Case Study: IoT Data Integration for Higher Education Institution

Abstract

Nowadays the Internet of Things (IoT) is one of the most trending technologies. It is expected that by year 2020 there will be 50 billion of Internet-connected devices. Fields like smart cities and smart homes largely rely on IoT phenomena by using a wide variety of sensors for data collection, analysis and corresponding actions. The paper describes how this trending and relatively new technology is applied at Riga Technical University for educational purposes.

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Information Technology and Management Science
71
ISSN 2255-9094 (online)
ISSN 2255-9086 (print)
December 2016, vol. 19, pp. 71–77
doi: 10.1515/itms-2016-0014
https://www.degruyter.com/view/j/itms
©2016 Krišjānis Pinka, Jānis Kampars, Vladislavs Minkevičs.
This is an open access article licensed under the Creative Commons Attribution License
(http://creativecommons.org/licenses/by/4.0), in the manner agreed with De Gruyter Open.
Case Study: IoT Data Integration
for Higher Education Institution
Krišjānis Pinka1, Jānis Kampars2, Vladislavs Minkevičs3
1–3 Riga Technical University
Abstract – Nowadays the Internet of Things (IoT) is one of the
most trending technologies. It is expected that by year 2020 there
will be 50 billion of Internet-connected devices. Fields like smart
cities and smart homes largely rely on IoT phenomena by using a
wide variety of sensors for data collection, analysis and
corresponding actions. The paper describes how this trending and
relatively new technology is applied at Riga Technical University
for educational purposes.
Keywords – Internet of Things, data integration, cloud
computing.
I. INTRODUCTION
The idea of the Internet of Things (IoT) suggests that rather
than having a small number of powerful computing devices
(e.g. laptop, tablet) you have a large number of devices, which
are less powerful (e.g., bracelet, connected light bulb).
Name itself implies that the IoT consists of two key parts –
network and various devices connected to it. The “Internet” part
could take various forms and serves as the medium for sending
and receiving the data, while the devices (referred to as
“Things”) act as data sources and actuators.
Explosive growth of smartphones and tablet PCs brought the
number of Internet-connected devices per person to more than
1 device in year 2010 for the first time in history [1]. In year
2015 the total number of connected devices reached 18.2 billion
[2]. Cisco predicts that there will be a total of 50 billion Internet-
connected devices by year 2020.
The IoT devices are present physically in the real world, in
your home, work, and car or worn around your body. This
means that they receive inputs from the physical world,
transform them into data and produce outputs that can be
collected and processed for further action, e.g., processing and
analysis [3]. The sensor originating data are turned into
information, knowledge, and, ultimately, wisdom. In this
context, the IoT becomes immensely important in enriching our
daily experience.
Even now the IoT has already made the Internet sensory with
real-time data about measurable properties, such as
temperature, pressure, vibration, light and moisture. This serves
as a driving force for creating cyber-physical systems that have
the potential of dramatically improving the way people live,
learn, work and entertain themselves. In addition, the Internet
is expanding into places that until now have been unreachable.
Patients are ingesting IoT devices into their own bodies to help
doctors diagnose and determine the causes of certain diseases.
Extremely small Internet-connected sensors can be placed on
plants, animals, geologic features and in buildings [1]. Those
sensors provide data that give real-time insights and can also be
used for predictive analysis. Lorence Heikell [4] has
documented an example from a dairy farm where IoT based
monitoring is used for improving milk production, smoothing
calving process and ensuring good health of the cows. The
German farmer has acknowledged that cow farming has
become much easier thanks to the applied IoT solution. The
time for evaluating the health of the animals has gone down
from 2–3 hours per day to a few minutes that can be spent while
looking at IoT based reports in a mobile application. Another
field of application is the building monitoring systems that
present data to experts in real-time allowing them to evaluate
the current state of a building, predicting possible damage or
even collapsing risks. Experiments documented in [5] show that
such IoT based building monitoring systems perform well and
meet experts’ requirements.
IoT solutions can be composed of a large number of devices
that should be provided with effective ways of data exchange.
