with a secure IoMT-based approach is considered. The Google net deep machine-learning model is used for accurate disease prediction in the smart healthcare industry 5.0. We can easily and reliably anticipate the lethal cancer disease in the human body by using the secure IoMT-based transfer learning approach. Furthermore, the results of the proposed secure IoMT-based Transfer learning techniques are used to validate the best cancer disease prediction in the smart healthcare industry 5.0. The proposed secure IoMT-based transfer learning methodology reached 98.8%, better than the state-of-the-art methodologies used previously for cancer disease prediction in the smart healthcare industry 5.0.

The Internet of Medical Things (IoMT) significantly impacts our healthcare system because it allows us to track and verify patient medical data before storing it in the cloud for future use. A rapidly expanding platform like IoMT requires high security to keep all data safe. The patient's prescription history and other sensitive information must be encrypted and managed with great care. Nevertheless, it is challenging to determine what data uses are acceptable while protecting patient privacy and security. Understanding the limits of current technologies and envisioning future research paths is crucial for establishing a safe and reliable data environment. An untrustworthy person can communicate with a trustworthy person via blockchain, a decentralized digital ledger that allows for end-to-end communication. Therefore, this research suggests that the healthcare industry with blockchain-integrated cyber-security based on artificial intelligence (BICS-AI) in medical care to preserve medical-related things. Blockchain applications have the potential to consistently identify the most severe, potentially life-threatening mistakes in the medical field. The use of blockchain for decentralized data protection helps to protect patient health records from compromise. With the help of an access control provider (ACP), here came up with a lightweight solution that addresses this issue by allowing the delegating of security operations. Medical data from IoMT and integrated devices can be collected and stored securely and distributed using a conventional in-depth approach combined with blockchain, making it suitable for healthcare professionals such as nursing homes, hospitals, and the healthcare industry where data exchange is required. The research findings indicate that the suggested system is viable and has a 94.84% security rate, a security performance of 96.4%, a success rate of 89.9%, and a 5.1% latency rate compared to traditional methods.

Due to emerging developments in sports games, the usage of bio-ankle sensors has been growing progressively. Whereas, Internet of Medical Things (IoMT) is an emerging network that boosts bio-inspired sensors’ performances onto the fog-cloud network. However, a sequence of processes is required to complete the healthcare process for one sportsman. Therefore, workflow-enabled bio-inspired sensors tasks scheduled in IoMT postures different challenges. For instance, cost-efficient scheduling, security, and data validation in distributed hospitals to share their data. In this paper, we devise bio-inspired robotics-enabled schemes in the blockchain-fog-cloud-assisted IoMT environment. The goal is to minimize execution cost and blockchain of applications. Based on the proposed system, the study devises bio-inspired robotics function blockchain task scheduling (BIR-FBTS) schemes, determining the optimal assignment of tasks to the available nodes. The simulation results show that the proposed methods minimized 50% of the service cost and 40% of mined cost in the system compared to all existing bio-inspired healthcare systems.

The entire world progression has ceased with the unexpected outbreak of the COVID-19 pandemic, and urges the requirement for contact-less and autonomous services and applications. Realizing these predominantly Internet of Things (IoT) based applications demands a holistic pervasive computing infrastructure. In this paper, we conduct a survey to determine the possible pervasive approaches for utilizing the Multi-Access Edge Computing (MEC) infrastructure in realizing the requirements of emerging IoT applications. We have formalized specific architectural layouts for the considered IoT applications, while specifying network-level requirements to realize such approaches; and conducted a simulation to test the feasibility of proposed MEC approaches.

