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International Journal for Research in Engineering Application & Management (IJREAM)
ISSN : 2454-9150 Vol-05, Issue-04, July 2019
6 | IJREAMV05I0452005 DOI : 10.35291/2454-9150.2019.0287 © 2019, IJREAM All Rights Reserved.
Applications of IoT Technology and Computer Vision
in Higher Education – A Literature Review *M. Lakshaga Jyothi,
#Dr. R.S.Shanmugasundaram
*Research Scholar,
#Professor, Department of Computer Science and Engineering, Vinayaka Mission’s
Kirupananda Variyar Engineering College, Vinayaka Mission’s Research Foundation (Deemed to be
University), Salem, TN, India. *lakshagamecse06@gmail.com,
#rsssslm32@yahoo.com
Abstract Nowadays, Higher Educational Institutions (HEIs) are adopting new technologies even before they are
recognized educationally making them compulsory technological parts of the educational system. To sustain with the
rapid educational changes and technological advancements, IoT seems to be the possible solution. IoT system senses,
collects and manipulates the data over Internet using its Unique Identifiers (UID). After few years, sum of devices
worldwide with IoT connections, ten years ahead will be approximately 75 billion worldwide. Its future lies with its
implementation. Raspberry Pi, a typical learning platform to study IoT technology has been explored. The Human
brain performs complex image processing functions, whereas the eye functions similar to a digital camera. Computer
Vision (CV) applications in Higher Education will date back to the 1980s. So, Computer Vision when coupled with IoT
technology in a Higher Education can give rise to smart, intelligent campuses improving student experiences and
positively enhancing educational perceptions. In this review paper, the literature works related to what/how different
styles, principles, and technologies that are not only related to IoT and Computer Vision technology but also any new
technological research works that can be possibly adopted into Higher Educational Environment will be discussed,
thus exploring its future with building automated intelligence in HEIs.
Keywords — Internet of Things, Computer Vision, IoT Applications, Raspberry Pi, Higher Education, Automated
Intelligence
I. INTRODUCTION
Higher Educational Institutions are integrating various
educational technologies into their educational space.
Educational transformation is becoming a trending jargon
through Computer and Telecommunication. Even though,
technologies are getting adopted; microcomputers have yet
to be effectively integrated into educational sector. From the
past two decades, Works from Researches of various fields
shows that, they have not yet succeeded in developing
complete solution for integrating educational technologies
into the educational environment. Various challenging
factors such as Space, Cost, Availability, Reliability,
Support or knowledge matters while considering their
adoption. Thus, collectively to fill these heterogeneous gaps
it is necessary to go for a better and promising solution,
here comes the technology called IoT- Internet of Things.
Nowadays, Automation has become a trending context to
refer to smartness in our everyday life. With the help of IoT,
which can act as a medium to interface physical and the
virtual worlds through the computation of the embedded
objects present in them through automation. But bringing
intelligence along with automation will stream a great start
for the future generations to work with.
With IoT concept integrated with connectivity, sensors,
analysis of data and making decisions based on the results
can simplify many real time problems in the classroom
where huge number of students will be gathered more often.
IoT will increase our everyday routines through intelligent
and strong systems making our life fast and easy in terms of
our priorities and preferences. An IoT based Higher
Educational Environment can not only provide smartness
through automation but can also provide efficient operation,
monitoring and maintaining of the routine activities inside
the classes. When we look into the difference between the
context ―smart‖ and intelligence, former is one where the
delivery of the content is quick and automatic but in the
latter one the delivery of the content cannot be quick but
will be very efficient and operational i.e., it can provide
users with the results what they are trying to do by means of
some computational works.
―A picture is worth a thousand words‖, like our vision, a
digital vision called Computer Vision came into existence
that captures and stores an image or image sets then
International Journal for Research in Engineering Application & Management (IJREAM)
ISSN : 2454-9150 Vol-05, Issue-04, July 2019
7 | IJREAMV05I0452005 DOI : 10.35291/2454-9150.2019.0287 © 2019, IJREAM All Rights Reserved.
transforming them into meaningful information to make
interesting decisions. Our eye sight is one of the important
and brilliant creations of God among our sensory systems.
In general, it is a fact that our surrounding environment is
composed of various visual signals or signs (cues). In the
context of Higher Education, Physical cues may be Student
Classroom Activities, Teaching Activities, Student
Engagement, Academic Performance Activities,
Administrative activities, Monitoring and Maintenance
Activities etc.
Computer Vision Engineering is a composition of
interdisciplinary fields such as Artificial Intelligence (AI),
Machine Learning (ML), Image Processing, Sensor
Technologies, Augmented Reality, Robotics etc. In recent
past, application of CV technologies where done on some
closed source platforms. As in combination with IP based
technologies, a new set of implementations, environments
or systems were created that were not made possible before
resulting in a revolution on IoT innovations. Once we are
ready connecting devices with the concept of IoT, then
there arises the next step towards making things more
intelligent by generating actionable data for a better
valuable insights. Apart from the theoretical knowledge,
the future and the full potential of any technology lies in its
implementation. Considering this, various IoT prototyping
platforms have been introduced such as Arduino, Beagle
board, Intel, Raspberry Pi etc. In this review work,
Raspberry Pi platform has been taken into consideration for
its state of the art related works and technologies.
This review paper has been set to investigate how
effectively IoT along with Computer Vision can be used to
create an intelligent educational environment promoting
automation and intelligence, thus its impact in higher
education. Towards the realization of this objective, the
goals of the proposal are to survey the literature works
related to the different methodologies and styles of IoT and
CV principles or technologies adopted into Higher
Educational Environment in essence with constructing
automated intelligence based IoT systems. Thus, enabling a
quick start in providing automation in educational
institutions especially in classrooms where the students will
be gathered more often than other areas. IoT based
automation has become a developing research area to
emphasize students in knowing the importance about the
technologies that will rule the future gatherings.
This paper has been organized as follows: Section I.
which gives an overview and implementation background of
IoT technology in HEIs and the possible applications of IoT
and CV technology in the HEIs. Section II. purely focuses
on the overview of Raspberry Pi consisting of its history,
core components and models. Section III. investigates on
the major area of interest considered for the literature study.
Section IV. describes the primary research methods used for
the study and Classification of data for further analysis
Section V. investigates the existing literature works that
were taken into account for further analysis of data in the
detailed manner. Sections VI. gives the literature review
results, Section VII. limitations of this study paper, Section
VIII. reveals the discussions and findings from the papers
that were selected for the survey, Section IX. finally forms
the conclusions.
A. Technological Evolution of IoT:
Educational technology has its main purpose as serving
the entities of the educational infrastructure. Its usages in
the Higher Educational Institutions (HEIs) have become a
trend in this digital era. When a technologist named Kevin
Ashton from Britain, coined the term ―Internet of Things‖ in
1999 would have known that IoT will become so popular
forming a remarkable age called ―IoT Age‖ making
ourselves as the IoT Generations. Educational History of
IoT dates back to the 1980s with the brief history of IoT use
cases in Higher Education is discussed as follows. As said
by Mark Weiser [1], The deepest technologies are the ones
that fade away‖, i.e., computational processing will be
invisible whereas the information processing will
overwhelm surrounding us. In this context, we try to focus
on IoT in Higher Education where ―Things‖ include objects
that are not only the electronic devices or the products of
any higher level technological developments (i.e., vehicles,
appliances, equipment) but also things that we ordinarily
use around us such as chair, table etc. inside the educational
environment. The supreme goal of IoT is to make things
connected at any time, or in any place, with anything and
also with anyone using any path or any services [2].
Automatic machines can reduce man power work along
with perfection [3]. An overview of various IoT contexts
applicable to Higher Education is discussed below (Table
1.).
Table Shows Core components of Raspberry Pi
Names Term Coined By Year
Ubi.Comp
AmI
Pervas.Comp
Cont.
Affec.Comp
IoT
CPS
WoT
IoE
IıoT
IoS
IoWT
Ubiquitous Computing
Ambient Computing
Pervasive Computing
Context-Aware Computing
Affective Computing
Internet of Things
Cyber-Physical Systems
Web of Things
Internet of Everything
Industrial Internet of
Things
Internet of Signs
Internet of Wearable Things
1988
1990’s
1993
1994
1997
1999
2006
2008
2012
Late 2012
Late 2012
Recent
International Journal for Research in Engineering Application & Management (IJREAM)
ISSN : 2454-9150 Vol-05, Issue-04, July 2019
8 | IJREAMV05I0452005 DOI : 10.35291/2454-9150.2019.0287 © 2019, IJREAM All Rights Reserved.
Table 1. In above table mentioned outlines the various
IoT contexts that are widely used in Higher Education
B. Exploring IoT Systems in Higher Educational
Environment:
Teaching. Teaching where teachers are the primary focus
with students considered to be secondary. When inducing
teaching through IoT, to concentrate on new pedagogical
techniques and approaches [4] Smart teaching methods
useful for IoT implementation such as [5-8], and how to
make a smart classroom[9], promotion of ubiquitous
learning using mobile personal device can also be made
possible, positive effects on ICT by enhancing education
process and learning effective and motivate [10].
Learning. A smart learning concept [11] based on
context-aware ubiquitous learning methods for a good
learning experience, learner-centric and service-oriented
educational paradigm without just focusing on the learning
devices [12]. Learner engagement and independent learning
can be made possible through richer contexts and
augmented techniques, in contrast, smart learning can be
supported by smart devices and intelligent technologies
[13], record lecture data and share it with e-learning
platform [14], Student-centered learning methods through
projects, problems of discovery etc were discussed [15],
better learning through connected devices [16], more
collaborative e-learning becomes vital with IoT [17].
Educational Administration. With IoT, admin can
maintain the student’s attendance record [18], students as
well as teachers, can identify their presence with
smartphones [19] to perform learning analytics on the
educational data of student’s learning activities [20],
various contexts arise for smart education. By sensing eye
gazed, emotional stress, motion detectors for gestures and
various neurological parameters to monitor them effectively
[21], through multimodal learning analytics, sensors can be
placed inside the classroom to potentially record the
activities such as temperature, capture audio feeds, wearable
wristband to sense their biological factors effects, motion
capture sensors to sense hand, eye, head, face gestures[20].
College premises. Various studies are made through IoT
to create smart environments in [22-24],There were studies
on how e-learning data be captured and analyzed to improve
teaching and learning through personalized learning [25],
Electronic equipments such as printers, scanners be
integrated with bar codes, and QR tags for better
identification, integrating embedded devices or UID to
interact with intelligent network structure to monitor objects
[26], monitoring of parking slots and number of vacancies,
message alerts can be given to reduce security violations,
Air monitors can be used to reduce noise pollution, garbage
collection systems, auto water flows in gardens, e-books
with 3D modeling of the image using QR codes can be used
in libraries.
Classroom. A teacher can maintain his/her control over
the classroom with IoT by identifying student engagement
or interest by identifying the concentration levels [27]. After
lectures are given, real-time feedback on the quality of the
lectures be arrived [28], intelligent classroom with tele-
education methods [29], automatic adjustment of lights in
the classroom, RFID tags with student ID cards [30]/ in
entrance of campus [31] or student-teacher classroom
relationship enhancement [32], transform the dumb
classroom-technology that just require human control to
smart space as ―intelligent classroom‖ with ubiquitous
computing and ambient intelligence [33].
C. Possible Applications of IoT and Computer Vision in
Higher Education:
This below mentioned diagram can give a brief
skeleton of ideas with the amalgam of Computer Vision
Technology and IoT that can be implemented in Higher
Educational Institutions.
Figure 1. Applications of IoT and Computer Vision
II. RASPBERRY PI-AN INTRODUCTION
A. An Overview of Raspberry Pi:
In the previous sections, we saw various impacts of
IoT in different educational infrastructures. However, IoT is
an integration of the various numbers of heterogeneous
technologies such as Embedded Devices, Cloud Computing,
Artificial Intelligence, Big Data and Machine Learning etc.
To make its implementation easier an alternative hands-on
platform is chosen, which is Raspberry Pi. Raspberry Pi is a
very tiny credit card sized single board computer with
affordable cost and size [34]. It can function as a multi-task
performing desktop computer. Raspberry Pi was formed by
UK registered charity in 2008 and later launched in 2012.
International Journal for Research in Engineering Application & Management (IJREAM)
ISSN : 2454-9150 Vol-05, Issue-04, July 2019
9 | IJREAMV05I0452005 DOI : 10.35291/2454-9150.2019.0287 © 2019, IJREAM All Rights Reserved.
Drastically, more IoT Raspberry Pi models were developed
such as Pi1, Pi2, Pi3, and Pi3 is the most recent. This
platform can be used as a brilliant solution to explore IoT
technological uses in education and this paper aims at
reviewing the applications of Raspberry Pi and Computer
Vision technology in Higher Education. Below is a typical
Raspberry Pi Model.
Educational implementation of Raspberry Pi started in
2012, and once after its initial release to nurture the basic
computational education in the UK. It is basically used to
learn coding skills, build DIY (Do-It-Yourself) or hardware
projects, automation etc. It has a set of GPIO (General
Purpose Input Output) pins to control physical computing
and IoT. It is not open source but it operates in an open
source environment, with its primary OS as Raspbian.
Raspberry Pi is used to make people learn programming
languages such as Python and it is manufactured consisting
of a three boards configured through licensed manufacturers
dealt by Newark element14 (Premier Farnell), RS
Components and Egoman. Thus, they started to sell
Raspberry Pi kits online.
B. History of Raspberry Pi:
Raspberry Pi Foundation was started in 2006, with
researchers named Eben Upton, Rob Mullins, Alan Mycroft
and Jack Lang from Computer Laboratory of Cambridge
University. The team developed various models of
prototypes from 2006 to 2008 and then finally released the
first version titled ―Raspberry Pi‖. By 2008, mobile device
processors became powerful against cheap cost. Thus the
team founded the Raspberry Pi Foundation. Raspberry Pi
has become a trending jargon for researchers in IoT
technology. Within few years, various generations of
Raspberry Pi models such as Model A to Model B has been
sold for various projects on supercomputer development,
drones, games, robotics, consoles etc.
C. Core Components of Raspberry Pi:
Raspberry Pi consists of the following core components.
Figure 2. Pictorial representation of Raspberry pi and
its components. (Courtesy: Anand Nayyar (2015),
[145])
Table Shows Core components of Raspberry Pi
Computing
Parameters
Functionality
1. Micro USB
Supply
It requires 5volts (V) per USB2.0
standard with Micro-USB type B-
interface, connecting A connector on
one side and B connector on the other.
2. SD Card Slot Required to run Operating System. It
supports Secure Digital High Capacity
and Secure Digital eXtended Capacity.
Best suited is Class 10 with 10MB/sec
speed.
3. USB Ports Each model has USB ports depending
on the models such as B with 2 and B+
with 4 ports.
