Internet Of Things Healthcare Research Articles

From Data to Diagnosis: Leveraging Deep Learning Architectures in Healthcare IoT

From Data to Diagnosis: Leveraging Deep Learning Architectures in Healthcare IoT

Integrating Homomorphic Encryption in IoT Healthcare Blockchain Systems

Integrating Homomorphic Encryption in IoT Healthcare Blockchain Systems

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.

An IoT Healthcare System With Deep Learning Functionality for Patient Monitoring

ABSTRACTCurrently, healthcare systems operate under conventional management practices and entail storing and processing substantial medical data. Integrating the Internet of Things (IoT) and wireless sensor networks (WSNs) technologies has facilitated the development of IoT‐enabled healthcare, which possesses advanced data processing capabilities and extensive data storage. This paper proposes a WSN and IoT framework for patient monitoring in high‐speed 5G communications. Based on an artificial neural network (ANN), an intelligent health monitoring system was developed using IoT technology to monitor a person's blood pressure, heart rate, oxygen level, and temperature. Furthermore, the system helps the elderly being in critical cases in their homes to communicate and update their medical condition with the hospital, especially in critical cases, to be treated as soon as possible, especially in remote areas. The experimental results showed the superiority and effectiveness of the proposed system. Moreover, relying on ANNs to extract the basic features, the accuracy reached 96%. The proposed system was implemented practically, and the results were displayed in real time and compared with commercial medical devices. Maximum relative errors are heart rate (2.19), body temperature (2.94), systolic blood pressure (3.4), diastolic blood pressure (2.89), and SpO2 (1.05). On the other hand, the proposed system is much faster than other wireless communication methods, regardless of the detection quality.

Enhanced Hybrid Congestion Mitigation Strategy for ‘6LoWPAN-RPL based patient-centric IoHT’

The Internet of Healthcare Things (IoHT) is rapidly evolving, providing new opportunities to enhance healthcare delivery. However, the resource limitation of connected medical sensing devices leads to congestion, resulting in reduced network performance, delayed data transmission, and loss of critical medical information, which can have significant consequences in healthcare. To address this issue, this paper proposes an Enhanced Hybrid Congestion Mitigation Strategy (EHCMS) for IPv6 over low-power wireless personal area networks (6LoWPAN) and routing protocol for low-Power and lossy networks (RPL) based patient-centric IoHT (PC-IoHT). The EHCMS combines several techniques, including traffic management, network topology optimization, and load balancing, to enhance network performance and reduce congestion. The proposed framework is a hybrid strategy that utilizes resource- and traffic-control mechanisms to alleviate congestion in the 6LoWPAN-RPL-based patient-centric IoHT network. For the resource-control-based approach, a congestion-aware composite objective function is designed using a few congestion-specific routing metrics and formulated as a multi-attribute decision-making problem solved using Grey relational analysis (GRA). In addition, a non-linear multi-criteria optimization problem-based transmission rate adaptation mechanism is contrived as a traffic-control scheme for congestion mitigation. The effectiveness of the proposed EHCMS is evaluated using simulations on the Cooja simulator in the Contiki-3.0 OS and compared with existing congestion-alleviating strategies. The results demonstrate that the proposed framework can significantly reduce congestion in the IoHT network, perform better than existing works, and improve the quality of service. This research paper contributes to the field of IoHT by proposing an effective congestion mitigation strategy that enhances the reliability and performance of the PC-IoHT network, ultimately improving the quality of healthcare delivery.

Introduction to the Transformative Potential of the Internet of Things (IoT) in East Africa

The internet of things (IoT) has emerged as a global transformational force, connecting physical devices and transmitting data remotely over the internet. In East Africa, this shift has the potential to bring socio-economic development by solving local challenges. The region-specific topics are mentioned here. Various diverse economies and cultures within the region have already started to use IoT in agriculture, healthcare, transportation, and utilities. This abstract primarily focuses on presenting the key concepts, challenges, and opportunities associated with IoT networking in East Africa. The author highlights the potential of IoT in addressing social issues and promoting sustainable growth. The study examines the networking aspects of IoT, the social and economic factors that influence its adoption, state policies, financial incentives, and the role of public-private partnerships. This study likely takes into account network infrastructure, which includes broadband connectivity and emerging buzzwords such as LoRaWAN and NB-IoT, and also discusses the obstacles associated with these technologies. This study investigates emerging buzzwords such as LoRaWAN and NB-IoT, as well as the challenges associated with these technologies. This study unveils the role of IoT in East Africa, highlighting its usefulness in sectors such as agriculture, healthcare, transport, and utilities. It highlights its role in combating various issues, enhancing productivity, and promoting inclusivity. It addresses various issues and contributes to boosting access, productivity, and inclusive growth. We utilised relevant published data (2004–2014) from diverse, reliable databases. Therefore, policymakers must emphasise the need for broad IoT policies, investment in digital infrastructure, and inter-sectoral coordination to fully leverage the IoT opportunities for the sustainable development of East Africa. Keywords: Internet of Things (IoT), East Africa, socio-economic development, networking infrastructure, transformative potential.

