=Paper=
{{Paper
|id=Vol-2864/paper30
|storemode=property
|title=Conceptual Model of IoT-based Laboratory for Study the Electrical Engineering and Electronics
|pdfUrl=https://ceur-ws.org/Vol-2864/paper30.pdf
|volume=Vol-2864
|authors=Oleksandr Osolinskyi,Lubomyr Kolodiychyk,Hrystyna Lipyanina-Goncharenko,Anatoliy Sachenko,Lukasz Kopania,Volodymyr Kochan
|dblpUrl=https://dblp.org/rec/conf/cmis/OsolinskyiKLSKK21
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==Conceptual Model of IoT-based Laboratory for Study the Electrical Engineering and Electronics==
https://ceur-ws.org/Vol-2864/paper30.pdf
Conceptual model of IoT‐based Laboratory for study the
Electrical Engineering and Electronics
Oleksandr Osolinskyia, Liubomyr. Kolodiichukb, Hrystyna. Lipyanina-Goncharenkoa,
Anatoliy. Sachenkoc,a Lukasz Kopaniac, Volodymyr Kochana, Diana Zahorodniaa
a
West Ukrainian National University Department for Information Computer Systems and Control, 3 Peremoga
Square, Ternopil, 46020, Ukraine
b
SE NULES of Ukraine "Berezhany Agrotechnical Institute", 20 Academichna st., Berezhany, Ternopil region,
47501, Ukraine
c
Kazimierz Pułaski Technology and Humanitarian University,Malczewskiego St 29, 26-600, Radom, Poland
Abstract
Abstract - The popularity of IoT has been growing rapidly in recent years. This is due to the
reduction in the cost of devices that take place in IoT solutions, the creation of user-friendly
software development systems and the development of cloud services. This, in turn, has led
to the transition of the educational process to a completely different level, where higher
education students can obtain most of the knowledge (both theoretical and practical) remotely
without the need to physically visit the classroom. In this regard, a conceptual model of IoT
hybrid laboratory is proposed, in which students have the opportunity to conduct their
research both remotely and in patiently. The structure of the control system and general
operating scenarios are described. The issue of optimizing the use of electricity in the
proposed laboratories, which is associated with an increase in electrical load several times
relative to classical laboratories.
Keywords 1
Conceptual model, Arduino; Raspberry; IoT, IoT-based Laboratory
1. Introduction
The Internet of Things is a concept of building an environment in which static physical objects
(mechanical, digital, people, animals and other objects) are connected to the World Wide Web and
can communicate with each other and exchange information to solve everyday problems [1-3]. The
main idea of IoT [4,5] is that these objects can interact with each other, perceive and collect data from
the environment without the need for human intervention or the need to communicate with her.
According to researchers, IoT infrastructure could reach 1.5 billion devices in 2022 [6-9]. Given that
the prices of microcontrollers have fallen to the prices of everyday food [10], it is possible to
automate and monitor all processes in life. This trend has not bypassed the learning process either
[11]. In particular, there is a growing tendency to conduct remote experiments without spending time
on physical visits to laboratories, which does not reduce the workload of laboratories and staff, but
also allows not to reduce the quality of training, because students still work with real equipment.
However, this approach makes it difficult to monitor all devices in such laboratories. Requires control
of equipment that is not currently used, but consumes electricity [12]. In addition, given that the
Proceedings Name, Month XX–XX, YYYY, City, Country
EMAIL: osolinskiy.oleksandr@gmail.com (O. Osolinskyi); kollub@ukr.net (L. Kolodiichuk); xrustya.com@gmail.com (H. Lipyanina-
Goncharenko); as@wunu.edu.ua (A. Sachenko); l.kopania@uthrad.pl (L. Kopania); volodymyr.kochan@gmail.com (V. Kochan);
dza@wunu.edu.ua (D. Zahorodnia)
ORCID: 0000-0002-0136-395X(O. Osolinskyi); 0000-0003-1172-8972 (L. Kolodiichuk); 0000-0002-2441-6292 (H. Lipyanina-
Goncharenko); 0000-0002-0907-3682 (A. Sachenko); 0000-0002-7318-4803 (L. Kopania); 0000-0001-8376-4660 (V. Kochan); 0000-0002-
9764-3672 (D. Zahorodnia)
© 2020 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
laboratories conduct not only remote experiments, but also the actual presence of students, it is also
necessary to control the level of lighting and air conditioning in the room.
