Internet Of Things Healthcare Research Articles

Design and Evaluation of a Low-Power Wide-Area Network (LPWAN)-Based Emergency Response System for Individuals with Special Needs in Smart Buildings.

The Internet of Things (IoT) is a growing network of interconnected devices used in transportation, finance, public services, healthcare, smart cities, surveillance, and agriculture. IoT devices are increasingly integrated into mobile assets like trains, cars, and airplanes. Among the IoT components, wearable sensors are expected to reach three billion by 2050, becoming more common in smart environments like buildings, campuses, and healthcare facilities. A notable IoT application is the smart campus for educational purposes. Timely notifications are essential in critical scenarios. IoT devices gather and relay important information in real time to individuals with special needs via mobile applications and connected devices, aiding health-monitoring and decision-making. Ensuring IoT connectivity with end users requires long-range communication, low power consumption, and cost-effectiveness. The LPWAN is a promising technology for meeting these needs, offering a low cost, long range, and minimal power use. Despite their potential, mobile IoT and LPWANs in healthcare, especially for emergency response systems, have not received adequate research attention. Our study evaluated an LPWAN-based emergency response system for visually impaired individuals on the Hazara University campus in Mansehra, Pakistan. Experiments showed that the LPWAN technology is reliable, with 98% reliability, and suitable for implementing emergency response systems in smart campus environments.

Exploitation of healthcare IoT–fog-based smart e-health gateways: a resource optimization approach

Exploitation of healthcare IoT–fog-based smart e-health gateways: a resource optimization approach

Students health physique information sharing in publicly collaborative services over edge-cloud networks

Data privacy is playing a vital role while facing the digital life aspects. Today, the world is being extensively inter-connected through the internet of things (IoT) technologies. This huge interconnectivity is bringing very wonderful capabilities for improving the quality of life (QoL) with itself, for instance, in distributed healthcare. On the other hand, there are new challenges in the interconnectivity per use. One of the most challenging issues of IoT use in social systems and digital life is secure, trustable, and reliable interactions over IoT networks such that safety, security, and privacy in both aspects of cyber and physical worlds for humankind should be planned and controlled.Due to the less physical activity of most people in the current world, fitness and aerobic sports are now an important need at any age to help them keep healthy in their cyber-physical life, specifically, for the younger student that are still in the growth ages. However, these sport activities need to be monitored seriously and closely to not put their life in danger. Herewith, healthcare services through IoT is becoming more applicable. Therefore, health information privacy for athletes is now a hot topic of investigation as we present the topic here. We propose an IoT-based physique healthcare system considering private information sharing for athletes based on data hiding at the edge of a collaborative system. The proposed system pays attention to the key factors of healthcare IoT infrastructure but it is bringing its new suggestions for more safety. Moreover, many evaluations based on different kinds of healthcare data are provided.

A blockchain based federated deep learning model for secured data transmission in healthcare Iot networks

The wide use of sensors in healthcare applications has made it necessary to have secure communication in healthcare Internet of Things (IoT) networks. The sensor data is sensitive, and can contain extremely confidential information such as medical diagnosis, clinical records, vital signs and health data of patients. The emergence of blockchain as a technology ensures consensus and trust among systems, and is now considered to be a new trend used to achieve high scalability, data integrity and privacy. Federated learning is a new technology based on distributed learning that exploits the concept of trust. In federated learning, each user builds an individual distributed model to help a central server that is accessible only to a trusted user group. This paper harnesses the potential of these approaches and proposes an attack detection model to discern normal user behaviours from that of adversaries in an IoT network. This model is called the Blockchain enabled Federated Learning model for secured communication in healthcare IoT (BFL-hIoT), to secure data in healthcare IoT networks. This model is trained and tested on a standard dataset and demonstrates the highest classification accuracy of 97.16 % for normal, 0.9546 for backdoors, 0.9618 for XSS etc., outperforming other blockchain and deep learning models.

