CC BY-NC-ND
30
DOI: 10.17323/2587-814X.2021.2.47.59
IoT systems in the process of multidisciplinary training of personnel for the digital economy and their design
Vladimir A. Komarov a,b ©
E-mail: [email protected]
Albert V. Sarafanov c
E-mail: [email protected]
a JSC "Academician M.F. Reshetnev Information Satellite Systems" Address: 52, Lenin Street, Zheleznogorsk 662972, Russia
b Siberian Federal University Address: 79, Svobodny Prospect, Krasnoyarsk 660041, Russia
c Vitte Consulting Co. (I-Teco Group) Address: 15, Kedrova Street, Moscow 117036, Russia
Abstract
In the context of digitalization of the most knowledge-intensive sectors of the domestic economy, the development of an industrial training system in the field of electronic instrumentation is of great importance. The key areas of its development with the use of information and communication technologies include the development and improvement of the technological basis for training and retraining of personnel in engineering educational programs. One of the elements of this basis is the service of multi-user remote access via the internet to a high-tech experimental equipment laboratory as a service based on internet of things (IoT) systems. Within the framework of this service, an urgent problem is to increase the functional saturation of automated stands/installations, which is currently characterized by a paucity of scientific research. The purpose of the research is to expand the areas of experimental research carried out in the mode of multiuser remote access based on specialized IoT systems. As a result, a method ofmultidisciplinary application of specialized IoT systems was developed. This consists of the technical implementation of possibilities for additional research: research into technologies underlying both multi-user distributed measuring and control systems and IoT systems in general; research into technologies used in their end-to-end computer-aided design; research into
joint interaction of several geographically distributed automated stands/installations, implemented on the basis of a four level IoT reference architecture. A methodology for the design of multi-user distributed measuring and control systems as specialized IoT systems has also been developed, focused on solving multidisciplinary research problems in an interactive dialogue mode based on single sample of experimental equipment. The methodology mobilizes organizational, technical and methodological support for the process of creating such systems with specified target characteristics. In general, the method and methodology developed open up opportunities for systematically implementing the basic principles of the "Education 4.0" concept in the preparation and retraining of engineering personnel in the field of electronic instrumentation.
Key words: internet of things (IoT); IoT system; laboratory as a service; technical teaching tools; multiuser distributed measuring and control systems; design methodology.
Citation: Komarov V.A., Sarafanov A.V. (2021) IoT systems in the process of multidisciplinary training of personnel for the digital economy and their design. Business Informatics, vol. 15, no 2, pp. 47—59. DOI: 10.17323/2587-814X.2021.2.47.59
Introduction
The accelerated pace of digitization of various areas of human activity is reflected primarily in the meaningful, methodical and technological components of industry training systems (ITS). At the same time, a feature of the current situation in the world which imposes a number of restrictions on traditional approaches to education is the pandemic COVID-19 [1]. In these conditions, the relevance of the following current trends in ITS is increasing:
♦ system-based use in various types of information and communication technology (ICT) training;
♦ increased emphasis on personalization of learning, the introduction of flexible learning schedules and the abandonment of rigid binding of trainees to specific locations;
♦ modernization of the traditional infrastructure of educational institutions — the introduction of digital library systems, the widespread use of ICT tools and specialized systems for simultaneous lectures in many different locations for online and offline modes, the use of
cyber-physical systems, as well as internet of things (IoT systems) systems, in particular, as means of conducting experiments in specialized distributed laboratories;
♦ ICT learning as a continuous process that accompanies a person throughout his life [2-10].
The Education 4.0 strategy highlights the following key aspects [11]:
♦ education on demand;
♦ personalization of education;
♦ learning at a convenient time anywhere;
♦ life-long learning.
The practical implementation of these aspects relies on the development and improvement of the ICT base for training and retraining in engineering education programs in the field of electronic instrumentation.
Thus, in the preparation and retraining of engineering personnel for the digital economy, the implementation of such aspects as "training at a convenient time anywhere" and "personalization of education" within digital educational environments are based on improving
the service of access to high-tech experimental equipment and modern specialized software — "laboratory as a service" [12—14]. This service allows trainees to perform in-kind and computational experimental research in remote access mode on the internet from any location. The relevance of the development of this service is associated with an increase in its efficiency. The main areas of service efficiency include increasing the capacity of laboratory equipment that is part of remote laboratories, as well as its functional saturation in terms of experimental research, both in the context of multidisciplinary and acquired in the course of training practices. Ways to solve the problem in terms of increasing the capacity of high-tech experimental equipment by the authors of this article have already been outlined in a number of works [14—15], and the approach to solving the problem of increasing the functional saturation of experimental equipment is the subject of this article.
