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The integration of artificial intelligence (AI) has caused information and communication technology (ICT) to undergo a number of recent rapid fluctuations. These changes have primarily affected the areas of management, end-to-end device interconnectivity, resource organization, communication, networking, and application-related aspects of ICT. Owing to the complex structure of applicational connectedness, evaluating each of the aforementioned opportunities concurrently reflects the idea of heterogeneity. The association of multiple end devices, particularly in interoperable space, integrity, privacy protection, security, provenance, and the massive volume of everyday media data generated in the modern healthcare setting could also provide significant issues. To address these issues, decentralized, secure, economical resource optimization, and intelligent network activities and organization are necessary. Blockchain technology plays a crucial role in providing distributed storage data organization, sharing, and exchange for automated decision-making, privacy, and security in AI-enabled machine learning (ML) models. However, machine learning models—support vector machine, in particular—have a significant impact on the growth of distributed consortium networks and the exchange of information among connected nodes, resolving issues with resource management, scalability, and data processing. By resolving the three main problems of seamless data integrity, peer-to-peer communication between nodes, and infrastructure security, we provide a novel interoperable technique in this proposed architecture. The approach is unique, as demonstrated by the simulation-based results, which display huge differences of 1.37%, 1.56%, and 1.87%, respectively. The background for the evaluation consists of the following three areas: (i) infrastructure security to protect automated decision-making; (ii) integrity between smooth data sharing and exchange; and (iii) network resource optimization to enable smooth communication across heterogeneous devices. [ABSTRACT FROM AUTHOR]

This work presents an on-device machine learning model with the ability to identify different mobility gestures called human activity recognition (HAR), which includes running, walking, squatting, jumping, and others. The data is collected through an Arduino Nano 33 BLE Sense board with a sampling rate of 119 Hz, which is embedded with an Inertial Measurement Unit (IMU) sensor. The same board is used as a microcontroller to identify human gestures by developing an end-to-end edge computing application. A deep neural network model is trained and then compressed for deployment on the board to create a self-contained, embedded device capable of identifying the type of gesture performed. Three deep learning models, namely Multi-Layer Perceptron (MLP), Convolutional Neural Network – Long Short Term Memory (CNN-LSTM) & CNN-Gated Recurrent Unit (CNN-GRU), are evaluated in the identification of the mobility gestures. The observed accuracy of the models is 96%, 97.1% and 97.8%, respectively, MLP, CNN-LSTM & CNN-GRU across the different gesture categories. The study shows the utility of embedded devices with deep neural network-based models, which can provide low cost, minimal power usage, and meet data privacy requirements in HAR. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) devices, as well as the Internet of Things phenomenon itself, are gaining a new group of customers every day, for whom it is almost a matter of course to use a wide range of devices, such as Internet-connected complex life support equipment or "smart" watches monitoring basic life parameters. With the growing popularity of such devices, however, questions about the safety of their users begin to arise, because almost in proportion to the number of benefits associated with the use of these products, the number of risks associated with them increases – eg improper functioning of Internet-connected life support equipment, in addition to threatening the life or health of its user, may affect the physical security of the product itself, the security of both personal and technical (eg non-personal) data processed by the specific product, or finally the cyber-security of the product. While the issues related to the protection of personal data and privacy, in general, have been discussed many times by the doctrine, the issues related to the protection of users of these devices under consumer law have not been considered much. In this context, the question arises whether the current legal regulations provide an adequate and sufficient level of protection for IoMT users. In particular, whether the average IoMT user can actually exercise their rights under the provisions of consumer law and whether the protection afforded to him – both in terms of the scope of their rights and the scope of obligations and liability of manufacturers and suppliers of these devices – is not only illusory? In order to answer the above questions, the author will evaluate the prevailing market practices – still focused around the doctrine of "caveat emptor" or "let the buyer beware" – and compare them with these regulations and juxtapose them with relevant legal regulations. However, given the lack of geographical borders in the field of cyber security and privacy, the author will not only analyse EU cyber security legislation, but also US legislation in a comparative legal analysis. The choice of jurisdictions to be compared is also related to the size and importance of both the US and the EU for the global IoMT market. It should be noted that the United States has a dominant position in the IoMT, while the European Union is estimated to have the second largest IoMT market globally. At the same time, however, there are differences in legal systems between the two economic areas. An analysis carried out in this way will make it possible not only to answer the question posed above, but also to possibly identify those areas of regulation that need to be changed or adapted to the realities of IoMT. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of healthcare, the integration of Artificial Intelligence (AI) and Natural Language Processing (NLP) holds immense promise for revolutionizing data analytics and decision-making processes. Current techniques for personalized medicine, disease diagnosis, treatment recommendations, and resource optimization in the Internet of Medical Things (IoMT) vary widely, including methods such as rule-based systems, machine learning algorithms, and data-driven approaches. However, many of these techniques face limitations in accuracy, scalability, and adaptability to complex clinical scenarios. This study investigates the synergistic potential of AI-driven optimization techniques and NLP applications in the context of the IoMT. Through the integration of advanced data analytics methodologies with NLP capabilities, we propose a comprehensive framework designed to enhance personalized medicine, streamline disease diagnosis, provide treatment recommendations, and optimize resource allocation. Using a systematic methodology data was collected from open data repositories, then preprocessed using data cleaning, missing value imputation, feature engineering, and data normalization and scaling. Optimization algorithms, such as Gradient Descent, Adam Optimization, and Stochastic Gradient Descent, were employed in the framework to enhance model performance. These were integrated with NLP processes, including Text Preprocessing, Tokenization, and Sentiment Analysis to facilitate comprehensive analysis of the data to provide actionable insights from the vast streams of data generated by IoMT devices. Lastly, through a synthesis of existing research and real-world case studies, we demonstrated the impact of AI-NLP fusion on healthcare outcomes and operational efficiency. The simulation produced compelling results, achieving an average diagnostic accuracy of 93.5% for the given scenarios, and excelled even further in instances involving rare diseases, achieving an accuracy rate of 98%. With regard to patient-specific treatment plans it generated them with an average precision of 96.7%. Improvements in early risk stratification and enhanced documentation were also noted. Furthermore, the study addresses ethical considerations and challenges associated with deploying AI and NLP in healthcare decision-making processes, offering insights into risk-mitigating strategies. This research contributes to advancing the understanding of AI-driven optimization algorithms in healthcare data analytics, with implications for healthcare practitioners, researchers, and policymakers. By leveraging AI and NLP technologies in IoMT environments, this study paves the way for innovative strategies to enhance patient care and operational effectiveness. Ultimately, this work underscores the transformative potential of AI-NLP fusion in shaping the future of healthcare. [ABSTRACT FROM AUTHOR]

