Medical IoT Devices Research Articles

Blockchain-Based Privacy Preservation Scheme for Misbehavior Detection in Lightweight IoMT Devices.

The Internet of Medical Things (IoMT) has risen to prominence as a possible backbone in the health sector, with the ability to improve quality of life by broadening user experience while enabling crucial solutions such as near real-time remote diagnostics. However, privacy and security problems remain largely unresolved in the safety area. Various rule-based methods have been considered to recognize aberrant behaviors in IoMT and have demonstrated high accuracy of misbehavior detection appropriate for lightweight IoT devices. However, most of these solutions have privacy concerns, especially when giving context during misbehavior analysis. Moreover, falsified or modified context generates a high percentage of false positives and sometimes causes a by-pass in misbehavior detection. Relying on the recent powerful consolidation of blockchain and federated learning (FL), we propose an efficient privacy-preserving framework for secure misbehavior detection in lightweight IoMT devices, particularly in the artificial pancreas system (APS). The proposed approach employs privacy-preserving bidirectional long-short term memory (BiLSTM) and augments the security through integrating blockchain technology based on Ethereum smart contract environment. The effectiveness of the proposed model is bench-marked empirically in terms of sustainable privacy preservation, commensurate incentive scheme with an untraceability feature, exhaustiveness, and the compact results of a variant neural network approach. As a result, the proposed model has a 99.93% recall rate, showing that it can detect virtually all possible malicious events in the targeted use case. Furthermore, given an initial ether value of 100, the solution's average gas consumption and Ether spent are 84,456.5 and 0.03157625, respectively.

On the Physical Layer Security of Federated Learning Based IoMT Networks.

Internet of Medical Things (IoMT) connects different medical devices, health sensors and hospital records to data platforms using wireless communications. Federated Learning (FL) is an emerging collaborative learning technique that can be beneficial for IoMT due to reduced communication overhead and enhanced security. This paper provides an overview of different architectures used in FL and potential approaches for FL based IoMT. We also discuss how Physical Layer Security (PLS) can be used for efficient privacy preservation of data in FL based IoMT. We highlight the recent work in this area and major research challenges related to PLS assisted FL in IoMT. We also provide a case study demonstrating that clustering of IoMT devices (such that a single device in each cluster acts as a cluster head) enhances the secrecy rate of the FL based IoMT network as compared to its non-clustered counterpart. Finally, we also discuss future opportunities and open research questions related to PLS assisted FL in IoMT.

Smart Healthcare Applications over 5G Networks: A Systematic Review

Provisioning of health services such as care, monitoring, and remote surgery is being improved thanks to fifth-generation cellular technology (5G). As 5G expands globally, more smart healthcare applications have been developed due to its extensive eMBB (Enhanced Mobile Broadband) and URLLC (Ultra-Reliable Low Latency Communications) features that can be used to generate healthcare systems that allow minimizing the face-to-face assistance of patients at hospital centers. This powerful network provides high transmission speeds, ultra-low latency, and a network capacity greater than that of 4G. Fifth-generation cellular technology is expected to be a means to provide excellent quality of medical care, through its technological provision to the use of IoMT (Internet of Medical Things) devices. Due to the numerous contributions in research on this topic, it is necessary to develop a review that provides an orderly perspective on research trends and niches for researchers to use as a starting point for their work. In this context, this article presents a systematic review based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), with article selection based on inclusion and exclusion criteria that avoid bias. This research was based on research questions that were answered from the included works. These questions focus on technical characteristics, health benefits, and security protocols necessary for the development of smart healthcare applications. We have identified that a high percentage of existing works in the literature are proposals (56.81%, n = 25) and theoretical studies (22.73%, n = 10); few implementations (15.91%, n = 7) and prototypes (4.55%, n = 2) exist, due to the limited global deployment of 5G. However, the panorama looks promising based on proposals and future work that these technological systems allow, all based on improving healthcare for people.