Maximising the inter-device communication distance and
minimising the power consumption are an important challenge
in the area of IoT, and new technologies are emerging to address
it [6]. One of such technologies is the Wireless Sensor Network
(WSN). It is an architectural model containing a number of
sensor nodes and a central node (hub). In the WSN, the
collected data from the sensor nodes are transferred to the
central node for processing and further actions. One of the
communication protocols that can be applied for IoT node
communication is the Bluetooth low energy (BLE) wireless
communication protocol. Another option providing low energy
and relatively wide communication range is the Zigbee wireless
communication standard. Zigbee supports creation of IoT
networks with up to 64 000 devices [7]. The difference between
BLE and Zigbee lies in the frequency, channel bandwidth,
number of cell nodes and supported range.
The objective of this paper is to present the IoT data
integration solution that was developed at Riga Technical
University (RTU) for demonstrating the IoT capabilities to the
new generation of IT professionals. The first version of our
solution is ready for deployment and we are planning to engage
our students in extending it. To make it more appealing for the
students we have integrated our IoT solution with social media.
Design science is chosen as the research method for this paper.
The organisation of this paper is as follows. Section II
presents a literature review of related studies. Section III
describes the IoT data integration solution developed at RTU.
Section IV concludes with final remarks and a summary of our
plans for expanding the current IoT solution.
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II. RELATED RESEARCH
When IoT devices are connected to a network, they work
together in cooperation to achieve a common goal – to provide
a specific service. This can be beneficial for many real-life
applications and services like smart-homes where some devices
collect data (sensors), while others (actuators) use it for
counteraction triggering.
The IoT can be applied to create extremely large-scale
solutions like smart cities consisting of thousands of sensors
and actuators [8]. In smart cities, administration and inhabitants
can get access to the real-time data about the surrounding
environment. This can then be transformed into information and
knowledge used for making better decisions on a daily basis and
urban planning [9].
Concepts like smart homes, which are more private IoT
networks at a smaller scale, are gaining popularity because
sensors and boards (microcontrollers and microcomputers) are
available at low prices and a sufficient number of educational
materials are freely available on the Internet. Smart homes can
be defined as home automation systems consisting of various
sensors and actuators used for improving the comfort for
residents. BLE, Wi-Fi and RFID are typically used as the
communication technologies. Sensors such as temperature,
light, power meter are installed and connected wirelessly to a
board following the WSN architectural concept. Usually the
board is also connected to the Internet making data available to
ones with appropriate access level. Smart homes are often
complemented with a mobile application, thus taking the
advantage of modern smartphone capabilities and delegating
some of the control functions to the resident’s smartphone. In
this way, the monitoring data can be easily accessed from any
place with an Internet connection [10].
Integrating the IoT with social media brings more
possibilities in information sharing and reaching larger
audiences. This can be specifically beneficial for smart-cities
and other scenarios with a large user base. In this concept, the
sensor data are sent to a central node for analysis and after that
it is made available to the general public through the Internet
and various social networks. Results of the research by Lee et
al. [11] show that the combination of IoT and social network
services not only helps with introduction of IoT products to
general public but also brings a potential business value. Sensor
information from the surrounding area can be correlated with
user activities in social networks. Using this kind of
information, it is easier to predict the places that a user might
like and also the contextual conditions (e.g., high temperature,
no rain) [12], [13].
The concept that is most related to this research is smart
universities. The data are made publicly available and gathered
by a wide range of various sensors. Typically these are:
 environment related sensors measuring properties, such as
temperature, light, air quality and humidity;
 security sensors detecting motion, opening and closing
doors or windows;
 safety sensors detecting smoke or other gases.
The architectural model described in [14] shows that all these
devices are connected to a hardware board (Arduino, Raspberry
Pi, Banana Pi) that wirelessly transmits data to a central node,
which is another hardware board. Similar to smart homes, this
also follows the WSN architectural principle. The received data
are sent to a web server and database server that are deployed
locally or in the cloud. Cloud provides the flexibility of scaling
the IoT database and computing nodes according to the load of
the system and provides a good quality of service with a
minimal number of resources [15].
The architect of the IoT solution can choose different
platforms for end-users. The most common ones are
smartphones and tablets; however, these are not the only
options. Smart TVs can also be used as devices for displaying
the sensor originating information in real time [16]. This is
particularly suitable for smart university use cases. The screen
of a Smart TV performs well, presenting data for a large number
of people at once. High resolution allows showing various
graphical charts and data, which would be harder to see on a
small screen of a smartphone. If the TV is mounted in a social
place, it will certainly attract people and popularise the IoT
technology.
