IoT Healthcare System Research Articles

Software architecture for pervasive critical health monitoring system using fog computing

Because of the existence of Covid-19 and its variants, health monitoring systems have become mandatory, particularly for critical patients such as neonates. However, the massive volume of real-time data generated by monitoring devices necessitates the use of efficient methods and approaches to respond promptly. A fog-based architecture for IoT healthcare systems tends to provide better services, but it also produces some issues that must be addressed. We present a bidirectional approach to improving real-time data transmission for health monitors by minimizing network latency and usage in this paper. To that end, a simplified approach for large-scale IoT health monitoring systems is devised, which provides a solution for IoT device selection of optimal fog nodes to reduce both communication and processing delays. Additionally, an improved dynamic approach for load balancing and task assignment is also suggested. Embedding the best practices from the IoT, Fog, and Cloud planes, our aim in this work is to offer software architecture for IoT-based healthcare systems to fulfill non-functional needs. 4 + 1 views are used to illustrate the proposed architecture.

Application and Challenges of IoT Healthcare System in COVID-19.

The importance of the IoT is increasing in every field of life, and it especially has a significant role in improving the efficiency of the healthcare system. Its demand further increased during COVID-19 to facilitate the patient remotely from their home digitally. Every time the COVID-19 patient visited the doctor for minor complications, it increased the risk of spreading the virus and the cost for the patient. Another alarming situation arose when a patient was in a critical position and may not claim an emergency service from the nearby healthcare system, increasing the death rate. The IoT uses healthcare services to properly monitor COVID-19 patients by using the interconnected network to overcome these issues. Through the IoT, the patient is facilitated by the health care system without spreading the virus, decreasing the death ratio during COVID-19. This paper aims to discuss different applications, technologies, and challenges of the IoT healthcare system, related to COVID-19. Different databases were searched using keywords in PubMed, ResearchGate, Scopus, ACM, Springer, Elsevier, Google Scholar, etc. This paper is trying to discuss, identify, and highlight the useful applications of the IoT healthcare system to provide guidelines to the researchers, healthcare institutions, and scientists to overcomes the hazards of COVID-19 pandemics. Hence, IoT is beneficial by identifying the symptoms of COVID-19 patients and by providing better treatments that use the healthcare system efficiently. At the end of the paper, challenges and future work are discussed, along with useful suggestions through which scientists can benefit from the IoT healthcare system during COVID-19 and in a severe pandemic. The survey paper is not limited to the healthcare system and COVID-19, but it can be beneficial for future pandemics or in a worse situation.

IoT Technology Applications and Healthcare System's Performance in the era of 4IR: Evidence from Bangladesh

IoT Technology Applications and Healthcare System's Performance in the era of 4IR: Evidence from Bangladesh

MCNN: a multi-level CNN model for the classification of brain tumors in IoT-healthcare system.

The classification of brain tumors is significantly important for diagnosing and treating brain tumors in IoT healthcare systems. In this work, we have proposed a robust classification model for brain tumors employing deep learning techniques. In the design of the proposed method, an improved Convolutional neural network is used to classify Meningioma, Glioma, and Pituitary types of brain tumors. To test the multi-level convolutional neural network model, brain magnetic resonance image data is utilized. The MCNN model classification results were improved using data augmentation and transfer learning methods. In addition, hold-out and performance evaluation metrics have been employed in the proposed MCNN model. The experimental results show that the proposed model obtained higher outcomes than the state-of-the-art techniques and achieved 99.89% classification accuracy. Due to the higher results of the proposed approach, we recommend it for the identification of brain cancer in IoT-healthcare systems.

