Healthcare IoT Architectures, Technologies, Applications, and Issues

2019 ◽  
pp. 235-265 ◽  
Author(s):  
Karthick G. S. ◽  
Pankajavalli P. B.
Keyword(s):  
Medical Devices ◽  
Healthcare Systems ◽  
Streaming Data ◽  
Machine Learning Techniques ◽  
Layered Architecture ◽  
Smart Healthcare ◽  
Learning Techniques ◽  
The Internet Of Things ◽  
Human Healthcare ◽  
Patient Centric

The internet of things (IoT) revolution is improving the proficiency of human healthcare infrastructures, and this chapter analyzes the applications of IoT in healthcare systems with diversified aspects such as topological arrangement of medical devices, layered architecture, and platform services. This chapter focuses on advancements in IoT-based healthcare in order to identify the communication and sensing technologies enabling the smart healthcare systems. The transformation of healthcare from doctor-centric to patient-centric with the diversified applications of IoT is discussed in detail. In addition, this chapter examines the various issues to be emphasized on designing an effective IoT-based healthcare system. It also explores security in healthcare systems and the possible security threats that may be vulnerable to the security essentials. Finally, this chapter summarizes the procedure of applying machine learning techniques on healthcare streaming data which provides intelligence to the systems.

Security in Smart Healthcare System: A Comprehensive Survey

2017 ◽  
Vol 7 (10) ◽  
pp. 39
Author(s):  
P. Jeyadurga ◽  
S. Ebenezer Juliet ◽  
I. Joshua Selwyn ◽  
P. Sivanisha
Keyword(s):  
Secure Communication ◽  
Smart Cities ◽  
Healthcare Systems ◽  
Internet Security ◽  
Security Risk ◽  
System A ◽  
Smart Healthcare ◽  
Comprehensive Survey ◽  
Healthcare Monitoring ◽  
The Internet Of Things

The Internet of things (IoT) is one of the emerging technologies that brought revolution in many application domains such as smart cities, smart retails, healthcare monitoring and so on. As the physical objects are connected via internet, security risk may arise. This paper analyses the existing technologies and protocols that are designed by different authors to ensure the secure communication over internet. It additionally focuses on the advancement in healthcare systems while deploying IoT services.

Malware Detection Approaches and Analysis for the Internet of Medical Things Enabled Healthcare Systems

2021 ◽  
Vol 10 (6) ◽  
pp. 3117-3121
Keyword(s):  
Denial Of Service ◽  
Malware Detection ◽  
Healthcare Systems ◽  
Information And Communications Technology ◽  
The Internet ◽  
Smart Healthcare ◽  
Man In The Middle ◽  
Internet Of Medical Things ◽  
Detection Schemes ◽  
The Internet Of Things

The advancement of information and communications technology has changed an IoMT-enabled healthcare system. The Internet of Medical Things (IoMT) is a subset of the Internet of Things (IoT) that focuses on smart healthcare (medical) device connectivity. While the Internet of Medical Things (IoMT) communication environment facilitates and supports our daily health activities, it also has drawbacks such as password guessing, replay, impersonation, remote hijacking, privileged insider, denial of service (DoS), and man-in-the-middle attacks, as well as malware attacks. Malware botnets cause assaults on the system's data and other resources, compromising its authenticity, availability, confidentiality and, integrity. In the event of such an attack, crucial IoMT communication data may be exposed, altered, or even unavailable to authorised users. As a result, malware protection for the IoMT environment becomes critical. In this paper, we provide several forms of malware attacks and their consequences. We also go through security, privacy, and different IoMT malware detection schemes

scholarly journals Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement

Machines ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 38 ◽  
Author(s):  
Fabrizio Balducci ◽  
Donato Impedovo ◽  
Giuseppe Pirlo
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Agricultural Field ◽  
Heterogeneous Information ◽  
Learning Techniques ◽  
Farm Model ◽  
Machine Learning Applications ◽  
Crop Harvest ◽  
The Right ◽  
The Internet Of Things

