IoMT-Based Wearable Body Sensors Network Healthcare Monitoring System

Adeniyi, Emmanuel Abidemi; Ogundokun, Roseline Oluwaseun; Awotunde, Joseph Bamidele

doi:10.1007/978-981-15-9897-5_6

Cited by 67 publications

(21 citation statements)

References 64 publications

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“…Definition 2 (SubsetEval). If the connection between an individual component of when the association between a function and the outside parameter is understood, as well as the intercorrelation across each set of parameters, the connection between a standardized test made up of the combined modules and the outside parameter can be estimated in (6).…”

Section: Deep Autoencoder (Dae)mentioning

confidence: 99%

“…Since the cloud has altered how individuals and organizations communicate and perform business online, cyberspace plays an important role in today's societies and economies [4,5]. As a result, the IIoT encompasses a variety of devices, software, and facilities that bridge the gap between the virtual and physical worlds [6]. Due to the connectivity of information technology (IT) and organizational technology (OT), industrial systems that depend on locked and exclusive communication systems are vulnerable to a wide range of interference activities [7,8].…”

Section: Introductionmentioning

confidence: 99%

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Intrusion Detection in Industrial Internet of Things Network‐Based on Deep Learning Model with Rule‐Based Feature Selection

Awotunde

Chakraborty

Adeniyi

2021

Wireless Communications and Mobile Computing

Self Cite

127

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The Industrial Internet of Things (IIoT) is a recent research area that links digital equipment and services to physical systems. The IIoT has been used to generate large quantities of data from multiple sensors, and the device has encountered several issues. The IIoT has faced various forms of cyberattacks that jeopardize its capacity to supply organizations with seamless operations. Such risks result in financial and reputational damages for businesses, as well as the theft of sensitive information. Hence, several Network Intrusion Detection Systems (NIDSs) have been developed to fight and protect IIoT systems, but the collections of information that can be used in the development of an intelligent NIDS are a difficult task; thus, there are serious challenges in detecting existing and new attacks. Therefore, the study provides a deep learning-based intrusion detection paradigm for IIoT with hybrid rule-based feature selection to train and verify information captured from TCP/IP packets. The training process was implemented using a hybrid rule-based feature selection and deep feedforward neural network model. The proposed scheme was tested utilizing two well-known network datasets, NSL-KDD and UNSW-NB15. The suggested method beats other relevant methods in terms of accuracy, detection rate, and FPR by 99.0%, 99.0%, and 1.0%, respectively, for the NSL-KDD dataset, and 98.9%, 99.9%, and 1.1%, respectively, for the UNSW-NB15 dataset, according to the results of the performance comparison. Finally, simulation experiments using various evaluation metrics revealed that the suggested method is appropriate for IIOT intrusion network attack classification.

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Section: Deep Autoencoder (Dae)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intrusion Detection in Industrial Internet of Things Network‐Based on Deep Learning Model with Rule‐Based Feature Selection

Awotunde

Chakraborty

Adeniyi

2021

Wireless Communications and Mobile Computing

Self Cite

127

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“…For diabetic patients, the use of such IoT devices has made considerable progress that is commendable, and due to this progress, the accuracy of glucose monitoring in the blood has increased exponentially. The use of such vitals while monitoring a critical diabetic patient can be lifesaving [45]. IoMT stands for Internet of Medical Things, and as the name suggests, it is the IOT specially designed for the medical field [45].…”

Section: Blood Glucose Monitoringmentioning

confidence: 99%

“…The use of such vitals while monitoring a critical diabetic patient can be lifesaving [45]. IoMT stands for Internet of Medical Things, and as the name suggests, it is the IOT specially designed for the medical field [45]. The IoMT consists of the heart rate sensor, blood glucose monitoring, endoscopic capsules, etc., and this interconnected network of sensors that communicates using IoT forms the IoMT diabetic-based WBSN monitoring system.…”

Section: Blood Glucose Monitoringmentioning

confidence: 99%

“…The paper [53] describes an e-healthcare monitoring system that checks and manages health through the internet using the SPO2 and other heart monitoring sensors. References [54,66] use a pulse oximeter sensor to sense the blood oxygen saturation and to find the pulse of the user [3,19,45]. Reference [33] explains a model that uses R-pi and interfaces an oximeter and airflow sensors to monitor the patient's breathing [51].…”

Section: Respiration Sensorsmentioning

confidence: 99%

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Recent Advances on IoT-Assisted Wearable Sensor Systems for Healthcare Monitoring

et al. 2021

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IoT has played an essential role in many industries over the last few decades. Recent advancements in the healthcare industry have made it possible to make healthcare accessible to more people and improve their overall health. The next step in healthcare is to integrate it with IoT-assisted wearable sensor systems seamlessly. This review rigorously discusses the various IoT architectures, different methods of data processing, transfer, and computing paradigms. It compiles various communication technologies and the devices commonly used in IoT-assisted wearable sensor systems and deals with its various applications in healthcare and their advantages to the world. A comparative analysis of all the wearable technology in healthcare is also discussed with tabulation of various research and technology. This review also analyses all the problems commonly faced in IoT-assisted wearable sensor systems and the specific issues that need to be tackled to optimize these systems in healthcare and describes the various future implementations that can be made to the architecture and the technology to improve the healthcare industry.

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