III. IOT DATA INTEGRATION SOLUTION AT RTU
This section looks at the technology, methods and devices
that were used to implement the IoT data integration solution at
RTU. Sensors are used for data collection and are connected to
the boards, which integrate and transmit the data to a central
cloud-deployed node containing a database and REST
(Representational State Transfer) endpoints. Smart TV is used
for visualising data, and Twitter integration provides ways for
interacting with the IoT solution.
A. Architecture
Various technologies and their combinations can be used to
implement an IoT project and very similar results can be
achieved using different boards and sensors. The devices used
in the research were chosen based on their availability in
Latvia’s retail stores and popularity among developers. The
objective of our solution is to educate the students and present
the possibilities of IoT. Social media (Twitter) and Smart TV
are used for making the solution interactive. The list of the used
devices and general architecture of the solution is given in
Fig. 1.
The central node of the IoT solution is deployed to RTU’s
cloud computing platform, thus providing persistent storage
and virtually unlimited computing power. Currently, it is a
virtual machine with random access memory (RAM) 1 GB and
1 x 2.39 GHz central processing unit (CPU). These resources
are sufficient for running a web server and a database server,
which are needed for aggregating the sensor data and
transmitting them to Smart TV. If more resources are needed,
database and web-server can be deployed to separate nodes with
larger memory and CPU capacity. The first release of IoT
solution has Apache 2 used as the web-server, while MySQL
performs the role of the relation-database. Web server uses PHP
server-side scripting language for processing REST
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web-service requests. JSON (JavaScript Object Notation) is
used as the data format for transmitting data between devices.
Microcomputer Raspberry Pi and microcontroller Arduino are
connected to each other with a universal serial bus (USB) cable,
and Arduino is used for processing dust and CO sensor
(analogue) data, while Raspberry Pi gathers (digital) data from
other sensors and transmits them over Wi-Fi to the previously
mentioned cloud node.
Fig. 1. The IoT Architecture used for the case study.
Both of the hardware boards are currently located at the
premises of the Institute of Information Technology, RTU;
however, they can be moved to any other location with a Wi-Fi
connection. The devices can be powered from a standard USB
cable or a portable power bank (generally used for charging
mobile phones while being on the move), so it is possible to
collect data in places with no power sources.
The Arduino program is written in C programing language.
It processes measurements from CO and dust sensors and
transfers the results to Raspberry Pi, which receives these data
and then collects measurements from temperature, humidity
and light sensors. A Python based program is used for
implementing the data processing and transmission logic. If
connection cannot be established due to network problems, the
data collection continues and another connection attempt is
performed later.
Samsung Smart TV is used for data visualisation and allows
users to interact with the IoT data integration solution using the
remote control of Smart TV. TV is connected to the Internet
through Wi-Fi and is supplied with near real-time sensor data
updates from the cloud-based node. Twitter integration
provides additional options for users and makes our solution
available from one of the leading social networks.
By tweeting a specific word in the text (e.g., HumidityDay
#RTU-IoT), users can change the data to be displayed on the
Smart TV screen. The cloud-based node is continuously
scanning tweets under specific hashtag and searching for
commands. If a syntactically valid command is detected, the
central cloud-based node contacts the Smart TV and changes
the data being displayed.
The approximate budget of our solution is 750 euro where
the Smart TV takes around 80 % of expenses. The cost can be
significantly reduced by using a cheaper TV. Components that
were used to implement the IoT solution are further described
in the following sections.
B. Sensors
DHT11 sensor is used for temperature and humidity
measurements, TSL2561 sensor provides luminosity
measurements, GP2Y1010AU0F sensor serves as an optical
dust sensor and MQ-7 measures carbon monoxide level.
Figure 2 illustrates the wiring to hardware boards for all the
used sensors.
DHT11 Temperature & Humidity Sensor has a digital output
function and uses exclusive techniques for measuring physical
conditions and converting the resulting samples into digital
numeric values. High reliability and long-term stability are
ensured by calibration of each DHT11 element in the laboratory
environment. The single-wire serial interface makes system
integration quick and easy. The small size of the sensor ensures
low power consumption and long distance signal transmission.
Sensor measurement range for humidity is 20–90 % RH
(accuracy ±5 % RH) and for temperature 0–50 Ԩ (accuracy
±2 Ԩሻ. The required power supply is 3–5.5 V DC [17].