The Prospects and Challenges of Internet of Things (IoT)-based Public Healthcare System in Bangladesh

Purpose: The purpose of this study is to discern the prospects and challenges of integrating Internet of Things (IoT) into the public healthcare sector of Bangladesh. This is timely because the present public healthcare status of the country needs dire improvement. Hence, this study explores the future scope of IoT-based public healthcare systems to be introduced to the Directorate General of Health Services (DGHS) of Bangladesh henceforth recommends how the country can integrate the implementation of this technology to deliver better public healthcare services to its population. Methodology: The authors reviewed extensively literature from several reputed sources to identify the current public healthcare conditions, as well as the benefits and impediments of IoT-based public healthcare system in Bangladesh. Based on the searches pertaining to the data base, this study then outlines appropriate future scopes of IoT in the public healthcare of Bangladesh. Findings: This research identified major impediments such as lack of infrastructure, low motivation, awareness and acceptance as well as poor healthcare startups prevalent in the public health services of Bangladesh. To surmount the inefficiencies of the current services, the authors deliberated how IoT based public healthcare system could foster better monitoring; management and rehabilitation thereby improve the overall Bangladesh’s public healthcare system. Practical Implications: IoT healthcare organizations can also use technology to improve patient outcomes and increase efficiencies, which will lower the cost of treatment, reduce death rates and make it possible to give public healthcare to people who live in rural areas. Originality/Value: The researchers of this study are confident that the findings of this study will benefit other researchers, policymakers, healthcare service providers/designers and professionals to realize the potentials of IoT-based public healthcare system that could overcome the challenges faced and finally could deliver sound public healthcare facilities and services for Bangladeshis in the near future. Limitations: In the future, we plan to develop the Central Repository of patient database system using blockchain technology.

RCME: A Reputation Incentive Committee Consensus-Based for Matchmaking Encryption in IoT Healthcare

RCME: A Reputation Incentive Committee Consensus-Based for Matchmaking Encryption in IoT Healthcare

Optimizing blockchain for healthcare IoT: a practical guide to navigating scalability, privacy, and efficiency trade-offs

The adoption of blockchain technology provides significant disruptive benefits to internet-of-things (IoT) applications in healthcare in vital aspects like security, integrity, transparency, and efficiency. Nevertheless, in order to fully realize the potential of blockchain-driven solutions, healthcare organizations have to address intricate compromises between essential factors including scalability, privacy and resource utilization considering that the data sensitivity alongside strict regulatory compliance requirements characterize this sector. This research discusses the fundamental aspects of these trade-offs, including the range of consensus protocols (e.g. proof-of-work, proof-of-stake) and cryptographic techniques (e.g. zero-knowledge proofs, homomorphic encryption). A systematic choice matrix is created, which relates specific use cases of the healthcare IoT to the optimal tailored blockchain structures on such critical metrics as transaction volume, frequency, privacy level and resource restrictions. The suggested framework provides solid, actionable recommendations to healthcare organizations in order to help them benefit from the enormous promise of the blockchain for connected IoT healthcare by finding a balance between decentralization advantages and performance, security and compliance requirements.