DACBT: deep learning approach for classification of brain tumors using MRI data in IoT healthcare environment

The classification of brain tumors (BT) is significantly essential for the diagnosis of Brian cancer (BC) in IoT-healthcare systems. Artificial intelligence (AI) techniques based on Computer aided diagnostic systems (CADS) are mostly used for the accurate detection of brain cancer. However, due to the inaccuracy of artificial diagnostic systems, medical professionals are not effectively incorporating them into the diagnosis process of Brain Cancer. In this research study, we proposed a robust brain tumor classification method using Deep Learning (DL) techniques to address the lack of accuracy issue in existing artificial diagnosis systems. In the design of the proposed approach, an improved convolution neural network (CNN) is used to classify brain tumors employing brain magnetic resonance (MR) image data. The model classification performance has improved by incorporating data augmentation and transfer learning methods. The results confirmed that the model obtained high accuracy compared to the baseline models. Based on high predictive results we suggest the proposed model for brain cancer diagnosis in IoT-healthcare systems.

An Efficient Fog Layer Task Scheduling Algorithm for Multi-Tiered IoT Healthcare Systems

IoT-based healthcare systems are becoming popular due to the extreme benefits patients, families, physicians, hospitals, and insurance companies are getting. Cloud is used traditionally for almost every IoT application, but cloud located far away from the devices resulted in an uncertain latency in providing services. At this point, fog computing emerged as the best alternative to provide such real-time services to delay-sensitive IoT applications. However, with the surge of patients, fog's limited resources may fail to handle the explosive growth in requests requiring advanced monitoring-based prioritization of tasks to meet the QoS requirements. To this end, in this paper, a level monitoring task scheduling (LMTS) algorithm is proposed for healthcare applications in fog to provide an immediate response to the delay-sensitive tasks with minimum delay and network usage. The proposed algorithm has been simulated using the Cloudsim simulator, and the results obtained demonstrated the efficacy of the proposed model.

Application of Internet of Things and Sensors in Healthcare.

The Internet of Things (IoT) is an innovative technology with billions of sensors in various IoT applications. Important elements used in the IoT are sensors that collect data for desired analyses. The IoT and sensors are very important in smart cities, smart agriculture, smart education, healthcare systems, and other applications. The healthcare system uses the IoT to meet global health challenges, and the newest example is COVID-19. Demand has increased during COVID-19 for healthcare to reach patients remotely and digitally at their homes. The IoT properly monitors patients using an interconnected network to overcome the issues of healthcare services. The aim of this paper is to discuss different applications, technologies, and challenges related to the healthcare system. Different databases were searched using keywords in Google Scholar, Elsevier, PubMed, ACM, ResearchGate, Scopus, Springer, etc. This paper discusses, highlights, and identifies the applications of IoT healthcare systems to provide research directions to healthcare, academia, and researchers to overcome healthcare system challenges. Hence, the IoT can be beneficial by providing better treatments using the healthcare system efficiently. In this paper, the integration of the IoT with smart technologies not only improves computation, but will also allow the IoT to be pervasive, profitable, and available anytime and anywhere. Finally, some future directions and challenges are discussed, along with useful suggestions that can assist the IoT healthcare system during COVID-19 and in a severe pandemic.

Prospective RFID Sensors for the IoT Healthcare System

The outbreak of COVID-19 has attracted people’s attention to our healthcare system, stimulating the advancement of next-generation health monitoring technologies. IoT attracts extensive attention in this advancement for its advantage in ubiquitous communication and sensing. RFID plays a key role in IoT to tackle the challenges in passive communication and identification and is now emerging as a sensing technology which has the ability to reduce the cost and complexity of data collection. It is advantageous to introduce RFID sensor technologies in health-related sensing and monitoring, as there are many sensors used in health monitoring systems with the potential to be integrated with RFID for smart sensing and monitoring. But due to the unique characteristics of the human body, there are challenges in developing effective RFID sensors for human health monitoring in terms of communication and sensing. For example, in a typical IoT health monitoring application, the main challenges are as follows: (1) energy issues, the efficiency of RF front-end energy harvesting and power conversion is measured; (2) communication issues, the basic technology of RFID sensors shows great heterogeneity in terms of antennas, integrated circuit functions, sensing elements, and data protocols; and (3) performance stability and sensitivity issues, the RFID sensors are mainly attached to the object to be measured to carry out identification and parameter sensing. However, in practical applications, these can also be affected by certain environmental factors. This paper presents the recent advancement in RFID sensor technologies and the challenges for the IoT healthcare system. The current sensors used in health monitoring are also reviewed with regard to integrating possibility with RFID and IoT. The future research direction is pointed out for the emergence of the next-generation healthcare and monitoring system.