1. Multidisciplinary use of remotely accessible experimental equipment
Remote laboratories are based on technical training tools (TTT) [16] developed using end-to-end digital technology, including internet of things technology [2—4, 17—19]. Other TTT include multiuser distributed measuring and control systems (DMCS) that implement high-tech equipment (measuring equipment, computing equipment, automated laboratory layouts/stands/installations) based on the concept of multi-tenancy [14]. The DMCS integration scheme into the IoT's four-tiered architecture is shown in Figure 1 [20, 21].
The process of creating DMCS samples as relevant IoT systems is based on the integration of a whole pool of digital technologies, which in turn may be the subject of individual experimental studies in addition to targeted domain research determined by appro-
priate automated layouts/stands/installations (Figure 1). In addition, advanced design technologies for such IoT systems can also serve as a separate subject of laboratory research in the context of end-to-end cycles of automated design of both individual elements and these systems as a whole. Thus, the current technical means of training in the form of DMCS (as appropriate IoT systems) are characterized by the following areas of laboratory research:
♦ in-kind experimental research in the target subject area determined by a specific ALL or their totality (purpose of the DMCS);
♦ research into the technologies underlying the functioning of DMCS (in addition to the intended purpose);
♦ research into technologies used in the design of DMCS (addition to the intended purpose).
Each of these areas is characterized by a specific area of research and its own set of training practices in Table 1. For a more detailed analysis of the components of the experiment, Table 1 introduces the following clarifications:
4 a "learning experiment" is a natural experimental study provided by relevant training courses and/or discipline programs;
4 the "scientific experiment" includes inkind experimental studies aimed at obtaining information on new, previously unexplored processes and/or phenomena (as part of the educational process).
Taken together, these areas allow the appropriate DMCS to be used as TTT to develop multidisciplinary training courses. This, in turn, allows students to develop skills such as "systemic abilities," "cognitive abilities" and "solving complex problems" that are quite popular in the major sectors of the digital economy. In addition, a number of areas allow for contextual training, for example, in the field of research, such as end-to-end automated design technology. Table 1.
Network
loT system
H2M
Customers
Physical entity
Fig. 1. DMCS as an loT system with four-tier architecture: a - loT levels; b - DMCS as loT system; ALL - automated lab layout; H2M, M2M - how to organize an interaction (Human-to-Machine, Machine-to-Machine)
a
b
Table 1.
Directions of multidisciplinary research based on multiuser distributed measuring and control systems (DMCS)
No Direction of research Research area Types of experimental research Developed development/research practices
1. The subject area defined by the TTT, as well as the properties of the DMCS as IoT systems as a whole 1.1. Physical processes or phenomena in individual functional nodes or electronic devices, as well as devices, complexes and electronic systems 1.2. Methods of organizing external (network) interaction between elements of IoT systems on the principle of M2M Educational / Scientific 1. The use of modern information and communication technology tools to analyze the processes taking place in electronic devices. 2. Applying the principles of building IoT systems as one of the promising directions in instrumentation. 3. The use of DMCS to automate the educational and scientific experiment as a segment of the modern digital educational environment*). 4. The use of electronic pedagogy methods that improve the quality of engineering teaching*). 5. Managing individual educational trajectories in a digital educational environment*).
2. The technologies behind the functioning of DMCS 2.1. Remote control of objects and processes over computing networks Educational The use of data technology in IoT systems
2.2 Digital data technologies and protocols Educational / Scientific
2.3. Statistical patterns of digital traffic transmission Scientific
2.4. Operating principles and scheduling in DMCS as in interactive dialog systems (queueing systems) Educational / Scientific 1. Applying query service disciplines with priorities in queueing systems. 2. Study of methods to increase the bandwidth of the DMCS as queueing systems. 3. The use of software to investigate the dynamics of the queueing systems.
2.5. User behavior in ergatic (human-machine) systems Scientific Methods of formal description of the operator's patterns in human-machine systems.