Fig. 2. Sensor wiring to hardware boards.
The TSL2561 luminosity sensor is an advanced digital light
sensor, which can be used for measuring a wide range of light
situations. Its accuracy allows performing exact Lux
calculations, and it can be configured for different gain and
timing ranges to detect light ranges from 0.1 to 40,000+ Lux.
One of its advantages is that it contains both infrared and full
spectrum diodes allowing for separate measurement of infrared,
full-spectrum or human visible light. The sensor has a digital
inter-integrated circuit (i2c) interface and built-in analogue-to-
digital converter (ADC), i.e., it can be used with a wide range
of boards (even ones having no analogue inputs like Raspberry
Pi). The current draw is extremely low making it power-
effective [18].
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Sharp’s GP2Y1010AU0F is an optical air quality sensor
designed to sense dust particles. An infrared emitting diode and
a phototransistor are diagonally arranged allowing one to detect
the reflected light of dust in the air. It is especially effective in
detecting very fine particles and is commonly used in air
purifier systems. The sensor has a very low current
consumption and can be powered with up to 7 VDC. The output
of the sensor is analogue voltage proportional to the measured
dust density, with a sensitivity of 0.5 V / 0.1 mg/m3. Due to the
analogue output, this sensor can be used only with boards that
have analogue inputs (e.g., Arduino) [19].
MQ-7 Carbon Monoxide (CO) sensor is suitable for sensing
CO concentrations in the air. It can detect the highly poisonous
CO-gas concentrations in parts-per million (ppm). This sensor
has a high sensitivity and fast response time. The sensor output
is analogue resistance, so it can be used only together with
boards that have analogue inputs [20].
C. Boards
Data collection is provided by two boards: Arduino Due and
Raspberry Pi 2B. The Raspberry Pi is a single-board computer
created by the Raspberry Pi Foundation. It is based on the
Broadcom BCM2836, which is a multimedia application
processor geared towards mobile and embedded devices. The
usage of ARMv7 processor allows Raspberry Pi 2B to run full
range of advanced RISC machine (ARM) GNU/Linux
distributions, including Snappy Ubuntu Core, as well as
Microsoft Windows 10. It also contains several other
components to support USB, RCA connections and SD cards as
a means of persistent data storage [21].
The key features of Raspberry Pi 2 are:
 a 900 MHz quad-core ARM Cortex-A7 CPU;
 1 GB RAM;
 4 USB ports;
 40 GPIO pins;
 full HDMI port;
 Ethernet port;
 combined 3.5mm audio jack and composite video;
 camera interface (CSI);
 display interface (DSI);
 Micro SD card slot;
 Video Core IV 3D graphics core.
An excerpt from Raspberry’s Python based sensor data
collection program is given below:
if __name__ == '__main__':
try:
instance = dht11.DHT11(pin = 14)
while True:
dht11()
tsl = TSL2561(debug=True)
lux = tsl.lux()
CO_DUST()
mysql()
time.sleep(10)
except error:
GPIO.cleanup()
destroy()
def dht11():
result = instance.read()
if result.is_valid():
global temp, hum
temp = result.temperature
hum = result.humidity
The code shows that dht11 sensor (temperature and
humidity) is connected to pin number 14. The data are read and
if the validity test has been passed, the values of global variables
temp
and
hum
are updated. The
main
program transmits the data
to the cloud-based server once in every 10 seconds.
Arduino is an open source single-board microcontroller
hardware [22] that is coupled with a programming language and
an Integrated Development Environment (IDE) [21]. It is based
on the Atmel AVR processor and provides many inputs and
outputs in only one self-sufficient piece of hardware. Arduino
has become one of the most popular and successful examples
of bringing the open source concept to the hardware world [22].
In this project we use the Atmel SAM3X8E ARM Cortex-M3
CPU based Arduino Due microcontroller board. It is the first
Arduino board based on a 32-bit ARM core microcontroller.
The key features of Arduino Due are:
 32-bit core;
 84 MHz clock speed;
 512 KB of Flash memory for code;
 54 digital input/output pins;
 12 analogue inputs;
 4 universal asynchronous receiver/transmitter (UARTs)
hardware serial ports;
 USB OTG capable connection;
 2 digital to analogue (DAC) pins.
The Arduino Due is compatible with all Arduino shields that
work at 3.3V and are compliant with the 1.0 Arduino pinout
[23].