Hybrid software defined network-based deep learning framework for enhancing internet of medical things cybersecurity

<p>The risk of cyber-attacks has increased significantly with the rapid development of the Internet of Medical Things (IoMT). The proliferation of IoMT devices in healthcare facilities has made conventional intrusion detection approaches challenging to employ. Our study proposes a novel hybrid framework leveraging Software Defined Network (SDN) controllers and deep learning techniques, specifically Convolutional Neural Networks (CNN) and Bidirectional Long-Term Memory (Bi-LSTM), to address these challenges. Our framework introduces a unique combination of SDN and deep learning, allowing for dynamic and efficient management of IoMT security. The integration of CNN and Bi-LSTM enables the system to handle diverse data types encountered in IoMT, offering a comprehensive approach to threat detection. Unlike traditional methods, our hybrid solution adapts seamlessly to the evolving threat landscape of healthcare IoT systems. The urgency of our research is affirmed by the critical need to fortify IoMT security amid escalating cyber threats. The conventional methods struggle to cope with the complex nature of IoMT networks, making our exploration of a hybrid SDN-based deep learning framework imperative. With a background in cybersecurity and a dedicated focus on healthcare IoT, we recognize the urgency to develop a solution that not only enhances detection accuracy but also ensures real-time responsiveness in healthcare settings. The proposed method has been validated using the “IoT-Healthcare security” dataset, revealing its efficacy in detecting numerous frequent threats and outperforming current state-of-the-art techniques in terms of high detection accuracy of 99.97% and less than 1.8 (s) in terms of speed efficiency.</p>

Advanced Encryption Techniques in Healthcare IoT: Securing Patient Data in Connected Medical Devices

As a result of the fast development of Internet of Things (IoT) technology, the healthcare sector has undergone a transformation. This transformation has been brought about by the deployment of linked medical devices that provide immediate monitoring and data collecting. Despite the fact that these innovations promise to bring about considerable gains in patient care and operational efficiency, they also bring about major security issues, especially with regard to the protection of personally identifiable information about patients. Within the context of the Internet of Things (IoT) ecosystem for healthcare, this study investigates sophisticated encryption approaches that are aimed to protect patient data stored in linked medical equipment. One of the first things that the research does is investigate the specific security needs and risks that are linked with Internet of Things (IoT) devices in the healthcare industry. These include concerns around data privacy, integrity, and authentication. Advanced Encryption Standard (AES), Elliptic Curve Cryptography (ECC), and Quantum Key Distribution (QKD) are some of the encryption standards and protocols that are often used in the process of protecting Internet of Things (IoT) connections. This article presents a complete examination of these encryption standards and protocols. Following that, it digs into sophisticated encryption approaches that are especially targeted to the limits and needs of healthcare IoT systems. These techniques include lightweight cryptographic algorithms, key management strategies, and secure multi-party computing (MPC) frameworks.

Enhancing Hospital Efficiency through IoT and AI: A Smart Healthcare System

In the rapidly evolving healthcare landscape, the integration of Internet of Things (IoT) and Artificial Intelligence (AI) is transforming hospital efficiency. This study explores how these technologies can enhance hospital operations by optimizing resource management, improving patient care, and reducing operational costs. IoT devices enable real-time monitoring of patient health and hospital assets, facilitating timely interventions and maintenance. Concurrently, AI-driven analytics improve decision-making processes by predicting patient needs and optimizing resource allocation. The synergy between IoT and AI creates a smart healthcare system that offers advanced data processing and actionable insights, leading to improved patient outcomes. Despite challenges such as data privacy concerns and infrastructure investments, the potential benefits of IoT and AI in healthcare are substantial. This paper presents a comprehensive framework for integrating these technologies into hospital operations, highlighting their impact on efficiency and patient care. The findings suggest that IoT and AI can significantly enhance hospital performance, paving the way for a smarter healthcare system.

Lab-in-a-Vial Rapid Test for Internet of Things-Embedded Point-of-Healthcare Protein Biomarker Detection in Bodily Fluids.

Amateurs often struggle with detecting and quantifying protein biomarkers in body fluids due to the high expertise required. This study introduces a Lab-in-a-Vial (LV) rapid diagnostic platform, featuring hydrangea-like platinum nanozymes (PtNH), for rapid, accurate detection and quantification of protein biomarkers on-site within 15min. This method significantly enhances detection sensitivity for various biomarkers in body fluids, surpassing traditional methods such as enzyme-linked immunosorbent assays (ELISA) and lateral flow assays (LFA) by ≈250 to 1300 times. The LV platform uses a glass vial coated with specific bioreceptors such as antigens or antibodies, enabling rapid in vitro evaluation of disease risk from small fluid samples, similar to a personal ELISA-like point-of-care test (POCT). It overcomes challenges in on-site biomarker detection, allowing both detection and quantification through a portable wireless spectrometer for healthcare internet of things (H-IoT). The platform's effectiveness and adaptability are confirmed using IgG/IgM antibodies from SARS-CoV-2 infected patients and nuclear matrix protein (NMP22) from urothelial carcinoma (UC) patients as biomarkers. These tests demonstrated its accuracy and flexibility. This approach offers vast potential for diverse disease applications, provided that the relevant protein biomarkers in bodily fluids are identified.

