From edge to cloud: Design and implementation of a healthcare Internet of Things infrastructure

Masouros, Dimosthenis; Bakolas, Ioannis; Tsoutsouras, Vasileios; Siozios, Kostas; Soudris, Dimitrios

doi:10.1109/patmos.2017.8106984

Cited by 10 publications

(3 citation statements)

References 18 publications

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“…The integrated edge, fog, healthcare IoT-based cloud infrastructure was implemented by Dimosthenis et al. [ 11 ]. Their architecture consists of three layers for acquiring operation, data storage, and decision-making in real-time.…”

Section: Related Workmentioning

confidence: 99%

Ambient Healthcare Approach with Hybrid Whale Optimization Algorithm and Naïve Bayes Classifier

Alwateer

Almars

Areed

et al. 2021

Sensors

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There is a crucial need to process patient’s data immediately to make a sound decision rapidly; this data has a very large size and excessive features. Recently, many cloud-based IoT healthcare systems are proposed in the literature. However, there are still several challenges associated with the processing time and overall system efficiency concerning big healthcare data. This paper introduces a novel approach for processing healthcare data and predicts useful information with the support of the use of minimum computational cost. The main objective is to accept several types of data and improve accuracy and reduce the processing time. The proposed approach uses a hybrid algorithm which will consist of two phases. The first phase aims to minimize the number of features for big data by using the Whale Optimization Algorithm as a feature selection technique. After that, the second phase performs real-time data classification by using Naïve Bayes Classifier. The proposed approach is based on fog Computing for better business agility, better security, deeper insights with privacy, and reduced operation cost. The experimental results demonstrate that the proposed approach can reduce the number of datasets features, improve the accuracy and reduce the processing time. Accuracy enhanced by average rate: 3.6% (3.34 for Diabetes, 2.94 for Heart disease, 3.77 for Heart attack prediction, and 4.15 for Sonar). Besides, it enhances the processing speed by reducing the processing time by an average rate: 8.7% (28.96 for Diabetes, 1.07 for Heart disease, 3.31 for Heart attack prediction, and 1.4 for Sonar).

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Section: Related Workmentioning

confidence: 99%

Ambient Healthcare Approach with Hybrid Whale Optimization Algorithm and Naïve Bayes Classifier

Alwateer

Almars

Areed

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The theoretical maximum data throughput of different BLE versions was studied in Reference [29]. Masouros et al [30] explored a three-layered architecture able to acquire, process, and store healthcare data as well as to provide real-time ECG signal arrhythmia detection. The performance of the classic Bluetooth protocol and BLE 4.1 was quantified in terms of packet transmission delay, CPU utilization, and power consumption.…”

Section: Related Workmentioning

confidence: 99%

Data Transmission Efficiency in Bluetooth Low Energy Versions

Bulić

Kojek

Biasizzo

2019

Sensors

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One important aspect when choosing a Bluetooth Low Energy (BLE) solution is to analyze its energy consumption for various connection parameters and desired throughput to build an optimal low-power Internet-of-Things (IoT) application and to extend the battery life. In this paper, energy consumption and data throughput for various BLE versions are studied. We have tested the effect of connection interval on the throughput and compared power efficiency relating to throughput for various BLE versions and different transactions. The presented results reveal that shorter connection intervals increase throughput for read/write transactions, but that is not the case for the notify and read/write without response transactions. Furthermore, for each BLE version, the energy consumption is mainly dependable on the data volume. The obtained results provide a design guideline for implementing an optimal BLE IoT application.

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“…Those nodes are termed as slack nodes. This may occur due to elected cluster head (CH) or exposure of employment nodes[25].Therefore, probabilities of occurrence of those nodes have to be attained initially. For this purpose, mapping of sensor field to non-overlapping cluster circle with the radius 'r' and consider an assumption that nodes outside the cluster circle boundary are secondary nodes, whereas the remaining nodes in the cluster are cluster members.…”

mentioning

confidence: 99%

Modelling an Adaptive Cluster Head Positioning Based Map Reducing Strategy for Data Transmission in Medical IoT

2019

IJEAT

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Internet-of-things (IoT) based health monitoring systems have turned out to be an interesting topic to enhance quality of health care services. Moreover, there is no advanced IoT based continuous monitoring of glucose systems in real time and some prevailing techniques have numerous limitations. Here, a continuous and invasive glucose monitoring system for transmitting the condition of individuals simultaneously utilizing IoT is modelled and a general system architectural design for processing back end systems to provide body temperature, real time glucose and contextual data in human readable and graphical forms to the physicians or patients is anticipated. As well, a protocol designs for monitoring the continuous data from IoT devices in order to overcome the short comings of existing methods is provided. The design of an energy efficient routing algorithm is a hot topic in the research of IoT based Data mining. Cluster Heads (CH) form backbone of inter-cluster communication. The selection of reliable and efficient cluster head is another important issue. In most of the clustering process, failure of CH occurs due to energy depletion and if the distance between sink and CH is more, it ultimately leads to failure in transmission. During transmission, nodes may fail that means sudden energy loss or node gets out of coverage. Due to relaying high data traffic, some of nodes quickly exhaust their energy and increase the risk of node failure. As a baseline to node failure, data packet loss also occurs in a CH due to congestion and poor link quality. Hence, one of the most crucial feature in designing a protocol is to minimize energy consumption for betterment of network functioning. Here, a clustering routing protocol based on data mining techniques is applied for sensor nodes in medical field called Adaptive Positioning of Cluster Head based Map reducing (APCH-MR) is proposed. Routing table based code book is generated for privacy concern, in which the process of mapping and reducing the data for dissemination is performed. The simulated outcomes depicts that the total number of packets transmitted in round 500 is 11200, total number of dead nodes during round 500 is 58, and time consumed by nodes at 500 rounds is 0.3751s respectively. The proposed method shows better trade off in contrast to conventional techniques.

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From edge to cloud: Design and implementation of a healthcare Internet of Things infrastructure

Cited by 10 publications

References 18 publications

Ambient Healthcare Approach with Hybrid Whale Optimization Algorithm and Naïve Bayes Classifier

Ambient Healthcare Approach with Hybrid Whale Optimization Algorithm and Naïve Bayes Classifier

Data Transmission Efficiency in Bluetooth Low Energy Versions

Modelling an Adaptive Cluster Head Positioning Based Map Reducing Strategy for Data Transmission in Medical IoT

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