A lightweight blockchain and fog-enabled secure remote patient monitoring system

IoT has enabled the rapid growth of smart remote healthcare applications. These IoT-based remote healthcare applications deliver fast and preventive medical services to patients at risk or with chronic diseases. However, ensuring data security and patient privacy while exchanging sensitive medical data among medical IoT devices is still a significant concern in remote healthcare applications. Altered or corrupted medical data may cause wrong treatment and create grave health issues for patients. Moreover, current remote medical applications’ efficiency and response time need to be addressed and improved. Considering the need for secure and efficient patient care, this paper proposes a lightweight Blockchain-based and Fog-enabled remote patient monitoring system that provides a high level of security and efficient response time. Simulation results and security analysis show that the proposed lightweight blockchain architecture fits the resource-constrained IoT devices well and is secure against attacks. Moreover, the augmentation of Fog computing improved the responsiveness of the remote patient monitoring system by 40%.

A secure communication using multifactor authentication and key agreement techniques in internet of medical things for COVID‐19 patients

AbstractCurrently, Internet of Medical Things (IoMT) gained popularity because of an ongoing pandemic. A few developed countries plan to deploy the IoMT for improving the security and safety of frontline workers to decrease the mortality rates of COVID‐19 patients. However, IoMT devices share the information through an open network which leads to increased vulnerability to various attacks. Hence, electronic health management systems remain many security challenges, like recording sensitive patient data, secure communication, transferring patient information to other doctors, providing the data for future medical diagnosis, collecting data from WBAN, etc. In addition, the sensor devices attached to the human body are resource‐limited and have minimal power capacity. Hence, to protect the medical privacy of patients, confidentiality and reliability of the system, the register sensor, doctor and server need to authenticate each other. Therefore, rather than two factors, in this work, a multifactor authentication protocol has been proposed to provide more secure communication. The presented scheme uses biometric and fuzzy extractors for more security purposes. Furthermore, the scheme is proved using informal and formal security verification BAN logic, ProVerif and AVISPA tools. The ProVerif simulation result of the suggested scheme shows that the proposed protocol achieves session key secrecy and mutual authentication

Learning and Game Based Spectrum Allocation Model for Internet of Medical Things (IoMT) Platform

The Internet of Medical Things (IoMT) paradigm provides pervasive healthcare services in-home monitoring networks. Nowadays, these services play an imperative part in the life of human beings. However, excessive requirements of health services result in insufficient spectrum resources and service delays. In this study, a novel spectrum allocation scheme is proposed for the IoMT system platform. The main challenge of our scheme is to effectively share the limited spectrum resource while dynamically handling different service requests. To achieve a mutually desirable solution for multiple IoMT devices, our proposed scheme is designed as a bi-level control algorithm using the ideas of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-agent reinforcement learning (MARL)</i> and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Balakrishnan-Gómez-Vohra (BGV)</i> solution. At the first level, each IoMT device selects its salient point according to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MARL</i> model. At the second level, the spectrum resource is distributed through the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BGV</i> solution, which is implemented by considering the selected salient point of each device. Through the sequential interactions of intelligent devices, our bi-level control approach can effectively guide individual IoMT devices to choose cooperation strategies while optimizing the spectrum allocation process. Finally, numerical results show the effectiveness of our proposed scheme through the comparisons with benchmark protocols. We demonstrate the performance improvement of our method in terms of the normalized device payoff, IoMT system throughput and device fairness.

Edge-Enabled Heart Rate Estimation from Multisensor PPG Signals.

Heart rate (HR) estimation from multisensor PPG signals suffers from the dilemma of inconsistent computation results, due to the prevalence of bio-artifacts (BAs). Furthermore, advancements in edge computing have shown promising results from capturing and processing diversified types of sensing signals using the devices of Internet of Medical Things (IoMT). In this paper, an edge-enabled method is proposed to estimate HRs accurately and with low latency from multisensor PPG signals captured by bilateral IoMT devices. First, we design a real-world edge network with several resource-constrained devices, divided into collection edge nodes and computing edge nodes. Second, a self-iteration RR interval calculation method, at the collection edge nodes, is proposed leveraging the inherent frequency spectrum feature of PPG signals and preliminarily eliminating the influence of BAs on HR estimation. Meanwhile, this part also reduces the volume of sent data from IoMT devices to compute edge nodes. Afterward, at the computing edge nodes, a heart rate pool with an unsupervised abnormal detection method is proposed to estimate the average HR. Experimental results show that the proposed method outperforms traditional approaches which rely on a single PPG signal, attaining better results in terms of the consistency and accuracy for HR estimation. Furthermore, at the designed edge network, our proposed method processes a 30 s PPG signal to obtain an HR, consuming only 4.24 s of computation time. Hence, the proposed method is of significant value for the low-latency applications in the field of IoMT healthcare and fitness management.