For such purposes, the concept of IoT is best suited, which will allow for mixed experiments in
laboratories, with some students working with equipment and others conducting experiments remotely
in parallel. The system fully controls the energy consumption and the turn of the experiments. This
will save electricity in general in the laboratory and increase the number of students involved without
increasing the physical presence of people.
2. Related work
In [19], a comprehensive approach to the implementation of a hardware laboratory based on FPGA
and ignited as a service in a hybrid cloud. This laboratory can also be scaled in the public cloud with
the possibility of free connection of IoT devices, which are implemented using FPGA [15]. The
presented laboratory uses FPGA to work with peripherals and cloud services.
The implemented controller on the FPGA simultaneously works with the server and receives
commands, as well as, on the other hand, receives data from sensors, which are sent to the cloud using
the Internet protocol [16]. After that, cloud services use FPGA to store, store data, analyze and
visualize data. In order for such an environment to work at the IoT level, the student first prepares IoT
peripheral designs in Verilog-HDL and server-side software. In addition, Docker [17] and Docker
Swarm containers were used for the operation of the system. This part is implemented in Go in the
form of templates and each student can easily customize them for their own needs. Using Docker
tools also reduces application development and human resource usage time. Thanks to the
microservice architecture, the laboratory can dynamically change its configuration according to the
workload of students. There are several work strategies in this system: students can conduct
experiments on their own or join group experiments. But first, the student must develop their own
Verilog-HDL for each FPGA, as well as management software to process data and work with the
cloud using Docker tools that will implement the IoT infrastructure.
Another implementation of the remote laboratory [11] is based on the most popular systems for
development and prototyping, namely Arduino and Raspberry PI. The main module of this system is
the Arduino debug board, which serves as a means of data collection and control for other devices. In
this implementation, the board is connected to the LED cube, mechanical crane, sensor, etc.
Execution of all functionality is performed on the Arduino board, and the assembly and download of
executable code is implemented using Raspberry PI. Hardware communication between the two
devices is via a USB port. The number of devices that can be connected to the Raspberry PI is limited
by their technological capabilities. At the same time, the laboratory administrator once a week sets up
stands for research according to user requests, indicating the port - the device file that will be used by
the Arduino board, the device name - the device ID that will be displayed on the website where the
Arduino processor type is specified, the programmer is the type of programmer according to the
processor to be used, Baud is the connection speed and the termination program. This program
configures the device with its default configurations. Thus, the laboratory allows students to create a
full-fledged system of measurement or control. In addition, the system uses webcams so that the
student can visually check the behavior of the test (device) that he performed (created).
The system [12] is a smart laboratory, where all devices are connected to a set of intelligent IoT
equipment, and the whole set is placed on a single printed circuit board. All physical parameters in the
laboratory are measured in real time and can be viewed on the information panel. Data transfer
between all devices is performed using the MQTT protocol. All devices are connected to the Wi-Fi
module ESP 8266 and act as MQTT clients, the tool for visual programming of data streams Node-
RED (Raspberry Pi 3) acts as an MQTT broker (server). The user can use the smartphone to turn on /
off any device in the laboratory via the MQTT server. Also, the condition of the equipment and
information about the consumed electricity can be seen on the control panel. The ThingSpeak IoT
service was used for data processing and transmission. This whole complex allows you to
significantly reduce electricity consumption in the laboratory.
Another interesting implementation is the learning management system using IoT described in
[13]. This system implements software and hardware, as well as IoT devices, which are used for
interesting experiments in the field of chemistry and robotics. This system is also essentially a module
and integrated into the Moodle LMS system, which uses IoT devices and services that improve the
learning process for both teachers and students. With this application, students can use Internet of
Things sensors and robotic tools to remotely perform an experiment via LMS (Learning Management
Systems). At the same time, students gain full control over the equipment and devices for the
experiment. In addition, during the experiment, the student always has access to real-time information
about the characteristics of the environment and the parameters of the experiment using sensors of the
Internet of Things. All these indicators are transmitted to the LMS through the IoT module. The
proposed application is integrated into LearnSmart LMS, which is based on Moodle.