EMAKAS: An efficient three-factor mutual authentication and key-agreement scheme for IoT environment

The adoption of IoT in healthcare revolutionizes remote patient monitoring and healthcare efficiency. Yet, it brings notable security and privacy challenges, particularly in resource-constrained environment. We propose a secure and efficient three-factor lightweight mutual authentication and key agreement scheme, designed for IoT-based smart healthcare systems, addressing these critical concerns. The scheme employs a fuzzy extractor, a one-way hash function, Elliptic Curve Discrete Logarithm and XOR operations for efficient cryptographic transformations, creating a robust framework for secure data handling. The scheme's design focuses on security and privacy while minimizing computational demands, making it ideal for resource-constrained IoT devices. We utilized both informal and formal security analyses to validate our scheme, employing the Random Oracle Model (ROM), Scyther tool and Burrows-Abadi-Needham (BAN) logic. The security and performance analysis showed that our scheme offers more security features across 15 defined criteria with minimal communication and computational costs compared to other related schemes. The scheme is not only robust against security threats but also practical for implementation in IoT healthcare environment, offering a solution for secure IoT communication by achieving mutual authentication and key agreement with minimized computational requirements.

Privacy Protection for Blockchain-Based Healthcare IoT Systems: A Survey

To enable precision medicine and remote patient monitoring, internet of healthcare things (IoHT) has gained significant interest as a promising technique. With the widespread use of IoHT, nonetheless, privacy infringements such as IoHT data leakage have raised serious public concerns. On the other side, blockchain and distributed ledger technologies have demonstrated great potential for enhancing trustworthiness and privacy protection for IoHT systems. In this survey, a holistic review of existing blockchain-based IoHT systems is conducted to indicate the feasibility of combining blockchain and IoHT in privacy protection. In addition, various types of privacy challenges in IoHT are identified by examining general data protection regulation (GDPR). More importantly, an associated study of cutting-edge privacy-preserving techniques for the identified IoHT privacy challenges is presented. Finally, several challenges in four promising research areas for blockchain-based IoHT systems are pointed out, with the intent of motivating researchers working in these fields to develop possible solutions.

Systematic survey on data security in wireless body area networks in IoT healthcare system.

In the Internet of Things (IoT) healthcare sector, the wireless body area network (WBAN) is being used to optimize medical results by tracking and treating patients as they go about their daily lives. Health insurance has also been one of the cybercriminal's main goals. The Systematic Review of IoT Healthcare systems particularly wireless body area networks is significant, to reach the benefits and challenges faced by existing methods in the domain. This study provides a systematic survey of WBAN data protection. Various types of devices are used in medical science to detect and diagnose diseases. The network is an integral part of medical science in today's era. In medical sciences, sensors take data from a problematic place like cancerous cells. This research discussed a lot of techniques in the literature review. Most of them are not able to fulfill the requirements. If an unauthorized person reaches the data that can be a severe issue, like the diagnosed disease was blood cancer, and after unauthorized access manipulation can change even the diagnosed issue in the database. A doctor can prescribe the medication based on provided data that has been manipulated by unauthorized persons. Several existing schemes are explored in the literature to determine how the protection of sharing patients' healthcare data can be improved. The systematic literature review (SLR) of multiple security schemes for WBAN is presented in this survey paper.