IoT-BASED AUTOMATED HEALTH CARE MONITORING SYSTEM FOR SMART CITY

The Internet of Things (IoT) is essential in innovative applications such as smart cities, smart homes, education, healthcare, transportation, and defense operations. IoTapplications are particularly beneficial for providing healthcare because they enable secure and real-time remote patient monitoring to improve the quality of people’s lives. 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; IoT; healthcare; monitoring; remote.

Preserving Privacy of IoT Healthcare Data using Differential Privacy and LSTM

The Internet of Things (IoT) is a powerful technology creating revolutions in multiple industries for ex: Traffic and Healthcare domains. The patient data collected by continuous monitoring using IoT will support in treating the patients and make a positive impact on patients' well-being and increase the efficiency of healthcare workers. It is crucial to be aware of certain drawbacks and risks associated with protecting the privacy of the patient data which is one of the major problems being faced in the healthcare domain. Harmful individuals/Agencies will use IoT devices to obtain private data of patients. It’s of prime importance to protect privacy in healthcare. To improve the privacy of IoT Healthcare data, Geometric data perturbation along with Noise addition is introduced in this study utilizing Laplace Noise which comes under the framework of Differential Privacy. To increase accuracy, a deep learning technique Long Short-Term Memory (LSTM) is applied in this paper. LSTM has proven to be a superior model in accuracy when compared with other models like Decision Tree and Naive Bayes.

Smart Seizure Detection System: Machine Learning Based Model in Healthcare IoT.

Epilepsy, the tendency to have recurrent seizures, can have various causes, including brain tumors, genetics, stroke, brain injury, infections, and developmental disorders. Epileptic seizures are usually transient events. They normally leave no trace after the postictal recovery period has passed. An electroencephalogram (EEG) can only detect brain activity during the recording. It will be detected if an epileptogenic focus or generalized abnormality is active during the recording. This work demonstrated a smart seizure detection system for Healthcare IoT, which is a challenging problem of EEG data analysis. The study suggested an integrated methodology in recognition of the drawbacks of manual identification and the significant negative effects of uncontrollable seizures on patients' lives. The research shows remarkable accuracy, up to 100% in some experiments, by combining classifier ensembles like Decision Trees, Logistic Regression, and Support Vector Machine with different signal processing techniques like Discrete Wavelet Transform, Hjorth Parameters, and statistical features. The results were compared using the kNN classifier, compared with other datasets and other state-of-the-art techniques. Healthcare IoT is further utilized by the methodology, which takes a comprehensive approach using classifier ensembles and signal processing approaches resulting in real-time data to help them make better decisions. This demonstrates how well the suggested method works for smart seizure detection, which is a crucial development for better patient outcomes.

Advancing Healthcare Networks: Optimizing Multi-Objective RPL for Diverse Traffic in Low-Power, Lossy Environments

RPL: Powering modern healthcare IoT Networks. The Routing Protocol for Low Power and Lossy Networks(RPL) is a protocol specifically designed for routing in networks characterized by low power and lossy connections built on IPv6. It is specifically designed for the growing use of Instantaneous IoT (Internet of Things) applications. These networks cater to the specialized requirements of Low Power (resource-constrained) and Lossy Networks(unstable networks), also known as LLNs, where routing data smoothly and prioritizing traffic types are the significant challenges. Studies have shown that standard RPL, which makes use of a set of routing rules, struggles to provide the level of performance that many IoT applications in modern-day healthcare demand. This limits RPL’s potential in scenarios where we have a mix of data traffic, which is very common in healthcare settings. To handle the diverse data traffic found in healthcare settings, our method involves distributing several instances of RPL settings throughout the network. The main objective of this strategy is to improve the effectiveness of important and critical healthcare applications. We have developed and thoroughly tested our system using Contiki, which is an open-source IoT operating system. We have three instances in our MultiInstance RPL setup, each of which is intended to handle different kinds of network traffic and provide varying Quality of Service (QoS) measures. Different sets of Objective Function (OF) rules are used by each instance to decide which route is optimal for the data it processes. We have made use of OF0 (Objective Function 0) and MRHOF (Minimum Rank with Hysteresis Objective Function). We have rigorously tested our solution. Our focus has been on metrics like delay, average energy consumption and Packet Delivery Ratio (PDR). Compared to standard RPL, our approach improved packet delivery rates and significantly reduced delays for high priority data packets. This research shows how to better address the complex data traffic needs of real-time healthcare IoT networks.