Medical information system, like the Internet of Medical Things (IoMT), has gained more attention in recent decades. Disease diagnosis is an important facility of the medical healthcare system. Wearable devices become popular in a wide range of applications in the health monitoring system and this has stimulated the increasing growth of IoMT. Recently, a smart healthcare system has been more effective, and various methods have been developed to classify the disease at the beginning stage. To capture the patient’s information and detect the disease, a new framework is designed using the developed Conditional Auto regressive Mayfly Algorithm (CAMA)-based Deep Residual Network (DRN). Initially, pre-processing is done by the T2FCS filtering technique to increase the image quality by eliminating noises. The second step is segmentation. Here, the segmentation of brain tumor is done using U-Net. After that, data augmentation is performed to enhance image dimensions using the techniques, such as flipping, shearing, and translation to solve the issues of data samples. After processing the data augmentation mechanism, the next step is brain tumor detection, which is done using DRN. Here, DRN is trained by the proposed CAMA, which is the integration of conditional auto regressive value at risk (CAViaR) with the mayfly algorithm (MA). The developed model reduces computational complexity and increases effectiveness and robustness. The proposed CAMA-based DRN outperformed with an utmost testing accuracy of 0.921, sensitivity of 0.931, specificity of 0.928, distance of 52.842 and trust of 0.697. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) is a subset of the Internet of Things (IoT) where medical devices communicate with one another to share sensitive data. The integration of medical devices into the IoT has greatly assisted the development of the IoMT. These advancements facilitate effective communication and providing care for patients in the healthcare sector. However, they also face specific security and privacy concerns, such as malware attacks and denial of service (DoS) attacks. To overcome this problem, intrusion detection systems (IDS) are introduced, specifically employing deep learning (DL) methodologies. This study proposes a deep learning-based binary dragonfly algorithm (BDA) with a dynamic architecture for arti- ficial neural networks2 (DAN2) model for implementing a robust and accurate IDS in IoMT. The IDS has the following stages: collection of data, preprocessing, selection of features, and classification. The IoMT dataset is employed to train the model to get improved outcomes. The standard scalar technique is used for the data preprocessing process. The BDA algorithm is used for feature selection (FS) of the preprocessed data. The DAN2 model is implemented to classify the selected data and to improve the classification accuracy. The dataset was further divided for training and testing of the model. The performance of the BDA-DAN2 model is assessed utilizing the evaluation parameters of accuracy, recall, precision, and F1-score. The BDA-DAN2 model demonstrates superior performance with 99.12% accuracy, 99.28% precision, 99.40% recall, and 98.56% F1-score during training, and 98.92% accuracy, 98.50% precision, 98.68% recall, and 97.90% F1-score during testing. Experiments confirmed that the binary dragonfly algorithm with the DAN2 (BDA-DAN2) model has the highest accuracy compared to the existing models. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT), an extension of the Internet of Things (IoT), is still in its early stages of development. Challenges that are inherent to IoT, persist in IoMT as well. The major focus is on data transmission within the healthcare domain due to its profound impact on health and public well-being. Issues such as latency, bandwidth constraints, and concerns regarding security and privacy are critical in IoMT owing to the sensitive nature of patient data, including patient identity and health status. Numerous forms of cyber-attacks pose threats to IoMT networks, making the reliable and secure transmission of critical medical data a challenging task. Several other situations, such as natural disasters, war, construction works, etc., can cause IoMT networks to become unavailable and fail to transmit the data. The first step in these situations is to recover from failure as quickly as possible, resume the data transfer, and detect the cause of faults, failures, and errors. Several solutions exist in the literature to make the IoMT resilient to failure. However, no single approach proposed in the literature can simultaneously protect the IoMT networks from various attacks, failures, and faults. This paper begins with a detailed description of IoMT and its applications. It considers the underlying requirements of resilience for IoMT networks, such as monitoring, control, diagnosis, and recovery. This paper comprehensively analyzes existing research efforts to provide IoMT network resilience against diverse causes. After investigating several research proposals, we identify that the combination of software-defined networks (SDNs), machine learning (ML), and microservices architecture (MSA) has the capabilities to fulfill the requirements for achieving resilience in the IoMT networks. It mainly focuses on the analysis of technologies, such as SDN, ML, and MSA, separately, for meeting the resilience requirements in the IoMT networks. SDN can be used for monitoring and control, and ML can be used for anomaly detection and diagnosis, whereas MSA can be used for bringing distributed functionality and recovery into the IoMT networks. This paper provides a case study that describes the remote patient monitoring (RPM) of a heart patient in IoMT networks. It covers the different failure scenarios in IoMT infrastructure. Finally, we provide a proposed methodology that elaborates how distributed functionality can be achieved during these failures using machine learning, software-defined networks, and microservices technologies. [ABSTRACT FROM AUTHOR]

In most developed countries, the medical healthcare system is experiencing rapid development from the stage of clinical information to the stage of information dissemination. In all of these countries, it is undeniable that the Internet of Medical Things (IoMT) technologies have contributed in order to develop information medical healthcare. In reality, the development of smart medical healthcare has been hindered by the protection of medical privacy, according to research and acceptance. This is especially true as telecommunications systems continue to expand and wireless sensor networks (WSN) develop, as well as ways to penetrate those checks that have become increasingly difficult. In the smart healthcare system, protecting users' information remains an outstanding issue. IoMT features and the protection of privacy and security have led to the development of an extended privacy homomorphism algorithm based on scrambling matrixes, an encryption algorithm enhanced by Modified RSA (mRSA), and a method of encrypted data compression that ensures data confidentiality. For the above purpose, we built a prototype system on a demo temporary domain using both hardware and software. According to the results, the proposed scheme protects E-healthcare from potential threats by providing stakeholders with a secure interface and preventing unauthorized users from accessing the mCloud, thus ensuring privacy. E-healthcare services based on cloud technology are protected by the proposed scheme because it is simple and robust. [ABSTRACT FROM AUTHOR]