Internet of Healthcare Things Based Detection of EEG Epileptic Seizures: A Smart System

Background: A seizure is a sudden and uncontrolled electrical activity in the brain that can cause a variety of symptoms, depending on the location and severity of the abnormal activity. It can be a symptom of an underlying neurological disorder or can occur without an apparent cause. Epilepsy is one of the most common causes of seizures. Overactive electrical discharges disrupt normal brain electrical activity and interfere with nerve cell communication. Aim and Objective: A comprehensive analysis of the literature revealed that several CAD system designs have shown to be useful to radiologists in routine medical practice as second-opinion aids for epileptic seizure detection in circumstances where a clear differentiation cannot be formed subjectively. CAD systems are made to help radiologists by automating the examination of medical data and images, improving the efficiency and accuracy of diagnosis. These systems examine patterns in medical imaging using machine learning approaches, which can be quite helpful in spotting small abnormalities that the human eye can miss. Moreover, the objective of this study was to design a smart healthcare system using a combination of DWT, Hjorth, and statistical parameters for seizure detection. Method: In this research article, the authors proposed the framework of the Internet of Healthcare Things (IoHT) for performing seizure detection. The authors used different pre-processing techniques and extracted different features like Hjorth, wavelets, and statistics, which were classified using different machine-learning techniques. This novel methodology combines a number of technologies and techniques to improve seizure detection's precision and dependability. Results: DWT + Hjorth + Statistical parameters with bior 1.5 as the pre-processing technique yielding the best outcomes. 86% accuracy was obtained with kNN for k = 5, 93% accuracy was obtained with a linear kernel for an SVM classifier, and 95.5% accuracy was obtained using a decision tree and logistic regression. The authors also considered another dataset for validation and received 96.83% accuracy with decision tree and logistic regression classifiers considering the bior1.5 wavelet filter as a preprocessing technique. Conclusion: The IoHT framework offers a multi-modal, adaptive method of seizure detection that enables the dynamic modification of detection parameters and the incorporation of extra sensor signals. This improves seizure detection's precision and dependability, which has important implications for patient care and monitoring. This work shows how IoHT and machine learning can be combined to build a reliable, real-time seizure detection system. These developments, which make it possible for prompt interventions and individualized treatment plans, can significantly improve the quality of care for individuals with epilepsy.

The Integration of Portable Sensors with a Mobile Application for Self-Observation

IoT in mobile healthcare significantly benefits patients by reducing treatment costs, saving time, and reducing the time and effort required for minor complications. Smart healthcare systems have significantly reduced the death rate worldwide by providing real-time monitoring of critical conditions, enabling immediate assistance from doctors. The proposed work introduces an IoT mobile application for healthcare monitoring, utilizing wearables and biosensor patches to regularly monitor both inpatients and outpatients. The system will facilitate the connection, monitoring, and decision-making for a precise evaluation of health changes.

RADAR-IoT: An Open-Source, Interoperable, and Extensible IoT Gateway Framework for Health Research.

IoT sensors offer a wide range of sensing capabilities, many of which have potential health applications. Existing solutions for IoT in healthcare have notable limitations, such as closed-source, limited I/O protocols, limited cloud platform support, and missing specific functionality for health use cases. Developing an open-source internet of things (IoT) gateway solution that addresses these limitations and provides reliability, broad applicability, and utility is highly desirable. Combining a wide range of sensor data streams from IoT devices with ambulatory mHealth data would open up the potential to provide a detailed 360-degree view of the relationship between patient physiology, behavior, and environment. We have developed RADAR-IoT as an open-source IoT gateway framework, to harness this potential. It aims to connect multiple IoT devices at the edge, perform limited on-device data processing and analysis, and integrate with cloud-based mobile health platforms, such as RADAR-base, enabling real-time data processing. We also present a proof-of-concept data collection from this framework, using prototype hardware in two locations. The RADAR-IoT framework, combined with the RADAR-base mHealth platform, provides a comprehensive view of a user's health and environment by integrating static IoT sensors and wearable devices. Despite its current limitations, it offers a promising open-source solution for health research, with potential applications in managing infection control, monitoring chronic pulmonary disorders, and assisting patients with impaired motor control or cognitive ability.

Predicting Chronic Diseases Using Nonlinear Systems

Healthcare heavily relies on advanced analytics to predict diseases and risks, with an abundance of health data being gathered through IoT and smart healthcare. Nonlinear systems and synchronization techniques play a crucial role in analyzing this data and predicting chronic diseases, such as cancer, cardiometabolic disease, and Parkinson’s disease. Using machine learning and computational intelligence, nonlinear analysis offers valuable insights into the enormous amounts of data collected in smart healthcare settings, enabling more accurate and efficient disease prediction. This chapter explores the various aspects of nonlinear systems and synchronization techniques in predictive analytics, providing a holistic view of their applications in chronic disease prediction