Health Internet Of Things Research Articles

Health IoT Threats: Survey of Risks and Vulnerabilities

The secure and efficient collection of patients’ vital information is a challenge faced by the healthcare industry. Through the adoption and application of Internet of Things (IoT), the healthcare industry has seen an improvement in the quality of delivered services and patient safety. However, IoT utilization in healthcare is challenging due to the sensitive nature of patients’ clinical information and communicating this across heterogeneous networks and among IoT devices. We conducted a semi-systematic literature review to provide an overview of IoT security and privacy challenges in the healthcare sector over time. We collected 279 studies from 5 scientific databases, of which 69 articles met the requirements for inclusion. We performed thematic and qualitative content analysis to extract trends and information. According to our analysis, the vulnerabilities in IoT in healthcare are classified into three main layers: perception, network, and application. We comprehensively reviewed IoT privacy and security threats on each layer. Different technological advancements were suggested to address the identified vulnerabilities in healthcare. This review has practical implications, emphasizing that healthcare organizations, software developers, and device manufacturers must prioritize healthcare IoT security and privacy. A comprehensive, multilayered security approach, security-by-design principles, and training for staff and end-users must be adopted. Regulators and policy makers must also establish and enforce standards and regulations that promote the security and privacy of healthcare IoT. Overall, this study underscores the importance of ensuring the security and privacy of healthcare IoT, with stakeholders’ coordinated efforts to address the complex and evolving security and privacy threats in this field. This can enhance healthcare IoT trust and reliability, reduce the risks of security and privacy issues and attacks, and ultimately improve healthcare delivery quality and safety.

Generisch-Net: A Generic Deep Model for Analyzing Human Motion with Wearable Sensors in the Internet of Health Things.

The Internet of Health Things (IoHT) is a broader version of the Internet of Things. The main goal is to intervene autonomously from geographically diverse regions and provide low-cost preventative or active healthcare treatments. Smart wearable IMUs for human motion analysis have proven to provide valuable insights into a person's psychological state, activities of daily living, identification/re-identification through gait signatures, etc. The existing literature, however, focuses on specificity i.e., problem-specific deep models. This work presents a generic BiGRU-CNN deep model that can predict the emotional state of a person, classify the activities of daily living, and re-identify a person in a closed-loop scenario. For training and validation, we have employed publicly available and closed-access datasets. The data were collected with wearable inertial measurement units mounted non-invasively on the bodies of the subjects. Our findings demonstrate that the generic model achieves an impressive accuracy of 96.97% in classifying activities of daily living. Additionally, it re-identifies individuals in closed-loop scenarios with an accuracy of 93.71% and estimates emotional states with an accuracy of 78.20%. This study represents a significant effort towards developing a versatile deep-learning model for human motion analysis using wearable IMUs, demonstrating promising results across multiple applications.

Fed-MStacking: Heterogeneous Federated Learning With Stacking Misaligned Labels for Abnormal Heart Sound Detection.

Ubiquitous sensing has been widely applied in smart healthcare, providing an opportunity for intelligent heart sound auscultation. However, smart devices contain sensitive information, raising user privacy concerns. To this end, federated learning (FL) has been adopted as an effective solution, enabling decentralised learning without data sharing, thus preserving data privacy in the Internet of Health Things (IoHT). Nevertheless, traditional FL requires the same architectural models to be trained across local clients and global servers, leading to a lack of model heterogeneity and client personalisation. For medical institutions with private data clients, this study proposes Fed-MStacking, a heterogeneous FL framework that incorporates a stacking ensemble learning strategy to support clients in building their own models. The secondary objective of this study is to address scenarios involving local clients with data characterised by inconsistent labelling. Specifically, the local client contains only one case type, and the data cannot be shared within or outside the institution. To train a global multi-class classifier, we aggregate missing class information from all clients at each institution and build meta-data, which then participates in FL training via a meta-learner. We apply the proposed framework to a multi-institutional heart sound database. The experiments utilise random forests (RFs), feedforward neural networks (FNNs), and convolutional neural networks (CNNs) as base classifiers. The results show that the heterogeneous stacking of local models performs better compared to homogeneous stacking.

