Internet Of Things Based Healthcare System Research Articles

Smart Medical Systems Based IoT: Exploring the Relationship Between Essential Algorithms and Service Orientation

This study examines the most recent research publications that focus on the Internet of Things (IoT) in medical systems including health monitoring, fitness programs, etc., as a consequence of the IoT's explosive growth in these areas. The monitoring efficiency of IoT-based healthcare systems has been the subject of much study. In this paper, we go into the architecture of the Internet of Things, namely the cloud-integrated systems. As accuracy and power consumption are major issues in the Internet of Things, studies that aim to enhance the efficiency of healthcare systems that rely on the IoT are highlighted. This research also provides a thorough examination of the strategies used for data management in IoT-based healthcare systems that make use of cloud services. The benefits and drawbacks of the IoT-based healthcare system are discussed, as well as its performance. Studies often have a high percentage of success in identifying multiple symptoms and making illness predictions. When it comes to keeping tabs on the health of the elderly population, the Internet of Things-based healthcare system developed specifically for them is an effective answer. Existing systems have many drawbacks, including high power consumption, limited resources, and security concerns brought on by the use of several devices which are discussed in the paper.

Secure medical sensor monitoring framework using novel optimal encryption algorithm driven by Internet of Things

Recently, healthcare monitoring systems have emerged as significant tolls for constant monitoring of patient's physiological characteristics. These systems use implanted sensors. IoT (Internet of Things) have revolutionized healthcare systems where health care equipment's are equipped with many sensors that actively collect data from patients and pass it on to cloud based storages using gateway sensors. Securing data have been significant barriers in many applications as false information get injected, or important information are modified or stolen at different phases of health care systems dependent on IoT. The attacks can also result in fatalities making it imperative to secure IoT based health care systems. A Hybrid technique combining MOAES (Modified Optimal Advanced Encryption Standard) with CM (Chaotic Map) Encryptions called HMOAES-CM technique is proposed. This technique can be helpful in securely accessing the patient data over online mode, and in addition, the data sharing can be performed in an encrypted form for the necessary targets of stakeholders. The proposed authentication approach is aimed at IoT, which is resilient to all kinds of network attacks and its implementation is also simpler. Comparing the suggested work to similar works, the level of evaluation is much improved.

Efficient load balancing Adaptive BNBKnapsack Algorithm for Edge computing to improve performance of network

INTRODUCTION: In present days, Automation of everything has become essential. Internet of things (IoT) play an important role among all medical advances of IT. In this paper, feasible solutions are discussed to compare and design better healthcare systems. A thorough investigation and survey of suitable approaches were done to select IoT based systems in hospitals consisting of various high precision sensors. OBJECTIVES: The challenge healthcare system face is to manage the real time patient’s data with high accuracy. Second challenge is at fog devices level to manage the load distribution to all sensors with limited availability of bandwidth. METHODS: This paper summarizes the selection criterions of suitable load balancing algorithms to reduce energy consumption and computational cost of fog devices and increase the network usage that are supposed to be used in IoT based healthcare systems. According to the survey BNBKnapack algorithm has been selected as best suitable approach to analyze the overall performance of fog devices and results are also verify the same. RESULTS: Comparative analysis of Overall performance of fog devices has been proposed with using SJF algorithm and Adaptive BNBKnapsack algorithm. It has been observed by analysing system performance, which is found as best among other load balancing algorithm Adaptive BNBKnapsack is successfully reduce the energy consumption by (99.29%), computational cost by (98.34%) and increase the network usage by (99.95%) of system CONCLUSION: It has been observed by analysing system performance, Adaptive BNBKnapsack Load balancing is successfully able to reduce the computational cost and energy consumption also increase the network usage of the fog network. The performance of the system is found best among other load balancing algorithm.

IoT-Enabled Framework for Early Detection and Prediction of COVID-19 Suspects by Leveraging Machine Learning in Cloud.