Internet of medical things-based real-time digital health service for precision medicine: Empirical studies using MEDBIZ platform.

The aim of this study was to introduce the implemented MEDBIZ platform based on the internet of medical things (IoMT) supporting real-time digital health services for precision medicine. In addition, we demonstrated four empirical studies of the digital health ecosystem that could provide real-time healthcare services based on IoMT using real-world data from in-hospital and out-hospital patients. Implemented MEDBIZ platform based on the IoMT devices and big data to provide digital healthcare services to the enterprise and users. The big data platform is consisting of four main components: IoMT, core, analytics, and services. Among the implemented MEDBIZ platform, we performed four clinical trials that designed monitoring services related to chronic obstructive pulmonary disease, metabolic syndrome, arrhythmia, and diabetes mellitus. Of the four empirical studies on monitoring services, two had been completed and the rest were still in progress. In the metabolic syndrome monitoring service, two studies were reported. One was reported that intervention components, especially wearable devices and mobile apps, made systolic blood pressure, diastolic blood pressure, waist circumference, and glycosylated hemoglobin decrease after 6 months. Another one was presented that increasing high-density lipoprotein cholesterol and triglyceride levels were prevented in participants with the pre-metabolic syndrome. Also, self-care using healthcare devices might help prevent and manage metabolic syndrome. In the arrhythmia monitoring service, during the real-time monitoring of vital signs remotely at the monitoring center, 318 (15.9%) general hikers found abnormal signals, and 296 (93.1%) people were recommended for treatment. We demonstrated the implemented MEDBIZ platform based on IoMT supporting digital healthcare services by acquiring real-world data for getting real-world evidence. And then through this platform, we were developing software as a medical device, digital therapeutics, and digital healthcare services, and contributing to the development of the digital health ecosystem.

Advance IoT Intelligent Healthcare System for Lung Disease Classification Using Ensemble Techniques

In healthcare systems, the Internet of Things (IoT) innovation and development approached new ways to evaluate patient data. A cloud-based platform tends to process data generated by IoT medical devices instead of high storage, and computational hardware. In this paper, an intelligent healthcare system has been proposed for the prediction and severity analysis of lung disease from chest computer tomography (CT) images of patients with pneumonia, Covid-19, tuberculosis (TB), and cancer. Firstly, the CT images are captured and transmitted to the fog node through IoT devices. In the fog node, the image gets modified into a convenient and efficient format for further processing. advanced encryption Standard (AES) algorithm serves a substantial role in IoT and fog nodes for preventing data from being accessed by other operating systems. Finally, the preprocessed image can be classified automatically in the cloud by using various transfer and ensemble learning models. Herein different pre-trained deep learning architectures (Inception-ResNet-v2, VGG-19, ResNet-50) used transfer learning is adopted for feature extraction. The softmax of heterogeneous base classifiers assists to make individual predictions. As a meta-classifier, the ensemble approach is employed to obtain final optimal results. Disease predicted image is consigned to the recurrent neural network with long short-term memory (RNN-LSTM) for severity analysis, and the patient is directed to seek therapy based on the outcome. The proposed method achieved 98.6% accuracy, 0.978 precision, 0.982 recalls, and 0.974 F1-score on five class classifications. The experimental findings reveal that the proposed framework assists medical experts with lung disease screening and provides a valuable second perspective.

