Internet Of Things Healthcare Research Articles

IoT-based external attacks aware secure healthcare framework using blockchain and SB-RNN-NVS-FU techniques.

In recent times, there has been widespread deployment of Internet of Things (IoT) applications, particularly in the healthcare sector, where computations involving user-specific data are carried out on cloud servers. However, the network nodes in IoT healthcare are vulnerable to an increased level of security threats. This paper introduces a secure Electronic Health Record (EHR) framework with a focus on IoT. Initially, the IoT sensor nodes are designated as registered patients and undergo initialization. Subsequently, a trust evaluation is conducted, and the clustering of trusted nodes is achieved through the application of Tasmanian Devil Optimization (STD-TDO) utilizing the Student's T-Distribution. Utilizing the Transposition Cipher-Squared random number generator-based-Elliptic Curve Cryptography (TCS-ECC), the clustered nodes encrypt four types of sensed patient data. The resulting encrypted data undergoes hashing and is subsequently added to the blockchain. This configuration functions as a network, actively monitored to detect any external attacks. To accomplish this, a feature reputation score is calculated for the network's features. This score is then input into the Swish Beta activated-Recurrent Neural Network (SB-RNN) model to classify potential attacks. The latest transactions on the blockchain are scrutinized using the Neutrosophic Vague Set Fuzzy (NVS-Fu) algorithm to identify any double-spending attacks on non-compromised nodes. Finally, genuine nodes are granted permission to decrypt medical records. In the experimental analysis, the performance of the proposed methods was compared to existing models. The results demonstrated that the suggested approach significantly increased the security level to 98%, reduced attack detection time to 1300 ms, and maximized accuracy to 98%. Furthermore, a comprehensive comparative analysis affirmed the reliability of the proposed model across all metrics. The proposed healthcare framework's efficiency is proved by the experimental evaluation.

Enhancing Medical Data Analysis with Federated Learning in the Internet of Medical Things

The Internet of Things refers to physical items, which are equipped with software, sensors, computing power, and other technologies, and that communicate with other electronic devices and systems over communication networks or the Internet. A collection of medical devices and software programmes known as the Internet of Medical Things (IoMT) link to healthcare networks via internet computing. Machine-to-machine communication, which is the foundation of IoMT, is made feasible by medical equipment that includes Wi-Fi. IoMT devices have the ability to analyse and store collected data by connecting to cloud services. IoMT is a different moniker for IoT in healthcare. Since data is transferred via the internet and the IoMT creates a lot of data, privacy concerns are important. The vast volume of data produced by IoMT devices calls for big data processing, and federated learning tackles privacy issues as a way to overcome these difficulties. The big data health care framework for IoMT is discussed in this article. It is built on federated learning.

HealthNet: IoT-based Healthcare Monitoring and Management System

Abstract: The Internet of Things (IoT) is a network of interconnected devices that can collect, store, analyze and transmit data over the Internet. IoT has the potential to revolutionize the healthcare industry by enabling remote patient care, telemedicine, electronic medical records, medication management, imaging and monitoring. The Internet of Things can also improve patient safety, reduce healthcare costs, increase healthcare accessibility, and increase efficiency. However, IoT in healthcare still faces challenges such as security, privacy, wearability, and design. This article reviews the latest research on IoT in healthcare, focusing on the technology, applications, benefits, and challenges of IoT in healthcare

A Blockchain Based Security system framework in Healthcare Domain using IoT

In the contemporary healthcare domain, the integration of Internet of Things (IoT) technologies has revolutionized patient care, enabling real-time monitoring, enhanced diagnostics, and personalized treatment plans. However, this digital transformation is accompanied by unprecedented challenges in ensuring the security and privacy of sensitive healthcare data. Traditional cybersecurity measures often fall short in addressing the complexity and dynamism of IoT ecosystems. This research introduces a novel blockchain-based security system framework specifically designed for the healthcare sector utilizing IoT. By leveraging the inherent security, transparency, and immutability features of blockchain technology, the proposed framework provides a robust solution to safeguard healthcare data integrity, ensure privacy, and enable secure patient data sharing among authorized entities. Through a comprehensive system architecture, this paper delineates the integration of IoT devices with a blockchain network, highlighting the deployment of smart contracts for automated access control, and data encryption mechanisms to preserve confidentiality. An empirical evaluation demonstrates the framework's effectiveness in enhancing data security while maintaining system efficiency. The discussion extends to the implications of this framework for the future of healthcare IT security, addressing potential challenges and suggesting avenues for further research. This study not only contributes to advancing the state of cybersecurity in healthcare IoT but also lays the groundwork for future innovations in the field.