The rapid growth of IoT has significantly changed modern technology by allowing devices, systems, and services to connect easily across different areas. Due to the growing popularity of Internet of Things (IoT) devices, attackers focus more and more on finding new methods, ways, and vulnerabilities to penetrate IoT networks. Although IoT devices are utilized across a wide range of domains, the Internet of Medical Things (IoMT) holds particular significance due to the sensitive and critical nature of medical information. Consequently, the security of these devices must be treated as a paramount concern within the IoT landscape. In this paper, we propose a novel approach for detecting various intrusion attacks targeting Internet of Medical Things (IoMT) devices, utilizing an enhanced version of the LSTM deep learning algorithm. To evaluate and compare the proposed algorithm with other methods, we used the CICIoMT2024 dataset, which encompasses various types of equipment and corresponding attacks. The results demonstrate that the proposed novel approach achieved an accuracy of 98% for 19 classes, which is remarkably high for classifications and presents a significant and promising outcome for IoMT environments.

Accurate disease monitoring is an extremely time-consuming task for medical experts and technocrats involved, requiring technical support for diagnostic systems. To overcome this situation, we developed an Internet of Medical Things (IoMT) based on Tsukamoto Type 2 Fuzzy Inference System (TT2FIS) that can easily handle diagnostic and predictive aspects in the medical field. In the proposed system, we developed a Tsukamoto type 2 fuzzy inference system that takes the patient’s symptoms as input factors and the medical device as the output factor of the result. The aim of this work is to demonstrate the usefulness of type 2 fuzzy sets in Tuberculosis and Alzheimer’s disease diagnostic system. Numerical calculations are also performed to illustrate the applicability of the proposed method. A validation of the proposed derivation of the proposed IoMT model is also discussed in the results and conclusions section.

The emergence of the Internet of Things (IoT) has sparked a profound transformation in the field of digital health, leading to the rise of the Internet of Medical Things (IoMT). These IoT applications, while promising significant enhancements in patient care and health outcomes, simultaneously present a myriad of ethical dilemmas. This paper aims to address these ethical challenges by introducing the Adaptive Ethical Framework for IoT in Digital Health (AEFIDH), a comprehensive evaluation framework designed to examine the ethical implications of IoT technologies within digital health contexts. The AEFIDH is developed using a mixed-methods approach, encompassing expert consultations, surveys, and interviews. This approach was employed to validate and refine the AEFIDH, ensuring it encapsulates critical ethical dimensions, including data privacy, informed consent, user autonomy, algorithmic fairness, regulatory compliance, ethical design, and equitable access to healthcare services. The research reveals pressing issues related to data privacy, security, and user autonomy and highlights the imperative need for an increased focus on algorithmic transparency and the integration of ethical considerations in the design and development of IoT applications. Despite certain limitations, the AEFIDH provides a promising roadmap for guiding the responsible development, deployment, and utilization of IoT technologies in digital health, ensuring its relevance amidst the rapidly evolving digital health landscape. This paper contributes a novel, dynamic framework that encapsulates current ethical considerations and is designed to adapt to future technological evolutions, thereby fostering ethical resilience in the face of ongoing digital health innovation. The framework’s inherent adaptability allows it to evolve in tandem with technological advancements, positioning it as an invaluable tool for stakeholders navigating the ethical terrain of IoT in healthcare.

Internet of Medical Things (IoMT) have been utilized in plentiful medical institutions. Nevertheless, since the security of EMRs in IoMT cannot be guaranteed, the EMRs should be encrypted before uploading to cloud server. Public key Encryption with Keyword Search (PEKS) can help the doctors to search encrypted EMRs, but traditional PEKS algorithms cannot resist to quantum computing attacks and without considering access control. To bridge the gap, we propose an attribute-based public key searchable encryption scheme based on lattice, named AAQ-PEKS. Initially, based on the LWE hardness, we first introduce the attribute-based PEKS that can resist quantum attacks for IoMT. Secondly, we combine Attribute-based Encryption (ABE) into AAQ-PEKS to realize access control for sensitive EMRs in the IoMT. Thirdly, the computational security analysis illustrates that our scheme achieves correctness, Indistinguishability against Chosen Plaintext Attack (IND-CPA) and Indistinguishability against Chosen Keyword Attack (IND-CKA). Lastly, comprehensive performance evaluation in practice elaborates that our AAQ-PEKS is more efficient compared with other existing top-tier schemes. To conclude, our scheme has the characteristics of resisting quantum attacks and fine-grained access control for EMR in the IoMT. [ABSTRACT FROM AUTHOR]

This paper explores the transformative impact of the Internet of Medical Things (IoMT) on healthcare. By integrating medical equipment and sensors with the internet, IoMT enables real-time monitoring of patient health, remote patient care, and individualized treatment plans. IoMT significantly improves several healthcare domains, including managing chronic diseases, patient safety, and drug adherence, resulting in better patient outcomes and reduced expenses. Technologies like blockchain, Artificial Intelligence (AI), and cloud computing further boost IoMT’s capabilities in healthcare. Blockchain enhances data security and interoperability, AI analyzes massive volumes of health data to find patterns and make predictions, and cloud computing offers scalable and cost-effective data processing and storage. Therefore, this paper provides a comprehensive review of the Internet of Things (IoT) and IoMT-based edge-intelligent smart healthcare, focusing on publications published between 2018 and 2024. The review addresses numerous studies on IoT, IoMT, AI, edge and cloud computing, security, Deep Learning, and blockchain. The obstacles facing IoMT are also covered in this paper, including interoperability issues, regulatory compliance, and privacy and data security concerns. Finally, recommendations for further studies are provided. [ABSTRACT FROM AUTHOR]