4. Ethernet jack To install or update software with
Ethernet connection consisting of RJ45
Ethernet Jack supporting CAT5/6
cables for Wireless Router Connection
or any Internet connectivity sharing
device.
5. HDMI (High
Definition
Multimedia
Interface)
To connect Raspberry Pi top TV via
HDMI cable with maximum resolution
1920X1200. Evan it can stream MPEG-
4 videos.
6. Video out (RCA
Cable)
Connection to standard monitor or TV
using RCA cable and 3.5mm stereo
cable for audio facility. Less expensive
than HDMI.
7. Status Led’s 5LED to perform functions such as
ACT: (Color-Green); Show card status
when an activity done by end user.
PWR: (Color-Red): Power which is
continuously ON when Pi is ON.
FDX: (Color-Orange): If the Ethernet
connection is of full Duplex mode.
LNK: (Color-Orange): Powered ON
with Ethernet connection establishment
and Packet transfer is started.
100: (Color-Orange): LED gets ON,
when 100 Mbps connection speed is
available and is switched OFF when it
is at 10Mbps speed.
8. GPIO Pins (General Purpose Input Output): used
to connect peripheral devices to Pi kit.
It has 40 pins, 26 for digital
inputs/outputs. 9-14 for inputs/outputs.
Also supports onboard UART, SPI,
I2C.
9. CSI Camera
Connector
Raspberry Pi has (MIPI) Camera serial
interface-2 to connect small camera
modules to Broadcom BCM 2835
processor. Version 1.01 supports 4 data
lanes, each lane carrying 1Gbps
bandwidth. PHY specification is
defined to indicate the physical and
hardware layer interface between
camera and processor id fast data
exchange is needed.
10. DSI Display
Connector
To connect Pi to LCD panel through
15-pin ribbon cable. It helps to feed
graphics data to the display through the
DSI connector.
11. System on Chip ARM based processor. To support
videocore 4 GPU, fast 3D core,
openGL, Blueray, H.264 video
playback etc.
International Journal for Research in Engineering Application & Management (IJREAM)
ISSN : 2454-9150 Vol-05, Issue-04, July 2019
10 | IJREAMV05I0452005 DOI : 10.35291/2454-9150.2019.0287 © 2019, IJREAM All Rights Reserved.
Table 2. In above table mentioned outlines the various
core components of a typical Raspberry pi Model.
D. Raspberry Pi Model:
There are many different models released from 2012
till now. Each model has its unique features and
characteristics to perform and support the system.
Following are the Raspberry Pi Models available in the
market.
i. Raspberry Pi A+,
ii. Raspberry Pi 2 Model B,
iii. Raspberry Pi Model B+,
iv. Raspberry Pi 3 Model B+,
v. Computer Module,
vi. Compute Module 3 and
vii. Computer Module 3 Lite.
To look out for a Pi model to suit our requirements,
first and foremost is to gather important resources needed
for the Pi project.
Table Shows Computing Parameters of a Raspberry Pi
model
Computing
Parameters
Functionality
i. Speed Faster the system, more tasks in short
span of time.
ii. RAM/ROM For large programs, more RAM
needed.
iii. Physical Size and
Weight
It depends on the project-specific
requirements.
iv. Cost Budget needed for the project
implementation.
v. I/O facility Based on GPIO pins available.
vi. Networking If project demands internet, then
Ethernet or Wi-Fi modules be
required.
Table 3. In above table mentioned outlines the Shortlist
the model based on the computing parameters needed
for the project
Table Shows Core components of Raspberry Pi
Selection Criteria based on Computing
Parameters
Model
Low cost, Small programs, Less memory Pi Model A
Most powerful kit for DIY projects Pi Model B
Industrial Application, Strong CPU processing Pi Model
Compute
Low constrained environment, without
wireless capacity
Pi Model Zero
Low constrained environment with Wireless
networking capacity
Pi Model Zero
W
Powerful kit to suit higher level projects Pi 3 Model B+
Table 4. In above table mentioned outlines the Selection
criteria based on which the Raspberry Pi model can be
selected that suits the project.
III. MAJOR AREA OF INTEREST
A. Major topics considered for the study:
The major topics based on which the following study
has been done, are listed below:
Computer Vision Technology:
Image Processing
Augmented Reality/Virtual Reality
Robotics
3D (3Dimensional) Technology
IoT Technology:
Raspberry Pi
There are many papers available in the above
mentioned fields. However, for the purpose of this review
work and also due to constrained time limit, only around
104 papers related to the above mentioned technologies
were explored which were collected from Google Scholar,
IEEE Xplore, Academic papers, Journals, Conference
proceedings etc.
Automated Intelligence:
Automation: Creating methods/system through which
manually operated systems can be then transformed into a
machine-driven system.
Intelligence: Can be Artificial Intelligent system that
basically uses computers to find a solution to a problem that
usually needs human level understanding.
Automated intelligence is a special stream in which a
special software or hardware can be used to do the task or
the things automatically without any human intervention.
But automation alone cannot make a task very easy to be
completed. It makes it more efficient, decision making has
to be done by the system on its own without a human
instruction. There comes into the picture the ―Intelligence‖.
So, Automation can or cannot be necessarily an artificially
intelligent system i.e., Automatic system can be artificially
intelligent or it can be a system with program fix
instructions. It depends on the system specific requirements.
Artificial Intelligence is a branch of science which deals
with the computer program development using several
algorithms such as problem-solving, logical reasoning,
tackling perceptions, learning, and understanding of the
language etc. In simple words, Through Automated
intelligence, Smart systems can be transformed into
intelligent decision-based processing systems.
Papers related to IoT based Automated Intelligence are
very few and yet in the developing stage. Current research
papers are primarily on how IoT devices/ objects can be
identified, connected and managed. In this study paper, an
attempt has been made to enhance the perspective on how
IoT devices can be worked with intelligence also. Primarily,
Literature works related to IoT/Raspberry Pi use cases in
Higher Educational Sector has been explored in
combination with Computer Vision Technologies. Finally,
how Recent trends are getting evolved with Pi devices
working with Intelligence in Higher Educational Sector.
Thus, its usage in Higher Educational Institutions on
International Journal for Research in Engineering Application & Management (IJREAM)
ISSN : 2454-9150 Vol-05, Issue-04, July 2019
11 | IJREAMV05I0452005 DOI : 10.35291/2454-9150.2019.0287 © 2019, IJREAM All Rights Reserved.
promoting Automation Intelligence with Raspberry Pi
technology has been explored.
B. Exploring the IoT/Raspberry Pi Applications in Higher
Education:
Combining Bluetooth and kinetics for designing smart
conference rooms [35], Bio sensors can be used to identify
students and stored in database using Raspberry Pi like
Moodle through Wi-Fi for attendance recording [36], smart
projectors to upload and download any PowerPoint
presentations without use of laptop in each classes, even
they can store their PPT is separate servers logging into
their unique credentials [35], PPT navigation with gesture
recognition [37], Google voice assistant using Raspberry Pi
can be used for effective classroom [38], It can also be used
for lecture projection systems using NETIO application,
controlled using Wired or Wireless connections [39],
controlled through mobile applications [40], controlled and
sharing of displays with remote desktops [41], controls to
android phones in intranet structures [42], using Virtual
Network Computing protocol for remote access in
projections [43], Real-time attendance tracker and recorder
systems using RFID tags and Knowledge Base System
(KBS) and Twilio cloud platform [44], Smart dustbin can
be used inside college premises as in [45], ELAB IoT
platform with Xively for smart learning environment [46],
Concept called ―Magic Mirror: to function as an interactive
display to give updates or information through voice
commands [47], Smart security in hostels of colleges can be
made though RFID and MATLAB with Face and thumb
recognition [48], Online exam assessments using Wi-Fi with
PHP [49], voice command based controls can be used
inside the college or classroom for effective management
[50], security system using PIR sensor also with night vision
capturing facility [51], Classroom temperature monitoring,
occupancy etc [52], automated blood bank services inside
college and direct link between donor and recipient using
GSM and Raspberry Pi and message alerts at emergency
situations [53], Digital Notice board using Mobile phones
alerts through Wi-Fi for sending notices in the form of text
[54], monitoring weather inside the college environment
using temperature, air, moisture sensors etc using
ThingSpeak on Zigbee communications and send results on
mobile apps [55], Garbage monitoring using Zigbee and
GSM on GUI platform [56], Control electrical appliances
using Flask and SQLite3 on GUI platform [57], Recycling
college drinking water treatment and monitoring using
Raspberry Pi and ultrasonic sensors [58], lecture delivery
system based on IoT using Raspberry Pi to record class
lectures in audio and video formats and through FTP
protocol and Python using VNC application [59], Learning
Management System can be implemented using Raspberry
Pi [60], Smart Blackboard/Whiteboard Cleaning using
Raspberry Pi as the control module to track the eraser
position [61], Classroom timetables can be managed
effectively using RFID, GSM and Raspberry pi [62], based
on Real-Time Clock, automated college bells can be used
without any demand from human control [63], To
demonstrate an experiment using robots inside classrooms
or labs based on human computer interaction giving voice
commands to robots [65], and also RFID based Intelligent
vehicle safety surveillances be used for precautions inside
the colleges [66]. Affective computation of student
activities- where emotions are analyzed from the behavior
or emotions of the students using Face Recognition Systems
and Open CV concepts [67].
IV. DATA COLLECTION AND
CLASSIFICATION
A. Data Collection:
This study reviews literature selected with the prime
focus on IoT technology and also Raspberry Pi and its
related technologies and its inferences to higher education,
instructional design and educational technology.
Google Scholar, IEEE Xplore were the websites used
to explore and find academic papers, journals, conference
proceedings based on the keywords ―IoT and Computer
Vision Technology in Higher Education‖ and ―Raspberry Pi
and Computer Vision Technology in Higher Education‖.
The papers analyzed include mutually quantitative and
qualitative data on the educational applications of these
technologies worldwide. For the intention of this study, the
data collected resulted in identifying around 100 papers
related to possible applications of Raspberry Pi in Higher
Education. Based on the results, it has been clearly seen that
all the articles were cited by the researchers. Since these
papers were citied they can be considered as a typical
quality representation of the literature works in this area of
study.
B. Data Classification:
Thus, this paper can give the readers with the
glimpse of the recent advancements in IoT/Raspberry Pi
technology on coupling with Computer Vision principles, so
that it can form a smart or intelligent higher educational
environment promoting automated intelligence in the
Higher Educational Sector.
Articles were separately classified in terms of
quantitative and qualitative methods. Quantitative analysis
was performed to organize the papers with the categories
such as Image Processing, Robotics, Artificial Intelligence,
3Dimensions, Augmented Reality and Virtual Reality
according to their publication years, Qualitative analysis
was done by identifyting the key concept/ key terms in each
of the literature work chosen as specified in the table below
as well as described in the following paragraphs.
To provide the readers, researchers an overview of
International Journal for Research in Engineering Application & Management (IJREAM)
ISSN : 2454-9150 Vol-05, Issue-04, July 2019
12 | IJREAMV05I0452005 DOI : 10.35291/2454-9150.2019.0287 © 2019, IJREAM All Rights Reserved.
all the literature works, related to this paper are listed from
Table 5. to Table 9. Table 5 represents the list of possible
applications of IoT technology in Higher Education. Table
6 represents the possible applications of Raspberry Pi and
Augmented Reality, Virtual Reality and 3D. Table 7
represents the possible applications of Raspberry Pi and
Image Processing technology, Table 8 represents the
possible applications of Raspberry Pi and Robotics
technology, Table 9 represents the possible applications of
Raspberry Pi and its Recent developments.
V. REVIEW ANALYSIS
A. Internet of Things:
IoT technologies that were implemented in Higher
Educational Institutions (HEIs) were listed from the year
1989 to 2018. In 1989, Arthur Leuhrmann made Intellectual
Amplifier. Similarly, Janette et. al (2000), Tried using
Ubiquitous computing devices for teaching, learning,
communications along with portable technologies, With the
same context, Bryan Alexander (2004), have Ubiquitous e-
learning environment using mobile devices in higher
educational environment with wireless technologies. Later,
A Student Educational Device (SED) was prepared on
interaction with smart classroom using Wi-Fi devices
offering assignments, classes, textbooks and videos by
KevinMcREynold (2008).
In (2009) Smart e-learning system along with HCI
based on polka dots and color band pattern recognition
along with Augmented Reality using Speeded up robust
features for feature extraction techniques. Swati Vitkar
(2012), to make e-learning over cloud environment,
ubiquitous devices were tried communicating through cloud
networks. Ubiquitous Scientific Device Transfer (USDT)
using RFID tags and Context-aware ubiquitous learning
with sensing technology to analyze real world student’s
behavior in Gwo-Jen Hwang (2012), Further, in 2013,
Usage of ubiquitous computing devices such as Pad-type
appliances, interactive whiteboards, telepresentations using
cellular, NFC, Wi-Fi or Bluetooth inside the Classroom. In
the same year (2013), holography (3DHT) is used as a
learner’s stimulus agent in the smart learning environment
was introduced. Educational framework, where everything
in the educational domain is automated and also pervasive
computing related to cognitive domain. Similarly, (2013),
Gopinath et. al used PyBlueZ a python library module to
connect Raspberry Pi via HDMI to a touchscreen interface
using Bluetooth 2.1 dongle along with a calibrator program
of Xinput-calibrator using Radio Frequency Communication
Protocol. Uganda et. al in 2013, to teach science tried to
study the development of Epi- prototype as an educational
tool for using Raspberry Pi in educational sector especially
in Uganda in order to teach science, computing and
engineering. With a scenario of setting temperature sensor
and ADC convertor through GPIO pins in Epi to implement
a digital advertising board.
Valeriu Manuel IONESCU et. al (2014), based on GPIO
sensors with Oracle JDK 1.8 based on Multiple Client
Single server communication over TCP/IP and user
interface was added to support usability to secure data
transfer AES algorithm was also implemented. Folakemi et.
al(2014), tried using pervasive computing devices like
PDA’s, wearables, display boards, etc with 3D printing,
Games, Virtual Assistants, Daily Self-trackers. AR-based
games can be created which was made possible by (Boris
Pokric et.al) using Environment monitoring using ARGenie
and ekoNET platform integrating IoT solutions with AR
based games on a CoAP network. In 2015, 3Dholographic
Technology (3DHT) is used for teaching and learning
environment. Similar to Valeriu’s work, A Remote
controlled digital advertising board with Raspberry pi 3
model B dividing the system into multiple levels and
regions and multiple screens are also used for LCD display
via HDMI with backend as PHP and frontend as HTML5
bootstrap. E-learning environment with ubiquitous devices
and systems by (Matthew Montebello et. al, 2017), Majid
Bayani Abbasy et. al worked on Descriptive theoretical
research on IoT with Education forming a novel Internet of
Educational Things model (IoET) Various features of IoET
were statistically analyzed such as classroom engagement,
Creativity, e-Learning, self-learning, research opportunity,
collaboration etc. Mirjana Maksimoviƈ et. al (2017),
developed his work on Collaborative based IoT supported
education in classroom environment with IoT enabled
devices such as Interactive whiteboards, Smart document
camera. Jay R.Porter (2017), Building automation with
IoT Building Monitoring Device (IBMD) along with 3D
printers is introduced to teachers and students with various
STEM concepts using Edukas Model, Ant Colony
Optimization, Random Forest and Principal Component
Analysis. In 2017, Luka Petroviƈ et. al, Web based
application using PHP and Laravel 5.3 framework, MYSQL
along with RFID tags and QR codes on Moodle LMS
interconnection with plant watering scenario and Vuforia
task to recognize Raspberry pi system and complete IoT
schemas to test knowledge on the subject by assigning tasks
of different difficulty levels. Works related to combining AI
and AR for teaching and learning practices by Stefan A. D.