The authors of [14] proposed a remote laboratory for the study of photovoltaic systems for remote
experiments. The system consists of 3 subsystems: power electronics, sensors and communications.
The remote lab is controlled by a dsPIC30F microcontroller, which acts as an intermediary between
the web server and the power system. The communication subsystem consists of two parts:
communication with the application on the PC via EM203 and communication with the power system
and sensors via dsPIC. The graphical interface was implemented in Visual Basic Programming for
date storage, user identification, development of measurement strategies, power system management.
The maximum duration of one session is one hour. But in reality, the experiment can be performed in
30 minutes, in addition, for each lesson is offered double the time in case students have problems, or
there is a need to conduct more tests. Another interesting feature of this system is the feedback of
students and teachers through the system or service feedback on the experiment.
From the literature it is clear that there are two main areas of development of smart laboratories:
the first is the so-called virtual laboratories, where all work with the equipment is performed
remotely and students do not have direct access to the equipment, which in some cases is
necessary, for example, when monitoring power equipment;
the second - smart laboratories that provide comfort in a real laboratory and optimization of the
workflow and electricity consumption. But they in turn impose restrictions on the number of
students who can experiment with real equipment.
For example, if several research stands are connected to a specialized PC and the student uses only
one, then why is it not possible for another student to be able to use the free stand remotely.
Therefore, it is proposed to combine these two concepts and develop a smart laboratory where
students could work both permanently and remotely. In addition, the system should optimize the
energy consumption of such a complex. It is also advisable to use an artificial intelligence system for
the so-called deferred experiments, which is described below, as well as introduce into the system a
submodule of equipment accounting [20] based on NFC technology.
3. System structure
Given the previous work, it is proposed to develop the concept of a smart laboratory, which would
combine the capabilities of a remote laboratory and a regular classroom for practical training. In
addition, it would be advisable to integrate a system of control and management of electricity
consumption by devices located in the laboratory. Due to the fact that such a concept involves
complex management of facilities, it is proposed to use as a management manager the ANN
apparatus, which would work in conjunction with the IoT cloud service. Figure 1 shows a simplified
structure of such a laboratory. As can be seen from Figure 1, the system is divided into two
subsystems - control of electrical appliances to ensure comfort in the room and control of training
stands. These two subsystems are independent of data transmission and control channels.
The control subsystem of electrical equipment includes all the equipment that provides the
educational process, namely the projector, air conditioners and laboratory heating, lighting and other
equipment needed to ensure a comfortable learning environment in the laboratory.
In the current implementation, the control subsystem includes the following equipment:
Smart projector for presentation of lectures or other teaching materials
Smart Condition for air conditioning control and temperature control in the laboratory room.
Smart Light - intelligent lighting control system in the laboratory
Additional control systems can be included in the current subsystem based on the wishes of
lecturers and students or faculty management.
Each such device, such as a projector, according to Figure 2, is equipped with additional modules -
a smart meter for electricity consumption and a specialized relay to control the operation of the
projector.
Global IoT Service
DB st DB usr
Collecting data Collecting data Data analisis
Collecting data from labs kits
Analysis user’s data
Local IoT Service
DB st DB usr
Collecting data Collecting data Artificial Intelligent Data analisis Control
1 Collecting data from devices 4 Analysis user’s data
Raspberry Raspberry 2 Collecting data from labs kits 5 Make decisions
3 Analysis power consumption 6 Control devices
The control subsystem of electrical GPIO GPIO
1 2 . .n 1 2 . .n
equipment
Relay control
Wi-Fi Ethernet
Smart 220V PC
Projector 220V Smart
Wi-Fi
USB
data
data meter
meter Arduino
Relay control
DC motor controler
control
Relay
Condition Wi-Fi data
220V Smart data Wi-Fi Wi-Fi
meter Smart 220V PC
meter USB
Relay V A
A control
V
The control subsystem of electrical
A
equipment
Figure 1: Simplified structure of IoT-based Laboratory
Data collection from the SmartMeter and relay control is performed via a network control
controller, such as a Raspberry Pi or BeagleBone board. Such a device serves as a gateway between
the IoT service and the equipment. Its main functions include collecting data on the state of the
equipment (data is read directly from smartmeters or from the cloud, or simultaneously) and control
the inclusion of devices (commands from the local IoT service).
