Technology get, acknowledge, and reciprocate data, simplifying and

Technology and education have come to the point of
harmony now. Internet of Things (IoT) refers, to technological advancements in
the networking with the help of which real-world entities can be connected to communicate
with each other over the internet. In general, replacing the teacher or making
the teaching process faster is not the objective anymore. It is about obtaining
the offering of education and better features. To do so, schools of tomorrow
should study towards emerging technologies today. An IoTs enabled classroom, or
Personal PC is without question adequately equipped to provide made-to-measure
learning for students with individual needs, interactive learning experiences,
faster feedback, easy collaboration access for both students and teachers and
facilitation for remote learning. The efficiency and cost benefits from an
administrative point of view are also enormous. In this paper, it will discuss
the study the impact and model of IoT based e-Learning system refer to
technological advancements in the networking with the help of which real-world
objects can be connected to communicate with each other over the internet and
also conclude how machine learning algorithms enhance the performance of IoT enabled
E-learning systemSome of the modern IoT artillery
in this domain includes digital highlighters, smarter boards. It means the
printed text could be digitally conveyed to the smartphone or any other app at
an incredible speed through tools like Scan marker and c-pen. Interactive
boards can get, acknowledge, and reciprocate data, simplifying and stimulating
the overall learning activity. Just imagine an outline where students sitting
in a classroom or front of PC at their home can interact with their friends,
classmates, teachers, and educators scattered over the world. Now, let’s
suppose the lesson of the day has focused on sea life. To give students an
especially exciting – and profoundly educational – experience, the mentor
decides to access live information caused by sensors and live feeds monitoring
a particular body of water. The IoT refers to a better vision whereby ‘things’
(objects) such as everyday objects (entity), places, and environments have
interconnected with one another via the Internet. An example of a simple IoT
object now available in some homes is a thermostat which can determine when
people occupy certain rooms and alter levels of heating, lighting and other
functions in the house accordingly. By widening the Internet from “a network of
interconnected computers to a network of interconnected objects,” the IoT will
cover a vast and complex network of devices. These devices will add sensors to
measure the data of environment around them, actuators which physically act
back into their environment such as processors to handle, opening the door and
store the massive data generated, nodes to send the information and organizers
to help manage sets of these parts. Through this, it has the potential to
significantly extend, enrich and even shift the relationship between people and
the world around them. In fact, many are hoping that the IoT will play a
pivotal role in addressing many of today’s societal challenges such as an aging
society, deforestation, traffic congestion and recyclability. This
interconnection of physical objects is expected to magnify the profound
consequences that large-scale networked connections are having on our organization,
gradually resulting in a genuine paradigm shift 1. In this paper, it has been
review recent E-Learning-related literature associated with the IoT vision. One
aim is to provide a resource for the E-learners to understand the current state
of research associated with the new IoT agenda.

 In this section, some of the earlier works on the
subjects have cited. According to Cisco 2, the organizations have already
experienced the Internet of Things (IoT) – the networked connection of things, soon
some capabilities like context awareness, energy independence, and increased
processing power are added to these things then IoT becomes IoE (Internet of Everything). Also, according to their research, 99.4 percent of
physical objects which can be a part of IoE is yet to be connected 3. The
whitepaper concludes by saying, “There is tremendous value in connecting the
unconnected with intelligent networks across education. This paper demonstrates
IoE’s potential impact on making education more relevant, engaging and
motivating learners, and enabling faster time to mastery. However, to realize
the benefits of connecting people, processes, data, and things, reliable
connectivity and continuous access must be guaranteed. Additionally for IoE to
be accepted, both policymakers and educators must be well-prepared not only to
exploit but also to understand potential risks.” IoT will enable life-enhancing
services, regarding the role of IoT in education say, “In education,
mobile-enabled solutions will tailor the learning process to each student’s
needs, improving overall proficiency levels, while linking virtual and physical
classrooms to make learning more convenient and accessible 4. IoT might serve
as the backbone for the universal learning environment and enable active smart
environments to accept and identify objects and retrieve information from the
internet to facilitate their adaptive functionality 5. A learner may gain the
knowledge not only by connecting to the learning contents via networks by using
desktop computers or wireless handheld devices such as Personal Digital
Assistants (PDAs) and mobile phones but also by communicating to the
microprocessors (e.g., RFID – Radio Frequency Identification) embedded in
devices.” In the reference paperR, two groups 25 students each were enrolled
in a similar course. However, one group was taught using traditional methods
and other using an interactive system of the internet of things. After
conducting various tests and analysis, they concluded that “Internet of
Objects, applied as a tool to support the teaching process, improves student
academic performance”.

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I.         
IoT ENABLED APPLICATIONS

The following table 1 represents the related
works done on the IoT enabled applications for different domains.