A Suite of Design Quality Metrics for Internet of Things by Modelling Its Ecosystem as a Schema Graph

Internet of Things (IoT) infrastructure connects consumer electronics, household appliances, and other smart gadgets. For designing IoT systems and applications, different architectures and protocols are being used. The design quality of an IoT system and its services is assessed with quality of service (QoS) factors such as complexity, functional appropriateness, performance, efficiency, compatibility, maintainability, portability, and usability. The existing methods in the literature focus on measuring the quality of an IoT system during execution time; however, there is a need to define some standards that may be used to determine the quality of an IoT system from its design. This work models the IoT EcoSystem as a schema graph and presents a suite of metrics to determine the design quality of an IoT system and its services under the defined QoS factors. The presented metrics are mapped to the ISO-9126 QoS factors to ensure the standards and quality of IoT services during the design phase. The proposed metrics are validated with benchmarking on a case study smart healthcare IoT system. The results proved that the presented metrics are practical to quantify the quality of IoT design models under QoS factors, and the suite of metrics is able to compare differently designed IoT services.

Comprehensive survey of IoT based arduino applications in healthcare monitoring

The Internet of Things (IoT) enables a range of applications in the area of information technology, one of which is linked and smart health care. A successful Monitoring patients' health state in real time through an IoT healthcare system is the goal, to avert critical patient scenarios, and to enhance patient quality of life via smart IoT settings. Additionally, healthcare expenses are reduced, and patient outcomes are improved. Edge devices (glucose monitors, ventilators, pacemakers) are common in IoT systems. etc.). We reviewed the essential methodologies and cutting-edge technology for remote patient monitoring in this paper, taking into consideration data privacy and security concerns. The primary emphasis of this study is on the many solutions suggested in healthcare for IoT in real-time remote patient monitoring, blockchain technology in healthcare, and data security.

Secure Model for IoT Healthcare System under Encrypted Blockchain Framework

With the advancements in the new technologies like IoT, the healthcare sector is also growing rapidly. The core concept of introducing IoT in the healthcare centers is to make it remotely accessible, so that communication between doctor and patient can be easy, and diagnosis of any disease can be remotely done through the Internet in case of emergency. The major objective of this study is to develop a security framework for the healthcare industry that can encrypt secret data on cloud servers using block-based data encoding. Since any information that is available on the Internet is vulnerable to various attacks, and the medical records of the patients carry sensitive information that must not be accessible to any unauthorized or unauthentic person, in this context, the concept of Blockchain (BC) technology was introduced in the healthcare systems. In the past few years, a number of BC based healthcare models were proposed in the system; however, these systems are decentralized in nature, which is a feature of Blockchain technology, but in certain applications, especially in healthcare, this feature gave rise to a problem termed as disease overlapping when the count of chains gets increased. To solve this issue, a BC based framework is used in this work that has a feature of creating a separate block in the chain every time whenever any alteration in information about patient’s health, any allergies, new symptoms, medications, etc. is updated. In addition to this, securing the sensitive information of patients is taken into consideration by developing an improved and enhanced multilayer Blockchain based IoT data security approach that not only protects the data, but also builds the trust between patients/user and healthcare service providers. The main focused area of this research article is to propose a security framework for healthcare domain that can encrypt the confidential data over cloud server under block-based data encoding. In the proposed approach, 128-bit AES key is generated in order to support the SHA-256 hashing algorithm. Individual user’s data is subdivided to blocks and encrypted to secure it from unauthorized access. An interface-based system is designed to present the real-world usage of the proposed model in healthcare application. Finally, the effectiveness of the proposed approach is validated in the MATLAB software in terms of MAE, RMSE, MSE, encryption time, and decryption time. The RSA and DSA are the most trending encryption algorithms. These algorithms are crucial components of Blockchain technology, implemented in the proposed work, and later on, their performances are also compared with each other to check their effectiveness. The simulation results demonstrated that the proposed encrypting algorithm surpasses the other encryption approaches such as RSA and DSA in all factors to prove its supremacy. The experimental results show that the encryption and decryption time are lowest in the proposed framework; therefore, its performance is more effective and robust. It is also more effective and resilient.