IOT Assisted Biomedical Monitoring Sensors for Healthcare in Human

The Internet of Things (IoT) is a concept that refers to the deployment of Internet Protocol (IP) address sensors in health care systems to monitor patients’ health. It has the ability to access the Internet and collect data from sensors. Automated decisions are made after evaluating the information of illness people records. Patients’ health and well-being can be monitored through IoT medical devices. It is possible to trace the origins of biological, medical equipment and processes. Human reliability is a major concern in user activity and fitness trackers in day-to-day activities. The fundamental challenge is to measure the efficiency of the human system accurately. Aim to maintain tabs on the well-being of humans; this paper recommends the use of wireless body area networks (WBANs) and artificial neural networks (ANN) to create an IoT-based healthcare framework for hospital information systems (IoT-HF-HIS). Our evaluation system uses a server to estimate how much computing power is needed for modeling, and simulations of the framework have been done using data rate and latency requirements are implementing the energy-aware technology presented in this paper. The proposed framework implements several hospital information system case studies by building a time-saving simulation environment. As the world’s population ages, more and more people suffer from physical and emotional ailments. Using the recommended strategy regularly has been proven user-friendly, reliable, and cost-effective, with an overall performance of 95.2%.

Brain Tumor Diagnosis Using Sparrow Search Algorithm Based Deep Learning Model

Recently, Internet of Medical Things (IoMT) has gained considerable attention to provide improved healthcare services to patients. Since earlier diagnosis of brain tumor (BT) using medical imaging becomes an essential task, automated IoMT and cloud enabled BT diagnosis model can be devised using recent deep learning models. With this motivation, this paper introduces a novel IoMT and cloud enabled BT diagnosis model, named IoMTC-HDBT. The IoMTC-HDBT model comprises the data acquisition process by the use of IoMT devices which captures the magnetic resonance imaging (MRI) brain images and transmit them to the cloud server. Besides, adaptive window filtering (AWF) based image preprocessing is used to remove noise. In addition, the cloud server executes the disease diagnosis model which includes the sparrow search algorithm (SSA) with GoogleNet (SSA-GN) model. The IoMTC-HDBT model applies functional link neural network (FLNN), which has the ability to detect and classify the MRI brain images as normal or abnormal. It finds useful to generate the reports instantly for patients located in remote areas. The validation of the IoMTC-HDBT model takes place against BRATS2015 Challenge dataset and the experimental analysis is carried out interms of sensitivity, accuracy, and specificity. The experimentation outcome pointed out the betterment of the proposed model with the accuracy of 0.984.

A Survey of Internet of Medical Things (IoMT) Applications, Architectures and Challenges in Smart Healthcare Systems

Internet of Medical Things (IoMT) or Healthcare IoT is a technological under IoT catering to the healthcare sector. It refers to the interconnection of medical devices, sensors, applications and systems to the Internet. IoMT enables the collection, transmission and analysis of patient’s data in real-time, allowing for remote monitoring and early detection of health issues. IoMT systems present a promising opportunity for prevention, prediction, and monitoring of emerging infectious diseases such as COVID-19. This paper provides a survey of IoMT devices, applications, benefits, challenges, and its impact on the healthcare industry.

A review of security issues and solutions for precision health in Internet-of-Medical-Things systems

Precision medicine provides a holistic perspective of an individual’s health, including genetic, environmental, and lifestyle aspects to realize individualized therapy. The development of the internet of things (IoT) devices, the widespread emergence of electronic medical records (EMR), and the rapid progress of cloud computing and artificial intelligence provide an opportunity to collect healthcare big data throughout the lifespan and analyze the disease risk at all stages of life. Thus, the focus of precision medicine is shifting from treatment toward prediction and prevention, i.e., precision health. To this end, various types of data such as omics, imaging, EMR, continuous physiological monitoring, lifestyle, and environmental information, need to be collected, tracked, managed and shared. Thus, internet-of-medical things (IoMT) is crucial for assimilating the health systems, applications, services, and devices that can improve the speed and accuracy of diagnosis and treatments along with real-time monitoring and modification of patient behavior as well as health status. However, security has emerged as a growing concern owing to the proliferation of IoMT devices. The increasing interconnectivity of IoMT-enabled devices with health data reception, transmission, and processing significantly increases the number of potential vulnerabilities within a system. To address the security issues of precision health in IoMT systems, this study reviews the state-of-the-art techniques and schemes from the perspective of a hierarchical system architecture. We present an IoMT system model comprising three layers: the sensing layer, network layer, and cloud infrastructure layer. In particular, we discuss the vulnerabilities and threats to security in each layer and review the existing security techniques and schemes corresponding to the system components along with their functionalities. Owing to the unique nature of biometric features in medical and health services, we highlight the biometrics-based technologies applied in IoMT systems, which contribute toward a considerable difference between the security solutions of existing IoT systems. Furthermore, we summarize the challenges and future research directions of IoMT systems to ensure an improved and more secure future of precision health.