Summary: The rapid progression of the Internet of Things and its increasing use in healthcare has generated considerable concerns over the safeguarding and privacy of vital medical data. In response to these issues, blockchain has surfaced as a possible remedy, offering transparent, immutable, and decentralized storage. Nevertheless, conventional blockchain‐based systems still encounter constraints in maintaining anonymity, confidentiality, and privacy. Hence, this article suggests a framework based on a secure consortium blockchain that prioritizes data privacy and employs time‐based authentication to streamline patient data monitoring. First, we employ time‐based authentication to verify the identities of authorized users. This process utilizes the NIK‐512 hashing algorithm in conjunction with passwords and registered timestamps, which strengthens the confidentiality of data. Patient information undergoes encryption before transmission within the network. Further, our framework introduces a sensor registration service that the trusted node employs to assign a distinct identity to each sensor connected to a patient. The implementation of data processing and filtering techniques at the edge layer serves the purpose of mitigating disturbances that may occur during the collection of sensor‐based data. Finally, a comprehensive evaluation of performance and security has been carried out with various metrics. The findings indicate that the proposed solution effectively enhances the management of Internet of Medical Things data by providing improved privacy and security. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) is a version of the Internet of Things. It is getting the attention of researchers because it can be used in a wide range of smart healthcare systems. One of the main advancements employed recently is the IoMT-cloud, which allows users to access cloud services remotely over the internet. These cloud services require an efficient task scheduling approach that satisfies the Quality of Service parameters with a low energy consumption. This paper presents an overview of the integration of IoMT and cloud computing technologies. Besides,this work proposes an efficient Extended Water Wave Optimization (EWWO) task scheduling in the IoMT Cloud for healthcare applications. EWWO algorithm performs based on its operations propagation, refraction and breaking. The proposed EWWO scheduling technique minimizes the energy consumption, makespan time, execution time and increases the resource utilization. Cloudsim simulator is used to simulate the IoMT-Cloud environment to verify the effectiveness of EWWO technique. The performance has been evaluated based on various parameters such as energy consumption, makespan time and execution time. [ABSTRACT FROM AUTHOR]

The fusion of Internet of Things (IoT) technology into healthcare, known as the Internet of Medical Things (IoMT), has significantly enhanced medical treatment and operational efficiency. Real-time patient monitoring (RPM) and remote diagnostics enabled by IoMT allow doctors to treat more patients effectively and save lives. However, healthcare devices' interconnected nature makes them vulnerable to cyber-attacks, threatening patient privacy and security. Ensuring the security and accuracy of patient health data is paramount, as any tampering could have life-threatening consequences, especially in emergency situations. To address these challenges, this research focuses on developing robust security models to secure patient data in IoMT networks while meeting the growing demand for efficient healthcare services. Artificial intelligence (AI)-based technologies such as machine learning (ML) and deep learning (DL) have the potential to be employed as the methodology for intrusion detection. The goal of this research is threefold: firstly, the linear support vector machine (LinSVM) model; secondly, the convolutional support vector machine (ConvSVM) model; and finally, the categorical embedding (CatEmb) model, which have been proposed to overcome the issue of security in a network. This article offers the CatEmb model as the first effort to use a DL-based embedding approach to recognize intrusion in the IoMT environment, utilizing patient biometric and network traffic flow data. Our experimental results show the efficacy of the proposed DL models, with the LinSVM achieving a training accuracy of 99.78%, ConvSVM reaching 99.98%, and CatEmb achieving 99.84%. These models outperform existing methodologies by 2.61% in detecting network intrusions, as demonstrated through metrics such as detection rate and F1-score. Furthermore, the proposed approaches are thoroughly compared with the existing state-of-the-art studies. [ABSTRACT FROM AUTHOR]

The integration of cloud computing (CC) and Internet of Things (IoT) technologies in the healthcare industry has significantly boosted the importance of real-time remote patient monitoring. The Internet of Medical Things (IoMT) systems facilitate the seamless transfer of health records to data centers, allowing medical professionals and caregivers to analyze, process, and access them. This data is often stored in cloud-based systems. Nevertheless, the transmission of data and execution of computations in a cloud environment may lead to delays and affect the efficiency of real-time healthcare services. In addition, the use of edge computing (EC) layers has become prevalent in performing local data processing and storage to reduce service response times for IoMT applications. The main objective of this article is to develop an adaptive EC infrastructure for IoMT systems, with a specific emphasis on maintaining optimal performance for real-time health services. It also designs a model to predict the server resources required to meet service level agreements (SLAs) regarding response time. Simulation results demonstrate that EC significantly improves service response time for real-time IoMT applications. The proposed model can accurately and efficiently predict the computing resources required for medical data services to achieve SLAs under varying workload conditions. [ABSTRACT FROM AUTHOR]

This review addresses the disease diagnosis from brain, eye, and lung scan images based on non-invasive imaging technologies using the Internet of Medical Things (IoMT) and Artificial Intelligence (AI) systems, a topic that has been neglected in the recent literature. Combining imaging modalities with IoMT and AI is expected to enhance both medical diagnoses and personalized treatment plans. We searched various scientific databases for details on IoMT and AI in medical imaging technologies from 2019 to 2023, focusing on different imaging modalities. We investigated the performance of AI-based algorithms in imaging modalities such as X-ray, Computed Tomography, Magnetic Resonance Imaging, Positron Emission Tomography, and Optical Coherence Tomography using the following metrics: accuracy, precision, recall, sensitivity, specificity, and F-1 score, and then analyzed their balanced performance in six issues: enhancement of medical image quality, improvement of clinical diagnoses, support for clinical decision-making, consideration of input data, time efficiency, and data management. Advanced understanding of the IoMT and AI applications in medical imaging technologies would help identify unexplored opportunities and provide directions for future research to enhance the clinical applicability. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a rapidly expanding network of interconnected things that use embedded sensors to gather and share data in real‐time. IoT technologies have given rise to many networking applications in our everyday life such as smart homes, smart cities, smart transport, and so forth. Smart healthcare is one such application that has been revolutionized by the IoT, introducing a new branch of IoT known as the Internet of Medical Things (IoMT). IoMT encompasses an entire ecosystem consisting of smart wearable, implantable sensing equipment's or devices, transmitters that are critical for monitoring the patients remotely and continuing the real‐time and has opened the door to new innovative smart healthcare approaches while improving patient care outcomes. IoMT wearable and embedded sensing devices are commonly utilized in smart healthcare to capture medical data and transmit the medical data in a communication network stored in the cloud. The large volume of data generated and transmitted by these IoMT devices is rising at an exponential rate, resulting in an increase in security and privacy vulnerabilities of healthcare data. To ensure the Confidentiality and integrity of the IoMT devices and the sensitive medical data, there should be proper security and privacy measures such as access control, passwords, multifactor authentication, and encryption of data generated, transmitted, or processed in the IoMT framework. In this paper, we identified the internet of things and its applications in smart healthcare systems. Additionally, the paper focuses on the architecture of IoMT, and several challenges, including the IoMT security and privacy requirements, and attack taxonomy. Furthermore, the paper thoroughly investigates both cryptographic and non‐cryptographic based security and privacy‐enhancing techniques for IoMT or healthcare systems with particular emphasis on advancements in key areas such as Homomorphic Encryption, Differential Privacy, and Federated Learning. [ABSTRACT FROM AUTHOR]