Securing the Internet of Health Things: Embedded Federated Learning-Driven Long Short-Term Memory for Cyberattack Detection

The proliferation of the Internet of Health Things (IoHT) introduces significant benefits for healthcare through enhanced connectivity and data-driven insights, but it also presents substantial cybersecurity challenges. Protecting sensitive health data from cyberattacks is critical. This paper proposes a novel approach for detecting cyberattacks in IoHT environments using a Federated Learning (FL) framework integrated with Long Short-Term Memory (LSTM) networks. The FL paradigm ensures data privacy by allowing individual IoHT devices to collaboratively train a global model without sharing local data, thereby maintaining patient confidentiality. LSTM networks, known for their effectiveness in handling time-series data, are employed to capture and analyze temporal patterns indicative of cyberthreats. Our proposed system uses an embedded feature selection technique that minimizes the computational complexity of the cyberattack detection model and leverages the decentralized nature of FL to create a robust and scalable cyberattack detection mechanism. We refer to the proposed approach as Embedded Federated Learning-Driven Long Short-Term Memory (EFL-LSTM). Extensive experiments using real-world ECU-IoHT data demonstrate that our proposed model outperforms traditional models regarding accuracy (97.16%) and data privacy. The outcomes highlight the feasibility and advantages of integrating Federated Learning with LSTM networks to enhance the cybersecurity posture of IoHT infrastructures. This research paves the way for future developments in secure and privacy-preserving IoHT systems, ensuring reliable protection against evolving cyberthreats.

A deep convolutional neural network approach using medical image classification

The epidemic diseases such as COVID-19 are rapidly spreading all around the world. The diagnosis of epidemic at initial stage is of high importance to provide medical care to and recovery of infected people as well as protecting the uninfected population. In this paper, an automatic COVID-19 detection model using respiratory sound and medical image based on internet of health things (IoHT) is proposed. In this model, primarily to screen those people having suspected Coronavirus disease, the sound of coughing used to detect healthy people and those suffering from COVID-19, which finally obtained an accuracy of 94.999%. This approach not only expedites diagnosis and enhances accuracy but also facilitates swift screening in public places using simple equipment. Then, in the second step, in order to help radiologists to interpret medical images as best as possible, we use three pre-trained convolutional neural network models InceptionResNetV2, InceptionV3 and EfficientNetB4 and two data sets of chest radiology medical images, and CT Scan in a three-class classification. Utilizing transfer learning and pre-existing knowledge in these models leads to notable improvements in disease diagnosis and identification compared to traditional techniques. Finally, the best result obtained for CT-Scan images belonging to InceptionResNetV2 architecture with 99.414% accuracy and for radiology images related to InceptionV3 and EfficientNetB4 architectures with the accuracy is 96.943%. Therefore, the proposed model can help radiology specialists to confirm the initial assessments of the COVID-19 disease.

Bluetooth security analysis of general and intimate health IoT devices and apps: the case of FemTech

The number of digital health products is increasing faster than ever. These technologies (e.g. mobile apps and connected devices) collect massive amounts of data about their users, including health, medical, sex life, and other intimate data. In this paper, we study a set of 21 Internet of Things (IoT) devices advertised for general and intimate health purposes of female bodies (aka female-oriented technologies or FemTech). We focus on the security of the Bluetooth connection and communications between the IoT device and the mobile app. Our results highlight serious security issues in the current off-the-shelf FemTech devices. These include unencrypted Bluetooth traffic, unknown Bluetooth services and insecure Bluetooth authentication when connecting to the app. We implement Bluetooth attacks on the communication between these devices and apps, resulting in malfunctioning of the device and app. We discuss our results and provide recommendations for different stakeholders to improve the security practices of Bluetooth-enabled IoT devices in such a sensitive and intimate domain.

