Internet Of Medical Things Environment Research Articles

Cross vision transformer with enhanced Growth Optimizer for breast cancer detection in IoMT environment

The recent advances in artificial intelligence modern approaches can play vital roles in the Internet of Medical Things (IoMT). Automatic diagnosis is one of the most important topics in the IoMT, including cancer diagnosis. Breast cancer is one of the top causes of death among women. Accurate diagnosis and early detection of breast cancer can improve the survival rate of patients. Deep learning models have demonstrated outstanding potential in accurately detecting and diagnosing breast cancer. This paper proposes a novel technology for breast cancer detection using CrossViT as the deep learning model and an enhanced version of the Growth Optimizer algorithm (MGO) as the feature selection method. CrossVit is a hybrid deep learning model that combines the strengths of both convolutional neural networks (CNNs) and transformers. The MGO is a meta-heuristic algorithm that selects the most relevant features from a large pool of features to enhance the performance of the model. The developed approach was evaluated on three publicly available breast cancer datasets and achieved competitive performance compared to other state-of-the-art methods. The results show that the combination of CrossViT and the MGO can effectively identify the most informative features for breast cancer detection, potentially assisting clinicians in making accurate diagnoses and improving patient outcomes. The MGO algorithm improves accuracy by approximately 1.59% on INbreast, 5.00% on MIAS, and 0.79% on MiniDDSM compared to other methods on each respective dataset. The developed approach can also be utilized to improve the Quality of Service (QoS) in the healthcare system as a deployable IoT-based intelligent solution or a decision-making assistance service, enhancing the efficiency and precision of the diagnosis.

Internet of Medical Things Security Frameworks for Risk Assessment and Management: A Scoping Review

Internet of Medical Things Security Frameworks for Risk Assessment and Management: A Scoping Review

Advanced Heart Disease Prediction: Deep Learning-Enhanced Convolutional Neural Network in the Internet of Medical Things Environment

Using the large dataset from the UCI Machine Learning Repository, this study presents a state-of-the-art Hybrid Convolutional Neural Network (HCNN) for predicting heart disease. The HCNN design has convolutional layers, leftover blocks, and attention methods. These help pull more features from cardiovascular health data, even when the patterns are very complex. Utilizing these deep learning parts, the HCNN shows better prediction skills, getting higher accuracy, strong classification (AUC), and a fair F1 Score. Because the model can change to find complicated connections in the information, it could be used to make medical diagnosis better. The HCNN is unique because it can instantly learn hierarchical structures from raw data. This lets it find hidden features that are important for accurately predicting heart disease. The convolutional layers help the model find local patterns, and the residue blocks stop problems with disappearing gradients, which makes training for deep designs more efficient. Attention processes make the network even more focused on important traits, which adds to its amazing ability to tell them apart. This work opens the door for researchers, doctors, and data scientists to use deep learning, and especially HCNN, to improve cardiovascular health analytics. This study takes a big step toward more accurate and efficient heart disease prognosis by giving a complete overview of the model's architecture and performance metrics. This opens the door for more research and use of advanced neural networks in the field of predictive medical diagnostics.

Distributed Computation Offloading and Power Control for UAV-Enabled Internet of Medical Things

The advancement of the Internet of Medical Things (IoMT) has led to the emergence of various health and emotion care services, e.g., health monitoring. To cater to increasing computational requirements of IoMT services, Mobile Edge Computing (MEC) has emerged as an indispensable technology in smart health. Benefiting from the cost-effectiveness of deployment, unmanned aerial vehicles (UAVs) equipped with MEC servers in Non-Orthogonal Multiple Access (NOMA) have emerged as a promising solution for providing smart health services in proximity to medical devices (MDs). However, the escalating number of MDs and the limited availability of communication resources of UAVs give rise to a significant increase in transmission latency. Moreover, due to the limited communication range of UAVs, the geographically-distributed MDs lead to workload imbalance of UAVs, which deteriorates the service response delay. To this end, this paper proposes a UAV-enabled Distributed computation Offloading and Power control method with Multi-Agent, named DOPMA, for NOMA-based IoMT environment. Specifically, this paper introduces computation and transmission queue models to analyze the dynamic characteristics of task execution latency and energy consumption. Moreover, a credit assignment scheme-based reward function is designed considering both system-level rewards and rewards tailored to each MD, and an improved multi-agent deep deterministic policy gradient algorithm is developed to derive offloading and power control decisions independently. Extensive simulations demonstrate that the proposed method outperforms existing schemes, achieving \(7.1\% \) reduction in energy consumption and \(16\% \) decrease in average delay.