In this paper, a method for the classification of anomalous heartbeats from compressed ECG signals is proposed. The method operating on signals acquired by compressed sensing is based on a feature extraction stage consisting of the evaluation of the Discrete Cosine Transform (DCT) coefficients of the compressed signal and a classification stage performed by means of a set of k-nearest neighbor ensemble classifiers. The method was preliminarily tested on five classes of anomalous heartbeats, and it achieved a classification accuracy of 99.40%. [ABSTRACT FROM AUTHOR]

Protecting patient privacy has become a top priority with the introduction of Healthcare 5.0 and the growth of the Internet of Things. This study provides a revolutionary strategy that makes use of blockchain technology, information fusion, and federated illness prediction and deep extreme machine learning to meet the difficulties with regard to healthcare privacy. The suggested framework integrates several innovative technologies to make healthcare systems safe and privacy-preserving. The framework leverages the blockchain system, a distributed and unchangeable ledger, to secure the integrity, traceability and openness of private medical information. Patient privacy is better protected as a result, and there is less chance of data breaches or unauthorized access. The system makes use of the Linear Discriminant Analysis (LDA), Decision Tree, Extra Tree Classifier, AdaBoost, and Federated Deep Extreme Machine Learning algorithms to increase the accuracy and efficacy of illness prediction. This method allows for collaborative learning across many healthcare organizations without disclosing raw data, protecting privacy. The system obtains a thorough awareness of patient health, allowing for the early diagnosis of diseases and the development of individualized treatment suggestions. To further detect and reduce possible security risks in the IoMT environment, the framework also includes intrusion detection methods. Protecting patient data and infrastructure, the system can quickly identify and react to unauthorized actions or threats. High accuracy and privacy protection are shown by the results, making it appropriate for Healthcare 5.0 applications. The findings have important ramifications for researchers, politicians, and healthcare professionals who are seeking to develop safe and privacy-conscious healthcare systems. [ABSTRACT FROM AUTHOR]

Cardiovascular disease is a major global health concern, with early detection being critical. This study assesses the effectiveness of a portable ECG device, based on Internet of Medical Things (IoMT) technology, for remote cardiovascular monitoring during daily activities. We conducted a clinical trial involving 2000 participants who wore the HiCardi device while engaging in hiking activities. The device monitored their ECG, heart rate, respiration, and body temperature in real-time. If an abnormal signal was detected while a physician was remotely monitoring the ECG at the IoMT monitoring center, he notified the clinical research coordinator (CRC) at the empirical research site, and the CRC advised the participant to visit a hospital. Follow-up calls were made to determine compliance and outcomes. Of the 2000 participants, 318 showed abnormal signals, and 182 were advised to visit a hospital. The follow-up revealed that 139 (76.37%) responded, and 30 (21.58% of those who responded) sought further medical examination. Most visits (80.00%) occurred within one month. Diagnostic approaches included ECG (56.67%), ECG and ultrasound (20.00%), ultrasound alone (16.67%), ECG and X-ray (3.33%), and general treatment (3.33%). Seven participants (23.33% of those who visited) were diagnosed with cardiovascular disease, including conditions such as arrhythmia, atrial fibrillation, and stent requirements. The portable ECG device using the patch-type electrocardiograph detected abnormal cardiovascular signals, leading to timely diagnoses and interventions, demonstrating its potential for broad applications in preventative healthcare. [ABSTRACT FROM AUTHOR]

Brain tumour disease develops due to abnormal cell proliferation. The early identification of brain tumours is vital for their effective treatment. Most currently available examination methods are laborious, require extensive manual instructions, and produce subpar findings. The EfficientNet-B0 architecture was used to diagnose brain tumours using magnetic resonance imaging (MRI). The fine-tuned EffeceintNet B0 model was proposed for the Internet of Medical Things (IoMT) environment. The fine-tuned EfficientNet-B0 architecture was employed to classify four different stages of brain tumours from the MRI images. The fine-tuned model showed 99% accuracy in the detection of four different classes of brain tumour detection (glioma, no tumour, meningioma, and pituitary). The proposed model performed very well in the detection of the pituitary class with a precision of 0.95, recall of 0.98, and F1 score of 0.96. The proposed model also performed very well in the detection of the no-tumour class with values of 0.99, 0.90, and 0.94 for precision, recall, and the F1 score, respectively. The precision, recall, and F1 scores for Glioma and Meningioma classes were also high. The proposed solution has several implications for enhancing clinical investigations of brain tumours. [ABSTRACT FROM AUTHOR]

This study introduces a novel meta-learning method to enhance diabetes detection using wearable sensor systems in smart health applications. Wearable sensor technology often needs to operate accurately across a wide range of users, each characterized by unique physiological and behavioral patterns. However, the specific data for a particular application or user group might be scarce. Moreover, collecting extensive training data from wearable sensor experiments is challenging, time-consuming, and expensive. In these cases, meta-learning can be particularly useful. This model can quickly adapt to the nuances of new users or specific applications with minimal data. Therefore, to solve the need for a huge amount of training data and to enable the application of artificial intelligence (AI) in data-scarce scenarios, a meta-learning method is proposed. This meta-learning model has been implemented to forecast diabetes, resolve cross-talk issues, and accurately detect R peaks from overlapping electrocardiogram (ECG) signals affected by movement artifacts, poor electrode contact, electrical interference, or muscle activity. Motion artifacts from body movements, external conditions such as temperature, humidity, and electromagnetic interference, and the inherent quality and calibration of the sensor can all contribute to noise. Contact quality between the sensor and the skin, signal processing errors, power supply variations, user-generated interference from activities like talking or exercising, and the materials used in the wearable device also play significant roles in the overall noise in wearable sensor data and can significantly distort the true signal, leading to erroneous interpretations and potential diagnostic errors. Furthermore, discrete wavelet transform (DWT) was also implemented to improve the quality of the data and enhance the performance of the proposed model. The demonstrated results confirmed that with only a limited amount of target data, the proposed meta-learning and DWT denoising method can adapt more quickly and improve the detection of diabetes compared to the traditional method. Therefore, the proposed system is cost-effective, flexible, faster, and adaptable, reduces the need for training data, and can enhance the accuracy of chronic disease detection such as diabetes for smart health systems. [ABSTRACT FROM AUTHOR]