Internet of medical things (IoMT) communication has become an increasingly important component of 5G wireless communication networks in healthcare as a result of the rapid proliferation of IoMT devices. Under current network architecture, widespread access to IoMT devices causes system overload and low energy efficiency. 5G-based IoMT systems aim to protect healthcare infrastructure and medical device functionality for longer. Therefore, using energy-efficient communication protocols is essential for enhancing QoS in IoMT systems. Several methods have been developed recently to improve IoMT QoS; however, clustering is more popular because it provides energy efficiency for medical applications. The primary drawback of the existing clustering technique is that their communication model does not take into account the chance of packet loss, which results in unreliable communication and drains the energy of medical nodes. In this study, we concentrated on designing a clustering model named Whale optimized weighted fuzzy-based cluster head selection algorithm to facilitate successful communication for IoMT-based systems. The experimental study shows that the proposed strategy performs better in terms of QoS than compared approaches. Inferring from this, the proposed method not only reduces energy consumption levels of 5G-based IoMT systems but also uniformly distributes cluster-head over a network to improve QoS.

Due to the recent pandemic, the healthcare sector has been forced to incorporate new technologies into its systems, such as IoT and Fog Computing. However, being new technologies, they are prone to security breaches. From this context, it is identified that medical systems do not have a sufficient level of security, due to the use of new technologies such as IoT and the lack of controls to protect these new technologies. Therefore, a model for implementing an Internet of Medical Things (IoMT) Architecture with ISO/IEC 27001 security controls for remote patient monitoring is proposed. This model has 4 stages: Stage 1 selects an information security standard for the healthcare sector. Stage 2 selects the information security controls of the selected standard. Stage 3 selects and evaluates an IoMT architecture applicable to the healthcare sector. And Stage 4 designs the information security controls for each layer of the IoMT architecture. The IoMT architecture and information security controls are simulated and experimented with physicians (the productivity of the system) and with information security expert (the quality of the implemented controls). The results of the first experiment show that ""effectiveness"", ""productivity, and ""satisfaction"" regarding the use of the IoMT architecture have an average rating of 4.05 (high level). The results of the second experiment show that ""Information Security"", ""Awareness"" and ""Security Incident Management"" regarding the quality of the security controls implemented have an average rating of 4.05 (high level).

Abstract The rapid advancement of computation techniques and cloud computing has led to substantial advancements in Cyber-Physical Systems (CPS), particularly in the field of health care. There are a variety of ways in which CPS is used in healthcare today, including delivering intelligent feedback systems, automatically updating patient data digitally, monitoring patients passively with biosensors, etc. In recent years, cyber-physical systems have become capable of making lifesaving decisions as they are becoming more connected to the cloud. However, healthcare has become one of the most critical issues for many. A CPS network uses the Internet of Medical Things (IoMT) to continuously monitor patients’ health metrics such as body temperature, heart rate, etc. Due to physical connectivity restrictions, networks are more susceptible to security threats. In spite of the fact that the data is stored in the cloud, it is necessary to provide security regardless of device security and network security. Several cyber-security vulnerabilities have been identified in cloud-based healthcare systems in particular. To give patients a reliable healthcare experience, security concerns with CPSs need to be addressed carefully. In this context, this paper proposes a Cross-Breed Blowfish and MD5 (CBM) approach to improve the security of health data in the CPS cloud. The proposed model uses the wireless sensor network, in which data acquired by the network is transmitted via the transmitting node. Using the fuzzified effective trust-based routing protocol (FET-RP), the most efficient path for data travel is selected. The best route is determined using Butter-Ant Optimization (BAO) algorithm. The proposed method conveys data throughput encryption and decryption in a decoded format. The encrypted data is then stored in the cloud database for security reasons. The route finding algorithm is the one which is sending the data from one end to other end. The data is encrypted based on the source and destination. We compare the performance metrics of our recommended technique to those of other existing techniques, such as RSA, Two fish, ICC, and FHEA, in order to ensure that it performs optimally. The values of Cross Breed Blowfish and MD5 and FET-RP with regard to the performance metrics in terms of encryption (60 ms), decryption (55 ms), latency (60 s), throughput (97 mbps), security level (98%), and execution time (57 ms) which outperforms the conventional methods by 10–15%. Also the proposed encryption shows the considerable improvement in the level of security making our model a real world solution.