Integrating IoT with Virtual Healthcare: A theoretical framework for enhancing accessibility and efficiency in the U.S. healthcare sector

This paper presents an open source platform aimed at revolutionizing healthcare delivery in the United States by significantly reducing the cost of virtual healthcare software. The proposed platform addresses the pressing challenges of accessibility and efficiency in the U.S. healthcare sector through cloud native backend solutions and mobile apps driven by modular design and IoT-enabled devices. By leveraging IoT technology, virtual healthcare platforms can provide real-time monitoring, remote consultations, and personalized care, thereby improving patient outcomes and reducing healthcare costs. The platform emphasizes nationwide scalability, inclusivity, and a transition towards a proactive, patient-centric healthcare model. By leveraging the opensource community, modular design and a plugin architecture, it aims to enable feature extensibility beyond what is foreseeable today, promote interoperability, ensure data privacy and security, and promote technology adoption among healthcare providers and patients. The proposal presented in this paper lays the foundation for the implementation aimed at harnessing the full potential of IoT and virtual healthcare to enhance accessibility and efficiency in the U.S. healthcare sector, ultimately advancing the quality of care and well-being of the population.

Empowering IoT Healthcare Systems with Deep Learning: From Sensor Data Fusion to Predictive Modeling and Intervention

Adding Internet of Things (IoT) technology to healthcare systems has changed the way patients are cared for by letting them be monitored and data collected in real time. This essay looks at how deep learning can be used to improve IoT-enabled healthcare systems, with a focus on combining sensor data, making predictions, and coming up with ways to help.Sensor data fusion is a key part of putting together data from different sources, like medical equipment, smart tech, and electronic health records. Deep learning algorithms, especially CNN and RNN are very good at handling different types of data streams. This makes it possible to get a full picture of a patient's health. Healthcare professionals can get a full picture of a patient's health by combining information from many sources, such as bodily signs, exercise levels, and outdoor factors. Based on past data, predictive modeling uses the power of deep learning to guess what will happen with people's health in the future. IoT healthcare systems can predict how a disease will get worse, find risk factors, and suggest early treatment using methods like long short-term memory (LSTM) networks and attention mechanisms. These prediction models allow for quick treatments, methods for preventive care, and the best use of resources, which improves patient results and lowers healthcare costs in the long run.Deep learning also makes it easier to come up with smart management methods that are specific to each patient's needs. Machine learning algorithms can make personalized treatment suggestions and adaptable care plans by looking at real-time monitor data along with old patient records. These treatments could include changes to medications or lifestyles, or tips for medical workers. These give patients and healthcare staff more information to help them make better choices and better handle chronic conditions. When IoT technology and deep learning are combined, they have the ability to completely change the way healthcare is provided. IoT-enabled healthcare systems can improve patient tracking, analysis, and treatment by using advanced algorithms for sensor data fusion, predictive models, and smart actions. This leads to better quality of care and better health results.

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>

Enhancing emergency medical response through IoT: A focus on security and privacy

The healthcare industry is globally crucial. With an increasing demand for quality services, there is a need for innovative technologies like the Internet of Things (IoT) to enhance rapid and effective emergency response. This research explores IoT applications in emergency healthcare response, focusing on theoretical frameworks and design principles. It investigates the integration of IoT technologies into these systems while prioritising data security and privacy without relying on experimental data. The study offers innovative ideas for future research, highlighting the significance of addressing privacy and ethics in healthcare IoT usage. It emphasises protecting sensitive health data and promoting responsible IoT implementation in emergency healthcare settings. In conclusion, the study affirms the potential of IoT in enhancing emergency healthcare responses while emphasising the necessity of addressing privacy and ethical concerns within healthcare applications of these technologies. This research seeks to contribute to the ongoing development of IoT in healthcare by emphasising the importance of safeguarding patient information and ensuring that ethical considerations are at the forefront of implementation efforts.

INTEGRATING IOT IN PEDIATRIC HEALTHCARE: A SYSTEMATIC REVIEW OF CURRENT APPLICATIONS AND FUTURE DIRECTIONS FOR PANCREATIC DISEASES AND OBESITY Tolulope O. Olorunsogo1

