Recent Machine Learning and Internet of Things (IoT) Applications for Personalized Healthcare: Issues and Challenges

2020 ◽  
pp. 119-126
Author(s):  
Md Tabrez Nafis ◽  
Aksa Urooj ◽  
Siddhartha Sankar Biswas
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Personalized Healthcare ◽  
Iot Applications

scholarly journals Machine Learning and LPWAN Based Internet of Things Applications in Healthcare Sector during COVID-19 Pandemic

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1615
Author(s):  
Zeeshan Ali Khan ◽  
Ubaid Abbasi ◽  
Sung Won Kim
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
K Nearest Neighbors ◽  
Iot Applications ◽  
Processing Resources ◽  
Multiple Services ◽  
Applications Of Machine Learning

Low power wide area networks (LPWAN) are comprised of small devices having restricted processing resources and limited energy budget. These devices are connected with each other using communication protocols. Considering their available resources, these devices can be used in a number of different Internet of Things (IoT) applications. Another interesting paradigm is machine learning, which can also be integrated with LPWAN technology to embed intelligence into these IoT applications. These machine learning-based applications combine intelligence with LPWAN and prove to be a useful tool. One such IoT application is in the medical field, where they can be used to provide multiple services. In the scenario of the COVID-19 pandemic, the importance of LPWAN-based medical services has gained particular attention. This article describes various COVID-19-related healthcare services, using the the applications of machine learning and LPWAN in improving the medical domain during the current COVID-19 pandemic. We validate our idea with the help of a case study that describes a way to reduce the spread of any pandemic using LPWAN technology and machine learning. The case study compares k-Nearest Neighbors (KNN) and trust-based algorithms for mitigating the flow of virus spread. The simulation results show the effectiveness of KNN for curtailing the COVID-19 spread.

Machine Learning for Internet of Things

2019 ◽  
pp. 57-83 ◽  
Author(s):  
Ramgopal Kashyap
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Internet Of Things ◽  
Information Quality ◽  
Physical World ◽  
Machine Learning Techniques ◽  
Iot Applications ◽  
Learning Techniques ◽  
The Way

Fast advancements in equipment, programming, and correspondence advances have permitted the rise of internet-associated tangible gadgets that give perception and information estimation from the physical world. It is assessed that the aggregate number of internet-associated gadgets being utilized will be in the vicinity of 25 and 50 billion. As the numbers develop and advances turn out to be more develop, the volume of information distributed will increment. Web-associated gadgets innovation, alluded to as internet of things (IoT), keeps on broadening the present internet by giving network and cooperation between the physical and digital universes. Notwithstanding expanded volume, the IoT produces big data described by speed as far as time and area reliance, with an assortment of numerous modalities and changing information quality. Keen handling and investigation of this big data is the way to creating shrewd IoT applications. This chapter evaluates the distinctive machine learning techniques that deal with the difficulties in IoT information.

Machine Learning for Internet of Things

2021 ◽  
pp. 976-1002
Author(s):  
Ramgopal Kashyap
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Internet Of Things ◽  
Information Quality ◽  
Physical World ◽  
Machine Learning Techniques ◽  
Iot Applications ◽  
Learning Techniques ◽  
The Way

Fast advancements in equipment, programming, and correspondence advances have permitted the rise of internet-associated tangible gadgets that give perception and information estimation from the physical world. It is assessed that the aggregate number of internet-associated gadgets being utilized will be in the vicinity of 25 and 50 billion. As the numbers develop and advances turn out to be more develop, the volume of information distributed will increment. Web-associated gadgets innovation, alluded to as internet of things (IoT), keeps on broadening the present internet by giving network and cooperation between the physical and digital universes. Notwithstanding expanded volume, the IoT produces big data described by speed as far as time and area reliance, with an assortment of numerous modalities and changing information quality. Keen handling and investigation of this big data is the way to creating shrewd IoT applications. This chapter evaluates the distinctive machine learning techniques that deal with the difficulties in IoT information.

scholarly journals A Review on Identifying Suitable Machine Learning Approach for Internet of Things Applications

2021 ◽  
Vol 3 (3) ◽  
pp. 128-145
Author(s):  
R. Valanarasu
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Smart Transportation ◽  
Iot Applications ◽  
Enhance Efficiency ◽  
The Internet Of Things

Recently, IoT is referred as a descriptive term for the idea that everything in the world should be connected to the internet. Healthcare and social goods, industrial automation, and energy are just a few of the areas where the Internet of Things applications are widely used. Applications are becoming smarter and linked devices are enabling their exploitation in every element of the Internet of Things [IoT]. Machine Learning (ML) methods are used to improve an application's intelligence and capabilities by analysing the large amounts of data. ML and IoT have been used for smart transportation, which has gained the increasing research interest. This research covers a range of Internet of Things (IoT) applications that use suitable machine learning techniques to enhance efficiency and reliability in the intelligent automation sector. Furthermore, this research article examines and identifies various applications such as energy, high-quality sensors associated, and G-map associated appropriate applications for IoT. In addition to that, the proposed research work includes comparisons and tabulations of several different machine learning algorithms for IoT applications.

scholarly journals A Reliable Current Starved Inverter based Arbiter PUF Architecture for IoT Applications

2019 ◽  
Vol 9 (1S5) ◽  
pp. 163-167
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Process Variations ◽  
Fabrication Process ◽  
Ring Oscillator ◽  
Smart Devices ◽  
Physical Unclonable Functions ◽  
Iot Applications ◽  
Arbiter Puf ◽  
A Current

In the recent years, Physical Unclonable Functions(PUFs) are emerged to be one of the lightweight hardware security primitives for device authentication, identification, such as Internet of things (IoT). IoT comprises connection of multiple number of nodes (devices) for exchanging the information across different networks. PUFs can sense the minute and unavoidable process variations during the fabrication process and generates the unique number of challenge-response pairs(CRPs), which can be stored and extensively used for secure associations between smart devices in IoT. Arbiter PUFs and ring oscillator PUFs are most commonly used strong PUFs in current day scenario. The conventional Linear arbiter PUFs are suffers from low reliability and vulnerable to Machine Learning attacks. In this paper, we proposed a Current starved Inverter (CSI) based arbiter PUF which enhances the non- linearity and randomness. The Proposed architecture was simulated using cadence spectre CMOS 45nm technology and estimated its metrics such as uniqueness reliability and uniformity.

Detecting Abnormal Behavior of an IoT Device in the Network Based on a Traffic Model

Telecom IT ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 50-55
Author(s):  
D. Saharov ◽  
D. Kozlov
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Mobile Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Abnormal Behavior ◽  
Traffic Model ◽  
The Internet ◽  
Wide Spread ◽  
The Internet Of Things

The article deals with the СoAP Protocol that regulates the transmission and reception of information traf-fic by terminal devices in IoT networks. The article describes a model for detecting abnormal traffic in 5G/IoT networks using machine learning algorithms, as well as the main methods for solving this prob-lem. The relevance of the article is due to the wide spread of the Internet of things and the upcoming update of mobile networks to the 5g generation.

Machine Learning Based Activity Learning for Behavioral Contexts in Internet of Things (IoT)

2020 ◽  
Vol 46 (8) ◽  
pp. 626-635
Author(s):  
Muhammad Safyan ◽  
Sohail Sarwar ◽  
Zia Ul Qayyum ◽  
Muddasar Iqbal ◽  
Shancang Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things
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