COVID-19 is the repugnant but the most searched word since its outbreak in November 2019 across the globe. The world has to battle with it until an effective solution is developed. Due to the advancement in mobile and sensor technology, it is possible to come up with Internet of things-based healthcare systems. These novel healthcare systems can be proactive and preventive rather than traditional reactive healthcare systems. This article proposes a real-time IoT-enabled framework for the detection and prediction of COVID-19 suspects in early stages, by collecting symptomatic data and analyzing the nature of the virus in a better manner. The framework computes the presence of COVID-19 virus by mining the health parameters collected in real time from sensors and other IoT devices. The framework is comprised of four main components: user system or data collection center, data analytic center, diagnostic system, and cloud system. To point out and detect the COVID-19 suspected in real time, this work proposes the five machine learning techniques, namely support vector machine (SVM), decision tree, naïve Bayes, logistic regression, and neural network. In our proposed framework, the real and primary dataset collected from SKIMS, Srinagar, is used to validate our work. The experiment on the primary dataset was conducted using different machine learning techniques on selected symptoms. The efficiency of algorithms is calculated by computing the results of performance metrics such as accuracy, precision, recall, F1 score, root-mean-square error, and area under the curve score. The employed machine learning techniques have shown the accuracy of above 95% on the primary symptomatic data. Based on the experiment conducted, the proposed framework would be effective in the early identification and prediction of COVID-19 suspect realizing the nature of the disease in better way.

Clouds Proportionate Medical Data Stream Analytics for Internet of Things-Based Healthcare Systems

Internet of Things (IoT) assisted healthcare systems are designed for providing ubiquitous access and recommendations for personal and distributed electronic health services. The heterogeneous IoT platform assists healthcare services with reliable data management through dedicated computing devices. Healthcare services' reliability depends upon the efficient handling of heterogeneous data streams due to variations and errors. A Proportionate Data Analytics (PDA) for heterogeneous healthcare data stream processing is introduced in this manuscript. This analytics method differentiates the data streams based on variations and errors for satisfying the service responses. The classification is streamlined using linear regression for segregating errors from the variations in different time intervals. The time intervals are differentiated recurrently after detecting errors in the stream's variation. This process of differentiation and classification retains a high response ratio for healthcare services through spontaneous regressions. The proposed method's performance is analyzed using the metrics accuracy, identification ratio, delivery, variation factor, and processing time.

Wearable Medical Sensor Devices, Machine and Deep Learning Algorithms, and Internet of Things-based Healthcare Systems in COVID-19 Patient Screening, Diagnosis, Monitoring, and Treatment

Wearable Medical Sensor Devices, Machine and Deep Learning Algorithms, and Internet of Things-based Healthcare Systems in COVID-19 Patient Screening, Diagnosis, Monitoring, and Treatment

Layered Architecture for Internet of Things-based Healthcare System: A Systematic Literature Review

Internet of Things (IoT), known as a new paradigm, has shown to have a significant role in healthcare domains including remote vital sign monitoring systems, physical activity tracking, early disease diagnosis, and prevention of disease risks. Therefore, designing an integrated healthcare system based on Internet of Things is highly dependent on designing a layered architecture pattern. However, there are no comprehensive studies on Internet of Things layered architecture in the healthcare industry. The purpose of this study was to identify and scrutinize different types of layered architecture of Internet of Things in healthcare in terms of functions, and technologies. We evaluated studies proposing layered architecture of Internet of Things based on security aspects (security requirements and solutions). A systematic literature review was conducted by searching IEEE, PubMed, Scopus and Web of Science between 2005 and 2019. We were able to find 47 academic studies based on inclusion and exclusion criteria. We systematically reviewed applied functions and technologies and categorized them into three main layers namely, the perception, network, and application layers. This study also presented a comprehensive classification of sensor types. Only 28 out of 47 studies proposing Internet of Things architecture addressed security aspects among which privacy, authentication, and access control, confidentiality, and integrity had the highest rank. The layered architecture of Internet of Things is needed to provide an integrated framework for healthcare system, make better communication, and enhance the information management process. We suggest several potential solutions for future research directions according to technical, management, and security challenges