Data access control for named data of health things

The internet of health things (IoHT) represents an innovative network concept that significantly improving healthcare. However, security and privacy are the main concerns of IoHT because the transmitted health data is often sensitive data about patients’ health status, which needs to be secured and protected from unauthorized users and any leakage. Named data networking (NDN) is considered the most promising architecture for the future internet that perfectly fits with the requirements of IoHT systems, especially regarding security and privacy. In this paper, we exploit the fundamental features of NDN to design a robust system for IoHT to ensure secure communication and access to health data. This system presents a content access control model, which prevents attackers and unauthorized users from accessing health data, allows only authorized users to access these data, and prevents users from accessing “corrupted” or “fake” content. The simulation results show that the proposed mechanism slightly delays the secure retrieval of health data. However, this delay is tolerable since the mechanism protects the health data from unauthorized persons and those who try to inject untrusted data into the network.

Blockchained Federated Learning for Internet of Things: A Comprehensive Survey

The demand for intelligent industries and smart services based on big data is rising rapidly with the increasing digitization and intelligence of the modern world. This survey comprehensively reviews Blockchained Federated Learning (BlockFL) that joins the benefits of both Blockchain and Federated Learning to provide a secure and efficient solution for the demand. We compare the existing BlockFL models in four Internet-of-Things (IoT) application scenarios: Personal IoT (PIoT), Industrial IoT (IIoT), Internet of Vehicles (IoV), and Internet of Health Things (IoHT), with a focus on security and privacy, trust and reliability, efficiency, and data diversity. Our analysis shows that the features of decentralization and transparency make BlockFL a secure and effective solution for distributed model training, while the overhead and compatibility still need further study. It also reveals the unique challenges of each domain presents unique challenges, e.g., the requirement of accommodating dynamic environments in IoV and the high demands of identity and permission management in IoHT, in addition to some common challenges identified, such as privacy, resource constraints, and data heterogeneity. Furthermore, we examine the existing technologies that can benefit BlockFL, thereby helping researchers and practitioners to make informed decisions about the selection and development of BlockFL for various IoT application scenarios.

Privacy-aware quantum convolutional neural network for blockchain-based IoT health care data

Nowadays, the Internet of Things (IoT) is widely used in many applications, including healthcare to monitor the health condition of patients. However, it faces privacy and security issues due to the massive growth of hacking systems that provide illegal access to confidential health information. Blockchain (BC) is applied in IoT healthcare systems to manage healthcare data securely by using transparency features. In this research, a novel CAViaR Jellyfish Swarm Optimization enabled Quantum Convolutional Neural Network (CJSO-QCNN) is developed for the removal of noise in a privacy-aware BC-based IoT healthcare system. The CJSO is the combination of Conditional Autoregressive Value at Risk (CAViaR) and Jellyfish Search Optimizer (JSO). The data privacy is ensured according to the user preference by the service provider. The data is classified initially for identifying the sensitive data and is allowed for the treatment of noise, which is then stored in BC. Later, the user accesses the data by removing the noise using the CJSO-QCNN model. The valid data credentials are stored at the service provider according to user preferences. In addition, the superiority of the designed model is computed by comparing the performance with other prevailing approaches. The experimental results revealed that the CJSO-QCNN attained a maximum accuracy of 88.79%, a True Positive Rate (TPR) of 88.20%, and a True Negative Rate (TNR) of 88.61%.

DRL-Based URLLC-Constraint and Energy-Efficient Task Offloading for Internet of Health Things.

Internet of Health Things (IoHT) is a promising e-Health paradigm that involves offloading numerous computational-intensive and delay-sensitive tasks from locally limited IoHT points to edge servers (ESs) with abundant computational resources in close proximity. However, existing computation offloading techniques struggle to meet the burgeoning health demands in ultra-reliable and low-latency communication (URLLC), one of the 5G application scenarios. This article proposes a Multi-Agent Soft-Actor-Critic-discrete based URLLC-constrained task offloading and resource allocation (MASACDUA) scheme to maximize throughput while minimizing power consumption on the remote side, considering the long-term URLLC constraints. The URLLC constraint conditions are formulated using extreme value theory, and Lyapunov optimization is employed to divide the problem into task offloading and computation resource allocation. MASAC-discrete and a queue backlog-aware algorithm are utilized to approach task offloading and computation resource allocation, respectively. Extensive simulation results demonstrate that MASACDUA outperforms traditional DRL algorithms under different IoHT points and data arrival rate intervals and achieves superior performance in delay, bound violation probability, and other characteristics related to URLLC.