In recent years, the integration of Internet of Things (IoT) technologies into healthcare systems has shown immense promise in revolutionizing patient care, particularly in pediatric populations. This systematic review aims to explore the current landscape of IoT applications in pediatric healthcare, specifically focusing on pancreatic diseases and obesity, two significant health challenges affecting children worldwide. The review comprehensively analyzes existing literature, including peer-reviewed articles, conference papers, and relevant reports. By employing systematic search strategies across various databases, studies were identified, screened, and synthesized to elucidate the diverse applications of IoT in managing pancreatic diseases and obesity among pediatric patients. The findings reveal a multitude of IoT applications tailored to pediatric healthcare, ranging from wearable devices for continuous glucose monitoring in diabetic children to smart scales and activity trackers for managing pediatric obesity. These technologies offer real-time monitoring, data analytics, and personalized interventions, enhancing disease management, improving patient outcomes, and promoting patient engagement and adherence to treatment regimens. Furthermore, this review identifies critical challenges and limitations associated with current IoT implementations in pediatric healthcare, such as data privacy concerns, interoperability issues, and the need for robust regulatory frameworks. Addressing these challenges is crucial to maximizing the potential benefits of IoT in pediatric healthcare and ensuring its safe and effective integration into clinical practice. The review outlines promising future directions for IoT in managing pancreatic diseases and obesity in pediatric populations. These include integrating advanced sensors and artificial intelligence algorithms for predictive analytics, developing user-friendly and child-centric IoT solutions, and establishing collaborative networks for data sharing and knowledge exchange. This systematic review underscores the transformative impact of IoT technologies in pediatric healthcare, particularly in managing pancreatic diseases and obesity. By highlighting current applications and future directions, this study provides valuable insights for clinicians, researchers, and policymakers to harness the full potential of IoT in improving pediatric health outcomes.
 Keywords: IoT, Healthcare, Pancreatic, Disease, Obesity, Pediatric, Review.

IoT based Health Monitoring System

The Internet of Things (IoT) is essential in innovative applications such as smart cities, smart homes, education, healthcare, transportation, and defense operations. IoT applications are particularly beneficial for providing healthcare because they enable secure and real- time remote patient monitoring to improve the quality of people’s lives. This review paper explores the latest trends in healthcare-monitoring systems by implementing the role of the IoT. The work discusses the benefits of IoT-based healthcare systems with regard to their significance, and the benefits of IoT healthcare. We provide a systematic review on recent studies of IoT- based healthcare-monitoring systems through literature review. The literature review compares various systems’ effectiveness, efficiency, data protection, privacy, security, and monitoring. The paper also explores wireless- and wearable-sensor-based IoT monitoring systems and provides a classification of healthcare- monitoring sensors. We also elaborate, in detail, on the challenges and open issues regarding healthcare security and privacy, and QoS. Finally, suggestions and recommendations for IoT healthcare applications are laid down at the end of the study along with future directions related to various recent technology trends. Keywords: IoT, IoWT, healthcare, monitoring, remote

Anomaly detection in IoT-based healthcare: machine learning for enhanced security

Internet of Things (IoT) integration in healthcare improves patient care while also making healthcare delivery systems more effective and economical. To fully realize the advantages of IoT in healthcare, it is imperative to overcome issues with data security, interoperability, and ethical considerations. IoT sensors periodically measure the health-related data of the patients and share it with a server for further evaluation. At the server, different machine learning algorithms are applied which help in early diagnosis of diseases and issue alerts in case vital signs are out of the normal range. Different cyber attacks can be launched on IoT devices which can result in compromised security and privacy of applications such as health care. In this paper, we utilize the publicly available Canadian Institute for Cybersecurity (CIC) IoT dataset to model machine learning techniques for efficient detection of anomalous network traffic. The dataset consists of 33 types of IoT attacks which are divided into 7 main categories. In the current study, the dataset is pre-processed, and a balanced representation of classes is used in generating a non-biased supervised (Random Forest, Adaptive Boosting, Logistic Regression, Perceptron, Deep Neural Network) machine learning models. These models are analyzed further by eliminating highly correlated features, reducing dimensionality, minimizing overfitting, and speeding up training times. Random Forest was found to perform optimally across binary and multiclass classification of IoT Attacks with an approximate accuracy of 99.55% under both reduced and all feature space. This improvement was complimented by a reduction in computational response time which is essential for real-time attack detection and response.

