Fog Computing-Based IoT for Health Monitoring System

Paul, Anand; Hameed, P. Shahul; Hong, Won‐Hwa; Seo, Hyuncheol; Rho, Seungmin

doi:10.1155/2018/1386470

Cited by 103 publications

(38 citation statements)

References 11 publications

(11 reference statements)

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“…real-time monitoring, self-driving car, etc.). (3) Providing better privacy to users, as patient’s data can be analyzed locally instead of sending it to the cloud (4) Deploying fog servers in PMs decrease the required bandwidth for transmission, providing real-time data to doctors without the need of internet connection [ 66 ]. (5) Utilizing fog computing will not only help people to get ease on their basic health monitoring, but will also help those countries where there is less doctor to patient ratio [ 63 ].…”

Section: Main Components Of the Rpm Systemmentioning

confidence: 99%

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

et al. 2021

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Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMs.

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Section: Main Components Of the Rpm Systemmentioning

confidence: 99%

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

et al. 2021

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“…In the last few years, researchers have developed several emergency response systems based on fog technology. Paul et al (2018) have proposed a fog computing-based IoT for a health monitoring system to help patients who suffer from chronic diseases. The proposed system reduces the amount of data that is transferred between the cloud and sensor networks.…”

Section: A Fog Computingmentioning

confidence: 99%

IoT-Fog based Smart-Building Security System Design and Performance Evaluation

Medhat

Elshafey

Ali

2020

Journal of Computer Science

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This paper aims to develop a smart building-security system utilizing fog technology. Fog computing is an innovative solution to reduce latency and network congestion, in which the cloud is extended to the edge of the network. The proposed system can detect an intruder, fire and water leakage. It is implemented using Raspberry Pi, Arduino, Pi-camera and different sensors. The sensors are installed on Arduino and Pi-camera is mounted on Raspberry Pi. The Raspberry Pi is used as a fog node to fit the time-sensitive nature of the system. The performance was evaluated through the iFogSim toolkit. Fog reduces 33.26 and 46.8% average latency and sensor delay as compared to the cloud. Also, the value of the network usage decreases by 71% in the case of fog-based even with increased network load. Finally, the iFogSim was evaluated by measuring the simulation time, which gave acceptable results of about 8.5 sec.

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“…Edge devices (Entry Points) of the organization or companies are connected with data storage, applications. This is called Fog Layer (Paul et al, 2018) and different organizations are connected with this fog layer. This layer is maintained under the blockchain based cloud network service.…”

Section: Biot Network Architecturementioning

confidence: 99%

A Combined Model of Blockchain, Price Intelligence and IoT for Reducing the Corruption and Poverty

Akhtaruzzaman¹,

Kabir²,

Haque³

et al. 2020

International Conference on Poverty and Sustainable Development

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Corruption of price hike is common and one of the major issues in the third world as well as in developing countries. Corruption and price hike issues are correlated with one another. A number of recent studies have been examined to establish the relationship between corruption and poverty. The people of low and middle-income countries cannot take proper medical treatment and buy nutritional food due to price hike and corruption. As a result, every year, many children and poor people have been suffering and dying from malnutrition. Blockchain is a leading technology which is prohibitive to data manipulation and an open distributed computation system where data transactions occur between two parties efficiently. Price Intelligence technology is used for price monitoring at the market level. This study presents a technological model where we have used blockchain, price intelligence and IoT approaches for monitoring the price transaction in the medical, banking and educational areas. This proposed model represents a technological architecture where different people and organizations are connected to the cloud through the internet and money transactions happened through blockchain approach. As a result, people of differing abilities and resources can detect the price hike and corruption and poor people can get priority for better service. Finally, we have suggested the potential impact of the proposed model and have explained how poor people can get better service by using this model.

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Fog Computing-Based IoT for Health Monitoring System

Cited by 103 publications

References 11 publications

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

IoT-Fog based Smart-Building Security System Design and Performance Evaluation

A Combined Model of Blockchain, Price Intelligence and IoT for Reducing the Corruption and Poverty

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