A Deep Learning-Based Framework for Strengthening Cybersecurity in Internet of Health Things (IoHT) Environments

A Deep Learning-Based Framework for Strengthening Cybersecurity in Internet of Health Things (IoHT) Environments

Healthcare System using IoT and Building Health Monitor App

An improvement in technology and miniaturization of sensors, there have been attempts to utilize the new technology in various areas to improve the quality of human life. One main area of research that has seen an adoption of the technology is the healthcare sector. The people in need of healthcare services find it very expensive this is particularly true in developing countries. As a result, this project is an attempt to solve a healthcare problem currently society is facing. The main objective of the project was to design a remote healthcare system. It’s comprised of three main parts. The first part being, detection of patient’s vitals using sensors, second for sending data to cloud storage and the last part was providing the detected data for remote viewing. Remote viewing of the data enables a doctor or guardian to monitor a patient’s health progress away from hospital premises. The Internet of Things (IoT) concepts have been widely used to interconnect the available medical resources and offer smart, reliable, and effective healthcare service to the patients. Health monitoring for active and assisted living is one of the paradigms that can use the IoT advantages to improve the patient’s lifestyle. In this project, I have presented an IoT architecture customized for healthcare applications. The aim of the project was to come up with a Remote Health Monitoring System that can be made with locally available sensors with a view to making it affordable if it were to be mass produced. Hence the proposed architecture collects the sensor data through Arduino microcontroller and relays it to the cloud where it is processed and analyzed for remote viewing. Feedback actions based on the analyzed data can be sent back to the doctor or guardian through Email and/or SMS alerts

Design of Smart IoT Health and Social Distancing Monitoring System

Since the outbreak of the COVID-19 pandemic, many companies have started to work from home. As the pandemic recovers, companies slowly start to adapt to the situation by making the workers work from the office. This is an effort to reduce the risks of spreading diseases in the workplace during the endemic of COVID-19. An effective solution is urgently needed to reduce and control the transmission rate of COVID-19. This has motivated us to design an Internet of Things (IoT) Health and Social Distancing Monitoring System (IHDS). This system aims to support that initiative and introduce a system that can control the risks of infection among workers due to a worrying spike in the number of cases in the workplace. The proposed system monitors the health condition of the users and controls social distancing at the workplace by using IoT technology and machine learning. Extensive experiments were conducted to assess the performance of the proposed system. Four critical health metrics were closely monitored: body temperature, pulse rate, blood oxygen saturation, and cough detection, achieving impressive accuracy rates of 99.91%, 94.32%, 99%, and 80.5%, respectively. The proposed system initiates the assignment of red boxes to couples who are separated by less than 1 meter, while it designates green boxes for couples who maintain more than a 1-meter distance from each other.

Towards a robust scale‐free network in internet of health things against multiple attacks using an inter‐core based reconnection strategy

SummaryWireless sensor networks (WSNs) have attained a great attraction of researchers in the recent years. In these networks, many structures are considered that have different properties. This article offers a unique approach, the inter‐core based reconnection strategy (ICRS), which is intended to improve the robustness of Scale‐Free Networks (SFNs) in the setting of wireless sensor networks (WSNs), with a special emphasis on the Internet of Health Things (IoHT) network. SFNs' vulnerabilities to malicious assaults while remaining resilient to random attacks. The proposed ICRS overcomes this issue by offering a novel reconnection approach that employs separate edges between network centers. Destructive assaults that have a significant impact on network connectivity, emphasizing the importance of a robust network that can resist a variety of attacks. ICRS is positioned as a solution that optimizes the network via reconnection techniques, changing it into an onion‐like structure with increased robustness. The simulation results depict that ICRS outperforms the existing algorithms in terms of robustness enhancement. The results show that ICRS performs 48%, 29%, 22%, and 16% better than Barabasi Albert (BA), Hill Climbing (HC), Simulated Annealing (SA), Random Edge Swap Mechanism (RESM), and Robustness Strategy (ROSE), respectively.