Laplace Angular Displaced Secure Data Transmission for Internet of Things Based Health Care Systems

The Internet of Things (IoT) has changed the world into a more physically connected, ensuring higher order applications. As smart devices and patients surrounding are able to freely communicate with each other, more chances and conveniences are brought to us. However, as the information is kept inside these devices is revealed and distributed, security and privacy concerns call for an effective safeguarding process more than ever. Secured data transmission with higher voluminous data indulging with noisy instances, the computational cost and overhead incurred remains the major issues for IoT based health care system. The complexity of the inferred model may increase, and thereby the overall secured data transmission accuracy of the model may decrease. In this work, the above said issues are addressed via secure data transmission method, in order to minimize the computational cost and overhead incurred during transmission of large data and also improve the data transmission accuracy with minimum running time. The method is called as Delay-aware and Energy-efficient Laplace Angular Displacement (DE-LAD). The DE-LAD method involves three steps. They are data collection, data communication and data transmission. First data collection is performed via delayaware and energy-efficient model. Second data communication is said to be established using pairing-free Laplace Estimator, minimizing computational complexity involved during data collection. Finally, secured data transmission is achieved via Angular Displacement. Moreover, in WSN, the security of data being transmitted is calculated for IoT-based healthcare system. The simulation results of DE-LAD method provides enhanced performance in terms of security and complexity as compared to conventional methods.

A lightweight key management scheme for key-escrow-free ECC-based CP-ABE for IoT healthcare systems

The rapid increase in the implementation of Internet of Things (IoT) solutions in diverse healthcare sectors raises the IoT in the healthcare market. The real-time health monitoring to manage the chronic diseases is likely to drive the demand of IoT in healthcare. Moreover, the enhancement in communication technologies like real-time data transmission has improved the patients’ confidence in managing the chronic disease and medication dosage. Although IoT in healthcare makes a positive impact on patients and healthcare providers, however, it gets the challenges like data security and privacy. One means of sustaining security in IoT based healthcare system is through key management and at the same time an effective security mechanism for outsourced data to obtain fine-grained access control is the Ciphertext Policy-Attribute Based Encryption (CP-ABE). However, the overhead of complex decryption operations and key-escrow problem of CP-ABE hinders its applicability in IoT. Hence, in this work, we design a lightweight key management mechanism for the CP-ABE scheme using Elliptic Curve Cryptography (ECC). The novelty in this scheme is that irrespective of the secret key generation by the semi-trusted authority (honest, but curious to know the secret information), it is not capable to decrypt any message using these keys until and unless it has an additional private key of the receiver. For a constraint environment like IoT, the applicability of CP-ABE has two major issues: complex decryption operations and key-escrow problem. Hence, in this paper, a lightweight CP-ABE scheme for IoT based health care system using ECC has been designed. The proposed key management mechanism in the CP-ABE scheme is key-escrow free as well as significantly reduces the decryption overhead of the data receiver. The performance analysis shows that the proposed scheme is more effective as compared to the existing competing schemes.

Medical Internet of Things-based Healthcare Systems, Wearable Biometric Sensors, and Personalized Clinical Care in Remotely Monitoring and Caring for Confirmed or Suspected COVID-19 Patients

(Annis et al , 2020) 2 Conceptual Framework and Literature Review Groundbreaking technologies can be deployed to enhance access to services and delivery of care, in addition to decreasing unsatisfied mental health needs, especially for rural and mainly inadequately serviced communities throughout the COVID-19 outbreak Descriptive statistics of compiled data from the completed surveys were calculated when appropriate 4 Survey Methods and Materials The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States Study participants were informed clearly about their freedom to opt out of the study at any point of time without providing justification for doing so 5 Results and Discussion Remote monitoring can harmonize with in-person diagnostic assessment, and track progressing health status by use of medical Internet of Things-based healthcare systems (Hirko et al , 2020) 6 Conclusions and Implications Enlarging training sets and advancing predictive models encompassing preexistent risk factors can supply a full-scale tool driving the decisions of the telehealth providers by use of computer screening algorithms and wearable biometric sensors for COVID-19, with the aim of configuring personalized clinical care