Wireless Patient Monitoring System: IoT in Healthcare

Abstract: In the conventional medical field, diagnosis often requires numerous sensors and significant human effort, posing challenges for large-scale implementation due to the scarcity of medical professionals and infrastructure. Addressing this issue, this research proposes an IoT-based healthcare application aimed at continuous wireless monitoring of patients, utilizing both web and mobile platforms. The system's primary objective is to create a cost-effective solution for transmitting vital patient signs during emergencies. Sensors are employed to measure these signs, with data transmitted via a wireless network to the cloud for storage, facilitated by a Wi-Fi module connected to a controller. Cloud-based processing enables analysis of the data, with subsequent feedback provided remotely to medical professionals for further assessment. This remote viewing alleviates the burden on doctors while offering accurate patient health status updates. Additionally, in urgent situations, the system automatically alerts doctors, ensuring prompt attention to critical cases. These innovations promise to streamline healthcare delivery, enhancing patient care and reducing the strain on medical resources

Blockchain Targets Integrated IoT for Smart Healthcare Systems - A Bibliometric Analysis

Blockchain technology and the Internet of Things (IoT) for smart healthcare systems has emerged as a promising area of research, as evidenced by a bibliometric analysis. This analysis explores the scholarly literature on the integration of blockchain and IoT in healthcare, identifying key trends, influential publications, and research directions. By leveraging blockchain's decentralized and immutable nature alongside the IoT's ability to collect and transmit real-time health data from various devices, smart healthcare systems aim to enhance data security, interoperability, and patient privacy. Bibliometric analysis provides valuable insights into the evolution of research in this field, highlighting emerging topics, interdisciplinary collaborations, and potential applications that can revolutionize healthcare delivery and management in the future. This paper introduces the Weighted Key Signature for Smart Healthcare Systems (WKSS), a framework designed to optimize healthcare operations within smart environments. With advanced technologies such as blockchain, Internet of Things (IoT), and integration technologies, WKSS aims to improve patient care, resource utilization, and overall healthcare system efficiency. The framework encompasses key components including digital signature generation, computational efficiency, and bibliometric analysis to provide insights into emerging trends and key contributors within smart healthcare systems. Through a comprehensive analysis, including simulations and bibliometric studies, this paper demonstrates the effectiveness of WKSS in reducing patient waiting times, enhancing resource utilization rates, and improving healthcare provider efficiency. Through simulations and bibliometric analysis, significant improvements were observed in key metrics. The average patient waiting time decreased from 45 to 34 minutes, resource utilization rates increased from 70% to 85%, and examination room occupancy rose from 60% to 75%. Additionally, physician and nurse utilization rates improved from 80% to 90% and from 75% to 85%, respectively.

Automatic Medical Dispatcher with Dynamic Telemonitoring System Using IoT in Rural Areas