RETRACTED ARTICLE: IoT in healthcare: a review of services, applications, key technologies, security concerns, and emerging trends

RETRACTED ARTICLE: IoT in healthcare: a review of services, applications, key technologies, security concerns, and emerging trends

Performance Improvement for Reconfigurable Processor System Design in IoT Health Care Monitoring Applications

Performance Improvement for Reconfigurable Processor System Design in IoT Health Care Monitoring Applications

A Post-Quantum Compliant Authentication Scheme for IoT Healthcare Systems

A Post-Quantum Compliant Authentication Scheme for IoT Healthcare Systems

Secure Healthcare Model Using Multi-Step Deep Q Learning Network in Internet of Things

Internet of Things (IoT) is an emerging networking technology that connects both living and non-living objects globally. In an era where IoT is increasingly integrated into various industries, including healthcare, it plays a pivotal role in simplifying the process of monitoring and identifying diseases for patients and healthcare professionals. In IoT-based systems, safeguarding healthcare data is of the utmost importance, to prevent unauthorized access and intermediary assaults. The motivation for this research lies in addressing the growing security concerns within healthcare IoT. In this proposed paper, we combine the Multi-Step Deep Q Learning Network (MSDQN) with the Deep Learning Network (DLN) to enhance the privacy and security of healthcare data. The DLN is employed in the authentication process to identify authenticated IoT devices and prevent intermediate attacks between them. The MSDQN, on the other hand, is harnessed to detect and counteract malware attacks and Distributed Denial of Service (DDoS) attacks during data transmission between various locations. Our proposed method’s performance is assessed based on such parameters as energy consumption, throughput, lifetime, accuracy, and Mean Square Error (MSE). Further, we have compared the effectiveness of our approach with an existing method, specifically, Learning-based Deep Q Network (LDQN).

Development of Smart Pill Expert System Based on IoT

An improved version of the smart pill expert system known as SPEC 2.0 is presented in this paper to give a knowledge of IoT in healthcare. At the designated moment, the system strives to accurately dispense the recommended dosage of medication. The user-friendly interface of SPEC 2.0 is one of its standout features since it makes it simple for people of all ages to utilize among all the smart medicine dispensers. The main objective of this system is to deliver control and monitoring capabilities via an android application, free of in-app purchases or subscriptions. Numerous features, including the capacity to send alerts and SMS messages regarding pill distribution, are included in the android application. With the help of this tool, users can keep on schedule with their medicine and receive frequent reminders. In order to successfully manage the issue of overdosage, the system lays a special emphasis on preventing the consumption of medication in excess amounts. Users of SPEC 2.0 can reduce their risk of adverse drug reactions by following the directions for dose intervals. Daily tests have been conducted to ensure that SPEC 2.0 works as intended, and thorough records have been kept of each test. These tests offer important information about the system's dependability and its capacity to deliver precise drug dosages on a regular basis. In conclusion, the improved smart pill expert system reveals its potential to dramatically increase medicine adherence and avoid any health concerns brought on by ingesting the wrong dosage.

Combination of One-Class and Multi-Class Anomaly Detection Using Under-Sampling and Ensemble Technique in IoT Healthcare Data

Objectives: This study addresses the concept drift issue in anomaly detection for IoT systems. The objective is to develop a novel approach that effectively handles the dynamic nature of IoT data. Methods: The proposed COMCADSET (Combination of One-Class and Multi-Class Anomaly Detection Using Under-Sampling and Ensemble Technique) addresses the concept drift challenge. It adapts to evolving data distributions, detects anomalies in IoT healthcare data, mitigates class distribution imbalances through under-sampling, and enhances performance with ensemble techniques. The approach involves four phases: multi-class anomaly spotting, one-class anomaly isolation, concept-drift-free dataset creation, and robust anomaly detection using ensembles. Evaluation utilizes the "Heart Failure Prediction" dataset from Kaggle, with comprehensive experiments and three classification algorithms. COMCADSET's innovation merges one-class and multi-class anomaly detection, under-sampling, and ensemble classification. It's compared against gold standards for classification accuracy, concept drift management, and anomaly detection performance. Findings: Conduct comprehensive experiments using a concept drift dataset and three classification algorithms to evaluate the efficacy of the COMCADSET technique. The experimental result shows the proposed COMCADSET technique attains an impressive 98.401% accuracy, decisively enhancing classification accuracy by adeptly addressing concept drift and identifying anomalies in IoT data. Early detection of abnormal behaviour prevents more significant issues and potential security vulnerabilities in IoT systems. Novelty: The novelty of the COMCADSET technique lies in its ability to address the concept drift issue and improve anomaly detection accuracy in IoT systems. By integrating one-class and multi-class anomaly detection, under-sampling, and ensemble techniques, the proposed approach provides a robust solution for handling the dynamic nature of IoT data. Keywords: Anomaly Detection, Concept Drift, Ensemble Classification, Internet of Things, Under­Sampling

Cyber Security Challenges and Effective Security Measures for IOT-based Intelligent Healthcare Systems