An Intelligent IoT Based Healthcare System Using Fuzzy Neural Networks

Healthcare facilities in modern age are key challenge especially in developing countries where remote areas face lack of high-quality hospitals and medical experts. As artificial intelligence has revolutionized various fields of life, health has also benefited from it. The existing architecture of store-and-forward method of conventional telemedicine is facing some problems, some of which are the need for a local health center with dedicated staff, need for medical equipment to prepare patient reports, time constraint of 24–48 hours in receiving diagnosis and medication details from a medical expert in a main hospital, cost of local health centers, and need for Wi-Fi connection. In this paper, we introduce a novel and intelligent healthcare system that is based on modern technologies like Internet of things (IoT) and machine learning. This system is intelligent enough to sense and process a patient’s data through a medical decision support system. This system is low-cost solution for the people of remote areas; they can use it to find out whether they are suffering from a serious health issue and cure it accordingly by contacting near hospitals. The results of the experiments also show that the proposed system is efficient and intelligent enough to provide health facilities. The results presented in this paper are the proof of the concept.

IoT Based Healthcare System Model for quarantine centers Post Covid 19 Outbreak

The bright capability of showing up Internet of things (IoT) advancements for wellbeing gadgets and sensors assumed an essential job in the new age of Healthcare industry for secure patient care and maintaining social distancing. After Corona virus outbreak, it has been observed that there is an utter need for the automated healthcare system for the patients in need or elderly incapable or un-helped people. This paper proposed a Healthcare and quarantined install with IoT devices for the benefiting of quarantined people and Covid-19 patients.

Medical Internet of Things-based Healthcare Systems: Wearable Sensor-based Devices, Patient-Generated Big Data, and Real-Time Clinical Monitoring

Medical Internet of Things-based Healthcare Systems: Wearable Sensor-based Devices, Patient-Generated Big Data, and Real-Time Clinical Monitoring

Challenges and opportunities in IoT healthcare systems: a systematic review

In this study, the latest research articles which are involved in the Internet of Things (IoT) based healthcare system are analyzed as the IoT is growing enormously in the healthcare systems such as health monitoring, fitness programs, etc. Numerous research has been carried out in the IoT based healthcare system to improve monitoring efficiency. The architecture used in the IoT especially the cloud integrated systems are investigated in this work. The factors such as accuracy and power consumption are the important concern in the IoT, hence the research works which are involved in improving the performance of the IoT based healthcare systems are discussed. Data management methods in the IoT based healthcare system with cloud facilities are also systematically analyzed in this study. The performance of the IoT based healthcare system along with its advantages and limitations are reviewed. Most research works are efficient in detecting several symptoms and can accurately predict the diseases. The IoT based healthcare system designed especially for elders is an efficient solution in monitoring their healthcare issues. Major limitations in the existing systems are high power consumption, availability of fewer resources and security issues due to the utilization of many devices.

Atrial fibrillation classification using deep learning algorithm in Internet of Things-based smart healthcare system.

Detecting the electrocardiogram pattern in Internet of Things-based healthcare system and notifying this to the user is a challenging task. Using advance computing methods for classification of electrocardiogram signal is a notable research topic. In this research work, an intelligent electrocardiogram signal classification, employing deep learning algorithm, developed and tested in Internet of Things-based smart healthcare system was proposed. For classification of acquired electrocardiogram signal, a partitioned deep convolutional neural network was proposed. The electrocardiogram feature continuously in the Internet of Things-based monitoring system was learnt. To make use of learned features in the continuous time series data, it forms a higher order space in the server. We have made quantifiable comparative analysis with other classification algorithm with the same time series data collected from different atrial fibrillation samples in the Internet of Things-based e-health system. Our proposed algorithm learned features were tested in atrial fibrillation classified signal with other conventional classifiers with various performance indices. We obtained an accuracy of 96.3 percent with 93.5-percent sensitivity and 97.5-percent precision. From the obtained result, processing with proposed deep convolutional neural network provides reliable timely assist and accurate classification of electrocardiogram signal in Internet of Things-based smart healthcare system.