Unlock the potential: Unveiling the untapped possibilities of blockchain technology in revolutionizing Internet of medical things-based environments through systematic review and future research propositions

The Internet of Medical Things (IoMTs) has rapidly gained popularity recently, and the need for self-health assessment and the resulting reduction in treatment waiting periods have sped up the usage of IoMTs. Unfortunately, the growing usage of IoMTs has also brought up issues that have unintended effects, including a lack of data management, an unsafe network environment, privacy and security concerns. To address the problems with IoMT applications, distributed ledger technology, which forms the basis of blockchain technology (BCT), has emerged as a practical solution. The utility of BCT to support IoMT-based settings has been demonstrated by academic research. However, no analysis of the literature has been performed to comprehend BCT performance in IoMT scenarios. Therefore, the present study offers a thorough literature review of 182 studies on the use of BCT in an IoMT environment, drawn from 70 pioneering journals included in the WoS database. It also outlines recommendations for future research areas. This study adds to the body of literature in three main ways using a mix of bibliographic and content analysis. It starts out by outlining the structure of how BCT works in an IoMT setting. Secondly, it provides a roadmap that takes into account the factors that might aid in BCT proliferation in IoMT situations. Thirdly, the paper outlines the prerequisites for BCT's effective adoption and use in an IoMT environment. The implications of the recognized clusters and the factors influencing the properties and outcomes of BCT are covered in the article's conclusion. This is the first effort to investigate BCT's function within the context of the IoMT environment; this has become crucial since BCT has developed popularity in IoMT contexts. The article highlights many aspects that may help to improve comprehension of this important issue by utilizing the provided outline as a guide and identifying major knowledge gaps.

A Genetic-Algorithm-Based Dynamic Transmission of Data for Communicable Disease in IoMT Environment

Recent advancements in the field of the Internet of medical things (IoMT) have enabled the real-time monitoring and treatment of patients with communicable infectious diseases while minimizing human intervention. However, IoMT devices face challenges such as unbalanced energy consumption, memory constraints, computation power, and low latency, which can deter the efficient transfer of patient monitoring data. Thus, there is an urgent need to establish an energy-efficient infrastructure for IoMT devices to remotely monitor and collect data on communicable diseases. For this, a genetic algorithm (GA) based dynamic transmission of data for communicable diseases in the IoMT environment is proposed in this paper. The energy utilization of the IoMT is enhanced by considering the GA evolutionary processing based on the dynamic sensor range. The proposed work incorporates a periphery of the fixed area for deploying the IoMT devices to settle the energy hole problem. Multiple sinks and direct information collection concepts are also introduced which further improve the performance and reduce the movement of data packets. The proposed protocols not only optimize energy usage but also provide a robust approach for massive data collection and communication.

Fractional-order modified heterogeneous comprehensive learning particle swarm optimizer for intelligent disease detection in IoMT environment

This paper presents an alternative early disease detection technique based on the Internet of Medical Things (IoMT) to improve the healthcare system. Since it is necessary to address different aspects to help healthcare organizations to improve their efficiency. Early disease detection using a fast and efficient test, such as chest X-ray images, is one of the most important issues. In this paper, we develop an efficient model to allocate abnormalities in medical chest X-ray images. The developed model consists of a set of stages; the first stage is to segment the images using a multilevel thresholding technique to determine the lung inside the image, then extracting the features from the segmented objects using different extractor methods. Then, we proposed a Fractional-order modified Heterogeneous comprehensive learning particle swarm optimizer (FMHCLPSO) as a feature selection method to determine the relevant features used to improve the detection process. To evaluate the performance of the developed IoMT model, a set of three medical datasets is used, called Covid-19 & Pneumonia, X-raycovid, and Radiography. The results illustrate the high efficiency of the developed model to detect diseases based on performance measures. Furthermore, We compared the proposed method to several existing methods, and it showed significant performance.

EcoSync: An Energy-Efficient Clock Discipline Data Synchronization in Wi-Fi IoMT Systems

The growth of the Internet of Medical Things (IoMT) and healthcare data analytics allows wearable Wireless Body Area Networks (WBANs) and ambient sensors to collect the large quantities of physiological signals necessary for better patient diagnostics and treatments. Artificial intelligence and machine learning algorithms frequently require precisely synchronized signals from multiple sensors, which in turn require time-consuming and energy-inefficient synchronization methods with constant wireless network connectivity. We propose ecoSync, a highly energy-efficient time synchronization algorithm for Wi-Fi devices in IoMT applications. We demonstrated that ecoSync can correct the time difference error to ±42 µs with an hour between resynchronizations, using only 658 millijoules of energy. This is an 87% improvement in time difference error and a 99.93% reduction in energy usage over using TSF for synchronization alone over a 1 h period. Wireless synchronization of sensors allows placement of physiological sensors on objects of everyday use (Smart Stuff), which in turn allows seamless collection of physiological status data every time we interact with smart objects in an IoMT environment.