Knowledge Organization System for Partial Automation to Improve the Security Posture of IoMT Networks

Remote patient monitoring is a healthcare delivery model that uses technology to collect and transmit patient data from a remote location to healthcare providers for analysis and treatment. Remote patient monitoring systems rely on a network infrastructure to gather and transmit data from patients to healthcare providers through a network. While these systems become more prevalent, they may also become targets for cyberattacks. This paper deals with the development of a domain ontology to facilitate partial automation to improve the security posture of IoT networks used in remote patient monitoring. For this purpose, it captures the semantics of the concepts and properties of the main security aspects of IoT medical devices. This is complemented by a comprehensive ruleset, evaluated by using SPARQL queries, and automated reasoning over the aggregated knowledge.

A secured architecture for IoT-based healthcare system

Healthcare has gradually moved away from the model centered on traditional health centers due to the emergence of highly accurate sensors and Internet of Things (IoT) enabled medical equipment. Ambient intelligence takes whatever actions are required in response to a recognized event in order to enable continuous learning about patient data. The capabilities of IoT-assisted healthcare services might be improved by incorporating autonomous control and human-computer interface (HCI) technologies into ambient intelligence. Major unsolved issues include the privacy and security of information collected by medical IoT devices, both during transmission to and during cloud storage. This research explores different techniques, IoT factors, and features, with an emphasis on the data security concerns connected to data flow in medical IoT. In order to guarantee data security and privacy at all data levels, this study suggests a safe design for the IoT healthcare system.

Automated detection of cybersecurity attacks in healthcare systems with recursive feature elimination and multilayer perceptron optimization

Widespread proliferation of interconnected healthcare equipment, accompanying software, operating systems, and networks in the Internet of Medical Things (IoMT) raises the risk of security compromise as the bulk of IoMT devices are not built to withstand internet attacks. In this work, we have developed a cyber-attack and anomaly detection model based on recursive feature elimination (RFE) and multilayer perceptron (MLP). The RFE approach selected optimal features using logistic regression (LR) and extreme gradient boosting regression (XGBRegressor) kernel functions. MLP parameters were adjusted by using a hyperparameter optimization and 10-fold cross-validation approach was performed for performance evaluations. The developed model was performed on various IoMT cybersecurity datasets, and attained the best accuracy rates of 99.99%, 99.94%, 98.12%, and 96.2%, using Edith Cowan University- Internet of Health Things (ECU-IoHT), Intensive Care Unit (ICU Dataset), Telemetry data, Operating systems’ data, and Network data from the testbed IoT/IIoT network (TON-IoT), and Washington University in St. Louis enhanced healthcare monitoring system (WUSTL-EHMS) datasets, respectively. The proposed method has the ability to counter cyber attacks in healthcare applications.

Data practices of internet of medical things: A look from privacy policy perspectives