Controlled alternate quantum walks based privacy preserving healthcare images in Internet of Things

The development of quantum computers and quantum algorithms conveys a challenging scenario for several cryptographic protocols due to the mathematical scaffolding upon which those protocols have been built. Quantum walks constitute a universal quantum computational model which is widely used in various fields, including quantum algorithms and cryptography. Quantum walks can be utilized as a powerful tool for the development of modern chaos-based cryptographic applications due to their nonlinear dynamical behavior and high sensitivity to initial conditions. In this paper, we propose new encryption mechanism for privacy preserving Internet of Things-based healthcare systems in order to protect the patients’ privacy. The encryption/decryption processes are based on controlled alternate quantum walks. The proposed cryptosystem approach is composed of two phases: substitution and permutation, both based on independently computed quantum walks. Simulation results and numerical analysis of our data provide enough evidence to reasonably conjecture that our image encryption protocol is robust and efficient for protecting patients’ privacy protection.

IOT Enabled Smart Heart Activity Monitoring Application

Internet of Things (IoT) has drastically changed the functionality of many sectors like health, education, transportation, home, city, etc. Health care underwent a crucial change through IoT, but the major concern is the functionality of the medical devices and their security. Heart disease has become a common problem now days due to smoking and aging. IoT based health care systems are already available but their usage is still a challenge due to their complex structure and deployment of sensors in the patient body. In this paper we propose a novel heart activity monitoring system called SHAM (Smart Heart Activity Monitoring) it monitors the heart activity through a smart watch app. This smart watch monitors the heart activity of the patient if there any irregular activity it notifies the patient and if very critical activity is identified it automatically sends SOS call to the ambulance with GPS coordinates of the patient. The main aim of the proposed system is to make the heartbeat monitoring device simple thereby providing comfort to the patient in its usage and reducing the risk of death.

Special Issue on Recent Trends and Future of Fog and Edge Computing, Services and Enabling Technologies

Special Issue on Recent Trends and Future of Fog and Edge Computing, Services and Enabling Technologies

INTERNET OF THINGS BASED HEALTH MONITORING SYSTEM : OPPORTUNITIES AND CHALLENGES

Internet of Things (IoT) is a new and fast growing technology in which everything ( smart objects and smart devices) are connected to the internet for effective communication between these connected things. Internet of things serves as a catalyst for the healthcare and plays very important role in wide range of healthcare monitoring applications. Networked sensors devices, either worn on the body or embedded in living environments, make possible the gathering of rich information to evaluate physical and mental health condition of the patient by collecting body temperature, blood pressure, sugar level etc. Communicating this collected information to the doctor, making accurate decision on the data collected and notifying the patient is the challenging task in the Internet of things. In this paper author focus on review of IoT based Healthcare System and outline about Opportunities and Challenges for Internet of Things based Patient Health Monitoring System.

The internet of things in healthcare: An overview

Extensive research has been dedicated to the exploration of various technologies such as information technologies (IT) in complementing and strengthening existing healthcare services. In particular, the Internet of Things (IoT) has been widely applied to interconnect available medical resources and provide reliable, effective and smart healthcare service to the elderly and patients with a chronic illness. The aim of this paper is to summarize the applications of IoT in the healthcare industry and identify the intelligentization trend and directions of future research in this field. Based on a comprehensive literature review and the discussion of the achievements of the researchers, the advancement of IoT in healthcare systems have been examined from the perspectives of enabling technologies and methodologies, IoT-based smart devices and systems, and diverse applications of IoT in the healthcare industries. Finally, the challenges and prospects of the development of IoT based healthcare systems are discussed in detail.