Popenici et al, Fernando Moreira et. al also worked on
Conceptual approach to enhance the learning process of
TLP in the higher education by deploying IoT, Big Data,
and Cloud Computing technologies. A Smart advertising
board by Togo Similar to Valeriu’s work, implemented
using Raspberry Pi on Wi-Fi connection via HDMI display
and GUI based webpage is created using PHP language and
MYSQL for database communication. To evaluate student’s
attention inside classroom by integrating RFID system with
indoor positioning system from teacher’s perspective by
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Rawia Bdiwi et. al (2018). Ana-Maria SUDUC and
Shahbaz et. al investigated on enhancing teaching and
learning using various IoT technologies in education. Using
EDM(Educational Data Mining) techniques with respect to
Student’s profile, Learning content customization,
Performance predictors, concept maps, courseware
construction, classify characteristics, Undesirable behavior,
Group students, Social Network Analysis, Intelligent
Systems for instructor feedback etc. to help educators in
making decisions related to students academic performances
based on the data generated with Binary Edge Detection
Method, Adaptive thresholding, Ellipse fitting method,
Pixel sampling, Running video Average. Sensor technology
to track 3D objects by Diego Lǒpez de Ipiña and QR codes
to implement in higher education as QR codes based
textbooks, geolocation based information display, using
camera name and ratings of place be displayed by Ashmit
Kohli(2018). The above analysis is based on the references
[64-95]
B. Augmented Reality:
To possibly see the applications of Raspberry Pi and
Augmented Reality, Virtual Reality and 3D, it is visible that
in the recent years various works have been done such as in
2015 and 2017, Augmented Reality has been used with
wearable devices to create teaching and learning
environments by Suvarna et.al, Dharmateja et.al, Nikhil
et.al, and Sűleyman et.al. A Model to combine
Computational Experiment along with Computational
Thinking with physical computing environments connecting
Ejs or Scratch in Raspberry Pi for an inquiry based learning
activities with STEM epistemology practices by Sarantos
(2017), Energy Monitoring system by Jannish as interfaced
with Computer Vision and AR using Node-Red tool and
Influx DB, TP-Link HS110, MQTT. A Novel Mobile
learning with Augmented Reality using 3D models with
Motion Estimation, Page Determination, Text Recognition,
Model Rendering modules with OCR to explain theoretical
concepts by Xunyu (2018). Iain (2004) tried his work on
Blind WA in combination with Cisco Systems deploying 3D
sound models, Haptic systems and Braille concepts along
with visuo-spatial concepts for vision impaired students
with easy access to learning materials. Low cost tablets for
rural students by Putjorn (2015), Video streaming with
holograms (2017). The above analysis is performed from
the references [96-103]
C. Image Processing:
While focusing on Image Processing algorithms, An
Android based educational tool (Mobi4Ed) is used to study
the Image/Image Processing algorithms with client as
Google Nexus and Server with Intel Xeon CPU and
Windows Server 2008 R2 running OpenCV with Visual
C++ by Romy Ferzli et.al (2011). Detecting laser spot with
OpenCV Library by capturing an image through webcam
installed and connected to USB port on Raspberry Pi. It can
be used against a computer based image processor system
(Aryuanto et. al (2014)), A real time, finger gesture
recognition by Geraldine (2015), Similarly in 2015, Novel
technique to control wheelchair remotely based on eye
movement for the disabled persons based on image
processing techniques at different stages such as Eye
detection and Face detection, Edge detection, color
conversion, Hough Transformed, motion detection and
object tracking on OpenCV library installed on Raspberry
Pi (Shyam et. al) , In the same year, Varsha tired to detect
the vigilance of drivers using real time image processing
and computer vision techniques with automatic alarm
systems. Qifan proposed a novel method to detect fall alarm
and abnormal inactivity of the elderly people using
Computer Vision and Image processing techniques and alert
is sent through SMS or mail through TCP/IP protocol.
Hassna in 2015, Embedded agent for Cardiac MRI images
along with Java Agent Development (JADE) platform for
multi-agent system construction. Implementing Parallel
algorithm with C-means method executed on Raspberry Pi 2
devices. Marko in 2015, proposed, a multi-camera based
remote surveillance system supporting images with 1080
pixels consisting of a 10-second refresh rate. Raw image is
taken using tool called raspiyuv. Image is compressed with
Kvazaar HEVC encoder to output HEVC image in BPG
format. These images were broadcasted over camera nodes
to terminals over Internet with WebSocket Protocol and a
client server model with image broadcasted to client
through node.js and the client receiving images in full-
duplex communication without polling. QR codes on
integration with Raspberry Pi with image processing
techniques and UVC Camera driver and Open CV library
on TFT_LCD display by P.V.Vinod et.al (2015). Digital
image processing for road sign recognition system with
embedded computing platforms such as Raspberry Pi,
Capture images with OpenCV library and Image processing
algorithms such as OpenCV Contour and Edge detection,
Optical Character Recognition run on Ubuntu MATE with
GPIO pins interaction with Camera module. k-NN for
feature classification and geometric space by Enis et. al
(2016), (Jyoti) in 2016, proposed a Wi-Fi based
photography using hand gestures, In 2016, Shaik et. al
proposed A 2- dimensional game using Raspberry Pi using
Python Tkinter, Accelerometer and Hand Gesture
controlled camera using SimpleCV library and image
processing techniques, LM358 converts analog value to
digital, thus pi uses those values to control game through
display. Pi connection to laptop with SSH protocol. The
same way Nestor(2016), Integrating Augmented Reality
with Image processing techniques such as Color Markers,
Ad-hoc Marker selection, Single/Double Marker, Fiducial
Markers Transformations on Raspberry Pi 3 Model 2B to
detect the user and display a garmet cloths and accessories
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on the person. Fire pattern recognition, face recognition
systems were developed by Dhanujalakshmi et.al, Kiran
et.al and Lubasi et.al in 2017 and 2018. Pi Book Reader by
Aditi et. al (2018). The above analysis is performed from
the references [104]-[121].
D. Robotics:
In the area of Robotics, few papers were surveyed as
mentioned below, Keerthi et. al (2015), Robotic arm is used
with Raspberry Pi with 15 GPIO pins interfaced by
controlling the joints of the robotic arm and its movements
though Android application with the Wi-Fi connection.
Morgane et. al in 2017, proposed an educational robots
such as Thymio robot has been studied to determine how its
usage been perceived by the teachers in education. A
Collaborative Robotic Educational Tool with a main
module FGPA communicates with Smart devices via
Bluetooth, with secondary module integration with Arduino
shields. External elements included such as Servo
management, motor control, memory., etc. They have also
provided scenarios to implement in educational activities
such as 4-wheeled robot arm, Educational drones,
Educational Environment, Smart Devices, Internet remote
playground, Pedro et. al in (2016). A Controlling robotic
arm SCORBOT-ER III to do several industrial tasks
automatically using Raspberry Pi model B and Image
Processing techniques along with TP-LINK TL-SC3230
type IP camera connected via D-Link DGS-1024D Ethernet
switch to the Ethernet port. With this setup, after execution
the robotic arm will hook up the given marked object using
its green bottle stopper and placing it in the exact recipient,
irrespective of its position. Real-time acquisitioned image
with overlays of lines and circles for calculating for target
position of the robotic arm to move the object by Roland et.
al (2016). But in 2015, Miller et.al proposed SCmesh
allows camera for object tracking, event detection, file
transmission to monitor environment, habitat, surveillance
etc. with SlugCam and Wi-Fi. The above analysis is
performed from the references [122]-[126].
E. IoT in Teaching and Learning:
While in the recent advancements, Nikolas et. al
(2014), proposed a model integrating Raspberry Pi and its
software to teach Physics concepts through lessons, e-
books, experiments, code examples etc. along with
PHP/MYSQL on Linux with better user interfaces possible
by JS/jQuery, AJAX, JSON etc. emphasizing on its
importance to use Pi in educating schools as well as college
students at various distributed subjects with scientific
proofs. Again, in the same year, A Clustering of 8
Raspberry Pi Boards in the form of parallel supercomputers
along with Ethernet, USB with slave nodes for power
supply and master nodes for keyboard, mouse, display
connections forming the PiBrain to educate students in
concepts of Introduction to Programming supercomputers at
a very little cost running on ARM processor. It utilizes MPI
(message passing protocols) for communication in parallel
programming models by P.Turton et.al. Rebecca et. al
(2016), examined the classroom applications of Rpi in
teaching Database management course, computer
architecture course, Computer organization course as a
prerequisite for teaching IoT technology based on active
learning projects with a computer lab setup for Rpi in a
testbed environment. Diyana et. al (2015), For a virtual
laboratory, Raspberry Pi acting like a console server for
remotely accessing communication devices following steps
such as serial port selection, Serial port configuration based
on Speed/Band rate, Number of Bits, Parity, Number of
Stop Bits and Flow control etc. thus enhancing the
education in the computer science fields and its
correspondingly related subjects. Investigation of teacher’s
reaction towards CPD (Continuing Professional
Development) programme with Raspberry Pi to enhance the
skills in circuitry, python programming, hardware
configuration etc by doing workshops with Bridge21 model
to learn Raspberry Pi. With several stages such as set up
phase, warm up, investigation stage, planning stage, create
stage, present, reflect stage to do projects and learn with
Raspberry Pi by J. R. Byrne et. al (2016). Peter Jamieson et.
al (2016), Integrating Raspberry Pi into education thus
enhancing the student’s skills in Project based learning for
ECE students by introducing Pi related courses into
curriculum against several challenges such as Open Source
project accessibility, student progress etc. Marina et. al
(2015), installing Remote Experimental Labs (RExLab) for
teaching various physics concepts in the schools using
Raspberry Pi B model and PiFace interface. User
interaction module is developed by PHP with a web server
application such as Apache or Lightpd. Heat Propagation
Experiment was conducted with main server processing
PHP pages and Pi process commands of experiments.
Communication between each controller and main server.
Microcontroller will do control commands. Online
experimentation with a setup to run Python on Raspberry Pi
and receive the results for the experiments using a camera
module with the help of IEEE 802.15.4 WSN
communication solution to locally collect sensor data. LMS
Moodle platform to experience interaction with lab and
hence calculations are done and analyzed. Finally forming
management system with first-come, first served approach
to access remote lab by Hélia et. al (2015). Using Net IO,
A projector is remote controlled from a mobile phone via
Ethernet /Wi-Fi and Raspberry Pi 2 Model B+ providing an
unified server facility by Siddharth et. al (2016). Wi-Fi
Based Projector to display content of the screen on laptop
as well as mobile and Raspberry Pi through Virtual
Network Computing by Manaswi et. al (2016) . Kazuaki et.
al (2016), To study the networking concepts, real
networking systems are to be developed. Physical
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Visualization and Physical manipulations with two
equipments for Terminal and Router, Dials and buttons and
LED lumps for physical direct manipulation collectively
connected to Raspberry Pi with wheezy OS. Low-cost
laboratory using Raspberry Pi Model B+ to promote
electrical engineering education has been developed for
courses such as Digital Signal Processing and
Instrumentation with Ethernet, Wi-Fi network connection
for real time remote measurement with mySQL for data
storage by Ciira et.al (2016).
F. Recent trends in IoT and Raspberry Pi technology:
Real time emotion based facial recognition system using
Image processing techniques to detect facial emotions such
as anger, digust, happiness, surprise and neutral with
Raspberry Pi with CMU MultiPIE database with image
collections. With remote desktop connections, and Virtual
Network Connections (VNC) and putty software by
Suchitra et. al(2016). AI based smart mirror like interface
device equipped with Raspberry Pi, microphone , speakers
to display weather updates , news etc. with speech
recognition by Vaibhav et.al (2017). Speech based robot
connected to a Raspberry Pi board through Bluetooth or
Wi-Fi and further it is operated based on the commands
with APP AMR Voice by Renuka (2017). Drones and
raspberry pi technology used to educate students on robotics
course by making them build their own drones on the
Robotics OS using any web and SSH environment like a
base station. Equipped with a single piece plastic frame,
ESC driven DC motors along with reinforced shafts and
bearers via JavaScript interfacing to SkyLine3, camera and
IR distance sensors by Isaiah et. al (2018). A wireless
notice board to display text, image or pdf files on Projector
and mobile application to convert any voice message into
text format and sent back to the board using cloud storage
by Ganesh E.N. et. al (2018). 360-degree video processing
with raspberry pi to improve teaching and learning
experiences with AR/VR technology based on scenarios
such as Teacher training video analysis, Mobile lecture
recording and Group discussions by Micheal et. al (2018).
Investigation on the applicability of Raspberry Pi as an IoT
technology in the field of education considerably in a
teaching process along with Learning Management System
(LMS), Machine Learning, To examine student social
behaviour based on the facial expression with Face
detection technology, Feature Extraction, Classification
based on facial expression. Teachers feedback on lecture
quality by Salman et. al (2019). On some specific topics
trending nowadays, few literatures have been proposed such
as: Micheal Derntl et. al(2013), proposed a dynamic topic
mining model so as to address various challenges in
Learning Analytics and Knowledge (LAK) data sets and to
explore and visually analyse D-VITA a web based browsing
tools was developed for dynamic topic models. Some other
papers were related to IoT based smart education system as
in 2018, N.Indira et. al developed a voice controlled robot
arm with Raspberry Pi using Google Alexa and Artificial
Neural Network. Alberto Pacheco et. al (2018), tried
creating a Osmotic computing architecture model for a IoT
based smart classroom is used to test deep learning models
for person detection by comparative study with cloud, fog
microserver and mobile edge computing device against
some limitations with real time responses with IoT
applications. Similarly, Katalin Ferencz et. al have deployed
a self designed Raspberry Pi based extension board to
collect sensor data with Apache Cassandra, a database
cluster to store senor data in low cost servers on web based
Glassfish application server with Pi Java based platform.