2.6 Functional GUI testing technologies Educational / Scientific Application of functional testing methods of the user GUI for human-machine systems (object control systems, virtual measuring systems, etc.)
2.7. Methods for processing the results of experimental research Educational The use of specialized software for digital, statistical and other processing of experimental research results
2.8. Computer measurement technology Educational The use of computer measurement technologies in the development of IoT systems
No Direction of research Research area Types of experimental research Developed development/research practices
3. DMCS design technologies 3.1. End-to-end automated design technology / "Digital Twin" Educational 1. Applying mathematical modeling techniques in the design process of IoT systems and cyber-physical systems in general 2. The use of methods of identification and verification of mathematical models of radio components in the process of researching electrical characteristics of electronic devices. 3. Applying methods to develop modern electronic design documentation. 4. Exploring the specifics of methodical support for end-to-end automated design of radio electronics based on CAE/CAD/CAM/PLM systems (conveyor, cyclical and iterative solution of design tasks based on specialized engineering procedures)
3.2. Technology and tools for developing applied software (frontend / backend) of specialized IoT systems Educational Application of technologies and tools for the development of applied software for automated systems (object management systems, virtual measuring systems, etc.)
*) Developing practices in the process of retraining teaching staff
As you know, the increasing functional complexity and cost of devices, complexes and systems make it difficult to properly equip training and research laboratories with modern samples as objects of appropriate experimental research [22—24]. At the same time, the use of the properties of DMCS, such as the IoT system (Figure 1b), namely the interaction of several DMCS, provides an opportunity to study on their basis several variants of the implementation of devices/complexes/systems in general. The organization of such research is as follows. Appropriate automated laboratory layouts which implement the variability of the performance of functional nodes/devices/ complexes can be placed in one location and provide the possibility of connecting them at the physical level to organize their interaction, and can be distributed geographically across several locations. In this case, it is possible to
"collect" them (to emulate the process of their joint operation) as part of a common device/ complex/system at the service level (Figure 1) by organizing M2M interactions at the communication level. This type of interaction is realized by creating a digital "image" of relevant signals (digitization of their fragments) and then exchanging them between DMCS of different locations. In order to implement such interaction, certain instrument samples provide technical devices (means) to record digital counting of relevant signals and/or to generate signals on their digitized fragments.
As an example of such a system that implements this principle of interaction, Figure 2 provides an example of an IoT supersystem structural scheme to ensure the process of training and retraining specialists for the aerospace industry. This system, based on the
Location 1 / DMCS N01 as TTT
Network
Test
Transmitting earth station equipment layouts information CD CD
User preferences Digital "image" of the signal from transmitting station ^ Œ. O —5
i -o ^ =5 CD Q_ ~' O
Signal analyzer ID
User preferences (interaction H2M)
-<---------------------------
The received test information (H2M interaction)
Location 3 / DMCS N01 as TTT
Arbitrary waveform generator
Digital "image" of the signal from the transponder
Receiving earth station equipment layouts
User preferences
The received test information
3 co
cu
User preferences (interaction H2M)
SERVICES OF THE MODERN DIGITAL EDUCATIONAL ENVIRONMENT
User preferences (H2M interactions)
User preferences, test information (H2M interaction)
Location 2/ DMCS N01 as TTT
Arbitrary waveform generator
Digital "image" of the signal rom transmitting station
Radio channel emulator/ Transponder layout
T
User preferences
Signal aanalyzer
Digital "image" of the signal from the transponder
3
TJ
Digital "image" of the signal from the transmitting | station (M2M interaction)
_ _ _ _ _ _ _ _ _ _ _ _ J
Fig. 2. An example of a research model of a satellite communications system In the form of an loT supersystem, elements of which are Individual Instances of distributed location
to o to
combination of interactions H2M and M2M, implements a model of the satellite communication system through the on-board transponder of the satellite, elements of which are individual instances of distributed location DMCS.