A Multifactor Ring Signature based Authentication Scheme for Quality Assessment of IoMT Environment in COVID-19 Scenario

The quality of the healthcare environment has become an essential factor for healthcare users to access quality services. Smart healthcare systems use the Internet of Medical Things (IoMT) devices to capture patients’ health data for treatment or diagnostic purposes. This sensitive collected patient data is shared between the different stakeholders across the network to provide quality services. Due to this, healthcare systems are vulnerable to confidentiality, integrity and privacy threats. In the COVID-19 scenario, when collaborative medical consultation is required, the quality assessment of the framework is essential to protect the privacy of doctors and patients. In this paper, a ring signature-based anonymous authentication and quality assessment scheme is designed for collaborative medical consultation environments for quality assessment and protection of the privacy of doctors and patients. This scheme also uses a new KMOV Cryptosystem to ensure the quality of the network and protect the system from different attacks that hamper data confidentiality.

Deep Learning-Based Network Intrusion Detection System for Internet of Medical Things

This article presents a deep learning-based approach for network-based intrusion detection in the Internet of medical things (IoMT) systems using features of network flows and patient biometrics. The proposed approach effectively learns optimal feature representation by passing the information of network flows and patient biometrics into more than one hidden layer of deep learning. The network includes a global attention layer which helps to effectively extract the optimal features from the spatial and temporal features of deep learning. To avoid data imbalance, a cost-sensitive learning approach is integrated into the deep learning model. The proposed model showed a 10-fold cross-validation accuracy of 95 percent on network features, 89 percent on patient biometrics, and 99 percent on combined features. In addition to the IoMT environment, the robustness and generalization ability of the proposed model is shown by conducting experiments on other network-based intrusion datasets. The proposed approach outperformed the existing methods in all the test cases mainly showing a 3.9 percent higher accuracy on the IoMT intrusion dataset. The proposed model can be used as an IoMT network monitoring tool to safeguard the IoMT devices and networks from attackers inside the healthcare and medical environment.

Internet of Medical Things with a Blockchain-Assisted Smart Healthcare System Using Metaheuristics with a Deep Learning Model

The Internet of Medical Things (IoMT) is a network of healthcare devices such as wearables, diagnostic equipment, and implantable devices, which are linked to the internet and can communicate with one another. Blockchain (BC) technology can design a secure, decentralized system to store and share medical data in an IoMT-based intelligent healthcare system. Patient records were stored in a tamper-proof and decentralized way using BC, which provides high privacy and security for the patients. Furthermore, BC enables efficient and secure sharing of healthcare data between patients and health professionals, enhancing healthcare quality. Therefore, in this paper, we develop an IoMT with a blockchain-based smart healthcare system using encryption with an optimal deep learning (BSHS-EODL) model. The presented BSHS-EODL method allows BC-assisted secured image transmission and diagnoses models for the IoMT environment. The proposed method includes data classification, data collection, and image encryption. Initially, the IoMT devices enable data collection processes, and the gathered images are stored in BC for security. Then, image encryption is applied for data encryption, and its key generation method can be performed via the dingo optimization algorithm (DOA). Finally, the BSHS-EODL technique performs disease diagnosis comprising SqueezeNet, Bayesian optimization (BO) based parameter tuning, and voting extreme learning machine (VELM). A comprehensive set of simulation analyses on medical datasets highlights the betterment of the BSHS-EODL method over existing techniques with a maximum accuracy of 98.51%, whereas the existing methods such as DBN, YOLO-GC, ResNet, VGG-19, and CDNN models have lower accuracies of 94.15%, 94.24%, 96.19%, 91.19%, and 95.29% respectively.