For Real time analytics, Wajdi Hajji et. al (2016) for
constructing Pi based Cloud so as to study the feasibility of
big data analytics in real-time environment both in virtual
and native real world environments with Apache Spark,
HDFA and Docker platform on MAGEEC ARM Cortex
M4-based STM32F4DISCOVERY board for measuring the
energy consumed by a Raspberry Pi model. Nicholas
Constant et. al(2018), A prototype for smart fog gateway
along with Pi and Intel Edison for Wearable Internet of
Things(w-IoT) devices in achieving smart analytics and
selective transfer to cloud for long term storage and
temporal variability monitoring. Tested over e-textile
gloves. In the same years for Deep learning, works were
done by (Ajay Kushwaha et. al). Automatic theft alert
system to alarm the owner using camera based theft
detection with machine learning using Convolution Neural
Network on Raspberry Pi, Ashwani Kumar et. al, Raspberry
Pi with Artificial Intelligence using Artificial Neural
Network (ANN) of CIFAR-10 model along with
Tensorflow to help the visually impaired people based on
image and video processing techniques, Marcelo Worsley
et.al, Multimodal Learning Analytics based literature review
works that deeply emphasize on deep learning to develop
data collection tools to find a way to use MMLA through
Raspberry Pi also.
The above analysis for subsection E. and F. is based on
the references [127]-[168]. The above accomplished data
analysis has been given in the form of table format below
for user reference.
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ISSN : 2454-9150 Vol-05, Issue-04, July 2019
16 | IJREAMV05I0452005 DOI : 10.35291/2454-9150.2019.0287 © 2019, IJREAM All Rights Reserved.
Table Shows Relative Survey on possible applications of IoT technologies in Higher Educational Institutions.
Authors Year Concept CV Technology IoT Technology Application
Arthur
Leuhrmann
1989 Intellectual Amplifier - IoT Education
Janette et. al 2000 Ubiquitous computing using theoretical
framework for teaching, learning and
communications along with portable
technologies.
- Ubiquitous
teaching and
learning
Education
Bryan Alexander 2004 Mobile based ubiquitous learning in higher
education with wireless technologies.
Sensor Technology Mobile based
learning
Higher
Education
Kevin McReynold
2008 Student Educational Device (SED)
interact with smart classroom using WI-
FI.
Human Computer
Interaction
Mobile based
learning
Smart
Classroom
Sang Hwa Lee 2009 E-learning system with interactive HCI
based on polka dot and color band
pattern recognition and AR
Human Computer
Interaction, Augmented
Reality, Feature
Extraction
E-learning Smart Learning
systems
Swati Vitkar
2012 Cloud based e-learning for ubiquitous
computing environments.
- Ubiquitous
Computing
Higher
Education
Gwo-Jen Hwang
2012 Analyse real world student’s behaviour. Sensor technology Context-aware
Ubiquitous
learning, RFID
Higher
Education
Catherine et. al 2013 Usage of ubiquitous computing devices
such as Pad-type appliances, interactive
whiteboards, telepresentations using
cellular, NFC, Wi-Fi or Bluetooth inside
the Classroom.
- Ubiquitous
learning
Higher
Education,
Classroom
Robin A.Waliker 2013 Use holography (3DHT) as a learners
stimulus agent in the smart learning
environment
3D Holography Ubiquitous
computing
Education
Jasmine Norman 2013 Educational framework where everything
in the educational domain is automated
and cognitive
- Pervasive
computing
Education
Gopinath
Shanmugasundara
m et. al
2013 PyBlueZ is python library module used to
connect Raspberry Pi using Bluetooth 2.1
along with a calibrator program of
Xinput-calibrator using Radio Frequency
Communication Protocol
Human Computer
Interaction
IoT based
teaching
-
Murat Ali et.
al[2013]
2013 Epi- prototype as an educational tool
with Raspberry Pi to teach science,
computing and engineering.
- IoT based
teaching
Education
Valeriu Manuel
IONESCU et. al
2014 Implementing custom digital advertising
board based on GPIO sensors with Oracle
JDK 1.8 over TCP/IP and AES algorithm
for security.
- Internet of
Educational
Things
-
Folakemi et. al 2014 3D printing, Games, Virtual Assistants,
Daily Self-tracker can be used with
pervasive computing devices such as
PDA’s, Wearable devices, display
boards.
3D printing, Games,
Augmented Reality,
Human Computer
Interaction
Pervasive
computing ,
Wearable
Technology
Classroom
environment
Upadhye sudeep 2015 3Dholographic Technology (3DHT) is
used for teaching and learning
environment.
3D technology for
teaching and learning
- Higher
Education
Boris Pokric et.al 2015 Environment monitoring using ARGenie
and ekoNET platform
Augmented Reality
based games
IoT, CoAP
protocol
Smart
Environment
Umakant B.
Gohatre et. al
2015 Remote controlled digital advertising
board dividing the system into multiple
levels and regions and multiple with
backend as PHP and frontend as HTML5
bootstrap
- Raspberry pi 3
Model B, Internet
of Educational
Things
Education
Matthew
Montebello
2017 Smart Learning environments with
ubiquitous computing in e-learning.
- Ubiquitous
learning
E-Learning
Majid Bayani
Abbasy et. al
2017 Descriptive theoretical research on
forming Internet of Educational Things
model (IoET) to analyze classroom
engagement, Creativity, e-Learning, self-
learning, research opportunity,
collaboration etc.
- Internet of
Educational
Things
Education
Mirjana
Maksimoviƈ
2017 Collaborative based IoT supported
education in classroom environment with
IoT enabled devices such as Interactive
Human Computer
Interaction
Internet of
Educational
Things
Higher
Education
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ISSN : 2454-9150 Vol-05, Issue-04, July 2019
17 | IJREAMV05I0452005 DOI : 10.35291/2454-9150.2019.0287 © 2019, IJREAM All Rights Reserved.
whiteboards, Smart document camera
Jay R.Porter et. al 2017 Building Monitoring Device (IBMD)
along with 3D printers for teachers and
students with various STEM concepts
using Edukas Model, Ant Colony
Optimization, Random Forest, Principal
Component Analysis
3D printers IoT based
automation
Hıgher
Education
Luka Petroviƈ et.
al
2017 Web based application using PHP
and Laravel 5.3 framework, MYSQL on
Moodle LMS interconnection with
plant watering scenario and Vuforia
task to identify pi model,complete IoT
schemas to test knowledge on the
subject.
Human Computer
Interaction
IoT, Raspberry Pi
RFID, QR for
learning
Hıgher
Education
Stefan A. D.
Popenici et al
2017 Integration of AI and Augmented Reality
to teaching and student learning practice.
Artificial intelligence,
AR
- Education
Fernando Moreira
et. al
2017 Conceptual approach to uplift the learning
process of TLP in the higher education by
deploying Big Data, IoT, Cloud
Computing technologies
- IoT based
learning, Big
Data, Cloud
Education
Toge Swatee
Pralhad et. al
2017 Smart advertising board using Raspberry
Pi on Wi-Fi and GUI based webpage is
created using PHP language and MYSQL
for database communication.
- Internet of
Educational
Things
Education
Rawia Bdiwi et. al 2018 Indoor positioning system to evaluate the
attention of students from teacher’s
perspective.
- Internet of
Educational
Things
Smart Learning
Environment
Ana-Maria
SUDUC
2018 Survey work on educating students to
enhance their learning experiences
through IoT technologies.
- IoT based learning Higher
Education
Shahbaz et. al
2018 IoT enabled classroom environment to
promote teaching and learning process
with IoT tools and technologies based on
the data generated by IoT devices.
- IoT based
teaching and
learning
Hıgher
Education
Everton Gomede
et. al
2018 Educational Data Mining with respect to
Student’s profile, Learning content
customization, Performance predictors,
etc. to help educators in making decisions
related to students academic
performances based on the generated
data.
- Educational Data
Mining, IoT
Hıgher
Education
Diego Lǒpez de
Ipiña
2018 Sensor system that can track the target
using virtual 3D environment
3D, sensor technology - Education
Ashmit Kolli 2018 QR codes based textbooks, geolocation
based information display, using camera
name and ratings of place be displayed.
Augmented Reality, QR
codes
- Smart Learning
Table 5. In above table mentioned outlines the possible applications of IoT technologies in Higher Educational
Institutions based on the work done in the past 20 years.
Table Shows Relative Survey of possible applications of Raspberry Pi and Augmented Reality, Virtual Reality and 3D.
Authors Year Concept Algorithm Application
Suvarna kumar
Gokula et. al
2015 Augmented reality in OpenSpace 3D - Augmented
Reality.
Dharmateja et. al 2015 Wifi enabled touchscreen tablet
- Virtual
Reality
Nikhil
Paonikar at.
al
2017 AR and wearable voice-controlled interface with head mounted
display.
-
Augmented
Reality, Image
recognition.
Sűleyman Nıhat
Sad et. al
2017 Ubiquituous based mobile learning with augmentation in the
classroom environment
AICHE Model,
KOLB’s Four Stage
Learning Cycle
Augmented
Reality
Martin
Gonzalez
2017 AR with Kinetics for physics and maths education. Amazon’s Voice
Service.
Kinetics,
Augmented
Reality.
International Journal for Research in Engineering Application & Management (IJREAM)
ISSN : 2454-9150 Vol-05, Issue-04, July 2019
18 | IJREAMV05I0452005 DOI : 10.35291/2454-9150.2019.0287 © 2019, IJREAM All Rights Reserved.
Sarantos
Psycharis et. al
2017 Computational Experiment and Computational Thinking with physical
computing with Pi for inquiry based learning activities with STEM
epistemology practices.
Simulation,
augmentation
methods
Augmented
Reality
Nishant Shani et.
al
2018 Pygame development using Simple DirectMedia Layer(SDL) PyODE Python
Open Dynamics
Engine
Augmented
Reality
Jannish
A.Purmaissur et.
al
2018 Energy monitoring interfaced with Computer Vision and AR using
Node-Red tool and Influx DB, TP-Link HS110, MQTT.
Open CV
Augmented
Reality, Image
Processing
Xunyu Pan et. al
2018 Mobile learning with AR and 3D models Motion Estimation, Page
Determination, Text Recognition, Model Rendering modules with
OCR to explain theoretical concepts
Open CV
Augmented
Reality,3D
models
Iain
Murray
2004
Blind WA in combination with Cisco Systems to deploy 3D sound
models, Haptic systems and Braille concepts along with visuo-spatial
concepts for vision impaired students
Multimodal learning
3D models,
haptic models
Putjorn et. al 2015 Educational learning tool for rural students with low cost tablet
computers.
OBSY sensing
device
3D modeling,
3D printing.
Prajakta et. al 2017 Video streaming using holograms Pepper Ghost
Effect
3D technology
Table 6. In above table mentioned outlines the possible applications of Raspberry Pi and Augmented Reality, Virtual
Reality and 3D based on the work done in the past 5 years.
Table Shows Relative Survey of possible applications of Raspberry Pi and Image Processing Technology.
Authors Year Concept Algorithm Application
Romy Ferzli et. al 2011
Android based educational tool (Mobi4Ed) is to study the Image/Image
Processing algorithms with Google Nexus and Intel Xeon CPU and
Windows Server 2008 R2 running OpenCV with Visual C++.
Haar face-detection
algorithm
Image
Recognition,Mo
bile Cloud
Computing
Ajantha Devi et.
al
2014 Camera-based assistive device Marker-based image
recognition
Image
Processing,
Aryuanto
Soetedjo et. al
2014 Detecting laser spot with OpenCV Library through webcam on
Raspberry Pi
Laser spot detection
algorithm, Color
threshold
Image
Processing
Geraldine et. al 2015 Real-time finger gesture recognition Color markers
OCR with image
capturing techniques
Image
Processing
Shyam Narayan
Patel et al
2015 Remote controlled wheel chair with eye movement for the disabled
persons based on image processing techniques and motion detection
and object tracking on OpenCV library installed on Raspberry Pi.
Haar Cascade
Algorithm, Hough
transform method,
Gaussian Blur filter
Image
Processing
Varsha E.
Dahiphale et. al
2015 Real time driver vigilance system with CV and automatic alarm
systems
Haar Cascade
classifiers, Frontal
face detection
system, Viola-Jones
Face detection
Image
Processing
Qifan Dong et. al 2015 Detect fall alarm and abnormal inactivity of the elderly people using
CV and Image processing techniques and SMS alert or mail through
TCP/IP protocol
Gaussian Mixture
Model, TCP/IP
Image
Processing
Hassna BENSAG
et. al
2015 Embedded agent for Cardiac MRI images with Java Agent
Development (JADE) platform for multi-agent system construction.
C-means method of
Classification
Image
Processing
Marko Viitanen
et. al
2015 A multi-camera based remote surveillance sytem using raspiyuv.
Image is compressed and broadcasted over Internet with WebSocket
Protocol through node.js with client receiving images in full-duplex
communication without polling.
WebSocket, High
Effciency Kvazaar
HEVC encoder
(HEVC), Better
Portable Graphics
(BPG)
Image
Processing
P.V.Vinod Kumar
et . al
2015 QR codes Pi with image processing techniques and UVC Camera
driver and Open CV library on TFT_LCD display.
Halftone mask Image
Processing
Enis Bilgin et. al 2016 Road sign recognition system with Pi and capture images processing
algorithms on Ubuntu MATE with GPIO pins interaction with Camera
module. k-NN.
k-Nearest Neighbor,
OpenCV Contour
and Edge detection,
Optical Character
Recognition, feature
classification and
geometric space.
Image
Processing
Jyoti Yadav et. al 2016 Wifi based photography using hand gestures with Open CV. Color markers Wearable
technology
Image
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Processing.
Shaik Riyaz
Hussain et. al
2016 2D game using Python Tkinter, Accelerometer and Hand Gesture
controlled camera using SimpleCV library and image processing
techniques, LM358 converts analog value to digital, thus pi uses those
values to control game through display.
Secure Shell (SSH)
protocol
Image
Processing
Nestor Lobo et.al 2016 AR and Image processing techniques on Raspberry Pi 3 Model 2B to
detect the user and display a garmet cloths and accessories on the
person.
Color Markers, Ad-
hoc Marker
selection,
Single/Double
Markers, Fiducial
Markers
Transformations
Image
Processing
Dhanujalakshmi
et. al
2017 Fire pattern recognition with heat signature and alter message is send
through MMS
RGB filter
cieLAB filter
Image
Processing,
Pattern
recognition.
Aditi.S. et.al 2018 Pi Book reader with image to text and text to speech conversion with
Open CV
Optical Character
Recognition
Gesture Speech
Recognition,
Feature
Extraction.
Kiran kumar et. al 2018
Face recognition system with OpenCV for attendance recording using
Java, MYSQL.
Principal
Component
Analysis, Haar
Cascade Algorithm
Image
Processing,
Feature
Extraction.
Lubasi Kakwete
Musambo et. al
2018
Facial authentication using Object detection method, Open CV and QR
codes.