Figure 2: Smart Projector System Structure
The subsystem of work with educational stands works on a similar principle. The stand consists of
a PC, where the necessary software to remote access and communication with a debug board (in this
case Arduino) or other controller is installed in accordance with the tasks of the stand, sensors and
controls. For example, we will consider a stand for Investigation of the simplest DC circuits, the
structure of which is shown in Figure 3. The stand includes a PC with software installed, board for
getting electric values from investigated DC circuit, a set of sensors for measuring the current and the
voltage, etc.
Figure 3: "Smart" stand for Investigation of the simplest DC circuits
To measure the electricity consumed by the stand, a smart meter has been introduced, which sends
data on the power consumption of the PC and the controller or the computer power control relay
together with the stand. All data is collected and sent to a local cloud service where the student can
see the measurement results. Communication with the local cloud service and the PC is provided by a
secure local area network (Ethernet).
Figure 4: Local IoT Service
The local IoT service, presented in Figure 4, consists of the following subsystems: 1) user
database, which contains information about users and their access rights; 2) databases on measuring
equipment and experimental data for each user and stand separately; 3) apparatus of artificial neural
networks to control all elements of the laboratory in automatic mode [18]; 4) a set of software for
analyzing user data; 5) specialized software for equipment management in remote control mode and
in remote experiment mode.
The system also provides a global IoT service for working with remote users, the structure of
which is shown in Figure 5. This subsystem has limited functionality. The system has user and device
databases, as well as a data analysis system. That is, users can see the results of their experiments,
perform data analysis and order a so-called remote experiment, when measurements will be
performed only when the stand is vacated. In this implementation, the user will not have direct access
to the equipment, but only pre-order the experiment according to the configuration specified by him.
Figure 5: Global IoT Service
This layout enables dividing the work process in the laboratory and ensuring the security of the
whole complex [21], because remote users will not have a direct access to the PC and the stand.
In addition, learning should take place where students spend the most time. Today it is social
networks. The introduction of social networks [26-28] in a smart laboratory will allow first of all the
placement of a variety of materials:
Announcements - a function when it is necessary to inform as many students as possible in
a short time about changes in the educational process (change of class schedule),
competitions, Olympiads that will take place in the near future;
Survey - allows you to take into account the wishes and suggestions through various
surveys;
Photo album - a function that allows you to place educational material in drawings,
illustrations, photographs. This is necessary in order to speed up and facilitate the
assimilation of educational material;
Video - this feature allows you to demonstrate the experimental basis for the learning
process;
Materials for learning - diagrams, tables, text material, interesting facts, etc. (everything
that reflects the subject of study).
The social network allows the teacher to better remember students (correlate names and faces in
the audience), understand their interests and allows them to jointly create and improve the course.
Instead of simply consuming information, students become developers and experts in a virtual
learning group environment (creating messages, discussions, resources, and more).
Thus, the use of social networks can give the learning process more interactivity, positively affect
the results of cognitive activity of students, become an effective means of increasing motivation and
quality of learning, organizing teamwork, joint project activities, individualize the student's virtual
learning space.
4. Description the stand for Investigation of the simplest DC circuits and
experiment for it
The stand (see Fig. 3) includes a PC with the following software: LabVIEW [22] and Arduino_cc
[23]. LabVIEW is a system engineering software for applications that require testing, measurement,
and control with fast access to hardware and data analysis. The process of developing a SCADA
system in LabVIEW is simpler than in "traditional" development tools. It is also a fundamentally
different programming language called "G" and is a functional language that is similar in concept to C
++. Arduino_cc is an integrated cross-platform Java development environment that includes a code
editor, compiler and firmware transfer module. The environment is based on the programming
language Processing. It is similar to the Wiring language. In general, it is C ++, supplemented by
special libraries. This combination is chosen because different students have different levels of
programming skills and it is desirable for beginners to use the Arduino environment, and senior
students can develop projects of a more complex level, where it is advisable to use LabVIEW. The
Arduino Expansion Shield for Raspberry board was chosen as the hardware data acquisition and
control module [24] because it has more functionality than standard boards and the ability to integrate
with the Raspberry Pi.