An excerpt from Arduino’s C based sensor data collection
program is given below:
int measurePin = 6;
float voMeasured = 0;
float calcVoltage = 0;
float dustDensity = 0;
void loop(){
Serial.print(MQGetGasPercentage(MQRead(MQ_PIN)/Ro,
GAS_CO));
voMeasured = analogRead(measurePin);
calcVoltage = voMeasured * (3.3 / 1024);
dustDensity = (0.17 * calcVoltage - 0.1) * 100;
Serial.print(' ');
Serial.println(dustDensity);
delay(1000);
}
The code begins with defining variables for holding the
values from a dust sensor that has been wired to pin 6. It is
followed with a loop that takes CO and dust measurements once
per second. At the end of the loop, the values are sent to the
Raspberry Pi 2 via the serial connection.
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Samsung J5500 5 series Flat Full HD Smart LED TV is used
for presenting the data in a graphical format. Samsung J5500
5 series Samsung TV has built-in Wi-Fi that allows interacting
with the cloud-deployed node. The data retrieval and
presentation are implemented as a web-based application
running on the application engine of the Smart TV. The
developed application is basically a web page consisting of
Hypertext Markup Language (HTML), Cascading Style Sheets
(CSS) and JavaScript.
D. Cloud-Based Server
The server node is used for receiving, processing and storing
sensor data and communicating with the Smart TV application.
The board program sends a request for inserting sensor data in
the database, while TV application is polling the aggregated
sensor data for updating the user interface with current and
historical sensor data measurements.
The cloud-based server is deployed to the Apache
CloudStack open-source cloud computing platform. Ubuntu
14.04 LTS was chosen as the operating system. It is built on the
foundation of Linux, which is a member of the UNIX family.
UNIX is one of the oldest types of operating systems, and
together with Linux has provided reliability and security for
professional applications for almost half a century. Many
servers around the world storing data for popular websites (such
as YouTube and Google) run some variant of Linux or Unix
[24].
Apache 2.4 was ch osen a s the web se rver s oftwa re. It is ope n-
source software that was developed in 1995. It is available for
a variety of operating systems and is hosting half of world’s
active websites and, therefore, it is the most widely used web
server of today [25]. In case of larger number of sensors and
presentation devices, Apache can be substituted with
alternatives providing a higher number of concurrent
connections and better performance like NodeJs.
MySQL server 5.5 is used as the relation database
management system. The advantages of MySQL are reliability,
high-performance, easy installation and use, good support in a
wide range of programming languages. Similarly to Apache, it
can be substituted with other alternatives in case of a higher
number of read/write operations. NoSQL database systems and
specifically MongoDB would be a suitable option in such a
scenario.
E. Smart TV Program
The Smart TV program is written in JavaScript and provides
good performance and a wide range of functionality to front-
end users. The program makes an HTTP request to the cloud-
based node in order to get the sensor data, which are retrieved
from the database. The data are then returned in JSON format
and visualised by the Smart TV. The user-interface of the
program is shown in Fig. 3.
Fig. 3. Smart TV user interface.
The source code of the JavaScript function pulling data from
the cloud-based server is given below:
function getData(){
$.ajax({
url: "http://iot-server.rtu.lv/rest",
type: "POST",
dataType: 'json',
success: function (data) {
Temp = data.Temp;
Hum = data.Hum;
Lux = data.Lux;
CO = data.CO;
Dust = data.DUST;
},
error: function(jqXHR,error, errorThrown) {
logData(jqXHR);
setTimeout(getData,3000);
}
});
}
}
The function retrieves the current temperature, humidity,
light, CO and dust measurements and saves them into the local
variables. In case of a communication error, the details are
logged and request is repeated in 3 seconds.
I. Security of the IoT Solution
According to the latest news about Botnet made of IoT
devices, such as CCTV cameras and DVR system attacks, the
security measures should be in place such as regular security
updates. The operating system should be hardened and
unneeded services should be switched off [26]. The current
security of the system includes physical protection of devices
by putting them in RTU premises under video surveillance. This
way people without the needed permission are unable to
physically access the devices, and the risk of damaging the IoT
solution is minimised.
Authentication and access lists are used for securing REST
web-service endpoints. The same applies to Raspberry Pi and
Arduino, where authentication is required for changing device
settings and main program.