Frontal face
biometric algorithm,
Haar Classifier
Object
detection,
Image
recognition.
Table 7. In above table mentioned outlines the possible applications of Raspberry Pi and Image Processing Technology
based on the work done in the past 7 years
Table Shows Relative Survey of possible applications of Raspberry Pi and Robotics Technology.
Authors Year Concept Algorithm Application
Keerthi
Premkumar et. al 2015
Robotic arm is used with Pi with 15 GPIO pins interfaced by
controlling the joints of the robotic arm and its movements though
Android application via Wi-Fi.
- Robotics
Morgane
Chevalier et. al 2016 Thymio robot to determine its by teachers in education - Robotics
Pedro Plaza et. al 2016
A Collaborative Robotic Educational Tool with a FGPA main module
and secondary integrates with Arduino shields. A 4-wheeled robot arm
for Education based drones, Smart Devices Educational Environment,
Internet remote playground.
- Robotics
Roland Szabó et.
al 2016
Robotic arm SCORBOT-ER III to do industrial tasks with Pi and
Image Processing techniques connected over Ethernet. Robotic arm
will hook up the given marked object using its green bottle stopper and
thus placing it in the exact recipient, irrespective of the position.
HSV filtering,
OpenCV libraries Robotics
Miller et. al
2015
SCmesh allows camera for object tracking, event detection, file
transmission to monitor environment, habitat, surveillance etc. With
SlugCam and Wi-fi.
SC mesh’s
Multipath routing,
DSR protocol
Eyes of Things,
Image
Processing.
Table 8. In above table mentioned outlines the possible applications of Raspberry Pi and Robotics Technology based
on the work done in the past 4 years
Table Shows Relative Survey of possible applications of Raspberry Pi and its Recent developments that can enhance
automated intelligence.
Authors Year Concept Algorithm Applicable
Area
Micheal Derntl et.
al
2013 A dynamic topic mining model in addressing the challenges with
Learning Analytics and Knowledge(LAK) based data sets. To explore
and visually analyse D-VITA a web based browsing tools was
developed for dynamic topic models.
D-VITA Learning
Analytics
Nikolaos
K.Ioannou et.
al[2014]
2014 Raspberry Pi and its software to teach Physics concepts through
PHP/MYSQL on Linux with better user interfaces possible by
JS/jQuery, AJAX, JSON etc.
- IoT based
Smart
Classroom
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P.Turton et.
al[2014]
2014 Clustering of 8 Pi Boards for parallel supercomputers along with
Ethernet, USB with slave nodes for power supply and master nodes for
keyboard, mouse, display connections forming the PiBrain to educate
students in concepts of Introduction to Programming supercomputers
using MPI message passing protocols
MPI (Message Passing
Protocol)
IoT based
Smart
Classroom
Rebecca F. Bruce
et. al[2015]
2015 Rpi in teaching Database management course, computer architecture
course, and Computer organization course as a prerequisite for
teaching IoT technology based on active learning projects with a
computer lab setup for Rpi in a testbed environment.
SSH IoT based
Smart
Classroom
Diyana
Kyuchukova et.
al[2015]
2015 Pi virtual lab to control communication devices following steps such as
serial port selection and configuration based on Speed/Band rate,
Parity, Number of Bits, Number of Stop Bits and Flow control etc. thus
enhancing CS education.
SSH IoT based
Virtual Labs
J. R. Byrne et.
al[2015]
2015 Teacher’s reaction towards CPD (Continuing Professional
Development) programme with Pi to enhance the skills in circuitry,
python programming, hardware configuration etc doing workshops
with Bridge21 model.
Bridge21 model IoT based
Smart
Classroom
Peter Jamieson et.
al[2015]
2015 Pi to promote skills using Project based learning for ECE students by
introducing Pi related courses into curriculum against several
challenges such as Open Source project accessibility, student progress
etc.
Project Based
Learning (PBL), Open
Source Software
(OSS)
IoT based
Smart
Classroom
Marina Rocha
Daros et. al[2015]
2015 Remote Experimental Labs (RExLab) to teach physics concepts in
schools using PiFace interface. UI with PHP and a web server
application such as Apache or Lightpd. Heat Propagation Experiment
was conducted with main server processing PHP pages and Pi process
commands of experiments.
- IoT based e-
Lab
Hélia Guerra et.
al[2015]
2015 Pi based remotely controlled lab with online experiments running
Python on Pi and results are collected locally .LMS Moodle platform
interact with lab and hence calculations are done and analyzed. Finally
forming management system with first-come, first served approach to
access remote lab.
IEEE 802.15.4 IoT based e-
Lab with
Learning
Management
System
O.Dürr et. al 2015 Real time detection of face recognition using Convolution neural
network on Raspberry Pi using Fisherface classifier.
Fisherface classifier Real time
face
recognition
Siddharth Arun et.
al[2016]
2016 Using Net IO, projector is remote controlled from a mobile phone via
Ethernet /Wi-Fi and Raspberry Pi 2 Model B+ providing an unified
server facility.
- Augmented
Reality
Manaswi
R.Ganbavale[201
6]
2016 Wi-Fi Based Projector to display content on laptop as well as mobile
and Raspberry Pi through Virtual Network Computing
Remote Frame Buffer,
remote Desktop
Protocol
Augmented
Reality
Kazuaki
Yoshihara [2016]
2016 Real networking systems are developed with two equipments for
Terminal and Router, Dials and buttons, LED lump .,etc. for physical
direct manipulation collectively connected to Raspberry Pi with
wheezy OS.
- Simulation
environments
Ciira wa MAINA
et. al[2016]
2016 Pi Laboratory to promote electrical engineering education for courses
such as Digital Signal Processing and Instrumentation with Ethernet,
Wi-Fi network connection for real time remote measurement with
mySQL for data storage.
- Real time
analytics
Suchitra et.
al[2016]
2016 Real time emotion based facial recognition system using Image
processing techniques to detect facial emotions with Raspberry Pi with
CMU MultiPIE database with image collections. With VNC
Haar cascade, (26
facial points
extracted), features
extraction using
Active shape
Model(ASM), Viola-
jones face detection
and Adaboost
classifier
Real time
face
recognition
M.Karthikeyan et.
al[2016]
2016 To identify the real time object in the surveillance, army, traffic
monitoring, human machine interaction with Pi and Camera module
with robot motors
Foundation
subtraction,
Movement templates
Real time
object
detection
Wajdi Hajji et. al 2016 Constructing Pi based Cloud for feasibility study of the real time big
data analytics in virtual and native real world environments with
Apache Spark, HDFA and Docker platform on MAGEEC ARM Cortex
M4-based STM32F4DISCOVERY board to measure the consumed
energy of a Raspberry Pi technology.
- Real time Big
Data
Analytics
C.Balasubramaniy
an et. al
2016 IoT enabled Air Quality monitoring system with Raspberry Pi using
Grove sensors with ThingSpeak Cloud database with MATLAB code
to generate alert message and sent to TWITTER for real time
monitoring and analysis.
- Real time
Analytics
Vaibhav Kanna
et. al[2017]
2017 AI based smart mirror like interface device equipped with Raspberry
Pi, microphone, speakers to display weather updates, news etc. with
speech recognition
- Ambient
Intelligence
Renuka
P.Kondekar et. al
[2017]
2017 Speech based robot connected to Raspberry Pi through Bluetooth or
Wi-Fi and further it is operated based on the commands with APP
AMR Voice.
Mel Frequency
Spectral Coefficient
algorithm, ANN
Artificial
Intelligence,
Robotics
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Nicholas Constant
et. al
2017 A prototype for smart fog gateway along with Raspberry Pi and Intel
Edison for Wearable Internet of Things(w-IoT) devices for selective
transfer to cloud for long term storage and temporal variability
monitoring resulting in achieving smart analytics with Tested over e-
textile gloves.
- Fog
Computing,
Edge
Computing
Badraddin Alturki
et. al
2017 Distributed data analytics to collect and analyze data in edge or fog
devices with a hybrid approach to collect data locally and apply data
fusion techniques on resource constrained devices and send to cloud
for processing.
- Distributed
data
analytics, Fog
Computing
Katalin Ferencz
et. al
2018 Self designed Raspberry Pi based extension board to collect sensor data
with Apache Cassandra a database cluster to store senor data in low
cost servers on web based Glassfish application server with PiJava
based platform
Sensor data
acquisition and
visualization
Sensor data
analytics
Rabindra K. Barik
et. al
2018 Fog computing based framework called FogLearn to apply K-means
cluster in Ganga River Basin Management , also in detecting disease
such as diabetes mellitus in patients by using swart watches worn by
patients
DNN Fog
Computing
Isaiah Brand et.
al[2018]
2018 Pi and Drones to educate students on robotics course by making them
build their own drones on the Robotics OS using any web and SSH
environment like a base station along with reinforced shafts and
bearers via JavaScript interfacing to SkyLine3, camera and IR distance
sensors.
MultiWii Serial
Protocol
Robotics
Alberto Pacheco
et. al
2018 Osmotic computing architecture model for a IoT based smart
classroom is used to test deep learning models for person detection by
comparative study with cloud, fog microserver and mobile edge
computing device against some limitations with real time responses
with IoT applications.
DNN inference model Osmotic
Computing
Ganesh E.N. et.
al[2018]
2018 A wireless notice board to display on Projector and mobile application,
convert any voice message into text, sent back to the board using
cloud.
- IoT based
Smart
Classroom
Micheal S.
Feurstein [2018]
2018 360-degree video processing with pi to improve teaching and learning
experiences with AR/VR technology based on Teacher training video
analysis, Mobile lecture recording and Group discussions
- IoT based
Smart
Classroom ,
Augmented
Reality
Ajay Kushwaha
et. al
2018 Automatic theft alert system to alarm the owner using camera based
theft detection with machine learning using Convolution Neural
Network on Raspberry Pi
CNN Deep
Learning,
Smart
Security
Mahmut Taha
Yazici et. al
2018 Edge computation based machine learning both training and inference
on Raspberry Pi with a comparative study on their algorithms
Random Forest,
Support Vector
Machines, Multi-
Layer Perception
Machine
Learning
N.Indira et. al 2018 A voice controlled robot arm with Raspberry Pi using Google Alexa
and Artificial Neural Network.
Deep Learning IoT based
Smart
Classroom
Ashwani Kumar
et. al
2018 Raspberry Pi with Artificial Intelligence using Artificial Neural
Network (ANN) of CIFAR-10 model along with Tensorflow to help the
visually impaired people based on image and video processing
techniques
CNN Image
Processing,
Artificial
Intelligence
Delia Velasco-
Montero et.al
2018 Through shallow learning, Real time Deep Neural Networks inference
have been comparatively studied based on power consumption,
throughput, precision etc. on Raspberry Pi
- Deep
Learning
Marcelo Worsley 2018 Multimodal Learning Analytics based literature review works that
deeply emphasize on deep learning to develop data collection tools to
find a way to use MMLA through Raspberry Pi also.
Deep Learning Multimodal
Learning
Analytics
Salman Mahmood
et. al[2019]
2019 Raspberry Pi as an IoT technology in teaching process along with
Learning Management System (LMS), Machine Learning, Examine
student social behaviour, lecture quality with Feature Extraction, Face
detection technology, Classification on facial expression.
Teacher Student
framework (TSI).
IoT based
Smart
Education
Table 9. In above table mentioned outlines the possible applications of Raspberry Pi and its Recent trends for
automated intelligence based on the work done in the past 7 years
VI. REVIEW RESULTS
A. Quantitative Results:
From the 34 articles selected on ―IoT in Higher
Educational Applications‖, 7 papers are related to
Teaching, 8 papers are related to Learning, 6 are related to
College Administration, 6 are related to College Premises
and 7 papers are related to Classroom implementation in
general keyword search on ―IoT in Higher Education‖.
Following figure (Fig 3.) defines more papers were related
to Learning outcomes of the students. (Fig 4.) depicts that
Out of 31 articles, papers were from 1989 to 2018, with
highest paper works collected from the publication year
2017. Out of 33 Articles, papers were ranging from the
publication year 2011-2018, with the higher number of
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papers from 2015 related to possible IoT applications in
higher education as in (Fig 5.), Fig 6. shows the recent
advancements in this field, with more number of papers
from Real time analytics, second highest with Artificial
intelligence, Machine Learning and IoT based Classroom
setup and less number of papers in Fog/Edge Computing,
Simulation Environments, Robotics, Osmotic Computing,
Sensor data analytics, etc.
IoT in Higher Educational Applications
0
1
2
3
4
5
6
7
8
9
Tea
chin
g
Lear
ning
Col
lege
Adm
inis
trat
ion
Pre
mis
es
Cla
ssro
om
IoT in Higher Educational
Applications
Figure 3. Articles on “IoT Technology in Higher
Educational Applications” classified by Implemented
Region.
IoT possible applications in Higher Educational Institutions
0
1
2
3
4
5
6
7
8
9
1989
2000
2004
2008
2009
2012
2013
2014
2015
2017
2018
No. of Years [1989-2018]
No
of
Art
icle
s =
31
IoT possible applications in
Higher Educational
Institutions
Figure 4. Articles on “Possible Applications of IoT
Technology in Higher Education” classified by
Publication Year.
0
1
2
3
4
5
6
7
8
2011 2014 2015 2016 2017 2018
Years [2011-2019]
No
. o
f A
rtic
les =
33
Augmented Reality
Image Processing
3D technology
Robotics
Figure 5. Articles on “Raspberry Pi and Computer
Vision Technology in Higher Education” classified by
Publication Year.
0
1
2
3
4
5
6
2013 2014 2015 2016 2017 2018 2019
No. of Years [2013-2019]
No
. o
f A
rti
cle
s =
35
Artificial Intelligence Robotics Sensor data analytics
Fog/Edge Computing Real time analytics IoT based classroom education
IoT based Labs Learning Analytics Augmented Reality
Simulation Environments Ambient Intelligence Osmotic Computing
Figure 6. Articles on “Recent trends in Raspberry Pi
and Computer Vision Technology in Higher
Education” classified by Publication Year and
Trending technologies.
B. Qualitative Results:
The articles reviews are under wide range of
topics and categories which are related to IoT, Raspberry
Pi and Computer Vision in Higher Educational Institutions.