As part of the experimental studies carried out on the basis of this system, the user is given the opportunity to form a high-frequency signal with specified parameters (the number of signals (carriers) in the band, the data transfer rate, the type of modulation used, etc.), for example, a fragment of a complex envelope which is digitized and transmitted over the computer network to location 2. In location 2, this signal is reproduced and sent to the entrance of the transponder or radio channel simulator, simulating the conditions of the signal passing in the space system, taking into account the uneven amplitude-frequency characteristics and group time delay of the transponder, the non-linearity of the amplitude characteristics of its amplifier, phase noise heterodyne and etc. A digitized signal fragment from the transponder is transmitted over the computer network to location 3. Using technical means in location 3, on the basis of the received digital "image," a signal can be formed at the entrance of the models of the equipment of the receiving station to assess the possibility of its detection, reception, processing, etc., or to evaluate the generalized qualitative characteristics of the communication system under study (bit error rate, error vector magnitude, etc.) [25].
It should also be noted that under this approach, the equipment layouts located in locations 1—3 continue to be subjects of full-fledged experimental research for each training site conducted as part of the relevant training courses.
Consider the systematic areas of experimental research in Table 1. The implementation of the research in accordance with
items 2.1—2.2, 2.6 is based on the creation of a sample of the DMSC in the form of an open platform (with open software interfaces). Using this platform, the trainee can test and conduct functional testing of the self-developed and created user GUI to remotely control the automated laboratory layout on the basis of A pair of communication channels based on a personal computer, smartphone, laptop [26, 27] (using various software technologies and communication protocols based on various operating systems), as well as experimentally test the developed scheduling algorithms.
The study of statistical patterns and dynamics of the functioning of the DMCS as a queueing system (items 2.3—2.5) is carried out through processing the relevant statistical data. Such a given moment in time of sending and receiving the results of their implementation, the moment of arrival, beginning and end of the task on the measuring and control computer, the duration of the functional operations performed and other operations. Such data can be obtained during the operation of the DMCS, for example, by using the developed and tested software of the module which provides measurement and their centralized accumulation and storage [14].
Thus, the information content of DMCS as a TTT and their further multidisciplinary application within the designated areas should be laid down at the design stage through their implementation based on a pool of modern technologies, as well as using a scientific approach to their creation using modern computer-aided design tools. This approach should ensure the harmonization of the functional user tasks to be solved (in the corresponding subject area of ALL and the planned areas of interdisciplinary application) and the required technical characteristics of functioning as a queueing systems (average response time to user requests, the number of concurrent users, etc.).
2. Methodology for designing DMCS as specialized IoT systems
Based on the results of the research, a subject-oriented methodology for designing DMCS was developed, combining both well-known [28] and newly developed approaches:
♦ methods of increasing the efficiency of functioning (method of optimizing control operations, method of time division of multiple measurements, method of parallelizing functional operations);
♦ mathematical models of the dynamics of functioning, taking into account the heterogeneity of the behavior and service of users, the variability of the implemented scheduling algorithms, the dependence of the durations of the performed functional measurement and control operations on the parametric and functional content of tasks, etc., as well as adaptable software modules that implement them;
♦ procedures for solving particular design problems (designing hardware and software forming the boundary parameters of the dialogue scenario, calculating parametric sensitivity functions, etc.), which provide solutions to design problems in a formalized form;
♦ a set of technical solutions (a set of basic structures for hardware construction of DMCS, algorithms for scheduling a shared resource, algorithms for spectral measurement of signal parameters and software modules that implement them, basic unified virtual devices, basic design templates for a graphical user interface).
A generalized block diagram of the methodology is shown in Figure 3. Based on the built interaction of the components of the methodology and the complex application of modern computer-aided design tools within the DMCS design procedure as a specialized IoT system, a design solution with improved technical level indicators is sought. Additionally, in order to provide information support for the design process, the informational com-
position of the knowledge base of engineering solutions, accumulating and realizing the possibility of applying the experience of previous developments, is proposed.
The design results in the form of sets of electronic software and design documentation, the results of the analysis of various characteristics of DMCS, etc. are integrated as part of an interactive electronic technical manual [29]. Interactive electronic technical manual for corresponding samples of DMCS are also an auxiliary element of the educational process in the framework of the implementation of practices in the field of end-to-end computer-aided design technology. Maintenance of the process of pilot/industrial operation of industrial designs of DMCS is carried out on the basis of the proposed procedure for adapting to changing operating conditions (increase in the number of users, variability of users' thinking time, deployment of new access locations, etc.) [14].