This research paper presents an investigation into the efficacy of a novel diagnostic technology designed for the real-time monitoring of weightlifting athletes, focusing on precision, time efficiency, and user convenience compared to traditional diagnostic systems. The study introduces an advanced non-invasive sensor system, integrated into a cohesive Internet of Medical Things (IoMT) framework, which facilitates the immediate and accurate assessment of athletes' health parameters. To empirically test the benefits of this new technology, a pedagogical experiment was conducted involving two distinct groups: an experimental group that utilized the proposed technology for medical checkups, and a control group that continued with traditional diagnostic methods. Each group consisted of 30 athletes, and the outcomes were measured across three dimensions: the precision of diagnostic results, the time expended for medical checkups, and the user-reported convenience of the equipment. The findings indicate that the proposed technology not only significantly enhances the precision of health diagnostics but also reduces the time required for medical examinations, thereby increasing overall efficiency. Additionally, the higher convenience scores reported by the experimental group suggest improved user satisfaction and usability. These results demonstrate the potential of the proposed diagnostic system to transform athlete health monitoring by providing more accurate, efficient, and user-friendly medical assessments, suggesting a significant step forward in the application of advanced technologies in sports medicine.

This work presents an on-device machine learning model with the ability to identify different mobility gestures called human activity recognition (HAR), which includes running, walking, squatting, jumping, and others. The data is collected through an Arduino Nano 33 BLE Sense board with a sampling rate of 119 Hz, which is embedded with an Inertial Measurement Unit (IMU) sensor. The same board is used as a microcontroller to identify human gestures by developing an end-to-end edge computing application. A deep neural network model is trained and then compressed for deployment on the board to create a self-contained, embedded device capable of identifying the type of gesture performed. Three deep learning models, namely Multi-Layer Perceptron (MLP), Convolutional Neural Network – Long Short Term Memory (CNN-LSTM) & CNN-Gated Recurrent Unit (CNN-GRU), are evaluated in the identification of the mobility gestures. The observed accuracy of the models is 96%, 97.1% and 97.8%, respectively, MLP, CNN-LSTM & CNN-GRU across the different gesture categories. The study shows the utility of embedded devices with deep neural network-based models, which can provide low cost, minimal power usage, and meet data privacy requirements in HAR.

The Internet of Medical Things (IoMT) links medical devices and wearable, enhancing healthcare. To secure sensitive patient data over the IoMT, encryption is vital to retain confidentiality, prevent tampering, ensure authenticity, and secure data transfer. The intricate neural network architecture of deep learning models adds a layer of complexity and non-linearity to the encryption process, rendering it highly resistant to plaintext attacks. Specifically, the Cycle_GAN network is used as the leading learning network. This work suggests deep learning-based encryption for medical images using Cycle_GAN, a Generative Adversarial Network. Cycle_GAN changes images without paired training data that improves quality and feature preservation. Unlike conventional image-to-image translation techniques, Cycle_GAN doesn’t require a dataset with corresponding input–output pairs. Traditional methods typically needs paired data to learn the mapping between input and output images. Paired data can be challenging to obtain, specifically in medical imaging where gathering and annotating data can be time-consuming, laborious and expensive. The use of Cycle_GAN overwhelms this constraint by using unpaired data, where the input and output images are not explicitly paired. This method ensures confidentiality, authenticity, and secure transfer. Cycle_GAN consists of two major components: a generator used to modify the images, and a discriminator used to distinguish between real and fake images. Further, the Binary-Cross Entropy loss function is employed to train the network for precise predictions. The experiments are carried out on skin cancer datasets. The results demonstrate high-level efficient, systematic and coherent encryption as compared with other modernized medical image encryption methods. The proposed technique offers several benefits, including efficient encryption and decryption and robustness against unauthorized access.

With the advancement of cutting-edge technologies, the Internet of Medical Things (IoMT) has assisted the healthcare sector by facilitating interaction between healthcare service providers and patients in remote areas. In IoMT, wearable or implantable sensors collect the patient’s record and share the information through a public network. Health-related information about the patient must be protected from a variety of attacks by the adversary since it is sensitive and extremely vulnerable to attacks. The sensor equipment that is implanted in the patient is also resource-constrained and has a low power capacity. The entities involved in the communication must be authenticated with one another in order to protect patients’ health information, anonymity, and reliability. While several authenticated key agreement protocols have been proposed, many suffer from high computational costs and storage cost, making them unsuitable for lightweight applications. This paper proposes a secure three-factor robust Elliptic Curve Cryptography (ECC) based mutually authenticated and key agreement protocol known as RELAKA for the IoMT environment, utilizing the benefits of one-way hash function. In proposed scheme, all entities, including the healthcare service providers and wearable sensors, are authenticated by the medical server. Subsequently, a secret key is established for each communication session and shared between all the entities. Additionally, mechanism for appropriate user revocation and re-registration is integrated to provide additional security in cases where a user’s QR code is tampered with by the attacker. The privacy of the proposed protocol is investigated by the potential use of zero knowledge proof. Furthermore, the efficacy of the authentication is examined by challenge and response mechanism. The informal security analysis demonstrates its resistance to threats such as DoS, impersonation, message modification, password guessing, and so on. The performance evaluation of RELAKA protocol indicates that the execution, communication, and storage costs is reduced by 87.59%, 43% and 60.71% respectively. Moreover, the outcomes of the AVISPA simulation illustrate that the RELAKA successfully evades both active and passive attacks. In addition, real-world testbed environment is developed with Raspberry pi 4 model B and the experimental results verifies the robustness of the proposed protocol. According to theoretical analysis and experimental evaluation, the RELAKA scheme is more secure and efficient than the existing protocols.

