A study on medical Internet of Things and Big Data in personalized healthcare system

Jagadeeswari, V.; Subramaniyaswamy, V.; Ravi, Logesh; Vijayakumar, V.

doi:10.1007/s13755-018-0049-x

Cited by 253 publications

(119 citation statements)

References 71 publications

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“…In terms of Ehealth care system, Romero et al (30) proposed the human data interactions and human computer interaction for sociotechnical health monitoring system. Jagadeeswari et al (31) analysed that upcoming techniques for medical healthcare system to improve the quality of life. It provide E-heath services by focusing on internet of things, cloud computing, bigdata analytics and applications which is based on WBAN.…”

Section: Related Workmentioning

confidence: 99%

An Energy Efficient Health Monitoring System using Relay Node in Wireless Body Area Networks

Bilandi¹,

Verma²,

Dhir³

2019

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: In today's world, Wireless Body Area Networks (WBAN) has emanated as a budding field within the healthcare scenario for monitoring, detecting, predicting, and diagnosing various fatal diseases such as cancer, cardiovascular, diabetes and other infectious diseases such as tuberclosis, pneumonia and ebola. It is an assemblage of bio-sensors which could be put on or embedded in a patient's body to surveil the functionality of various body organs. OBJECTIVES: The purpose of that proposed protocol is to select the relay node and record the various physiological parameters like body temperature, blood pressure, rate of blood sugar of human being. Energy consumption is a major hurdle in WBAN as replacing as well as recharging the nodes pinioned to human body may bring about discomfort to the patient. Therefore, in this work, the emphasis is given to improve the performance of WBAN i.e. network lifetime, greater mobility, stability and energy efficiency. METHODS: To find the shortest energy-efficient route and avoid any kind of relay node failure, a novel routing scheme called Enhanced Opportunistic Protocol using Relay nodes (E-OPR) is proposed for data routing. CONCLUSION: Finally, an enhanced opportunistic protocol using relay node in WBAN has been proposed for cost effective real time health monitoring system. The proposed solution seems to give better results as compare single-hop, multi-hop routing protocols in terms of throughput, network lifetime, energy consumption and path loss.

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Section: Related Workmentioning

confidence: 99%

An Energy Efficient Health Monitoring System using Relay Node in Wireless Body Area Networks

Bilandi¹,

Verma²,

Dhir³

2019

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

show abstract

“…The study of such signals allows monitoring the status and health of organs, muscles, or body parts. Bioelectric studies can be evaluated in the area of e-health, in charge of the use of technological resources to improve the quality of life of the patients and general population [12].…”

Section: Related Workmentioning

confidence: 99%

Priority Schemes for Life Extension and Data Delivery in Body Area Wireless Sensor Networks with Cognitive Radio Capabilities

Chávez

Rivero-Ángeles

Jiménez

et al. 2019

Wireless Communications and Mobile Computing

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Wireless sensor networks have recently been used to monitor bioelectric signals generated by the human body. However, since the dimensions and capabilities of the network nodes are small and limited, a problem is generated inherently by the energy consumption and radioelectric interference. In view of this, we propose two priority schemes to improve the performance of the network. In the first one, we aim at increasing the system lifetime by reducing the number of transmissions of nodes with low energy levels. In the second scheme, we aim at conveying priority data to the sink node as fast and efficient as possible. In the former scheme, users can be monitored for long time periods considering a low data generation. On the other hand, the latter scheme allows expediting transmission of important data when energy efficiency is not relevant. In this article, we mathematically model, analyze, and study the performance of the system for both priority schemes considering cognitive radio capabilities to make efficient use of resources and limit the radioelectric interference when the network transmits both continuous monitoring and event detection information. Numerical results provided in this work allow a careful parameter selection for practical implementation of BANETs.

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“…The body sensors networks (BSN) are formed of disparate wearable or implantable devices, say accelerometer, cardioverter-defibrillator, and pacemaker, which sense as well as monitor the breathing rates, blood pressures, pulse, together with body temperature [4] of users. BSN functions as a basic component of IoT [5,6]. These devices are utilized to collect vital information like adequate alteration in wellbeing restraint and it refreshes the weightiness of the remedial parameters in a pre-said period [7].…”

Section: Introductionmentioning

confidence: 99%

An Efficient IoT-Based Patient Monitoring and Heart Disease Prediction System Using Deep Learning Modified Neural Network

Sarmah¹

2020

IEEE Access

149

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The leading causes of death worldwide are chronic illnesses suchlike diabetes, Heart Disease (HD), cancer as well as chronic respiratory malady. It is remarkably intricate to diagnose HD with disparate symptoms or features. With the augmentation in popularity of smart wearable gadgets, a chance to render an Internet of Things (IoT) solution has turned out to be more. Unfortunately, the survival rates are low for the people suffering from sudden heart attacks. Consequently, a patient monitoring scheme intended for heart patients utilizing IoT centered Deep Learning Modified Neural Network (DLMNN) is proposed to assist in the HD diagnosis, and medication is given accordingly. This proposed technique is executed via '3' steps: I) Authentication, ii) Encryption, and iii) Classification. First, by utilizing the substitution cipher (SC) together with the SHA-512, the heart patient of the specific hospital is authenticated. Subsequently, the wearable IoT sensor device, which is fixed to the patient's body, concurrently transmits the sensor data to the cloud. This sensor data is encrypted and securely transmitted to the cloud utilizing the PDH-AES technique. After that, the encrypted data is finally decrypted, and by employing the DLMNN classifier, the classification is done. The classified outcomes comprise '2'types of data: i) normal and ii) abnormal. It denotes the patient's heart condition and if the outcome is abnormal, an alert text is passed to the physician for treating the patient. The investigational outcomes are estimated and the DLMNN for HD diagnosis shows improvement as compared to existing algorithms. Additionally, the proposed PDH-AES used in support of secure data transmission results in the highest level of security i.e. 95.87%, and it is achieved in the lowest time for encryption along with decryption when weighted against the existent AES. INDEX TERMS Disease prediction, Healthcare Monitoring System, Internet of Things (IoT), Advanced Encryption Standard (AES), Modified Huffman Algorithm (MHA), Deep Learning Modified Neural Network (DLMNN), and Cuttlefish Optimization Algorithm (CFOA).

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A study on medical Internet of Things and Big Data in personalized healthcare system

Cited by 253 publications

References 71 publications

An Energy Efficient Health Monitoring System using Relay Node in Wireless Body Area Networks

An Energy Efficient Health Monitoring System using Relay Node in Wireless Body Area Networks

Priority Schemes for Life Extension and Data Delivery in Body Area Wireless Sensor Networks with Cognitive Radio Capabilities

An Efficient IoT-Based Patient Monitoring and Heart Disease Prediction System Using Deep Learning Modified Neural Network

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