Dimensions of artificial intelligence techniques, blockchain, and cyber security in the Internet of medical things: Opportunities, challenges, and future directions

Abstract The Internet of medical things (IoMT) is a modern technology that is increasingly being used to provide good healthcare services. As IoMT devices are vulnerable to cyberattacks, healthcare centers and patients face privacy and security challenges. A safe IoMT environment has been used by combining blockchain (BC) technology with artificial intelligence (AI). However, the services of the systems are costly and suffer from security and privacy problems. This study aims to summarize previous research in the IoMT and discusses the roles of AI, BC, and cybersecurity in the IoMT, as well as the problems, opportunities, and directions of research in this field based on a comprehensive literature review. This review describes the integration schemes of AI, BC, and cybersecurity technologies, which can support the development of new systems based on a decentralized approach, especially in healthcare applications. This study also identifies the strengths and weaknesses of these technologies, as well as the datasets they use.

Fed-ESD: Federated learning for efficient epileptic seizure detection in the fog-assisted internet of medical things

Epilepsy is a predominant paroxysmal neurological disturbance that is usually recognized as the incidence of impulsive seizures rarely seen in medicine. Automatic detection of epileptic seizures from electroencephalogram (EEG) signals is viewed as an effective diagnosis of patients on the Internet of Medical Things (IoMT). To design a robust detection service in an IoMT environment, the EEG signals of different patients are collected from geographically distributed patients to a centralized server. However, this makes the patient’s privacy prone to exposure and adds to the energy and communication costs. Also, the central server can be subject to malevolent attacks, resulting in non-efficient solutions. In this regard, for the first time, this paper presents a privacy-preserving federated learning framework for epileptic seizure detection (called Fed-ESD) from EEG signals in the fog-computing-based IoMT. A lightweight and efficient spatiotemporal transformer network is introduced to collaboratively learn spatial and temporal representations from the local data of each participant. The proposed Fed-ESD employs geographically situated fog nodes as local aggregators to enable sharing of location-based EEG signals for comparable IoMT applications. Moreover, a greedy method is introduced for deciding on the ideal fog node to be the coordinator node responsible for global aggregation during the training, thereby decreasing the reliance on the central server in the IoMT. Experimental evaluations demonstrate the efficiency of the proposed Fed-ESD in terms of detection performance, resource-efficiency, stability, and scalability for deployment in the IoMT.

An Improved Lightweight User Authentication Scheme for the Internet of Medical Things.

The Internet of Medical Things (IoMT) is used in the medical ecosystem through medical IoT sensors, such as blood glucose, heart rate, temperature, and pulse sensors. To maintain a secure sensor network and a stable IoMT environment, it is important to protect the medical IoT sensors themselves and the patient medical data they collect from various security threats. Medical IoT sensors attached to the patient's body must be protected from security threats, such as being controlled by unauthorized persons or transmitting erroneous medical data. In IoMT authentication, it is necessary to be sensitive to the following attack techniques. (1) The offline password guessing attack easily predicts a healthcare administrator's password offline and allows for easy access to the healthcare worker's account. (2) Privileged-insider attacks executed through impersonation are an easy way for an attacker to gain access to a healthcare administrator's environment. Recently, previous research proposed a lightweight and anonymity preserving user authentication scheme for IoT-based healthcare. However, this scheme was vulnerable to offline password guessing, impersonation, and privileged insider attacks. These attacks expose not only the patients' medical data such as blood pressure, pulse, and body temperature but also the patients' registration number, phone number, and guardian. To overcome these weaknesses, in the present study we propose an improved lightweight user authentication scheme for the Internet of Medical Things (IoMT). In our scheme, the hash function and XOR operation are used for operation in low-spec healthcare IoT sensor. The automatic cryptographic protocol tool ProVerif confirmed the security of the proposed scheme. Finally, we show that the proposed scheme is more secure than other protocols and that it has 266.48% better performance than schemes that have been previously described in other studies.

Deep Forest-Based Fall Detection in Internet of Medical Things Environment

This article introduces a new medical internet of things (IoT) framework for intelligent fall detection system of senior people based on our proposed deep forest model. The cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural networks. Moreover, the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated layer. The suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset, which is acquired from three-axis accelerometer in a smartwatch. It includes 92781 training samples and 91025 testing samples with two labeled classes, namely non-fall and fall. Classification results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0% compared to three machine learning models, i.e., K-nearest neighbors, decision trees and traditional random forest, and two deep learning models, which are dense neural networks and convolutional neural networks. By considering security and privacy aspects in the future work, our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.