Accessing quality healthcare in rural areas can be challenging, often requiring individuals to travel long distances to nearby towns or cities. To address this issue, the Anytime Medical Machine (AMM) project was developed, leveraging Internet of Things (IoT) technology to provide on-the-spot medical consultations. AMM, a part of the Internet of Medical Things (IoMT), is designed for primary deployment in rural areas. Through AMM, individuals can receive consultations from qualified doctors without the need to travel. Doctors can prescribe medications and provide treatment recommendations, reducing both time and expenses associated with travel. The system collects patient data using sensors for temperature, heart rate, and blood oxygen saturation (SPO2), transmitting this information to doctors via a Wi-Fi module. The doctor-patient interaction is facilitated through secure video conferencing, requiring a unique username and password for each user to prevent misuse. After analyzing the patient's health parameters, the doctor prescribes appropriate medication, which is then dispensed through AMM. Patient information is securely uploaded to a cloud server for future reference, and the system is maintained by an administrator who refills medications as needed. Keywords- Rural Healthcare, Remote Consultation, IoT Healthcare, Medical IoT, Telemedicine, Remote Monitoring, Cloud-Based Healthcare, Healthcare Accessibility

Towards intelligent healthcare: Investigating IoT, Big Data, and AI Collaboration

The rising prevalence of chronic disorders such as COVID-19 has reopened the worldwide demand for urgent healthcare services. The current pandemic highlights weaknesses in the established healthcare system, demonstrating that hospitals and clinics cannot handle this situation by themselves. One important aspect of technology supporting modern healthcare solutions is intelligent networked wearables. The Internet of Things (IoT) has advanced to the point that these wearables can collect data on an unparalleled scale. Wearable technology is used to collect context-specific data about our behavioral, psychological, and physical well-being. Managing the large amounts of data produced by wearables and other IoT healthcare devices can be difficult and have a detrimental impact on the decision centers ability to make informed decisions. Using big data (BD) analytics to mine and extract information and make predictions and deductions using knowledge has generated a lot of interest. Research in machine learning (ML) has been effective in addressing a range of networking issues, including resource allocation, routing, traffic engineering, and security. The use of ML-based approaches to enhance different IoT applications has increased. Even though ML and BD analytics have been deeply investigated, most of it talks concerning the way ML-based BD analysis approach is developing in the context of IoT healthcare. In this study, the Analysis of BD using ML techniques in the healthcare industry has been extensively discussed. The advantages and disadvantages of current methods are also discussed, along with a number of research challenges. Our research will help government organizations and healthcare professionals stay up to date on the most recent developments in ML-based data analysis for intelligent healthcare.

Explainable Anomaly Detection of Synthetic Medical IoT Traffic Using Machine Learning

In the context of the Internet of Things (IoT), particularly within medical facilities, the detection and categorization of Internet traffic remain significant challenges. While conventional methods for IoT traffic analysis can be applied, obtaining suitable medical traffic data is challenging due to the stringent privacy constraints associated with the health domain. To address this, this study proposes a network traffic simulation approach using an open-source tool called IoT Flock, which supports both CoAP and MQTT protocols. The tool is used to create a synthetic dataset, to simulate IoT traffic originating from various smart devices in different hospital rooms. The study shows a complete anomaly detection analysis of IoT-Flock-generated traffic, both normal and malicious, by leveraging and comparing traditional machine learning techniques, deep learning models with multiple hidden layers, and explainable artificial intelligence techniques. The results are very promising. For the binary classification, for example, the obtained accuracy is close to 100%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$100\\%$$\\end{document} in the case of the CoAP protocol. Good results are also obtained when the multinomial classification is performed, observing that CoAP packets are classified better than MQTT packets, even if the identification of the different MQTT packets reaches very high metrics for the most of the considered algorithms. Moreover, the obtained classification rules are also meaningful in the considered IoT context. The results indicate that IoT-Flock synthetic data can effectively be used to train and test machine and deep learning models for detecting abnormal IoT traffic in medical scenarios. This research attempts also to bridge the gap between IoT security and healthcare, providing useful insights into securing medical IoT networks in general.