The keywords from the articles are as follows:
Deep Learning, Convolution Neural Network(CNN),
Random Forest, Support Vector Machines, Multi-Layer
Perception, DNN inference model, MultiWii Serial
Protocol, Sensor data acquisition and visualization, Mel
Frequency Spectral Coefficient algorithm, Artificial
Neural Network (ANN), Foundation subtraction,
Movement templates, Haar cascade, Viola-jones face
detection, Adaboost classifier, features extraction using
Active shape Model(ASM), (26 facial points extracted ),
Remote Frame Buffer, remote Desktop Protocol, Project
Based Learning (PBL), Open Source Software (OSS),
Fisherface classifier, MPI (Message Passing Protocol),
SSH, D-VITA, HSV filtering, OpenCV libraries, Bridge21
model, Frontal face biometric algorithm, Principal
Component Analysis, RGB filter cieLAB filter, Color
Markers, Ad-hoc Marker selection, Single/Double
Markers, Fiducial Markers Transformations, k-Nearest
Neighbor, OpenCV Contour and Edge detection, Optical
Character Recognition, Halftone mask, WebSocket, High
Efficiency Kvazaar HEVC encoder (HEVC), Better
Portable Graphics (BPG), C-means method of
Classification, Gaussian Mixture Model, Viola-Jones Face
detection, Laser spot detection algorithm, Color threshold,
Marker-based image recognition, Haar face-detection
algorithm, Pepper Ghost effect, OBSY sensing device,
Multimodal learning, PyODE Python Open Dynamics
Engine, Amazon’s Voice Service, AICHE Model, KOLB’s
Four Stage Learning Cycle, Internet of Educational
Things, IoT based learning, Ubiquitous computing,
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Pervasive Computing, Context-aware Ubiquitous learning,
RFID, Drones, Real time gesture recognition, Learning
Analytics, Educational Data Mining, Osmotic Computing,
Fog Computing, Edge Computing, Ambient Intelligence,
PHP, MYSQL, Wearables, QR codes, Java Agent
Development Environment (JADE), Cloud, Holography,
3D printing, Games, Virtual Assistants, Daily Self-tracker
can be used with pervasive computing devices such as
PDA’s, Wearable devices, display boards.
VII. LIMITATIONS
Due to unnatural time limit, merely 150 articles were
considered for the review work. Articles available in Non-
English languages have not been considered for this study.
As the investigation was conducted only on Google
Scholar, IEEE Xplore, International Conferences, and
Journals, Few of the Academic databases were not
included for the study.
VIII. DISCUSSION AND FINDINGS
Looking into the review results, the rise of Raspberry Pi
and Computer Vision in Higher Education Sector can be
visible from the past 20 years until present. In the IoT
impacts on Higher Education is huge driving education
into a smart and intelligent educational environment. As
we have seen various works related to IoT based higher
educational development. Different literature works were
collected and reviewed separately for Raspberry Pi
applications in higher education and also its application in
higher education specific to Computer Vision Technology
such as Augmented Reality, Virtual Reality and 3D, Image
Processing technology and Robotics.
From this study, it reveals that a huge set of examples
are available in each corner of the higher educational
sector. Take these scenarios and choosing from the recent
trending technologies, these systems have a greater scope
of making new educational transformation in the real time
future. Keeping this as the first step, we can explore and
build more projects, products and systems by integrating
two or more technologies to form a highly potential
education system. It is just a start in deriving new
invaluable sights through deeper analysis of the huge
number of educational data using Data Mining and
Analytics, Artificial Intelligence and Machine Learning
Algorithms, Deep Learning Algorithms, Educational Data
Mining (EDM) and Learning Analytics and
Knowledge(LAK), Fog/Edge Computing, Osmotic
Computing, Sensor Data Analytics, Predictive Analytics.
Thus formed IoT generated big data can be helpful in
forming a more efficient, better operational and automated
intelligent educational infrastructure can be deployed. The
study reveals that only less number of papers are available
on Real time analytics, second highest with Artificial
intelligence, Machine Learning and IoT based Classroom
setup and less number of papers in Fog/Edge Computing,
Simulation Environments, Robotics, Osmotic Computing,
Sensor data analytics. Thus, it results in enhancing the
performances of the educational workflows in the coming
future.‖
IX. CONCLUSION
With the huge technological demand in the Higher
Education Sectors, the need to grow along with the
technological advancements have given rise to look for a
solution i.e., IoT Technology. It is familiar that IoT usage
in the educational history dates back to nineteenth Century.
But its potential reformation in the past few decades has
proven it to promote a promising future in the
technological upliftment of the Higher Education Sectors.
Narrowing down IoT technology in combination with
Computer Vision techniques will create a broader view on
how the surrounding environment is managed to evolve
with the visual signs resulting in innovative use cases for
the Higher Educational field.
In this review, we have gone through plenty of use cases
for the major domains under Computer Vision technology
such as Augmented Reality, Image Processing, Robotics,
3D technology. Thus, the results reveal that not only more
works were related to Image Processing techniques in the
past ten years, but also on possible applications of IoT
technology in the Higher Educational Framework. These
above mentioned articles were highest during the past 3
years. Since these papers were taken from academic
journals and research databases, this forms it to be a good
and valuable proof on usage of IoT and Computer Vision
in the Higher Education Sector, making it to form a huge
educational transformation.
In this review paper, further we have discussed various
applications of Raspberry Pi and Computer Vision
technology in the Higher Educational Institutions
especially of Pattern Recognition principles such as Face
Recognition, Object detection, Gesture recognition and for
Robotics. There were examples with Robotics arm
implementation for teaching and learning, and for 3D
technology use cases on deploying 3D models for teaching,
learning, holography, models for visually impaired., etc. In
the recent years, few publications were available on IoT
based classrooms, labs, teaching and learning. Thus, from
the above mentioned results, it can be concluded that future
classrooms will become an IoT connected environment for
both the staffs and the students.
We surprisingly came across various new 10 domains
such as Learning Analytics and Knowledge(LAK),
Educational Data Mining(EDM), Fog and Edge
Computing, Osmotic Computing, Sensor Data Analytics,
Predictive Analytics, Artificial Intelligence, Machine
International Journal for Research in Engineering Application & Management (IJREAM)
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24 | IJREAMV05I0452005 DOI : 10.35291/2454-9150.2019.0287 © 2019, IJREAM All Rights Reserved.
Learning, Deep Learning models and Simulation
environments with the trend set of forming the concept of
invoking automation and intelligence in the HEI
environments emphasizing on the importance on the field
of analytics, mining techniques related to education, node
level computations, sensor level analytics .,etc. This review
reveals that these with state-of-the art technologies, and
new trending technologies related to IoT and CV can help
the researchers, developers, and scientists based on this
area of interest to promote automated intelligence in the
HEI environment.
These summarized applications of Raspberry Pi in
forming a smart and intelligent educational infrastructure
can be considered as a starting guide to come up with new
ideas. This review paper limited to Computer Vision
technology will help those who are interested with IoT
based CV system deployment. The future of IoT concept
with CV seems to be more amusing as more connecting
things will start to communicate with less human
interaction bringing a shift in the way the higher education
and its environment will thrive to survive.
REFERENCES
[1] Shahla Gul, Muhammad Asif, Shahbaz Ahmad, Mahammad Yasir,
Muhammad Majid, M.Sheraz Arshad: A Survey on Role of
Internet of Things in Education. International Journal of
Computer Science and Network Security, vol. 17, no. 5, 2017.
[2] Keyur K Patel, Sunil M Patel, T.: Internet of Things- IoT,
Definition, Characteristics, Architecture, Enabling Technologies,
Application and Future Challenges. IJESC International Journal of
Engineering and Computing, vol. 6, Issue no. 5, ISSN: 2321
3361,2016.
[3] Nidhi Shukla, Sandhya Mishra.: Internet of Things(IoT): A Future
Vision On MODERN SOCIETY. International Journal of
Engineering and Techniques. Vol. 4, issue. 3, pp. 12-15, 2018.
[4] Tikhomirov V., Dneprovskaya, N., Yankovskaya E.: Three
Dimensions of Smart Education, 2015.
[5] Fitzgerald, R., A Smart Teaching System, 2013.
[6] Moazeni, S., Pourmohammadi, H. Smart teaching quantitative
topics through VARK learning styles model. IEEE Integrated
STEM Education Conference, 2013.
[7] Tilton., W., Adult professional development: can brain-based
Teaching strategies increase learning Effectiveness? Fielding
Graduate University, ProQuest, 2011.
[8] Tredowski, TN., Woods, AM.: Seven Student-Centered Principles
for Smart Teaching in Physical Education, Journal of Physical
Education, Recreation & Dance. vol. 86, no. 8, pp. 41-47, 2015.
[9] Zhu, ZT, Yu, MH, Riezebos, P.: A research framework of smart
education. Springer Open Online Published, Smart Learning
Environment (2016).
[10] T.Priestley.,: Everything is hyper connected in the internet of
things, 2014.
[11] Gwak.D.: The meaning and predict of Smart Learning. Smart
learning Korea Proceeding, Korean e-Learning Industry
Association, 2010.
[12] Kim T., Cho J.Y., Lee B.G.: Evolution to Smart Learning in Public
Education: A Case Study of Technologies for Networked Learning.
IFIP Advances in Information and Communication Technology,
Springer, Berlin, 2013.
[13] Kim, S., Song, S.M., & Yoon, Y.I.: Smart learning services based
on smart cloud computing- Sensors, vol. 11, no. 8, pp. 7835-
7850, 2011.
[14] Shivaraj Kumar T.H, Sriraksha T.A, Noor U Saba.: An IOT Based
Secured Smart e-Campus, International Journal of Humanities and
Social Science Invention, vol. 6, no. 3, pp. 88-93, 2017.
[15] Evigeny Osipov, Laurynas Riliskis: Educating Innovators of
Future Internet of Things.
[16] Shrinath, Vıkhyath, Shivani, Sanket, Shruti: IOT Application in
Education, International Journal of Advanced Research and
Development, vol. 2, no. 6, 2017. 17. Mercatus, 2015.
[17] Chang. C.H.: Smart Classroom roll caller system with IOT
architecture, Proceedings of 2nd International Conference, IBICA,
pp. 356-360, 2011.
[18] A.Alghamdi, S.Shetty: Survey toward a smart campus using
internet of things. Proceedings 2016 in IEEE 4th International
Conference, Future Internet of Things and Cloud, FiCloud, 2016.
[19] Kusmin, M., Laanpare, M., Saar, M, Rodriguez-Triana, M.J.: Smart
Schoolhouse as a Data-Driven Inquiry Learning Space for the Next
Generation of Engineering. IEEE EDUCON – Global Engineering
Education, 2017.
[20] Bajaj V., & Pachori, R. B.: Detection of human emotions using
features based on the multiwavelet transform of EEG signals.
Intelligent Systems Reference Library, 2015.
[21] Cornel. C, & Ph.D.: The Role of Internet of Things for a
Continuous Improvement in Education, vol. 2, no. 2, pp. 24-31,
2015.
[22] Mȁkitalo-Siegi, K. Zottmann, J. Kaplan, F & Eds, F.F.: The
Classroom in the Future: Orchestrating Collaborative Learning
Spaces, 2012.
[23] Selinger, M., Sepulveda, A., Buchan J.: Education and the internet
of everything: How ubiquitous connectedness can help transform
pedagogy. Education IoE Whitepaper, Cisco, 2013.
[24] Piccianio, A.G.: The evolution of big data and learning analytics in
American Higher Education, Journal of Asynchronous Learning
Networks, vol. 16, no. 3, pp. 9-20, 2012.
[25] Debasis Bandyopadhyay, Jaydip Sen.: Internet of Things –
Applications and Challenges in Technology and Standardization,
2011.
[26] A.Rythivaara.: Collaborative classroom management in a co-taught
primary school classroom. vol. 53, pp. 182-184, 2012.
[27] Chew. C.B.: Sensors enables Smart Attendance System using Nfc
and Rfid technologies. International Journal for New Computer
Architecture and their Applications. vol. 5, no. 1, pp. 19-28, 2015.
[28] Uskov V.L., Bakken J.P., Pandey A.: The Ontology of Next
Generation Smart Classrooms. İn: L.Uskov V., Howlett R., Jain
L. (eds) Smart Education and Smart e-Learning, Smart
Innovation, Systems and Technologies, vol. 41, Springer, 2015.
[29] Mary Catherine, O’Connor.: RFID Takes Attendance – and Heat
Retrieved (2005).
[30] Hung-Wei Chen: The Study of RFID Based Roll-Call and
Entrance Control System. Master thesis, Asia University,
National Chung Cheng University (2011).
[31] Andrew W. Wright.: RFID Classroom Management System.
Master thesis, Industrial and Manufacturing Engineering,
California Polytechnic State University (2011)
International Journal for Research in Engineering Application & Management (IJREAM)
ISSN : 2454-9150 Vol-05, Issue-04, July 2019
25 | IJREAMV05I0452005 DOI : 10.35291/2454-9150.2019.0287 © 2019, IJREAM All Rights Reserved.
[32] R.Josѐ, H.Rodriguez, N.Otero: Ambient Intelligence: Beyond the
Inspiring Vision. Journal of Universal Computer Science(J.UCS),
pp-1480-1499 (2010).
[33] https://www.raspberrypi.org/help/what-%20is-a-raspberry-pi/
[34] Dhaval Chheda et al.,: Smart Projectors using Remote Controlled
Raspberry Pi, International Journal of Computer Applications,
(2013).
[35] Arunkumar.L., Arun raja.: Biometrics Authentication Using
Raspberry Pi. International Journal For Trends In Engineering
Technology (2015).
[36] Ansari Imran, Bhatkar Firoz, Chaudhary Faisal, Khan Shahrukh,
Prof. Mubashir Khan.: Smart Lecture Delivery Using Raspberry Pi.
International Journal of Advanced Research in Computer
Engineering Technology (2015).
[37] Ch Sai Sudeep Reddy et al.: Smart Classroom using Raspberry Pi.
International Journal of Pure and Applied Mathematics, vol. 120,
no. 6, pp- 1871-1884(2018).
[38] Praneeth MSSR, Nikhilesh M., Tahrun Reddy.P., Priya B.K.:
Upgradataion of Wired to Wireless Projector using WLAN.
Recent Researches in Electrical Engineering, Dept of ECE,
Amrita College of Engineering
[39] Jaya Bharatho chintalapathi, Srinivasa Rao T.Y.S.: Remote
Computer Access through Android mobiles. IJCSI International
Journal of Computer Science Issues, vol. 9, no. 3 (2012).
[40] Suhashini Chaurasia.: Implementation of Remote Desktop Utility
Teamviewer. IOSR Journal of Computer Engineering, ISSN:2278-
0061,p-ISSN: 2278-8727
[41] Priyadharshini Raskarl, Seja Patel.: Virtual Network Computing- A
Technique to Control Android phones Remotely: International
Journal of Engineering and Computer Science ISSN:2319-7242,
vol. 3, issue. 2 (2014).
[42] Ajit Kotkar, Alok Nalawade.: Android Based Remote Desktop
Client. International Journal of Innovative Research in Computer
and Communication Engineering, vol. 1, issue. 2 (2013).
[43] Rashmi A Kalje, Prof. S.P.Kosbatwar.: Android Based Remote
control of Mobile Devices Using VNC System. IJCSIT
International Journal of Computer Science and Information
Technologies, vol. 5, no. 4 (2014).