Abstract The Internet of Things (IoT) is revolutionizing the healthcare industry by enhancing personalized patient care. However, the transmission of sensitive health data in IoT systems presents significant security and privacy challenges, further exacerbated by the difficulty of exploiting traditional protection means due to poor battery equipment and limited storage and computational capabilities of IoT devices. The authors analyze techniques applied in the medical context to encrypt sensible data and deal with the unique challenges of resource‐constrained devices. A technique that is facing increasing interest is the Physical Unclonable Function (PUF), where biometrics are implemented on integrated circuits' electric features. PUFs, however, demand special hardware, so in this work, instead of considering the physical device as a source of randomness, an ElectroCardioGram (ECG) can be taken into consideration to make a ‘virtual’ PUF. Such an mechanism leverages individual ECG signals to generate a cryptographic key for encrypting and decrypting data. Due to the poor stability of the ECG signal and the typical noise existing in the measurement process for such a signal, filtering and feature extraction techniques must be adopted. The proposed model considers the adoption of pre‐processing techniques in conjunction with a fuzzy extractor to add stability to the signal. Experiments were performed on a dataset containing ECG records gathered over 6 months, yielding good results in the short term and valuable outcomes in the long term, paving the way for adaptive PUF techniques in this context.

Abstract In modern healthcare, integrating Artificial Intelligence (AI) and Internet of Medical Things (IoMT) is highly beneficial and has made it possible to effectively control disease using networks of interconnected sensors worn by individuals. The purpose of this work is to develop an AI-IoMT framework for identifying several of chronic diseases form the patients’ medical record. For that, the Deep Auto-Optimized Collaborative Learning (DACL) Model, a brand-new AI-IoMT framework, has been developed for rapid diagnosis of chronic diseases like heart disease, diabetes, and stroke. Then, a Deep Auto-Encoder Model (DAEM) is used in the proposed framework to formulate the imputed and preprocessed data by determining the fields of characteristics or information that are lacking. To speed up classification training and testing, the Golden Flower Search (GFS) approach is then utilized to choose the best features from the imputed data. In addition, the cutting-edge Collaborative Bias Integrated GAN (ColBGaN) model has been created for precisely recognizing and classifying the types of chronic diseases from the medical records of patients. The loss function is optimally estimated during classification using the Water Drop Optimization (WDO) technique, reducing the classifier’s error rate. Using some of the well-known benchmarking datasets and performance measures, the proposed DACL’s effectiveness and efficiency in identifying diseases is evaluated and compared.

A healthcare-focused version of the Internet of Things (IoT), the Internet of Medical Things (IoMT) enables real-time monitoring and remote medical support via integrated medical devices, programs, and support solutions. However, patients' safety and anonymity are in jeopardy by the open access networks employed in IoMT, which makes the systems susceptible to several threats and security lapses. By harnessing the synergies of blockchain, cloud computing, and digital twins, this study presents a comprehensive architecture and a secure lightweight authentication mechanism (which integrates the benefits offered by Yu and Park, Yu et al., and Amintoshi et al.) that addresses these concerns. The suggested method entails using session keys for secure communication while authenticating medical professionals and patients through a gateway. Cloud computing offers a flexible and robust framework for managing and storing medical data. Additionally, it simulates digital twins to enable data-driven decision-making and predictive analysis, and the incorporation of blockchain offers a decentralized and immutable ledger for recording and validating patient data and transaction logs enhancing data integrity, transparency, and traceability. Healthcare systems may confidently embrace the potential of IoMT by implementing this framework since it offers promising solutions to enhance the security and confidentiality of patient data in IoMT while supporting the provision of the best healthcare services, especially in emergency scenarios like the COVID-19 pandemic. The suggested approach is subjected to a thorough security evaluation using AVISPA, demonstrating its resistance to various attacks. A comparative analysis has also been carried out to assess the performance and computational cost of IoMT-BADT in comparison with other authentication schemes. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) has significantly advanced healthcare, but it has also brought about critical security challenges. Traditional security solutions struggle to keep pace with the dynamic and interconnected nature of IoMT systems. Machine learning (ML)-based Intrusion Detection Systems (IDS) have been increasingly adopted to counter cyberattacks, but centralized ML approaches pose privacy risks due to the single points of failure (SPoFs). Federated Learning (FL) emerges as a promising solution, enabling model updates directly on end devices without sharing private data with a central server. This study introduces the BFLIDS, a Blockchain-empowered Federated Learning-based IDS designed to enhance security and intrusion detection in IoMT networks. Our approach leverages blockchain to secure transaction records, FL to maintain data privacy by training models locally, IPFS for decentralized storage, and MongoDB for efficient data management. Ethereum smart contracts (SCs) oversee and secure all interactions and transactions within the system. We modified the FedAvg algorithm with the Kullback–Leibler divergence estimation and adaptive weight calculation to boost model accuracy and robustness against adversarial attacks. For classification, we implemented an Adaptive Max Pooling-based Convolutional Neural Network (CNN) and a modified Bidirectional Long Short-Term Memory (BiLSTM) with attention and residual connections on Edge-IIoTSet and TON-IoT datasets. We achieved accuracies of 97.43% (for CNNs and Edge-IIoTSet), 96.02% (for BiLSTM and Edge-IIoTSet), 98.21% (for CNNs and TON-IoT), and 97.42% (for BiLSTM and TON-IoT) in FL scenarios, which are competitive with centralized methods. The proposed BFLIDS effectively detects intrusions, enhancing the security and privacy of IoMT networks. [ABSTRACT FROM AUTHOR]

The sensors used in the Internet of Medical Things (IoMT) network run on batteries and need to be replaced, replenished or should use energy harvesting for continuous power needs. Additionally, there are mechanisms for better utilization of battery power for network longevity. IoMT networks pose a unique challenge with respect to sensor power replenishment as the sensors could be embedded inside the subject. A possible solution could be to reduce the amount of sensor data transmission and recreate the signal at the receiving end. This article builds upon previous physiological monitoring studies by applying new decision tree-based regression models to calculate the accuracy of reproducing data from two sets of physiological signals transmitted over cellular networks. These regression analyses are then executed over three different iteration varieties to assess the effect that the number of decision trees has on the efficiency of the regression model in question. The results indicate much lower errors as compared to other approaches indicating significant saving on the battery power and improvement in network longevity. [ABSTRACT FROM AUTHOR]