Prediction and risk analysis of Cardio Vascular diseases in IoHT by enhanced CHIO-based Residual and dilated gated network with Attention Mechanism

Generally, computer networks automatically discover data based on the machine learning process. It is the execution of Artificial Intelligence (AI) without any specific program. Using both machine learning and AI, an effective optimization was done for Cardio Vascular Disease (CVD). CVD is associated with disorders that lead to hypertension and unexpected cardiac arrest. To promote good health and prevent risky situations, the Internet of Health Things (IoHT) helps to enhance the treatment's effectiveness. The most challenging task is to perform accurate heart disease prediction. Thus, a CVD prediction model is designed. At first, the needed information is collected from the benchmark dataset. After that, the pre-processing is done on the collected data using the data cleaning and scaling method. The pre-processed data are sent to the CVD prediction phase with the aid of Adaptive Residual and Dilated Long Short Term Memory with Attention Mechanism (ARDL-AM) and the parameters in this structure are optimized by the Enhanced Coronavirus Herd Immunity Optimizer (ECHIO). The obtained results from the ARDL-AM are used for the CVD prediction process. The simulations were carried out with various measures to calculate the recommended method's efficiency.

A lightweight logisticsed chaotic S-box encryption for IoT enabled smart health care applications

<p>The Internet of Things (IoT) acts as the major enabler for realizing more intelligent devices and establishes a new dimension of communication between humans and machines using the Internet. These intelligent devices find their role in numerous domains namely health care, automation, smart homes & other people-assisted applications. Although sensor-driven gadgets have significantly improved people’s daily lives, the majority of IoT systems have been plagued by security backlogs, which results in privacy issues. Recently, Advanced Encryption Standard (AES) provides immense light of research in maintaining security and privacy among IoT health care devices. But encrypted data generated still needs to be improvised to defend against the various IoT attacks. The proposed scheme is implemented in the IoT infrastructure that consists of real-time health care sensors interfaced with the ESP8266. Extensive experimentation has been carried out to evaluate the proposed schemes and S-box tests are conducted and analyzed. Additionally, the efficacy of the suggested methods is evaluated with regard to of time and memory usage in comparison to the other encryption schemes already in use. Experimental findings demonstrate that the suggested L-DAL-SBoX has outperformed the other existing algorithms and finds its strong place in IoT security.</p>

Privacy, Security, and Legal Issues in the Health Cloud: Structured Review for Taxonomy Development.

Privacy in our digital world is a very complicated topic, especially when meeting cloud computing technological achievements with its multidimensional context. Here, privacy is an extended concept that is sometimes referred to as legal, philosophical, or even technical. Consequently, there is a need to harmonize it with other aspects in health care in order to provide a new ecosystem. This new ecosystem can lead to a paradigm shift involving the reconstruction and redesign of some of the most important and essential requirements like privacy concepts, legal issues, and security services. Cloud computing in the health domain has markedly contributed to other technologies, such as mobile health, health Internet of Things, and wireless body area networks, with their increasing numbers of embedded applications. Other dependent applications, which are usually used in health businesses like social networks, or some newly introduced applications have issues regarding privacy transparency boundaries and privacy-preserving principles, which have made policy making difficult in the field. One way to overcome this challenge is to develop a taxonomy to identify all relevant factors. A taxonomy serves to bring conceptual clarity to the set of alternatives in in-person health care delivery. This study aimed to construct a comprehensive taxonomy for privacy in the health cloud, which also provides a prospective landscape for privacy in related technologies. A search was performed for relevant published English papers in databases, including Web of Science, IEEE Digital Library, Google Scholar, Scopus, and PubMed. A total of 2042 papers were related to the health cloud privacy concept according to predefined keywords and search strings. Taxonomy designing was performed using the deductive methodology. This taxonomy has 3 layers. The first layer has 4 main dimensions, including cloud, data, device, and legal. The second layer has 15 components, and the final layer has related subcategories (n=57). This taxonomy covers some related concepts, such as privacy, security, confidentiality, and legal issues, which are categorized here and defined by their expansion and distinctive boundaries. The main merits of this taxonomy are its ability to clarify privacy terms for different scenarios and signalize the privacy multidisciplinary objectification in eHealth. This taxonomy can cover health industry requirements with its specifications like health data and scenarios, which are considered as the most complicated among businesses and industries. Therefore, the use of this taxonomy could be generalized and customized to other domains and businesses that have less complications. Moreover, this taxonomy has different stockholders, including people, organizations, and systems. If the antecedent effort in the taxonomy is proven, subject matter experts could enhance the extent of privacy in the health cloud by verifying, evaluating, and revising this taxonomy.