CSFF-YOLOv5: Improved YOLOv5 based on channel split and feature fusion in femoral neck fracture detection

The Internet of Things has found extensive applications in the medical field, where the utilization of the You Only Look Once version 5 (YOLOv5) object detection network has played a crucial role. One notable application of the Internet of Things in healthcare is the detection of Femoral Neck Fractures (FNF) in pathology. Because of its subtle fissures, it is difficult to observe and is considerably hindered by treatment. Therefore, this paper proposes a new network model CSFF-YOLOv5 based on YOLOv5s network for the detection task of FNF. The network integrates the attention mechanism and channel split idea to make the network pay more attention to the parts of interest, better learn the feature information of fracture, and ensure the integrity of information through feature fusion to improve the detection accuracy of the network. Through a large number of ablation experiments to prove the performance of different modules, the experimental results show that compared with YOLOv5s, the detection effect of the method proposed in this paper is significantly improved, and there is a certain increase in precision, recall, F1, mAP@0.5 and mAP@0.5:0.95, especially in the mAP@50:95 index, which is increased by 4.985%, which is enough to show that CSFF-YOLOv5 has a good detection effect on femoral neck data.

Monitoring and analysis of physical activity and health conditions based on smart wearable devices

The rapid growth of the Internet of Things (IoT) brings sweeping changes in various industries. Healthcare industries have become a prime example where the Internet of Healthcare Things (IoHT) is making significant progress, particularly in how we approach real-time patient care. Traditional systems for monitoring older people and people with special needs are frequently expensive, require a large workforce, and fall short of providing real-time data. This paper introduces the “3-Tier Health Care Architecture,” an integrated approach to mitigating these issues. This architecture capitalizes on IoHT technologies and is constructed around three principal tiers: Sensor, Fog, and Cloud. The Sensor Tier employs Health Metrics Acquisition Units (HMAUs) fitted with an nRF5340 Development Kit, capturing an extensive range of health-related metrics via wearable sensors. These metrics are then relayed to the Local Processing Units (LPUs) in Fog Tier, which operates on Raspberry Pi Zero 2 W microprocessors for the initial data processing before forwarding to the cloud. The Cloud Tier uses a hybrid CNN-LSTM Machine Learning (ML) model to perform Real-Time Healthcare Monitoring (RTHM) status assessments and includes an Early Warning System for immediate alert issuance. The proposed architecture is resilient, scalable, and efficient, serving as a fortified and all-encompassing solution for RTHM. This enables quick medical interventions, thus elevating healthcare quality and potentially life-saving.

Internet of Things in Healthcare

The health monitoring system plays a pivotal role in healthcare management. Internet of Things (IoT) technology is an emerging field that facilitates the connectivity of devices to the internet. Consequently, patient data can be transmitted to the cloud for accessibility via the internet, enabling doctors to remotely monitor patients and vice versa. This paper explores the services and applications of IoT in the context of health monitoring systems, while also addressing key challenges. Given the sensitive nature of patient health data, encryption techniques for data protection are also examined. Keywords—Challenges in IoT, Data protection, Encryption, IoT technology, IoMT.

A Sustainable W-RLG Model for Attack Detection in Healthcare IoT Systems

The increasingly widespread use of IoT devices in healthcare systems has heightened the need for sustainable and efficient cybersecurity measures. In this paper, we introduce the W-RLG Model, a novel deep learning approach that combines Whale Optimization with Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) for attack detection in healthcare IoT systems. Leveraging the strengths of these algorithms, the W-RLG Model identifies potential cyber threats with remarkable accuracy, protecting the integrity and privacy of sensitive health data. This model’s precision, recall, and F1-score are unparalleled, being significantly better than those achieved using traditional machine learning methods, and its sustainable design addresses the growing concerns regarding computational resource efficiency, making it a pioneering solution for shielding digital health ecosystems from evolving cyber threats.

Internet of Things For Healthcare

The first principle of IoT (Internet of Things) is to connect smart objects - things- to the Internet in a transparent way. This leads to an exchange of data between all things, and bring users information in a more secure way. Cisco Systems estimates that IoT will consist of 50 billion devices connected to the Internet by 2020 and it is predictable that many physical objects, like computers, sensor actuators, will be distributed with unique addresses and the ability to transfer data, from the common daily activities to restricted medical records, in a secure way.

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.