[44] ChinjuPaul, Amal Ganesh, Sunitha C.: An IoT Based Smart
Environment for Classrooms, International Journal of Pure and
Applied Mathematics, vol. 119, no. 16, pp-239-245 (2018).
[45] Amrutha P.V., Chaithar B.N., Kavyashree D.R., Pooja S Kumar.:
IoT Based Waste Management Using Smart Dustbin.
[46] Konstantin Simić, marijana Despotovićr- Zrakić, Zivko Bojović.,
Branislav Jovanić, Đorde Knežević.,A Platform for a Smart
Learning Environment. Facta Universitatis, vol. 29, no. 3, pp-407-
417 (2016).
[47] Suryansh Chandel, Ashay Mandwarya, S.Ushasukhanya.:
Implementation of Magic Mirror using Raspberry Pi 3.
International Journal of Pure and Applied Mathematics.
[48] Sneha Sonar, Rajendra Patil.,: Hostel In Out Management and
Monitoring System Using RFID, Face and Thumb Recognition.
İnternational Journal of Innovation Research in Science,
Engineering and Technology, vol. 5, issue. 7, pp-12659- 12668
(2016).
[49] Karishma Sudharshan kalike, Vargantwar M.R.: Raspberry Pi
Based Online Examination System. Vol. 5, issue. 9, pp- 16547-
16552.
[50] Surinder kaur, Sanchit Sharma, Utkarsh Jain, Arpit Raj.: Voice
Command System Using Raspberry Pi. Advanced Computational
Intelligence: An International Journal (ACII), vol. 3, no. 3 (2016).
[51] Anabham bhavani, Tulasi Jami, Gajjala Ashok.: Low Cost Smart
Security with Night Vision Capability Using Raspberry Pi and
PIR Sensor. International Journal of Advanced Technology and
Innovative Research, vol. 8, issue. 21, pp-4053-4056 (2016).
[52] Kristian Hentschel, Dejice jab, Jeremy Singer, Matthew
Chalmers.,:Supersensors: Raspberry Pi Devices for Smart
Campus Infrastructure.
[53] Ashlesha C. Adsul., V.K.Bhosale.,: Automated Blood Bank System
using Raspberry Pi. International Journal for Scientific Research &
Development, vol. 5, issue 9, pp-1045-1047 (2017).
[54] Vinod B.Jadhav, Tejas S.Nagwanshi, Yogesh P.Patil, Deepak R
Patil.,: Digital Notice Board Using Raspberry Pi. International
Research Journal of Engineering and Technology, vol. 3, issue. 5,
pp. 2076-2079 (2016).
[55] Gaurav Jadhav, Kumal Jadhav, Kavita Nadlamani.: Environment
Monitoring System using Raspberry Pi. International Research
Journal of Engineering and Technology, vol. 3, issue. 4, pp. 1168-
1172 (2016).
[56] Harshita A.GawadurajKadam, Dona Jain, Nirav Patel.: An IOT
based Dynamic Garbage Level Monitoring System using Raspberry
Pi. International Journal of Engineering Research and Application,
vol. 7, issue. 7, pp-30-34 (2017).
[57] V.Hemanthraj, N.Dhamodharan, V.Mohankumar.: IoT Based
College Automation with Smart Classroom Integration Using
Raspberry Pi. Vol. 5, issue. 6, pp. 2120-2125 (2017).
[58] Prof.Amruta Nikam, Madhuri Urgunde, Priyanka Shinde.: IoT
Based Automatic Water Recycling Using Raspberry Pi.
International Journal of Innovations & Advancement in Computer
Science. Vol. 7, issue. 3, pp. 386-388 (2018).
[59] Shaik Yusuf Bahi Saheb, Gharade Abdul Mueed, Haider
Choudhary, Ziauddin Ansari.: IoT Based Lecture Delivery System
Using Raspberry Pi. International Journal of Advanced Research in
Computer Engineering & Technology, vol. 6, issue. 3, pp. 245-247
(2017).
[60] Khaleel Mershad, Pilar Wakim.,; A Learning Management System
Enhanced with Internet of Things Application. Journal of Education
and Learning, vol. 7, no. 3, pp. 23-40 (2018).
[61] Gu Yichen, Li Lan.: Smart Black/Whiteboard, Final Report (2016).
[62] Kanvitha.P., Sirisha., M.Kalpana Chowdary.,: Timetable
Management System Using Raspberry Pi and RFID. International
Research Journal of Engineering and Technology, vol. 4, issue. 9,
pp. 99-102.
[63] Devakate Atul Balaso, Londhe Sarita Dhanaji, Patil Dipali Netaji.:
Automatic College Bell Using Raspberry Pi. International Journal
of Emerging Trends in Science and Technology, vol. 3, issue. 6, pp.
4086-4089 (2016).
[64] N.Indira.: Human Interaction Artificial Intelligence by Using
Raspberry Pi. International Journal of Advanced Engineering and
Research Development, vol. 5, issue. 3, pp. 410-417 (2018).
[65] Arhikeyan.V.K., Kirubhasudha.N., Kiruthika.S., Monisha.T.,
Dr.Sivasankari.B.: Raspberry Pi Based Intelligent Autonomous
Campus Mobility Services, International Journal of Scientific
Research in Computer Science, Engineering and Information
Technology, vol. 3, issue. 1, pp. 1457-1461 (2018).
[66] Kalyani.A., Premalatha.B., Ravi Kiran.: Real-Time Emotion
Recognition from Facial Images using Raspberry Pi. International
Journal of Advanced Technology and Innovative Research, vol. 10.
İssue. 1, pp. 0013-0016 (2018).
[67] Arthur Luehrmann.: Technological innovations in higher
education: Are they possible?.Journal of Computing in Higher
Education. Vol.1, Issue. 1, pp. 82-92 (1989).
[68] Janette R. Hill, Thomas C. Reeves, Heike Heidemeier.:
Ubiquitous Computing for Teaching, Learning and
International Journal for Research in Engineering Application & Management (IJREAM)
ISSN : 2454-9150 Vol-05, Issue-04, July 2019
26 | IJREAMV05I0452005 DOI : 10.35291/2454-9150.2019.0287 © 2019, IJREAM All Rights Reserved.
Communicating: Trends, Issues & Recommendations. Copyright
2000.
[69] Bryan Alexander. Going Nomadic: Mobile learning in Higher
Educarion. EDUCAUSE Review (2004).
[70] 71.Kevin McReynolds.: Pervasive Computing in Higher Education:
A Smart Classroom Model. The technology Interface Journal/ Fall
(2008).
[71] Sang Hwa Lee, Junyeong Choi, and Jong-Il Park.: Interactive E-
Learning System Using Pattern Recognition and Augmented
Reality, IEEE Transactions on Consumer Electronics, Vol. 55,
No. 2, (2009).
[72] Catherine Marinagi.: Employing Ubiquitous Computing Devices
and Technologies in the Higher Education Classroom of the
Future. Vol. 73, pp. 487-494 (2013).
[73] Robin A.Walker.: Holograms as Teaching Agents. 9th
International Conference on Display Holography, IOP
Publishing, Journal of Physics: Conference Series (2013).
[74] Jasmine Norman.: Impact of Pevasive Computing in Education.
International Journal of Education and Learning, vol. 2, no. 2, pp.
39-48 (2013).
[75] Gopinath Shanmuga Sundaram, Bhanuprasad Patibandal, Harish
Santhanam, Sindura Gaddam, Vamsi Krishna Alla, Gautham Ravi
Prakash, Shiva Chaitanya Vıshwakarma Chandracha, Sindhu
Boppana and James M. Conrad, ―Bluetooth Communication using
a Touchscreen Interface with a Raspberry Pi‖, IEEE, 2013.
[76] Murat Ali, Jozef Hubertus Alfonsus Vlaskamp, Nof Nasser Eddin,
Ben Falconer and Colin Oram, ―Technical Development and
Socioeconomic Implications of the Raspberry Pi as an
educational tool in Developing Countries‖, 5th Computer Sicnec
and Electronic Engineering Conference (CEEC), 2013.
[77] Valeriu Manuel IONESCU, Florin SMARANDA, Adrain- Viorel
DIACONU, ―Control System For Video Advertising Based on
Raspberry Pi‖, RoEduNet Internationational Conference 12th
Edition: Networking in Education and Research, (2013).
[78] Folakemi Oluwagbemi, Sanjay Misra, Nicholas Omoregbe.:
Pervasive Computing in Classroom Environments and
Applications. International Conference on Education & eLearning
Innovations, (2014)
[79] Upadhye sudeep.: Use of 3D Hologram Technology in
Engineering Education, IOSR Journal of Mechanical and Civil
Engineering (IOSR-JMCE), pp. 62-67, (2015).
[80] Boris Pokric, Srdjan Krco, Dejan Drajic, M.Pokric,: Augmented
Reality renabled IoT services for environmental monitoring
utilising serious gaming concept, (2015).
[81] Umakant B.Gohatre, V.D.Chaudhari, ―Digital Advertising of Still
and Moving Images Using Raspberry Pi ‖ International Journal of
Engineering Research and Technology, Vol. 4, Issue. 2, (2015).
[82] Matthew Montebello.: Smart Ubiquitous Learning
Environments. International Journal of Education. Vol. 5,
no.3/4,pp. 17-24 (2017).
[83] Mirjana Maksimoviƈ.: IOT Concept Application in Educational
Sector using Collaboration. Teaching, Learning and Teacher
Education, vol. 1, No. 2, pp. 137-150, (2017).
[84] Dr. Jay. R. Porter, Dr.Josepgh A. Morgan, Dr.Micheal Johnson.,:
Building Automation and IoT as a Platform for Introducing STEM
Education in K-12. American Society for Engineering Education,
(2017).
[85] Luka Petroviƈ, Ivan Jezdoviƈ, Danijela Stojanoviƈ, Zorica
Bogdanoviƈ, Marijana Despotoviƈ-Zrakiƈ.: Development of an
educational game based on IoT. International Journal of
Electrical Engineering and Computing, vol. 1, no. 1, pp. 36- 45,
(2017).
[86] Stefan A.D. Popenici, Sharon Ken.: Exploring the impact of
artificial intelligence on teaching and learning in higher
education. Research and Practice in Technology Enhanced
Learning, (2017).
[87] Fernando Moreira, Maria João Ferreira, Abilio Cardoso.: Higher
Education Disruption Through IoT and Big Data: A Conceptual
Approach, Springer International Publishing, pp. 3009-405,
(2017).
[88] Toge Swatee Pralhad, Bhope Vishal, ―Smart Interactive
Advertising Board Using Raspberry Pi‖, International Journal
of Advance Research, Ideas And Innovations In Technology,
Vol. 3, Issue. 6, (2017)
[89] Rawia Bdiwi, Cyril de Runz, Sami Faiz, Arab Ali Cherlif,: Smart
Learning Environment: Teacher’s Role in assessing classroom
attention, Research in Learning Technology, Vol. 27, (2019).
[90] Ana-Maria SUDUC, Mihai Bizoi, Gabriel Gorghiu.,: A Survey on
IoT in Education. Revista Românească pentru Educaţie
Multidimensională, vol. 10, issue. 3, pp. 104-111, (2018).
[91] Dr.Shahbaz Pervez, Shafiq ur Rehman, Dr. Gasim Alandjani.:
Role of Internet of Things (IOT) in Higher Education. 4th
International Conference on Advances in Education and Social
Sciences, pp. 792-800 (2018)
[92] Everton Gomede, Fernando Henrique Gaffo, Gabriel Ulian
Briganó, Rodolfo Miranda de Barros, Leonardo de Souza
Mendes.: Applications of Computational Intelligence to
Imrpove Education in Smart Cities, MDPI,Sensors, (2018).
[93] Diego Lǒpez de Ipiña, Paulo R.S. Mendonça, Andy Hopper.:
TRIP: a Low-Cost Vision Based Location System for
Ubiquitous Computing.
[94] Ashmit Kolli.: Augmented Reality and Ubiquitous Computing
using HCI.
[95] Suvarna Kumar Gogula.,Sandhya Devi Gogula, Chanakya
Puranam.: Augmented Reality in Enhancing Qualitative
Education. International Journal of Computer Applications, vol.
132, no. 14, pp.41-45, (2015).
[96] Dharmateja.R., Dr.A.Ruhan Bevi.: Raspberry Pi Touchscreen
Tablet (Pi-Pad). International Journal of Recent and Innovation
Trends in Computing and Communication, vol. 3, issue. 2, pp.
600-605, (2015).
[97] Nikhil Paonikar, Samuel Kumar, Manoj Bramhe.,: Raspberry
Pi Augmentation: A cost effective solution to Google Glass.
International Research Journal of Engineering and Technology,
vol. 4, issue. 3, pp. 1439-1442, (2017).
[98] Sarantos Psycharis, Konstantinos Kalovrektis, Eva Sakellaridi,
Konstantinos Korres, and Dimitrios Mastorodimos, : Unfolding
the Curriculum: Physical Computing, Computational Thinking and
Computational Experiment in STEM’s Transdisciplinary
Approach, European Journal of Engineering Research and
Science Special Issue : CIE, 2017.
[99] Xunyu Pan, Joseph Shipway, Weijuan Xu.: Learning Enhancement
with Mobile Augmented Reality. International Symposium of
Electronic Imaging, Society for Image Science and Technology,
(2018).
[100] Iain Murray, Helen Armstrong.: A Computing Education Vision
For the Sight Impaired. ACE ’04 Proceedings of the Sixth
Australian Conference on Computing Education, vo. 30, pp. 201-
206, (2004).
[101] PuTjorn Pruet, Deravi Farzin, Chee Siang Ang, Naong Chaiwut.:
Exploring the Internet of Educational Things(IoET) in rural
unprivileged areas, IEEE (2015)
[102] 103.Prajakta Chavan, Gauri Bhatt, Neha Ture, Prof. K.K.Mathew.:
Real-Time Video Streaming using Holograms. International
International Journal for Research in Engineering Application & Management (IJREAM)
ISSN : 2454-9150 Vol-05, Issue-04, July 2019
27 | IJREAMV05I0452005 DOI : 10.35291/2454-9150.2019.0287 © 2019, IJREAM All Rights Reserved.
Journal of Advanced Research, vol. 5, issue. 8, pp. 1006-1010,
(2017).
[103] Romy Ferzli, Ibrahim Khalife.: Mobile Cloud Computing
Educational Tool for Image/Video Processing Algorithms, IEEE
(2011)
[104] V.Ajantha Devi., Dr.S.Santhosh baboo.: Embedded Optical
Character Recognition On Tamil Text Image Using Raspberry
Pi, International Journal of Computer Science Trends and
Technology (IJCST), vol. 2, issue. 4, pp. 127- 131, (2014).
[105] Aryuanto Soetedjo, Ali Mahmudi, M. Ibrahim Ashari, Yusuf
Ismail Nakhoda, ―Raspberry Pi Based Laser Spot Detection‖,
IEEE International Conference on Electrical Engineering
Computer Science, 2014.