This work aims to show how to manage heterogeneous information and data coming from real datasets that collect physical, biological, and sensory values. As productive companies—public or private, large or small—need increasing profitability with costs reduction, discovering appropriate ways to exploit data that are continuously recorded and made available can be the right choice to achieve these goals. The agricultural field is only apparently refractory to the digital technology and the “smart farm” model is increasingly widespread by exploiting the Internet of Things (IoT) paradigm applied to environmental and historical information through time-series. The focus of this study is the design and deployment of practical tasks, ranging from crop harvest forecasting to missing or wrong sensors data reconstruction, exploiting and comparing various machine learning techniques to suggest toward which direction to employ efforts and investments. The results show how there are ample margins for innovation while supporting requests and needs coming from companies that wish to employ a sustainable and optimized agriculture industrial business, investing not only in technology, but also in the knowledge and in skilled workforce required to take the best out of it.

A Micro-Cluster-Based Data Stream Clustering Method for P2P Traffic Classification

2012 ◽  
Vol 263-266 ◽  
pp. 1121-1126
Author(s):  
Guang Hui Yan ◽  
Ming Hao Ai
Keyword(s):  
Concept Drift ◽  
Real Data ◽  
Streaming Data ◽  
Machine Learning Techniques ◽  
Traffic Classification ◽  
Limited Memory ◽  
Learning Techniques ◽  
Data Stream Clustering ◽  
Effectiveness And Efficiency ◽  
P2p Applications

Many machine learning techniques were proposed to classify P2P traffic and each with reasonable successes. But in the real P2P network environment, new communities of peers often attend and old communities of peers often leave. It requires the identification methods to be capable of coping with concept drift and updating the model incrementally. In this paper, we presented a concept-adapting algorithm MCStream which was based on streaming data mining techniques to identify P2P applications in Internet traffic. The MCStream used two micro-cluster structures, potential micro-cluster structures and outlier micro-cluster structures, to classify the P2P traffic and discovered the concept drift with limited memory. Our performance studied over a number of real data which was captured at a main gateway router demonstrates the effectiveness and efficiency of our method.

scholarly journals Crop Management with the IoT: An Interdisciplinary Survey

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 181 ◽  
Author(s):  
Giuliano Vitali ◽  
Matteo Francia ◽  
Matteo Golfarelli ◽  
Maurizio Canavari
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Crop Management ◽  
Cropping System ◽  
Heterogeneous Data ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Crop Fields ◽  
Learning Techniques ◽  
The Internet Of Things

In this study, we analyze how crop management will benefit from the Internet of Things (IoT) by providing an overview of its architecture and components from agronomic and technological perspectives. The present analysis highlights that IoT is a mature enabling technology with articulated hardware and software components. Cheap networked devices can sense crop fields at a finer grain to give timeliness warnings on the presence of stress conditions and diseases to a wider range of farmers. Cloud computing allows reliable storage, access to heterogeneous data, and machine-learning techniques for developing and deploying farm services. From this study, it emerges that the Internet of Things will draw attention to sensor quality and placement protocols, while machine learning should be oriented to produce understandable knowledge, which is also useful to enhance cropping system simulation systems.

scholarly journals A new smart healthcare framework for real-time heart disease detection based on deep and machine learning

2021 ◽  
Vol 7 ◽  
pp. e646
Author(s):  
Haitham Elwahsh ◽  
Engy El-shafeiy ◽  
Saad Alanazi ◽  
Medhat A. Tawfeek
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Real Time ◽  
Heart Diseases ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Medical Decision ◽  
Rate Condition ◽  
Smart Healthcare ◽  
Learning Techniques