A classic voltage divider circuit is proposed as a research electrical circuit (Figure 6)
Figure 6: Voltage divider
The stand is based on the previous scheme and includes the following components:
1. controller of data collection from sensors and supply voltage control. In this particular
implementation (Figure 7), an Arduino board is proposed, but you can also use another module based
on different microcontrollers;
2. analog current sensor Arduino 30A ACS712 on the Hall effect;
3. analog voltage sensor (V1 ... V3);
4. variable resistors R1 and R2 based on digital potentiometer X9C103S.
Figure 7: Measurement part of stand
4.1. The experiment
The essence of the experiment is to experimentally check the basic relationships of electrical
quantities for DC circuits with series connection of resistors. Student Increasing the value of one of
the resistors from 100 * (Number of students) Ohm to 200 Ohm in 20 Ohm steps, fills in table 1.
Table 1
Basic parameters
R1 Ohm R2 Ohm I, Amps V, Volts VR1, Volts VR2, Volts
700 200 0.0133 12 9.31 2.66
700 220 0.013 12 9.1 2.86
700 240
4.2. Software for experiment and remote access
Figure 8 shows the concept of a graphical user interface for conducting an experiment. Despite the
simplicity of the interface, it fully provides the necessary functionality and is a network client, and
provides control of the queue of experiments and access rights of users (students) to the measuring
stand, collection of measurements and control of the change in resistance on digital potentiometers for
a specific student card.
Figure 8: User interface for experiment
In addition to this, the program transmits data to a foggy service for remote access and control.
There are two options for working:
1. When a student conducts an experiment locally, the control interface on the fog service will
become inactive and will be asked to stand in the queue for the experiment, although the process itself
will be visible to other students.
2. If the control and experiment are removed, the main interface on the PC will also become
inactive and the student will be asked to enroll in the queue or select another free stand in the
laboratory.
All parameters will be collected by the Arduino board and transferred to the PC, from where, after
pre-processing (if necessary), they are transferred to the local IoT service.
The control system of the PC itself includes two devices:
1. DELOCK 25242 power supply control controller: ATX Power Supply Controller or its
analogue, which will be connected to the Raspberry Pi (see Figure 2);
2. smart current-consumption meter smart-MAC [25], which will independently transmit data to
the local IoT service on the electricity consumed by a specific PC.
This information is needed to analyze the total power consumption of the laboratory stands and to
further intelligently control the PCs and stands described above, as well as to automatically control
the on / off and start of the PCs themselves.
5. Basic functions and algorithms of IoT laboratory
From the above structure of the system (see Fig. 1), the laboratory must have many scenarios for
working with users and operating scenarios. In addition, the structure and algorithms of work in this
system should be divided into two main classes - algorithms of the subsystem of control of
educational equipment in the laboratory and algorithms of software operation of test benches, where
research will be conducted (Fig. 9).
Figure 9: Activity diagram of smart projector system
The main functions of the control system of educational equipment include: control of equipment
operation, equipment management, collection of data on the use of equipment, collection of energy
consumption statistics of classroom equipment for each device separately and in total.
Below is a scenario of the projector, which is located in the laboratory. The main controller of the
projector control is a script that operates on a local IoT service and has access to a database of
schedules in a particular laboratory, where it loads the time of laboratory, also downloads
presentations of laboratory classes, if already downloaded by the teacher. If (before a certain lesson
the teacher) did not download the presentation files, the system simply turns on the projector in
standby mode relative to the schedule, and the teacher can use the Web interface to download the
presentation or directly connect to the projector and give a presentation. In parallel with the projector
works a smart electricity meter, which sends data on energy consumption by the projector. These data
are collected on the local cloud service for further analysis by the laboratory administrator (if
necessary), as well as for reporting to the accounting department.