[106] Geraldine Shirley.N., Jayanthy.S.: Virtual Control hand Gesture
Recognition System using Raspberry Pi, ARPN Journal of
Engineering and Applied Sciences, vol. 10, no. 7, pp. 2989-2993,
(2015)
[107] Shyam Narayan Patel, V.Prakash, ―Autonomous Camera based
Eye Controlled Wheelchair system using Raspberry- Pi‖, 2nd
International Conference on Innovations in Information Embedded
and Communication Systems, (2015).
[108] Varsha E. Dahiphale, Sathyanarayana Ramachandra Rao, A
Review Paper on Portable Driver Monitoring System for Real
Time Fatigue‖, IEEE Computer Society, 2015
[109] Qifan Dong, Yang Yang, Wang Hongjun, Xu Jian-Hua, ―Fall
Alarm and Inactivity Detection System Design and
Implementation on Raspberry Pi‖, ISBN 978-89-968650-5-6,
ICACT, (2015).
[110] Hassna BENSAG, Mohamed YOUSSFI, Omar BOUATTANE,
―Embedded Agent for medical image segmentation‖, 27th
International Conference on Microelectronics (ICM) ,2015.
[111] Marko Viitanen, Ari Koivula, Jarno Vanne, Timo D.
Hämäläinen, ―Kvazaar HEVC Still Image Coding on Raspberry
Pi 2 for Low-Cost Remote Surveillance‖, IEEE ,(2015).
[112] P.V.Vinod Kumar, K.Dhanunjaya, ―Kindling and Perception of
Qr-Images Using Raspberry-PI‖, International Journal of
Engineering and Computer Science, vol. 4, issue. 7, pp. 13082-
13085, (2015).
[113] Enis Bilgin, Dr. Stefan Robila, ―Road Sign Recognition System
on Raspberry Pi‖, IEEE Long Island Systems, Applications and
Technology Conference LISAT, (2016).
[114] Jyoti Yadav, Mohammaed Firash Khan, Muhammed Shahan
K.K., Sreedevi.P., Sumayya K., Manju V.M.: Raspberry Pi Based
Gesture Photography. International Journal of Advanced
Research in Computer and Communication Engineering+, vol. 5,
issue. 3, pp. 412-416 ,(2016).
[115] Shaik Riyaz Hussain, K. Rama Naidu, Chintala R.S.Lokesh,
Patchava Vamsikrishna, Goli Rohan, ―2D-Game Development
Using Raspberry Pi‖, International Conference On Information
Communication And Embedded System ICICES, (2016).
[116] Nestor Lobo, ―Intellie-Mirror:An Augmented Reality based IoT
System for Clothing and Accessory Display‖, 2016 International
Conference on Internet of Things and Applications (IOTA), (2016).
[117] Dhanujalakshmi.R., Divya.B., Divya.C., Robersingh.A.: Image
Processing Based Fire Detection System using Raspberry Pi
System, SSRG International Journal of Computer Science and
Engineering, vol. 4, issue. 4, pp. 18- 20, (2017).
[118] Aditi.S,Annapoorani.S.P.,Kanchana.P.:Book ReaderUsing
Raspberry Pi for Vısually Impaired. International Research
Journal of Engineering and Technology, vol. 5, issue. 1, pp. 555-
557, (2018).
[119] Kiran Kumar.R, Mekala.S.: Face Recognition Attendance System
Using Raspberry Pi. International Journal of Pure and Applied
Mathematics, vol. 118, no. 20, pp. 3061-3065, (2018).
[120] Lubasi kakwete Musambo, Jackson Phiri.: Student Facial
Authentication Model based on OpenCV’s Object Detection
Method and QR code for Zambian Higher Institutions of
Learning. International Journal of Advanced Computer Science
and Application, vol. 9, no. 5, pp. 88-94, (2018).
[121] Keerthi Premkumar, K Gerard Joe Nigel, ―Smart Phone Based
Robotic Arm Control Using Raspberry Pi, Android and Wi-Fi‖,
2nd International Conference on Innovations in Information
Embedded and Communication Systems, (2015).
[122] Morgane Chevalier, Fanny Riedo, Francesco Mondada.: How do
teachers perceive educational robots in formal education? A study
based on the Thymio robot (2016)
[123] Pedro Plaza, Elio Sancristobal, German Fernandez, Manuel
Castro,Clara Pérez, Collaborative Robotic Educational Tool
based on Programmable Logic and Arduino, IEEE (2016)
[124] Roland Szabó, Aurel Gontean, ―Industrial Robotic Automation
with Raspberry PI using Image Processing‖, ISBN 978-80-
261-0602-9, © University of West Bohemia, (2016).
[125] Leland Miller, Kevin Abas, Katia Obraczka.: SCmesh: Solar-
Powered Wireless Smart Camera Mesh Network. IEEE 24th
International Conference on Computer Communication and
Networks, (2015).
[126] Nikolaos Ioannou, George S. Ioannidis, George D.
Papadopoulos, Athanasios E.Tapeinos, ―A novel Educational
Platform, based on Raspberry Pi‖, Optimised to assist the
teaching and learning of young students, International
Conference on Interactive Collaborative Learning, IEEE ,(2014).
[127] 128.P.Turton,T.F.Turton, ―PiBrain – A Cost Effective
Supercomputer for Educational Use‖, 5th Brunei Internatinoal
Conference on Engineering and Technology BICET (2014) pp-
1-4.
[128] Rebecca F. Bruce, J.Dean Brock, Susan L.Reiser, ―Make Space
for the Pi‖, Proceedings of the IEEE SoutheaseCon, (2015).
[129] Diyana Kyuchukova, Georgi Hristov, Plamen Zahariev and
Svilen Borisov, ―A study on the possibility to use Raspberry Pi as
a console server for remote access to devices in virtual learning
environments‖, European Union (2015).
[130] .J. R. Byrne, L. Fisher, B. Tangney, ―Computer Science Teacher
reactions towards Raspberry Pi Continuing Professional
Development (CPD) workshops using the Bridge21 Model‖, 10th
International Conference on Computer Science & Education
(ICCSE 2015).
[131] Peter Jamieson, Jeff Herdtner, ―More Missing the Boat -
Arduino, Raspberry Pi, and Small Prototyping Boards and
Engineering Education Needs Them‖, IEEE (2015).
[132] Marina Rocha Daros, João Paulo Cardoso de Lima, Willian
Rochadel , Juarez Bento Silva, José Schardosim Simão, ―Remote
Experimentation in Basic Education using an Architecture with
Raspberry Pi‖ IEEE (2015).
[133] Hélia Guerra, Alberto Cardoso, Vitor Sousa, Joaquim Leitão,
Vitor Graveto, Luís Mendes Gomes ―Demonstration of
Programming in Python using a remote lab with Raspberry Pi‖,
IEEE (2015)
[134] Siddharth Arun, C. Aakash, V.Ajay, ―Design and
Implementation: Remote Control for Projectors Using Raspberry
Pi‖, Middle-East Journal of Scientific Research, Vol. 24, No. 11,
ISSN: 1990-9233, (2016).
[135] . Manaswi R.Ganbavale, Shubhangi C. Deshmukh, ―Raspberry Pi
Based Intelligent Projector‖, International Journal of Innovative
International Journal for Research in Engineering Application & Management (IJREAM)
ISSN : 2454-9150 Vol-05, Issue-04, July 2019
28 | IJREAMV05I0452005 DOI : 10.35291/2454-9150.2019.0287 © 2019, IJREAM All Rights Reserved.
Research in Computer and Communication Engineering, vol. 4,
issue. 8, pp. 14704- 14709.
[136] Kazuaki Yoshihara* and Kenzi Watanabe, Nobukazu Iguchi,
―New Educational Equipments for Networking Study by
Physical Visualizations and Physical Direct Manipulations‖, 30th
International Conference on Advanced Information Networking
and Applications, IEEE Computer Society, (2016).
[137] Ciira wa MAINA, Asaph MUHIA, James OPONDO, ―A Low
Cost Laboratory for Enhanced Electrical Engineering
Education‖, IST-Africa 2016 Conference ProceedingsPaul
Cunningham and Miriam Cunningham (Eds) IIMC International
Information Management Corporation, (2016).
[138] Suchitra, Suja P., Shikha Tripathi, ―Real-Time Emotion
Recognition from Facial Images using Raspberry Pi II‖ 2016 3rd
International Conference on Signal Processing and Integrated
Networks (SPIN) (2016).
[139] Vaibhav kanna, Yash Vardhan, Dhruv Nair, Preeti Panu,
―Design and Development of a Smart Mirror Using Raspberry
Pi‖, International Journal of Electrical and Data
Communication,ISSN2320-2084.
[140] Renuka P.Kondekar, A.O. Mulani, ―Raspberry Pi based voice
operated Robot‖, International Journal of Recent Engineering
Research and Development (IJRERD),vol. 02, Issue. 12, pp. 69-76,
(2017).
[141] Isaiah Brand, Josh Roy, Aaron Roy, John Oberlin, Stefanie
Tellex, ―‖PiDrone: An Autonomous Educational Drone using
Raspberry Pi and Python‖, RSJ International Conference on
Intelligent Robots and Systems, 2018.
[142] Micheal S.Feurstein, ―Towards an Integration of 360- degree
Video in Higher Education‖, proceedings of DeLFI Worksops
2018.
[143] Salman Mahmood, Sellapan Palaniappan, Raza Hasan, Kamal
Uddin Sarker, Ali Abass, Puttasamy Malali Rajegowda, ―
Raspberry PI and role of IoT in Education‖, IEEE (2019).
[144] Anand Nayyar, Vikram Puri, ―Raspberry Pi- A Small, Powerful,
Cost Effective and Efficient Form Factor Computer: A Review,
International Journal of Advanced Research in Computer Science
and Software Engineering, Vol. 5, Issue 12, (2015).
[145] Rabia M.Yilmaz.: Augmented Reality Trends in Education
between 2016 and 2017 Years. State of the Art Virtual Reality
and Augmented Reality Knowhow, (2018).
[146] Kim, S., Song, S.M., & Yoon, Y.I.: Smart learning services
based on smart cloud computing- Sensors, vol. 11, no. 8, pp.
7835-7850, (2011).
[147] Noufal Ibrahim KV.: Creating Physics Aware Games using
PyGame and PyODE. Proceedings of PYCon Asia-Pacific, (2010).
[148] Prof. Swati Vitkar.: Cloud Based Model for E-Learning in
Higher Education. IJAIR, ISSN: 2278-7844, (2012).
[149] Gwo-Jen Hwang, Chin-ChungTsai, Hui—Chun Chu, Kinshuk,
Chieh-Yuan Chen. A Context aware ubiquitous learning
approach to conducting scientificninquiry activities in a science
park, Australasian Journal of Educational Technology, vol. 28,
no. 5, pp. 931-947, (2012).
[150] Majid Bayani Abbasy, Enrique Vlíchez Quesada.: Predictable
Influence of IoT (Internet of Things) in the Higher Education.
International Journal of Information and Education Technology.
Vol. 7, no. 12, pp. 914-919, (2017).
[151] Sejal V. Gawande, Prashant R. Deshmukh, ―Raspberry Pi
Technology‖, International Journal of Advanced Research in
Computer Science and Software Engineering, Vol 5, Issue 4,
(2015).
[152] Ching-Hsang, Chang and Wei-Lin Wang.,: RFID System, Tsang
Hai Book, Taiwan, (2008).
[153] M.Karthikeyan, M.Kudalingam, P.Natrajan, K.Palaniappan,
A.Madhan Prabhu, ―Object Tracing Robot by Using Raspberry PI
with open Computer Vision (CV), International Journal of Trend
in Research and Development, Vol. 3, Issue. 3, ISSN: 2394-
9333, (2016).
[154] Dürr, Y.Pauchard, D.Browarnik, R.Axthelm, M.Loeser, ―Deep
Learning on a Raspberry Pi for Real time Face Recognition
(2015)
[155] Delia Velasco-Montero, Jorge Fernández-Berni, Ricardo
Carmona-Galan, Angel Rodríguez-Vázquez,‖Performance
Analysis of Real-Time DNN Inferencfe on Raspberry Pi‖,2018.
[156] Ajay Kushwaha, Ankita Mishra, Komal Kamble, Rutuja
Janbhare, ―Theft Detection using Machine Learning‖, IOSR
Journal of Engineering, Vol. 8, pp-67-71, (2018)
[157] Mahmut Taha Yazici, Shadi Basurra, Mohamed Medhat Gaber,
―Edge Machine Learning: Enabling Smart Internet of things
Applications‖, Big Data and Cognitive Computing, 2(26),
(2018).
[158] N.Indira, H.Sanofar Nisha, P.Vidhyabanu, M.Suganya,
―Human Interaction Artificial Intelligence By Using Raspberry
Pi‖, International Journal of Advance Engineering and Research
Development, Vol. 5, Issue. 3, 2018.
[159] Ashwani Kumar, Anlush Chourasia, ―Blind Navigation System
Using Artificial Intelligence‖, International Research Journal of
Engineering and Technology, Vol. 5, Issue. 3, 2018.
[160] Marcelo Worsley, ―Multimodal Learning Analytics’ Past, Present
and Potential Futures‖, Companion Proceedings 8th
International Conference on Learning Analytics and
Knowledge, 2018.
[161] Nicholas Constant, Debanjan Borthakur, Mohammedreza Abtahi,
Harishchandra Dubey, Kunal Mankodiya, ―Fog – Assisted wIoT: A
Smart Fog Gateway for End-to-End Analytics in Wearable
Internet of Things‖, 23rd IEEE Symposium on High Performance
Computer Architecture HPCA 2017.
[162] Katalin Ferencz, József Domokos, ―IoT Sensor Data Acquistion
and Storage System Using Raspberry Pi and Apache
Cassandra‖,2018.
[163] Wajdi Hajji, Fung Po Tso, ―Understanding the Performance of
Low Power Raspberry Pi Cloud for Big Data‖, MDPI
Electronics Journal, 5(29), 2016.
[164] C.Balasubramaniyan, D.Manivannan, ―IoT Enabled Air Quality
Monitoring System (AQMS) using Raspberry Pi‖, Indian
Journal of Science and Technology, Vol. 9(39), 2016.
[165] R.K.Barik, R.Priyadharshini, H.Dubey, V.Kumar and
K.Mankodiya, ―FogLearn: Leveraging Fog-based Machine
Learning for Smart System Big Data Analytics‖, International
Journal of Fog Computing, Vol. 1, Issue. 1, 2018.
[166] Micheal Derntl, Nikou Günnemann, Ralf Klamma, ―A Dynamic
Topic Model of Learning Analytics Research‖, 2013.
[167] Badradiin Alturki, Stephan Reiff-Margainee, Charith Perera, ―A
Hybrid Approach for Data Analytics for Internet of Things‖,
ACM, 2017.
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