The healthcare industry went through reformation by integrating the Internet of Medical Things (IoMT) to enable data harnessing by transmission mediums from different devices, about patients to healthcare staff devices, for further analysis through cloud-based servers for proper diagnosis of patients, yielding efficient and accurate results. However, IoMT technology is accompanied by a set of drawbacks in terms of security risks and vulnerabilities, such as violating and exposing patients' sensitive and confidential data. Further, the network traffic data is prone to interception attacks caused by a wireless type of communication and alteration of data, which could cause unwanted outcomes. The advocated scheme provides insight into a robust Intrusion Detection System (IDS) for IoMT networks. It leverages a honeypot to divert attackers away from critical systems, reducing the attack surface. Additionally, the IDS employs an ensemble method combining Logistic Regression and K-Nearest Neighbor algorithms. This approach harnesses the strengths of both algorithms to improve attack detection accuracy and robustness. This work analyzes the impact, performance, accuracy, and precision outcomes of the used model on two IoMT-related datasets which contain multiple attack types such as Man-In-The-Middle (MITM), Data Injection, and Distributed Denial of Services (DDOS). The yielded results showed that the proposed ensemble method was effective in detecting intrusion attempts and classifying them as attacks or normal network traffic, with a high accuracy of 92.5% for the first dataset and 99.54% for the second dataset and a precision of 96.74% for the first dataset and 99.228% for the second dataset. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) is an application of the Internet of Things (IoT) in the medical field. It is a cutting-edge technique that connects medical sensors and their applications to healthcare systems, which is essential in smart healthcare. However, Personal Health Records (PHRs) are normally kept in public cloud servers controlled by IoMT service providers, so privacy and security incidents may be frequent. Fortunately, Searchable Encryption (SE), which can be used to execute queries on encrypted data, can address the issue above. Nevertheless, most existing SE schemes cannot solve the vector dominance threshold problem. In response to this, we present a SE scheme called Vector Dominance with Threshold Searchable Encryption (VDTSE) in this study. We use a Lagrangian polynomial technique and convert the vector dominance threshold problem into a constraint that the number of two equal-length vectors' corresponding bits excluding wildcards is not less than a threshold t. Then, we solve the problem using the proposed technique modified in Hidden Vector Encryption (HVE). This technique makes the trapdoor size linear to the number of attributes and thus much smaller than that of other similar SE schemes. A rigorous experimental analysis of a specific application for privacy-preserving diabetes demonstrates the feasibility of the proposed VDTSE scheme. [ABSTRACT FROM AUTHOR]

Infectious diseases pose a very significant threat to development of the society and the world at large. With several outbreaks of diseases like Monkeypox, Lassa fever, SARS, COVID-19, etc, the global economy was grossly affected. The rate of transfer and mortality associated with similar outbreaks is alarming. This research presents a novel approach utilizing the Internet of Medical Things (IoMT) to develop a third-party notification model. This model uses IoMT's ubiquitous connectivity to notify even ordinary individuals of the presence of an infectious disease vector within a specified range. A four-tier architecture, including cloud and web API blocks, healthcare provider management, IoT sensory, and notification blocks forms the bedrock of the model. The research focuses on developing a Location Tracking Device (LTD) prototype that incorporates the Haversine formula for real-time distance calculation between individuals performed at the edge using the location data supplied by the LTDs as input parameters. The optimization of data reception rates was based on the average human walking speed in order to enhance response time of the system. Results from testing the prototype demonstrate an average of 4.68s response delay which corresponds to an offset of about 6.85m from the real vector distance calculation. The research implementation challenges include the internet connection speed, network availability, and topography. Despite these challenges, the IoMT-enabled model introduces a promising approach to infectious disease-carrier monitoring, integrating personalized carrier/vectorpresence awareness with associated risks within the disease control ecosystem. Hence, every user can use the LTD during an epidemic to help track the user’s nearness to a symptomatic person thereby helping to control the spread of infectious diseases during epidemics. [ABSTRACT FROM AUTHOR]

Today's era of smart technologies has shifted the paradigm of how things work in almost every domain. This change has also affected the utmost need of human beings, that is, sustainability in healthcare. With the help of technologies like the Internet of medical things (IoMT) devices and cloud‐based applications, doctors can now monitor and diagnoses their patients remotely. In the data analytics process for health‐care data, more precision and challenges are present. Data must be transferred from a lower layer to final storage (cloud‐based) during data ingestion so that subsequent operations such as mining, accessing, and streaming can be performed on these data. This article addresses some of the important issues to maintain sustainability in healthcare like authentication, smart scheduling of devices, the removal of redundant data on the final layer, and improving access times for stored data. Bloom filter (a probabilistic data structure)‐based solutions are proposed at different layers of IoMT (edge‐fog‐cloud). The comparative analysis of state‐of‐art techniques with the proposed has shown significant improvement over various metrics. The proposed framework has been evaluated experimentally on real health‐care datasets. [ABSTRACT FROM AUTHOR]

IoT has seen remarkable growth, particularly in healthcare, leading to the rise of IoMT. IoMT integrates medical devices for real-time data analysis and transmission but faces challenges in data security and interoperability. This research identifies a significant gap in the existing literature regarding a comprehensive ontology for vulnerabilities in medical IoT devices. This paper proposes a fundamental domain ontology named MIoT (Medical Internet of Things) ontology, focusing on cybersecurity in IoMT (Internet of Medical Things), particularly in remote patient monitoring settings. This research will refer to similar-looking acronyms, IoMT and MIoT ontology. It is important to distinguish between the two. IoMT is a collection of various medical devices and their applications within the research domain. On the other hand, MIoT ontology refers to the proposed ontology that defines various concepts, roles, and individuals. MIoT ontology utilizes the knowledge engineering methodology outlined in Ontology Development 101, along with the structured life cycle, and establishes semantic interoperability among medical devices to secure IoMT assets from vulnerabilities and cyberattacks. By defining key concepts and relationships, it becomes easier to understand and analyze the complex network of information within the IoMT. The MIoT ontology captures essential key terms and security-related entities for future extensions. A conceptual model is derived from the MIoT ontology and validated through a case study. Furthermore, this paper outlines a roadmap for future research, highlighting potential impacts on security automation in healthcare applications. [ABSTRACT FROM AUTHOR]