Security and Privacy in IoT Healthcare System: A systematic review

As technology advances worldwide, several governments and health organizations develop or implement mobile health systems and applications to treat and monitor patients. Several health systems based on the Internet of Things (IoT) applications have been built to monitor patients remotely. Different mechanisms and techniques have been used in healthcare applications, such as centralized, decentralized, and hybrid. In addition, many communication technologies were used, such as Bluetooth, GPS, WiFi, and others. This systematic analysis discusses these aspects, focusing on user security, privacy, effective data extraction, and confidential handling. This review investigates all researches published between 2015 and 2021, where four databases (IEEE Xplore, ScienceDirect, Research Gate, and Springer) were considered. A detail of used exclusion criteria and selection procedure to assess the collected publications were presented. Only thirteen papers have matched the criteria thoroughly examined and included in this study. The findings are provided throughout the papers to highlight gaps and concerns in an IoT-based healthcare system that will be constructed to effectively decrease potential dangers during messaging while preserving total user privacy and security.

A CNN-based modular classification scheme for motor imagery using a novel EEG sampling protocol suitable for IoT healthcare systems

A CNN-based modular classification scheme for motor imagery using a novel EEG sampling protocol suitable for IoT healthcare systems

IoT Information Status Using Data Fusion and Feature Extraction Method

The Internet of Things (IoT) role is instrumental in the technological advancement of the healthcare industry. Both the hardware and the core level of software platforms are the progress resulted from the accompaniment of Medicine 4.0. Healthcare IoT systems are the emergence of this foresight. The communication systems between the sensing nodes and the processors; and the processing algorithms to produce output obtained from the data collected by the sensors are the major empowering technologies. At present, many new technologies supplement these empowering technologies. So, in this research work, a practical feature extraction and classification technique is suggested for handling data acquisition besides data fusion to enhance treatment-related data. In the initial stage, IoT devices are gathered and pre-processed for fusion processing. Dynamic Bayesian Network is considered an improved balance for tractability, a tool for CDF operations. Improved Principal Component Analysis is deployed for feature extraction along with dimension reduction. Lastly, this data learning is attained through Hybrid Learning Classifier Model for data fusion performance examination. In this research, Deep Belief Neural Network and Support Vector Machine are hybridized for healthcare data prediction. Thus, the suggested system is probably a beneficial decision support tool for multiple data sources prediction and predictive ability enhancement.

Multiobjectives for Optimal Geographic Routing in IoT Health Care System

In numerous internet of things (IoT) appliances, messages might require to be distributed to certain specified nodes or objects with the multicast transmission. “The multicast routing protocol can be divided into nongeographic based and geographic based.” As locations of device are roughly extracted by GPS devices, geographic‐oriented multicast routing schemes were chosen, because it induces lesser overheads. Nevertheless, the extant geographic‐oriented routing models are found to have particular disadvantages. After the advent of the IoT systems for remote healthcare, medical services can be rapidly provided to patients in rural areas. The IoT network encapsulates flexible sensors in the environment to collect environmental information. This gathered sensor information is sent to the nursing stations for timely medical assistance. The IoT network is wireless, which leads to security breaches. Therefore, there is a necessity to have a secured data transmission in the context of healthcare. Hence, this study intends to propose a novel optimal route selection model in IoT healthcare by deploying optimized ANFIS. Here, the optimal routes for medical data are selected using a new self‐adaptive jellyfish search optimizer (SA‐JSO) that is the enhanced edition of the extant JSO model. Accordingly, the optimal route selection for medical data is performed under the consideration of “energy, distance, delay, overhead, trust, quality of service (QoS), and security (high risk, low risk, and medium risk).” In the end, the performances of adopted work are compared and proved over other extant schemes.