Table 1:
Related works done on the IoT enabled applications in different domains

Authors

Title of the paper

Application

Description

Theodoridis, Evangelos, Georgios Mylonas,
and Ioannis Chatzigiannakis 6

Developing an iot smart city framework

Smart city

Monitoring of parking
spaces availability in the city.

Noel, Adam, et al 7

Structural Health Monitoring using
Wireless Sensor Networks: A Comprehensive Survey

Structural
health

Monitoring of vibrations and material
conditions in buildings, bridges and historical monuments.

Majumder, AKM Jahangir A., et al 8

A wireless IoT
system towards gait detection in stroke patients

Smartphone
Detection

Detect iPhone and Android devices and in
general any device which works with WiFi or Bluetooth interfaces.

Ozger, Mustafa, Oktay Cetinkaya, and Ozgur
B. Akan 9

Energy
Harvesting Cognitive Radio Networking for IoT-enabled Smart Grid

Electromagnetic
Field Levels

Measurement of the energy radiated by cell
stations and WiFi routers.

Jeyasheeli, P. Golda, and JV Johnson Selva
10

An
IOT design for smart lighting in green buildings based on environmental
factors

Smart
Lighting

Intelligent and weather
adaptive lighting in street lights.

Keerthana, B., et al11

Internet
of Bins: Trash Management in India

Waste
Management

Detection of rubbish
levels in containers to optimize the trash collection routes.

Shaikh, Faisal Karim, Sherali Zeadally,
and Ernesto Exposito12

Enabling
technologies for green internet of things

Forest
Fire Detection

Monitoring of combustion
gases and preemptive fire conditions to define alert zones.

Obara, Kazushige, et al13

A densely distributed high-sensitivity
seismograph network in Japan

Earthquake
Early Detection

Distributed control in
specific places of tremors.

Weidhaas, Jennifer, Lian-Shin Lin, and Karen
Buzby 14

A case study for orphaned chemicals:
4-methylcyclohexanemethanol (MCHM) and propylene glycol phenyl ether (PPH) in
riverine sediment and water treatment processes.

Chemical
leakage detection in rivers

Detect leakages and
wastes of factories in rivers.

Gupta, Shikha Pranesh, and Umesh Kumar
Pandey 15

Automatic and Intelligent Integrated
System for Leakage Detection in Pipes for Water Distribution Network Using
Internet of Things

Water
Leakages

Detection of liquid
presence outside tanks and pressure variations along pipes.

El-Din, Hemdan Ezz, and D. H. Manjaiah
16

Internet
of Nano Things and Industrial Internet of Things

M2M
Applications

Machine auto-diagnosis
and assets control.

Tsai, Yao-Te, et al 17

Precise
Positioning of Marketing and Behavior Intentions of Location-Based Mobile
Commerce in the Internet of Things

Intelligent
Shopping Applications

Getting advices in the
point of sale according to customer habits, preferences, presence of allergic
components for them or expiring dates.

Tao, Fei, et al 18

Internet of Things in product life-cycle
energy management

Smart
Grid

Energy consumption
monitoring and management.

Veeramanickam, M. R. M.,
and M. Mohanapriya 19

IOT enabled Futurus Smart
Campus with effective E-Learning: i-Campus

E-learning

 In digital era
our College campus need of IoT technology for classy environment to
utilize 
effective E-learning.

Auer, Michael E., and
Danilo G. Zutin, eds 20

Online
Engineering & Internet of Things: Proceedings of the 14th International
Conference on Remote Engineering and Virtual Instrumentation REV 2017

Smart E-learning

IoT technology for classy
environment to utilize 
effective E-learning

                                                                                                                                    
II.        
The  IoT Enabled E-LEARNING

E-learning is currently implemented using various techniques and technologies.
Some technologies (Some of Listed in Table 1) have been specifically developed
for the same while others can be used as successful E-learning tools. Some
Technologies used in E-learning are:

Table 2: Related works done on IoT enabled E-Learning

 

Authors

Title of the paper

Keywords

Description

Bystrova, T. Yu.
Larionova, V. A.
Osborne, M.
Platonov, A. M. 21

Introduction of open e-learning system as
a factor of regional development

Information society
Educational paradigm
Regional development
Lifelong learning
E-learning
Open e-learning
Educational resources
Massive open online course
Nancial model
Economic efect

The description is made of the cost
options for open-type e-learning course development, investment parameters
for their establishment, as well as costs of implementing educational
programmes with the application of e-learning. The analysis of the activities
of Ural Federal University on implementing e-learning gives the opportunity
to further imagine the effect from the introduction of e-learning in other
universities in the region.

Islam, Nurul, Martin Beer, and Frances
Slack 22

E-learning challenges faced by academics
in higher education: a literature review

e-learning, higher education, academic
challenges, e-learning in Middlesex Universit

This paper references some of the research
work on the limitations of e-learning technology, categorises it in five
challenges that teachers are faced with and suggestions for a successful
e-learning outcome. This paper also discusses the use of e-learning
technology in Middlesex University and some of the challenges they face.