Smart TV is connected to Wi-Fi through Wi-Fi Protected
Access version 2 (WPA2) security protocol allowing only for
authorized use of the wireless network and safe data
transmission.
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IV. CONCLUSION
The IoT data integration solution is ready for deployment and
is planned to be installed by the end of year 2016. Currently our
solution can perform measurements only in a single location
since it uses a single set of boards and sensors; however, this
can be easily extended by adding more components. The unique
feature of our IoT data integration solutions is the integration
with Twitter, which is planned to be extended with support for
other social media networks, such as Instagram. Student
feedback will be gathered serving as input for specifying
additional requirements for future improvements. The current
data integration solution is only at its early stages and supports
data gathering from a single location; therefore, work on further
development will be related to expansion of system capabilities.
We are planning to extend the Twitter integration with
additional commands that can be used in tweets. For example,
users will be able to find out the data about real time conditions
at the university by just tweeting a syntactically valid command
(e.g., @rtu-iot get temperature) to the IoT solution’s Twitter
account and getting the response in form of a tweet. This would
provide easy access to the IoT data from tablets, smart phones
and desktop PCs.
The existing range of sensors can be extended with a sound
level detection sensor. Increasing the number of devices will
allow placing them in several rooms in such way gathering data
from a larger area. Our IoT solution will also be supplemented
with actuators performing various actions under certain context
conditions. For example, the conditioner in one of our labs
could be powered by a signal from an infrared (IR) transmitter
in case of temperature going above a predefined margin.
We are also planning to add a Wi-Fi adapter in the
monitoring mode that will be able to collect all broadcasted
SSIDs (Service Set Identifiers) that mobile phones or laptops
are broadcasting while trying to connect to Wi-Fi access points
that they have already been connected to previously. The result
will be displayed on Smart TV raising the awareness that our
devices are broadcasting excessive information. Bluetooth
adapter can also be used to collect information about mobile
phones or laptops that have their Bluetooth enabled.
Another potential function of the IoT data integration
solution is people counting using cameras and image processing
programs that are deployed to the cloud. However, in this case
the privacy aspects should be considered based on EU
Regulation on the protection of natural persons with regard to
the processing of personal data and on the free movement of
such data.
Currently only Wi-Fi is used as a means of communication;
however, we are planning to experiment with other more
perspective communication technologies. In next versions of
our solution, the connected sensors could be based on AVR
MCU and sensors could send information to the board
wirelessly. In this case, we also have to think about security
issues, for example, whether the information will be sent in
plaintext or encrypted form.
After setting up the number of sensors and monitored rooms,
we are planning to display a live map of the sensor locations on
the Smart TV. We are also planning to install customised
Bluetooth beacons for demonstrating the in-room positioning
capabilities and tracking the movement of users, who have
established a Bluetooth connection with our beacons.
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Information Technology and Management Science
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Krišjānis Pinka has earned his B. sc. ing. (in 2015) degree in Information
Technology from Riga Technical University, Latvia. He has been working at
the Faculty of Computer Science and Information Technology, Riga Technical
University since 2015. The current positions are Junior System Administrator
and Junior Researcher. He has participated in Cisco two-day IoE Creathon for
social good. His areas of interest are IoT technologies and cloud computing.
E-mail: Krisjanis.Pinka@rtu.lv
Jānis Kampars obtained his Doctoral degree in Information Technology from
Riga Technical University in 2012. His areas of interest are application
integration, data integration, web development and cloud computing. Jānis
Kampars works as an Assistant Professor and Researcher at the Faculty of
Computer Science and Information Technology, RTU, Latvia. He has been
working at RTU since 2004. Recently he has participated in FP7 project
Capability as a Service. Jānis Kampars also delivers study courses on web
programming, data analysis and processing, cloud computing, business system
programming at RTU.
E-mail: Janis.Kampars@rtu.lv
Vladislavs Minkevičs works as a Chief IT Security Officer at Riga Technical
University. His areas of interest are IT security, microcontrollers, and script
programming for IT security implementation purposes. Vladislavs Minkevičs
has been working as a Lecturer at the Faculty of Computer Science and
Information Technology, RTU for more than 10 years and delivers study
courses on IT security related material. Vladislavs Minkevičs has more than
15 years of experience in IT security area and has obtained CISA and CISSP
certification.
E-mail: Vladislavs.Minkevics@rtu.lv
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