A Novel Cloud-Based IoT Framework for Secure Health Monitoring

The growing use of Internet of Things (IoT) technologies in a variety of sectors, including healthcare, has opened up new possibilities for gathering and analyzing patient data. In some cases, the elderly are exposed to significant risk and even mortality as a result of the global aging problem, which has become a burden in recent years. Numerous IoT devices are being created to monitor, track, and record the actions of the elderly to reduce these hazards. This study proposed a novel, dependable, cloud-based remote system patient monitoring framework for IoT health detection. The main distinguished part of this research is that we rarely can find a framework in the literature that is based on real-time systems by considering heartbeat (BPM), blood oxygen (SpO2), and body temperature at the same time. The implementation and testing of this real-time system is classified into six distinctly separated phases for developing both the hardware and software. To verify the performance of the suggested system, data were gathered from BOT-IoT datasets. The outcome enhances patient satisfaction, secure data transmission, and healthcare outcomes by showing that the proposed framework is more efficient than other compared protocols in terms of the decision time, which is 16.3 seconds for 46 features, with 100% accuracy.

LW-PWECC: Cryptographic Framework of Attack Detection and Secure Data Transmission in IoT

In the present era, the number of Internet of Health Things (IoHT) devices and applications has drastically expanded. Security and attack are major issues in the IoHT domain because of the nature of its architecture and sorts of devices. Over the recent few years, network attacks have dramatically increased. Many detection and encryption techniques are existing however they lack accuracy, training stability, insecurity, delay etc. By the above concerns, this manuscript introduces a novel deep learning technique called Agnostic Spiking Binarized neural network with Improved Billiards optimization for accurate detection of network attacks and Light Weight integrated Puzzle War Elliptic Curve Cryptographic framework for secure data transmission with high security and minimal delay. Optimal features from the datasets are selected by volcano eruption optimization algorithm with better convergence for reducing the overall processing time. Wilcoxon Rank Sum and Mc Neymar’s tests are performed for proving the statistical analyses. The outcomes show that the introduced approach performs with an overall accuracy of 99.93% which is better than the previous techniques demonstrating the effectiveness.

Classifying wireless IOT ICU traffic with machine learning models

In this work, we classified the wireless internet of things (IoT) traffic of the IoT Health intensive care unit (IHI) dataset which belongs to three general classes: patient monitoring, environment monitoring, and network attack. We trained and tested 7 machine learning (ML) models with Orange 3 including kNN, Decision Tree (tree), SVM, Random Forest (RF), Neural Network (NN), Gradient Boosting (GB), and AdaBoost (AB). With the original dataset, 5 ML models performed perfect classification. After pruning the dataset columns by keeping the features with the highest correlations with the label in the dataset, good classifications were obtained with only 4 TCP/IP features by the Gradient Boosting, kNN, and RF models with MSEs in the range 0.008-0.011, and R2s in the range 0.978-0.984. With only 6 MQTT features, Gradient Boosting, RF, Tree, and NN were the top classifiers with MSEs in the range 0.073-0.074, and R2s in the range 0.856-0.859. This work demonstrates the effectiveness of guiding the feature pruning process by the values of the correlation coefficients in order to minimize the long training times of ML models while achieving good accuracies.