The use of avatars in the Metaverse and virtual reality technology in education is a rapidly developing field that has the potential to revolutionize the way students learn. In the context of the Indian EdTech market, virtual reality could play a significant role in enhancing the learning experience for students, although more research is needed to explore its full potential.ICT has already made a significant impact on education, with academic programming, technical instruction, and theoretical concepts being taught through digital platforms. Virtual reality has the potential to take this a step further, allowing students to experience immersive and interactive learning environments that simulate real-world scenarios.However, as mentioned in the paragraph, there is still limited research on the use of virtual reality in education, particularly in the Indian context. Further studies are needed to assess the effectiveness of this technology and ensure that it can be integrated effectively into the educational system.Overall, the use of virtual reality in education is an exciting development in the ICT industry and has the potential to make a significant impact on the learning experience for students. Various non-governmental organizations (NGOs) seek to improve women's lives and provide them with self-sufficiency training. The government is likewise concerned about this issue and has established a number of programs to assist women. Information and communication technology (ICT) is significant in this context. ICT has improved the security, knowledge, education, employability, confidence, and popularity of women. The significance of ICT for the empowerment of women is highlighted in this study. To illuminate the various facets of ICT's impact on society, various examples have been addressed. Because of ICT, new generations of women all over the globe have defied all expectations and proven themselves in every aspect of life, even the most complicated and time-consuming realm of enterprise. In this regard, ICT may possibly become a magic wand for improving women's current situations. In this research, the eight factors that influence how ICT affects rural women's empowerment were found.

The hype of the Internet of Things as an enabler for intelligent applications and related promise for ushering accessibility, efficiency, and quality of service is met with hindering security and data privacy concerns. It follows that such IoT systems, which are empowered by artificial intelligence, need to be investigated with cognisance of security threats and mitigation schemes that are tailored to their specific constraints and requirements. In this work, we present a comprehensive review of security threats in IoT and emerging countermeasures with a particular focus on malware and man-in-the-middle attacks. Next, we elaborate on two use cases: the Internet of Energy Things and the Internet of Medical Things. Innovative artificial intelligence methods for automating energy theft detection and stress levels are first detailed, followed by an examination of contextual security threats and privacy breach concerns. An artificial immune system is employed to mitigate the risk of malware attacks, differential privacy is proposed for data protection, and federated learning is harnessed to reduce data exposure.

The Internet of Medical Things (IoMT) has become a strategic priority for future e-healthcare because of its ability to improve patient care and its scope of providing more reliable clinical data, increasing efficiency, and reducing costs. It is no wonder that many healthcare institutions nowadays like to harness the benefits offered by the IoMT. In fact, it is an infrastructure with connected medical devices, software applications, and care systems and services. However, the accelerated adoption of connected devices also has a serious side effect: it obscures the broader need to meet the requirements of standard security for modern converged environments (even beyond connected medical devices). Adding up different types and numbers of devices risks creating significant security vulnerabilities. In this paper, we have undertaken a study of various security techniques dedicated to this environment during recent years. This study enables us to classify these techniques and to characterize them in order to benefit from their positive aspects.

Due to advancement in internet of medical things, the conventional health-care systems are transformed into smart health-care systems. The medical emergence services can be significantly enhanced by integration of IoMT and data analytic techniques. These technologies also examine the unexplored area of medical services that are still unseen and provide opportunity for investigation. Moreover, the concept of smart cities is not achievable without providing a smart connected healthcare scheme. Hence, the main purpose of this research is to come up with a smart healthcare system based on IoMT, Cloud and Fog computing and intelligent data analytic technique. The major objective of the proposed healthcare system is to develop a diagnostic model capable for earlier treatment of heart disease. The suggested scheme consists of distinct phases such as data acquisition, feature extraction, FogBus based edge/fog computing environment, classification, and evaluation. In data acquisition, different IoMT such as wearables and sensors devices are considered to acquire the data related to heart disease and the various features related to signal and data are extracted. Further, the deep forest technique is integrated into the proposed system for classification task and effective diagnosis capabilities of heart issues. The performance of the suggested scheme is evaluated through set of well-defined parameters. Comparison with other healthcare model was conducted for the purpose of performance evaluation. It is concluded that the proposed model has a superiority over other all other models in different aspects namely, the sensitivity measure, accuracy measure, and specificity.