Cardiovascular diseases (CVDs) are the most critical heart diseases. Accurate analytics for real-time heart disease is significant. This paper sought to develop a smart healthcare framework (SHDML) by using deep and machine learning techniques based on optimization stochastic gradient descent (SGD) to predict the presence of heart disease. The SHDML framework consists of two stage, the first stage of SHDML is able to monitor the heart beat rate condition of a patient. The SHDML framework to monitor patients in real-time has been developed using an ATmega32 Microcontroller to determine heartbeat rate per minute pulse rate sensors. The developed SHDML framework is able to broadcast the acquired sensor data to a Firebase Cloud database every 20 seconds. The smart application is infectious in regard to displaying the sensor data. The second stage of SHDML has been used in medical decision support systems to predict and diagnose heart diseases. Deep or machine learning techniques were ported to the smart application to analyze user data and predict CVDs in real-time. Two different methods of deep and machine learning techniques were checked for their performances. The deep and machine learning techniques were trained and tested using widely used open-access dataset. The proposed SHDML framework had very good performance with an accuracy of 0.99, sensitivity of 0.94, specificity of 0.85, and F1-score of 0.87.

scholarly journals Hybrid Deep-Learning and Machine-Learning Models for Predicting COVID-19

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Talal S. Qaid ◽  
Hussein Mazaar ◽  
Mohammad Yahya H. Al-Shamri ◽  
Mohammed S. Alqahtani ◽  
Abeer A. Raweh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
Healthcare Systems ◽  
Viral Pneumonia ◽  
Machine Learning Techniques ◽  
Full Potential ◽  
Learning Models ◽  
Local Data ◽  
Data Set ◽  
Learning Techniques

The COVID-19 pandemic has had a significant impact on public life and health worldwide, putting the world’s healthcare systems at risk. The first step in stopping this outbreak is to detect the infection in its early stages, which will relieve the risk, control the outbreak’s spread, and restore full functionality to the world’s healthcare systems. Currently, PCR is the most prevalent diagnosis tool for COVID-19. However, chest X-ray images may play an essential role in detecting this disease, as they are successful for many other viral pneumonia diseases. Unfortunately, there are common features between COVID-19 and other viral pneumonia, and hence manual differentiation between them seems to be a critical problem and needs the aid of artificial intelligence. This research employs deep- and transfer-learning techniques to develop accurate, general, and robust models for detecting COVID-19. The developed models utilize either convolutional neural networks or transfer-learning models or hybridize them with powerful machine-learning techniques to exploit their full potential. For experimentation, we applied the proposed models to two data sets: the COVID-19 Radiography Database from Kaggle and a local data set from Asir Hospital, Abha, Saudi Arabia. The proposed models achieved promising results in detecting COVID-19 cases and discriminating them from normal and other viral pneumonia with excellent accuracy. The hybrid models extracted features from the flatten layer or the first hidden layer of the neural network and then fed these features into a classification algorithm. This approach enhanced the results further to full accuracy for binary COVID-19 classification and 97.8% for multiclass classification.

scholarly journals A Review Of The Issues And Challenges In IoT Security Using Machine Learning Techniques

2021 ◽  
Vol 9 (4) ◽  
pp. 499-502
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Security Threats ◽  
X Rays ◽  
Iot Security ◽  
Learning Techniques ◽  
Human Endeavor ◽  
Security Challenges ◽  
Iot Devices ◽  
The Internet Of Things

In a typical IoT network, a sensor connects to a controller using a wireless connection. Controllers collect data from sensors and sends the data for storage and analysis[1]. These controllers work with actuators that translate an electrical input to a physical action. The internet of things (IoT), have found application in different areas of human endeavor including healthcare, government, supply chain, cities, manufacturing, etc. and it is estimated that the number of connected devices will reach 50 billion by 2020[2] With the increasing number of devices comes an increase in the the varying number of security threats to the IoT network [3]. To contain these threats, a secure-by-design approach should be adopted as this will help the IoT devices to anticipate and neutralize the ever changing nature of the threats as against older systems where security was handled as it presents itself [2] This paper x-rays the security challenges in IoT networks and the application of machine learning (Supervised learning, Unsupervised learning and Reinforcement learning) in tackling the security challenges

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