Feature Selection Using Selective Opposition Based Artificial Rabbits Optimization for Arrhythmia Classification on Internet of Medical Things Environment

Feature Selection Using Selective Opposition Based Artificial Rabbits Optimization for Arrhythmia Classification on Internet of Medical Things Environment

Encryption with User Authentication Model for Internet of Medical Things Environment

Internet of Medical Things (IoMT) enabled e-healthcare has the potential to greately improve conventional healthcare services significantly. However, security and privacy become major issues of IoMT because of the restricted processing abilities, storage, and energy constraints of the sensors. Therefore, it leads to infeasibility of developing traditional cryptographic solutions to the IoMT sensors. In order to ensure security on sensitive medical data, effective encryption and authentication techniques need to be designed to assure security of the patients and healthcare service providers. In this view, this study designs an effective metaheuristic optimization based encryption with user authentication (EMOE-UA) technique for IoMT environment. This work proposes an EMOE-UA technique aims to accomplish mutual authentication for addressing the security issues and reducing the computational complexity. Moreover, the EMOE-UA technique employs optimal multikey homomorphic encryption (OMKHE) technique to encrypt the IoMT data. Furthermore, the improved social spider optimization algorithm (ISSOA) was employed for the optimal multikey generation of the MKHE technique. The experimental result analysis of the EMOE-UA technique takes place using benchmark data and the results are examined under various aspects. The simulation results reported the considerably better performance of the EMOE-UA technique over the existing techniques.

Artificial-Intelligence-Based Decision Making for Oral Potentially Malignant Disorder Diagnosis in Internet of Medical Things Environment.

Oral cancer is considered one of the most common cancer types in several counties. Earlier-stage identification is essential for better prognosis, treatment, and survival. To enhance precision medicine, Internet of Medical Things (IoMT) and deep learning (DL) models can be developed for automated oral cancer classification to improve detection rate and decrease cancer-specific mortality. This article focuses on the design of an optimal Inception-Deep Convolution Neural Network for Oral Potentially Malignant Disorder Detection (OIDCNN-OPMDD) technique in the IoMT environment. The presented OIDCNN-OPMDD technique mainly concentrates on identifying and classifying oral cancer by using an IoMT device-based data collection process. In this study, the feature extraction and classification process are performed using the IDCNN model, which integrates the Inception module with DCNN. To enhance the classification performance of the IDCNN model, the moth flame optimization (MFO) technique can be employed. The experimental results of the OIDCNN-OPMDD technique are investigated, and the results are inspected under specific measures. The experimental outcome pointed out the enhanced performance of the OIDCNN-OPMDD model over other DL models.

Achieving Privacy-Preserving Online Diagnosis With Outsourced SVM in Internet of Medical Things Environment

Online diagnosis is one of the data services, which can use the machine learning model placed on the cloud and collected physical data from internet of medical things (IoMT) for better medical services. However, the collected user data, diagnosis results and the deployed machine learning model contain sensitive information of users and the healthcare provider, which may lead to serious privacy leakage. To achieve a secure outsourced diagnosis, both high security and low burden for users should be considered. However, the existing works can not solve these problems simultaneously. In this article, based on two non-colluding servers, a privacy-preserving cloud-aided diagnosis scheme for IoMT is proposed. Concretely, a hybrid data encryption method based on homomorphic encryption and AES is used to generate user requests in an efficient way. Besides, we propose a class of secure two-party protocols using homomorphic encryption, such as secure kernel function computation, secure multiplication, and secure comparison, and a privacy-preserving diagnosis scheme based on multi-class SVM with these building blocks is constructed, which can keep users offline in the diagnosis process. Finally, the security analysis and evaluation further illustrate that our scheme is superior to the prior works in terms of security and user-friendliness.

LMNet: Lightweight multi-scale convolutional neural network architecture for COVID-19 detection in IoMT environment

The COVID-19 disease, initially known as SARS-CoV-2, was first reported in early December 2019 and has caused immense damage to humans globally. The most widely used clinical screening method for COVID-19 is Reverse Transcription Polymerase Chain Reaction (RT-PCR). RT-PCR uses respiratory samples for testing, because of which, this manual technique becomes complicated, laborious and time-consuming. Even though it has a low sensitivity, it carries a considerable risk for the testing medical staff. Hence, there is a need for an automated diagnosis system that can provide quick and efficient diagnosis results. This research proposed a multi-scale lightweight CNN (LMNet) architecture for COVID-19 detection. The proposed model is computationally less expensive than previously available models and requires less memory space. The performance of the proposed LMNet model ensemble with DenseNet169 and MobileNetV2 is higher than the other state-of-the-art models. The ensemble model can be integrated at the backend of the smart devices; hence it is useful for the Internet of Medical Things (IoMT) environment.