Security & Privacy Concerns, Med-jacking and Attacks in IoT Healthcare System

Security & Privacy Concerns, Med-jacking and Attacks in IoT Healthcare System

Security framework for IoT and deep belief network-based healthcare system using blockchain technology

Nowadays, the computerisation and information systems need very fast, easier and secure data analysis models. So, blockchain technology and deep learning concepts play important roles for data security and analysis in field of healthcare applications. Blockchain technology has optimal reliability in various sectors like healthcare, security, information management, smart home, etc. In this paper, blockchain technology is used for the healthcare system to provide a secure and reliable data transmission. Furthermore, IoT-based wearable devices are used to collect the data from the patients. Moreover, deep belief network (DBN) is proposed to classify the obtained data in order to predict the type of disease or problems of the patients. The experimental results shows that the proposed framework and classification model abruptly outperforms when compared with other existing techniques. The efficiency of the proposed framework is analysed based on the performance metrics like throughput and latency. The obtained loss ratio for the proposed model seems very less of 0.0126 when compared with other existing techniques like MLP, SVM, ANN, and CNN. The accuracy obtained for the proposed DBN classifier is above 95% for the increase in the number of patients.

Security framework for IoT and deep belief network-based healthcare system using blockchain technology

Security framework for IoT and deep belief network-based healthcare system using blockchain technology

Chaos-Based Cryptographic Mechanism for Smart Healthcare IoT Systems

Smart and interconnected devices can generate meaningful patient data and exchange it automatically without any human intervention in order to realize the Internet of Things (IoT) in healthcare (HIoT). Due to more and more online security and data hijacking attacks, the confidentiality, integrity and availability of data are considered serious issues in HIoT applications. In this regard, lightweight block ciphers (LBCs) are promising in resource-constrained environment where security is the primary consideration. The prevalent challenge while designing an LBC for the HIoT environment is how to ascertain platform performance, cost, and security. Most of the existing LBCs primarily focus on text data or grayscale images. The main focus of this paper is about securing color images in a cost-effective way. We emphasis high confidentiality of color images captured by cameras in resource-constrained smartphones, and high confidentiality of sensitive images transmitted by low-power sensors in IoT systems. In order to reduce computational complexity and simulation time, the proposed Lightweight Symmetric Block Cipher (LSBC) exploits chaos-based confusion-diffusion operations at the inter-block level using a single round. The strength of LSBC is assessed by cryptanalysis, while it is ranked by comparing it to other privacy-preserving schemes. Our results show that the proposed cipher produces promising results in terms of key sensitivity and differential attacks, which proves that our LSBC is a good candidate for image security in HIoT.

Intelligent Disease Diagnosis Model for Energy Aware Cluster Based IoT Healthcare Systems

In recent days, advancements in the Internet of Things (IoT) and cloud computing (CC) technologies have emerged in different application areas, particularly healthcare. The use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud server. Therefore, energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT devices. At the same time, the advent of deep learning (DL) models helps to analyze the healthcare data in the cloud server for decision making. With this motivation, this paper presents an intelligent disease diagnosis model for energy aware cluster based IoT healthcare systems, called IDDM-EAC technique. The proposed IDDM-EAC technique involves a 3-stage process namely data acquisition, clustering, and disease diagnosis. In addition, the IDDM-EAC technique derives a chicken swarm optimization based energy aware clustering (CSOEAC) technique to group the IoT devices into clusters and select cluster heads (CHs). Moreover, a new coyote optimization algorithm (COA) with deep belief network (DBN), called COA-DBN technique is employed for the disease diagnostic process. The COA-DBN technique involves the design of hyperparameter optimizer using COA to optimally adjust the parameters involved in the DBN model. In order to inspect the betterment of the IDDM-EAC technique, a wide range of experiments were carried out using real time data from IoT devices and benchmark data from UCI repository. The experimental results demonstrate the promising performance with the minimal total energy consumption of 63% whereas the EEPSOC, ABC, GWO, and ACO algorithms have showcased a higher total energy consumption of 69%, 78%, 83%, and 84% correspondingly.