Practical application of the proposed methodology and procedure for adapting DMCS helped to reduce the time of their design by 20—30%, allowed 3 to 6 times to expand the set of alternative design solutions synthesized in accordance with the specified requirements of the technical task, to increase in some cases the number of simultaneously served user terminals by 30% or more, as well as to create a number of industrial designs of DMCS in order to provide resources for the processes of field experimental studies of both ITS and production activities. The proposed methodology, in combination with the procedure for adaptation, makes it possible to ensure the effective use of DMCS as part of the "laboratory as a service" service while minimizing the cost of hardware and software through the implementation of the multi-tenancy concept. The high efficiency of such a service, implemented on the basis of DMCS, contributes to the wide possibilities of using the methods of electronic pedagogy in the preparation and retraining of engineering personnel for the digital economy.
Formalized representation of the mathematical, software, procedure and other support for the design process of DMCS in terms of their properties as loT systems
г 1
Terms of reference for the development of DMCS
i ♦ a set of functional user tasks to be solved;
♦ the number of concurrently served user terminals; i ♦ efficiency of functioning, etc.
l _ _
г 2 ■
Procedure for forming boundary values of dialog scenario parameters
3 ■
Hardware and software design procedure
Hardware and software design procedure
r5-
Procedure for evaluating the parameters of the dialogue scenario
г 7-
Mathematical models of the dynamics of functioning
(forecasting of functioning)
Analytical modeling method
Simulation method
Database of the results of experimental studies of task transmission delays in DMCS over various types of computer networks
i-12-
Procedure for calculating parametric sensitivity functions
Д-14-
\t V if v
a A
4
A set of basic structures for hardware construction
(single-object / multi-object)
г 6-
Methods for improving the efficiency of functioning
N
ei
-¡a S2 iE
P ^
-о p о -fa
S S S R
g> « ■ Çy .g
|S Ц
"та fe il &
"ö .о
■S .3
CD
CD О
CT-
"o fc fc= S
T-i о
О С: о со
со 43 e
r10-
Complex of basic measuring solutions
11
Basic GUI design patterns
(H2M interaction)
13
Procedure for analyzing the technical characteristics of DMCS using CAD/CAE/CAM/CALS
DMCS design procedure
(provides for the choice of a design route, implemented on the basis of one of the methods: morphological synthesis, labyrinth design, a method of directed improvement of the prototype, etc.)
-15-
The set options for design solutions DMCS
Scheduling algorithms
&
Database of
engineering solutions DMCS
8
A variant of the design solution for DMCS
(terms of reference, reports on the results of research by using mathematical modeling, sets of design and program documentation, a set of target indicators, etc., integrated into the interactive electronic technical manual)
1 17 Procedure for adapting DMCS to changing operating conditions
Fig. 3. Methodology for designing DMCS with specified technical characteristics: blocks 2-7, 9-11 - solving design problems in research areas according to the items 2.1-2.8 of Table 1; blocks 3, 8, 11, 13, 14, 16, 17 - solving design problems in research areas according to the items 3.1, 3.2 of Table 1
The "laboratory as a service" service allows several educational institutions or training centers (as operating organizations) to move from the deployment and maintenance of high-tech experimental equipment to its temporary lease and remote access via the internet (implementation of the economic model of sharing economy) [12—15, 30].
Conclusion
The directions of multidisciplinary research considered and the proposed approach to their implementation on the basis of DMCS are provided due to the following factors:
♦ a wide range of experimental laboratory studies as part of modern educational environments, allowing students to study various aspects of IoT systems (principles of operation, design methods, purpose), taking into account the key aspects of the Education 4.0 strategy and the development directions of electronic instrumentation;
♦ the development of students in the process of educational and scientific experiments,
practices focused on solving a wide range of tasks in the field of digitalization of various spheres of human activity;
♦ development of students' skills within the framework of professional competencies, such as "systemic abilities," "cognitive abilities" and "solving complex problems" which are in demand in the main sectors of the digital economy;
♦ development of DMCS samples with specified characteristics (number of served users, response time) due to interrelated components of a domain-specific methodology which include the proposed methods, models, procedures and specialized software, and also ensure the effective use of modern computer-aided design systems;
♦ increasing the efficiency and functionality of the "laboratory as a service" service for the processes of training and retraining of personnel in the field of electronic instrumentation based on the methods of electronic pedagogy. ■
References
1. Kuzminov Ya.I. (2020) The Viral Revolution: how the pandemic will change our world. Available at: https://www.rbc.ru/opinions/society/27/03/2020/5e7cd7799a79471ed230b774 (accessed 29 March 2020) (in Russian).