The Internet of Medical Things (IoMT) is a growing trend within the rapidly expanding Internet of Things, enhancing healthcare operations and remote patient monitoring. However, these devices are vulnerable to cyber-attacks, posing risks to healthcare operations and patient safety. To detect and counteract attacks on the IoMT, methods such as intrusion detection systems, log monitoring, and threat intelligence are utilized. However, as attackers refine their methods, there is an increasing shift toward using machine learning and deep learning for more accurate and predictive attack detection. In this paper, we propose a fuzzy-based self-tuning Long Short-Term Memory (LSTM) intrusion detection system (IDS) for the IoMT. Our approach dynamically adjusts the number of epochs and utilizes early stopping to prevent overfitting and underfitting. We conducted extensive experiments to evaluate the performance of our proposed model, comparing it with existing IDS models for the IoMT. The results show that our model achieves high accuracy, low false positive rates, and high detection rates, indicating its effectiveness in identifying intrusions. We also discuss the challenges of using static epochs and batch sizes in deep learning models and highlight the importance of dynamic adjustment. The findings of this study contribute to the development of more efficient and accurate IDS models for IoMT scenarios.

The integration of medical equipment into the Internet of Things (IoT) led to the introduction of Internet of Medical Things (IoMT). Variation of IoT devices have been equipped in medical facilities. These devices provided convenience to healthcare provider since they can continuously monitor their patients in real-time, while allowing them to have greater physical flexibility and mobility. However, users of healthcare services (such as patients and medical staff) often are less concerned about security issues associated with IoT. These alleviate existing problems and jeopardize the lives of their patients by making them susceptible to attacks. Furthermore, IoMT applications have direct access to healthcare services because it handles sensitive patient information. Therefore, it is extremely important to preserve and establish the security and privacy of IoMT. This further justifies the need to investigate and address the related issues. Despite existing literature on security and privacy mechanisms, the domain still requires more attention. Therefore, this paper aims to discuss the security and privacy principles, as well as challenges associated with IoMT. Besides, a comprehensive analysis of privacy and security solutions for IoMT is also presented. In addition, we introduced a novel taxonomy of IoMT security and privacy based on cyber security principles such as “govern,” “protect,” and “detect”. In conclusion, this paper provides a discussion on existing challenges and future direction for researchers.

Secure management of Critical National Infrastructures (CNI) is a burning challenge to any state. As a CNI, Electronic Healthcare System (EHS) infrastructure records citizens’ medical records, raising security and privacy concerns. Traditional EHS functions independently where patients’ records are recorded and maintained in centralized systems that produce massive redundant data. Due to the non-coherence of these systems, data atomicity is not maintained; hence research results based on these data create questioning. Moreover, medical records are valuable for research but cannot be public due to security and privacy. Blockchain (BC) is currently considered a potential solution for the challenges. Blockchain can integrate every independent EHS as a bridging platform. The solution can ensure data uniqueness and overcome security issues. The prime difficulties for the integration are data synchronization of the traditional EHS and BC-based EHS. Furthermore, the autonomous interoperability between SQL and NoSQL database used in typical EHS and BC-based EHS, respectively, is a prime challenge. Therefore, this research proposes a Blockchain-based framework that bridges Traditional E-Health Systems(TEHS) and allows uninterruptible data exchanges between two systems, even for archive medical records. Beyond that, the framework shows an elevated way to overcome a single point of failure, data security, access control, etc., issues in a centralized system. Finally, the testbed implementation justifies the proposed architecture.