6. Conclusions
In this paper, the authors proposed a conceptual model of a training laboratory based on IoT,
which combines the functions of a remote laboratory and a regular classroom for practical classes.
The general structure of such system and the basic modules of functioning are presented.
Despite the fact that only one test bench is described, it is planned to implement about 10 test
benches for each PC (see Figure 1) for different types of tests.
The subsystem of control and management of electricity consumption by the equipment working
in the laboratory and the scenario of operation of measuring stands is described. Scenarios for
managing objects in this system and working with users are revealed.
In further works, the authors plan to implement the main modules of the system and the use of
Artificial Neural Networks for intelligent control and protection of the system as a whole.
7. Acknowledgements
The authors would like to thank the Ukrainian-German Education and Research Center of West
Ukrainian National University for the opportunity to use their training laboratories and support from
the Internet of Things project: Emerging Curriculum for Industry and Human Applications ALIOT
(573818-EPP-1-2016-1-UK -EPPKA2-CBHE-JP) for the provided equipment for measuring stands.
8. References
[1] D. Singh, G. Tripathi, and A. J. Jara, "A survey of internet-of-things: Future vision, architecture,
challenges and services." in 2014 IEEE worldforum on Internet of Things (WF-IoT). IEEE,
(2014): 287–292. DOI: 10.1109/WF-IoT.2014.6803174
[2] Srinivasan, C. R., Rajesh, B., Saikalyan, P., Premsagar, K., & Yadav, E. S. "A review on the
different types of internet of things (IoT)." Journal of Advanced Research in Dynamical and
Control Systems, 11(1), (2019). 154-158.
[3] Jeretta Horn Nord, Alex Koohang, Joanna Paliszkiewicz, "The Internet of Things: Review and
theoretical framework." Expert Systems with Applications, Volume 133, (2019). 97-108, ISSN
0957-4174, DOI: 10.1016/j.eswa.2019.05.014.
[4] P.P. Ray, "A survey on Internet of Things architectures." Journal of King Saud University -
Computer and Information Sciences, Volume 30, Issue 3, (2018). 291-319. DOI: 10.4108/eai.1-
12-2016.151714.
[5] O. Kanoun, T. Keutel, C. Viehweger, X. Zhao, S. Bradai, S. Naifar, C. Trigona, B. Kallel, I.
Chaour, G. Bouattour et al., "Next generation wireless energy aware sensors for internet of
things: A review." 15th International Multi-Conference on Systems, Signals & Devices (SSD).
IEEE, (2018).1–6. DOI: 10.1109/SSD.2018.8570695
[6] Ericsson Mobility Report, November 2020. URL:
https://www.ericsson.com/4adc87/assets/local/mobility-report/documents/2020/november-2020-
ericsson-mobility-report.pdf
[7] IoT evolution towards a super-connected world, URL: https://arxiv.org/abs/1907.02589
[8] Saravanan K., Julie E.G., Robinson Y.H. "Smart Cities & IoT: Evolution of Applications,
Architectures & Technologies, Present Scenarios & Future Dream." Internet of Things and Big
Data Analytics for Smart Generation (2018).135-151. DOI: 10.1007/978-3-030-04203-5_7.
[9] Ruth Ande, Bamidele Adebisi, Mohammad Hammoudeh, Jibran Saleem, "Internet of Things:
Evolution and technologies from a security perspective." Sustainable Cities and Society, Volume
54, (2020) 101728. DOI: 10.1016/j.scs.2019.101728.
[10] The shape of the MCU market URL: https://www.embedded.com/the-shape-of-the-mcu-market/
[11] A. Fernández-Pacheco, S. Martin and M. Castro, "Implementation of an Arduino Remote
Laboratory with Raspberry Pi." 2019 IEEE Global Engineering Education Conference
(EDUCON), Dubai, United Arab Emirates, 2019, pp. 1415-1418, DOI:
10.1109/EDUCON.2019.8725030.
[12] M. Poongothai, P. M. Subramanian and A. Rajeswari, "Design and implementation of IoT based
smart laboratory." 2018 5th International Conference on Industrial Engineering and Applications
(ICIEA), Singapore, 2018, pp. 169-173, DOI: 10.1109/IEA.2018.8387090.