Kong, Siu Cheung, et al 23

E-learning in School Education in the
Coming 10 Years for Developing 21st Century Skills: Critical Research Issues
and Policy Implications

E-Learning, School education, 21 st
century skils, Research issues, Policy implications

This paper aims to discuss the research
issues and policy implications critical for achieving such a curriculum goal.
A review of literature in the related fields indicates that K-12 schools
should take advantage of e-learning to maximize learning opportunities of
learners for the development of 21st century skills.

Charmonman, Srisakdi, et al 24

e-Learning and the Science of Instruction:
Proven Guidelines for Consumers and Designers of Multimedia Learning

Educational paradigm
Regional development
Lifelong learning
E-learning
Open e-learning
 

e-Learning and the Science of Instruction
is the ultimate handbook for evidence-based e-learning design. Since the
first edition of this book, e-learning has grown to account for at least 40%
of all training delivery media. However, digital courses often fail to reach
their potential for learning effectiveness and efficiency.

Charmonman, Srisakdi, et al 25

Applications of Internet of Things in
E-Learning

Internet of Things, IoT in
eLearning, IoT and instructional design,
IoT and training, Skills for IoT, Internet
of
Learning Things, IoT to transform
education, IoT to improve student
performance

This paper will
discuss IoT in eLearning and instructional
design, training employees on IoT
technology, six skills for IoT
applications,
Internet of Learning Things, IoT
potentials
to transform education, and IoT to improve
student performance

                                                                                                                       
III.       
Machine Learning Algorithms in IoT

The following table 3
depicts the related works done in IoT by using Machine Learning algorithms.

Table 3: Related Works done
on IoT using Machine Learning algorithms

 

Authors

Title of the paper

Keywords

Description

Zou, Han, et al 26

A fast and precise indoor localization algorithm
based on an online sequential extreme learning machine

Biomedical monitoring,
Biomedical monitoring,
Sensors,
Medical services,
Smart homes,
Logic gates,
Assisted living,
Ambient networks,
Internet of things

This article differs from seamlessly linking
multimodel data-collecting infrastructure and data analytics together in an
AAL platform. This article also outlines a multimodality sensor platform with
heterogeneous network connectivity, which is under development in the sensor
platform for healthcare in a residential environment (SPHERE)
Interdisciplinary Research Collaboration (IRC).

Lane, Nicholas D., et al 27

An early resource characterization of deep learning
on wearables, smartphones and internet-of-things devices

behavior and
ambient context, IoT, Deep Learning, smartphones, wearable systems.

The aim of this investigation is to begin to build
knowledge of the performance characteristics, resource requirements and the
execution bottlenecks for deep learning models when being used to recognize
categories of behavior and context.

Zou, Han, et al 28

An online sequential extreme learning machine
approach to WiFi based indoor positioning

IEEE 802.11
Standards,
Calibration,
Training,
Accuracy,
Testing,
Mathematical
model,
Heuristic algorithms

An indoor localization algorithm based on online
sequential extreme learning machine (OS-ELM) to address these problems
accordingly

Alsheikh, Mohammad Abu, et al 29

Machine learning in wireless sensor networks:
Algorithms, strategies, and applications

Wireless
sensor networks,
Routing,
Machine
learning algorithms,
Clustering
algorithms,
Algorithm
design and analysis,
Principal
component analysis,
Classification
algorithms

An extensive literature review over the period
2002-2013 of machine learning methods that were used to address common issues
in WSNs. The advantages and disadvantages of each proposed algorithm are
evaluated against the corresponding problem.

Lane, Nicholas D., et al 30

A large-scale web QoS prediction scheme
for the Industrial Internet of Things based on a kernel machine learning
algorithm
 

Kernel least
mean square
Quality of
services (QoS)
QoS
prediction
Pearson
correlation coefficient (PCC)
Industrial
Internet of Things (IIoT)
 

Apply the derived coefficients for the prediction of
missing web service QoS values. An extensive performance study based on a
public data set is conducted to verify the prediction accuracy of our
proposed scheme

                                                                                                                                                              
IV.       
CONCLUSION

Internet of Things (IoT)
already delivers connectivity to a broad range of devices, enabling the
development of innovative new services and applications. In the field of
education, IoT will take E-learning to the next level. This paper explains the
related works done on the implementation of IoT in different domains and the
applications that are utilizing IoT. And the detailed description of the work
done on IoT based E-Learning system and the IoT using Machine Learning
algorithms. In the future, this IoT based E-learning can leverage the power of
IoT to implement a smart learning environment that facilitates better learning
and higher retention rates. This advancement in education to produce better
individuals regarding skills and knowledge.