Aims and Background: In order to keep up with the rapid development of technology, cities must enhance their offerings in the areas of public security, medical care, transportation, and citizen well-being. Healthcare systems rely heavily on patient care. Patients' life and the services offered by doctors, nurses, clinicians, pharmaceutical companies, and governments stand to benefit from the implementation of Healthcare IoT. There has been a change in the medical industry, marked by the widespread adoption of wireless healthcare-monitoring-systems (HMS) in hospitals. Objectives and Methodology: However, the Internet-of-Things(IoT) concept frequently ignores security-privacy for linked things. Security and privacy protections must be implemented systematically throughout the entire healthcare and remote health monitoring system, from the creation of equipment to their interconnection, communication, storage, and eventual destruction. This research investigates the risks associated with using smart health devices and provides recommendations for addressing those risks. The present security and privacy of IoT devices in healthcare systems, as well as the difficulties in adopting security frameworks, are discussed, and recommendations for addressing these issues are offered. Results and Conclusions: This study also contributes in two ways to the ongoing effort to investigate security-privacy concerns in the circumstance of smart cities for healthcare purposes. Moreover, this article provides a summary of the many Internet of Things applications and the cyber threats they face. On the other hand, thorough assessments of methods to reduce cyber threats to 60% are presented.

Blockchain-Based Medical Record Sharing in Healthcare IoT: Building Trust and Transparency through Secure Provenance Tracking

Blockchain technology has been incorporated into the Healthcare Internet of Things (IoT) landscape as a revolutionary solution to tackle issues related to the sharing of medical records. This paper presents an innovative method that utilizes Temporal Blockchain for the purpose of Provenance Tracking. The introductory section provides context by delineating the significance of trust and transparency in medical data sharing within the healthcare IoT ecosystem. The study examines current blockchain solutions, delving into frameworks such as Hyperledger Fabric, Ethereum, Corda, and specialized approaches like temporal blockchain. The paper examines the difficulties associated with tracking the origin of data, concerns regarding privacy, problems related to scalability, and the need to comply with regulations. These challenges provide the context for the proposed methodology. The main emphasis is on Temporal Blockchain, integrating temporal elements to improve the tracking of origin and history. The evaluation parameters, such as security, provenance tracking, scalability, interoperability, privacy compliance, and performance, undergo a thorough assessment. The attained values demonstrate a strong emphasis on security at a high level, thorough tracking of origin and history, and strict adherence to privacy regulations. Nevertheless, the need for scalability and interoperability necessitates meticulous consideration. The study showcases the capacity of Temporal Blockchain to establish trust and enhance transparency in the sharing of medical records. The future scope focuses on tackling scalability challenges, improving interoperability, and making continuous optimization efforts. The proposed approach highlights notable accomplishments and emphasizes the continuous development and collaborative aspect of Blockchain-Based Medical Record Sharing in Healthcare IoT.

Realtime Anomaly Detection in Healthcare IoT: A Machine Learning-Driven Security Framework

Healthcare IoT, a fast-growing field, could revolutionize patient monitoring and intervention. This interconnection raises new security concerns, requiring real-time anomaly detection to protect patient data and device integrity. This study presents a novel security framework that uses combination of Hidden Markov Models (HMM) and Support Vector Machine (SVM) to detect anomalies in real-time healthcare IoT environments with high accuracy. The framework prioritizes real-time strength and efficiency. Sensor data from wearables, medical devices, and other IoT devices is carefully segmented into time intervals. Features are carefully derived from each segment, including statistical summaries, patterns, and frequency domain characteristics. Feature engineering is essential for accurate anomaly detection. Integrating HMM and SVM capabilities is the framework’s core. HMM accurately represent concealed states within divided data sequences and analyze patterns of change over time. After that, an independent-trained SVM examines each state. Using proximity to a decision hyperplane in feature space, this SVM can classify data points as normal or anomalous. This method augments HMM temporal capabilities with SVM classification efficiency, increasing sensitivity to anomalous patterns and reducing false positives. The framework’s exceptional performance is shown by extensive evaluations on the PhysioNet Challenge 2017 dataset, which includes diverse ECG recordings with labeled anomalies. HMM-SVM outperforms Naive Bayes, LSTM, and Random Forest with 98.66% accuracy. The framework also has high precision, recall, and F1-score, indicating a refined ability to detect real anomalies and reduce false alarms. The framework prioritizes real-time understanding and application alongside its remarkable precision. HMM-SVMs reveal hidden data state changes, revealing context and possible causes of anomalies. Modular design and efficient algorithms enable seamless integration of real-time functionality in low-resource IoT devices, enabling quick and effective security responses. To conclude, this study introduces an HMM-SVM framework for fast Healthcare IoT abnormality detection. The framework emphasizes comprehensibility and real-time applicability while achieving high accuracy. This framework can protect patient data, improve device security, and create a more reliable healthcare IoT ecosystem.