[13] K. Mershad and A. Hamieh, "Using Internet of Things to Enhance Remote Experiments in
Learning Management Systems." 2019 IEEE International Smart Cities Conference (ISC2),
Casablanca, Morocco, 2019, pp. 458-464, DOI: 10.1109/ISC246665.2019.9071786.
[14] A. D. Martin, J. M. Cano, J. R. Vazquez and D. A. López-García, "A Low-Cost Remote
Laboratory for Photovoltaic Systems to Explore the Acceptance of the Students." 2020 IEEE
Global Engineering Education Conference (EDUCON), Porto, Portugal, 2020, pp. 1333-1337,
DOI: 10.1109/EDUCON45650.2020.9125211.
[15] T. Gomes, S. Pinto, T. Gomes, A. Tavares and J. Cabral, "Towards an FPGA-based edge device
for the Internet of Things," 2015 IEEE 20th Conference on Emerging Technologies & Factory
Automation (ETFA), Luxembourg, 2015, pp. 1-4, DOI: 10.1109/ETFA.2015.7301601.
[16] P. G. Lopez , A. Montresor, D. Epema, A. Datta, T. Higashino, A. Iamnitchi, M. Barcellos, P.
Felber and E. Riviere, “Edge-centric Computing: Vision and Challenges”, in ACM SIGCOMM
Computer Communication Review, Volume 45, Number 5, October 2015, pp.37-42. DOI:
10.1145/2831347.2831354
[17] Docker URL: https://docs.docker.com/
[18] W. Raad, M. Bueno-Delgado, M. Deriche and W. Suliman, "An IoT Based Inventory System for
High Value Laboratory Equipment." 2019 Sixth International Conference on Internet of Things:
Systems, Management and Security (IOTSMS), Granada, Spain, 2019, pp. 314-319. DOI:
10.1109/IOTSMS48152.2019.8939259.
[19] N. Fujii and N. Koike, "IoT Remote Group Experiments in the Cyber Laboratory: A FPGA-
based Remote Laboratory in the Hybrid Cloud." 2017 International Conference on Cyberworlds
(CW), Chester, 2017, pp. 162-165, DOI: 10.1109/CW.2017.29.
[20] I. Turchenko et al., "Approach to neural-based identification of multisensor conversion
characteristic." 2009 IEEE International Workshop on Intelligent Data Acquisition and
Advanced Computing Systems: Technology and Applications, Rende, Italy, 2009, pp. 27-31,
DOI: 10.1109/IDAACS.2009.5343030.
[21] M. Komar et al., "High performance adaptive system for cyber attacks detection." 2017 9th IEEE
International Conference on Intelligent Data Acquisition and Advanced Computing Systems:
Technology and Applications (IDAACS), Bucharest, 2017, pp. 853-858, DOI:
10.1109/IDAACS.2017.8095208.
[22] LabVIEW Community Edition, URL: https://www.ni.com/ru-ru/shop/labview/select-
edition/labview-community-edition.html
[23] Arduino IDE URL: https://www.arduino.cc/
[24] Arduino Expansion Shield for Raspberry Pi model B, URL: https://www.dfrobot.com/product-
1148.html
[25] Smart meters for any kind of consumptions! URL: https://smart-mac.com/en/
[26] G. Marques and R. Pitarma, “Using IoT and Social Networks for Enhanced Healthy Practices in
Buildings,” Smart Innovation, Systems and Technologies, (2018) 424–432. DOI: 10.1007/978-3-
030-03577-8_47
[27] V. Tyagi and A. Kumar, "Internet of Things and social networks: A survey." 2017 International
Conference on Computing, Communication and Automation (ICCCA), Greater Noida, India,
(2017) 1268-1270, DOI: 10.1109/CCAA.2017.8230013.
[28] G. A. Stelea, V. Popescu, F. Sandu, L. Jalal, M. Farina and M. Murroni, "From Things to
Services: A Social IoT Approach for Tourist Service Management." in IEEE Access, vol. 8, pp.
153578-153588, 2020, DOI: 10.1109/ACCESS.2020.3018331.