The Internet of Medical Things (IoMT) is an important enabler for improving healthcare through collecting, processing, and analyzing sensitive medical data of unprecedented volume. While the data collection on the body, in the home, or in hospital settings helps revolutionize healthcare, it endangers privacy and exposes users to network vulnerabilities. Furthermore, there is a lack of transparency in the data practices of the IoMT devices. We seek to explore the data practices of IoMT devices found in smart homes and identify trends and potential outliers. We leveraged legal frameworks (i.e., HIPAA, CalOPPA, COPPA) and expert opinions to develop a Privacy Policy Assessment Questionnaire (PPAQ) to evaluate the privacy posture of IoMT devices (i.e., weight scales, blood pressure monitors, continuous glucose monitors). We evaluated 20 IoMT privacy policies according to the PPAQ's seven privacy factors: General Data Collection, Data Sharing, Data Retention, Data Protection, User Choice, and Children's Personal Information. The results show the presence of the privacy factors, but the statements are general and do not provide specificity to describe relevant privacy concerns. For example, more than 85% (17/20) of the privacy policies mention children, but more than half of those policies do not explain how they handle the unintentional collection of children's personal information. Additionally, most privacy policies indicate data retention, but only three privacy policies provided time frames for the retention periods. This study underscores that privacy policies fail to inform and support users and demonstrates a need to investigate the prevalence of device identifiers as a tracking technology among IoMT devices. • Most policies cover privacy-related factors, however the coverage lacks specificity. • Health information is the fourth most discussed data type in IoMT privacy policies. • Data protection responsibility is disproportionately placed on users. • IoMT devices use device identifiers for tracking.

An Intelligent Trust Cloud Management Method for Secure Clustering in 5G Enabled Internet of Medical Things

5G edge computing enabled Internet of Medical Things (IoMT) is an efficient technology to provide decentralized medical services while device-to-device (D2D) communication is a promising paradigm for future 5G networks. To assure secure and reliable communication in 5G edge computing and D2D enabled IoMT systems, this article presents an intelligent trust cloud management method. First, an active training mechanism is proposed to construct the standard trust clouds. Second, individual trust clouds of the IoMT devices can be established through fuzzy trust inferring and recommending. Third, a trust classification scheme is proposed to determine whether an IoMT device is malicious. Finally, a trust cloud update mechanism is presented to make the proposed trust management method adaptive and intelligent under an open wireless medium. Simulation results demonstrate that the proposed method can effectively address the trust uncertainty issue and improve the detection accuracy of malicious devices.

A Novel Resource Oriented DMA Framework for Internet of Medical Things Devices in 5G Network

The Internet of Medical Things (IoMT) mobile devices such as ambulance, medical done, and emergency mobile medical equipment face severe signal distortions due to interference, end-to-end packet loss, handoff delays, and lower throughputs during mobility. Network mobility basic support protocol (NBSP) has been proposed using the IP-based Wi-Fi solution to solve these issues. However, the weak signal, extra signaling overhead, and higherdelays were identified during handover due to patients' excessive requisites, resulting in radio link failure. Therefore, this article proposes a novel resource-efficient flow-enabled distributed mobility anchoring (FDMA) framework enhancing the functionalities of the centralized network entities and mobility entities.The performance of the proposed FDMA framework is evaluated and compared with the standard NBSP and proxy NEMO (PNEMO) scheme in terms of the variable number of cell residence time and mobile routers, where the proposed framework outperformed NBSP and PNEMO schemes for IoMT Mobile devices in 5G network.

STAKEHOLDERS' PERSPECTIVES ON WEARABLE INTERNET OF MEDICAL THINGS PRIVACY AND SECURITY

The Internet of Medical Things (IoMT) is, in fact, a fast-developing healthcare technology that has a great deal of room for improvement in terms of security. Like any other device that is linked to the internet, IoMT is susceptible to security flaws. These breaches have the potential to have an influence not just on the operation of the device, but also on the security and privacy of the data (S&amp;P). The fallout from these violations may have potentially catastrophic and even life-threatening effects. A stakeholder-centric approach was used in the technique that was developed, with the goal of increasing the level of security of transportable IoMT devices. The suggested technique to measure the level of security present inside wearable IoMT’s is based on a combination of S&amp;P characteristics that have been defined for such devices. Second, a technique was developed for measuring the level of security included inside such devices. At the end of the presentation, a case study was given to show how the conceptual framework may be used to the grading of Wearable IoMT’s with regard to features of S&amp;P. The purpose of this paper is to assist hesitant consumers in choosing an IoMT device that is secure, to encourage healthy competition among makers of IoMT devices, and to ultimately raise the bar for the safety of mobile IoMT devices