2. Auer M.E., Azad A.K.M., Edwards A., De Jong T., eds. (2018) Cyber-physical laboratories in engineering and science education. Springer. DOI: 10.1007/978-3-319-76935-6.
3. Angrisani L., Cesaro U., D'Arco M., Grillo D., Tocchi A. (2018) IOT enabling measurement applications in Industry 4.0: Platform for remote programming ATES. Proceedings of the Workshop on Metrology for Industry 4.0 and IoT, Brescia, Italy, 16—18 April 2018, pp. 40—45.
DOI: 10.1109/METR0I4.2018.8428326.
4. Elsaadany A., Soliman M. (2017) Experimental evaluation of Internet of Things in the educational environment. International Journal of Engineering Pedagogy, vol. 7, no 3, pp. 50—60.
DOI: 10.3991/ijep.v7i3.7187.
5. Chen H. (2017) Applications of cyber-physical system: A literature review. Journal of Industrial Integration and Management, vol. 2, no 3, article no 1750012. DOI: 10.1142/S2424862217500129.
6. Lee E.A. (2015) The past, present and future of cyber-physical systems: A focus on models. Sensors, vol. 15, no 3, pp. 4837-4869. DOI: 10.3390/s150304837.
7. SerdarAsan S.,Isikli E. (2020) Engineering education trends in the digital era. IGI Global.
8. Zaslavskaya O.Y., Kirillov A.I. (2017) New features of education informatization — "internet
of things". RUDN Journal of Informatization in Education, vol. 14, no 2, pp. 140—147 (in Russian). DOI: 10.22363/2312-8631-2017-14-2-140-147.
9. Heradio R., de la Torre L., Dormido S. (2016) Virtual and remote labs in control education: A survey. Annual Reviews in Control, vol. 42, pp. 1-10. DOI: 10.1016/j.arcontrol.2016.08.001.
10. Gomes L., Bogosyan S. (2009) Current trends in remote laboratories. IEEE Transactions on Industrial Electronics, vol. 56, no 12, pp. 4744-4756. DOI: 10.1109/TIE.2009.2033293.
11. Hussin A.A. (2018) Education 4.0 made simple: ideas for teaching. International Journal of Education & Literacy Studies, vol. 6, no 3, pp. 92-98. DOI: 10.7575/aiac.ijels.v.6n.3p.92.
12. Tawfik M., Salzmann C., Gillet D., Lowe D., Saliah-Hassane H., Elio Sancristobal E., Castro M. (2014) Laboratory as a Service (LaaS): A model for developing and implementing remote laboratories as modular components. Proceedings of the 11th International Conference on Remote Engineering and Virtual Instrumentation (REV 2014), Porto, Portugal, 26—28 February 2014, pp. 11-20.
DOI: 10.1109/REV.2014.6784238.
13. Deshko I.P., Kriazhenkov K.G. (2015) Labs as a service at study ICT courses. Perspectives of Science and Education, no 1 (13), pp. 70-74 (in Russian).
14. Komarov V.A., Sarafanov A.V., Tumkovskiy S.R. (2019) Multi-user distributed information-control systems as an element of modern digital educational environment. Information and Control Systems, no 2, pp. 89-94 (in Russian). DOI: 10.31799/1684-8853-2019-2-83-94.
15. Komarov V.A., Sarafanov A.V., Tumkovskiy S.R. (2019) Comparison of the effectiveness of methods to increase the throughput of experimental equipment with remote access. Information and Control Systems, no 6, pp. 68-76 (in Russian). DOI: 10.31799/1684-8853-2019-6-68-76.
16. Standardinform (2010) GOSTR 53626-2009. Information and communication technologies in education. Training equipment. General statements. Moscow: Standardinform (in Russian).
17. Ray P.P. (2018) A survey on Internet of Things architectures. Journal of King Saud University - Computer and Information Sciences, vol. 30, no 3, pp. 291-319. DOI: 10.1016/j.jksuci.2016.10.003.
18. El-Hasan T.S. (2019) Internet of Thing (IoT) based remote labs in engineering. Proceedings of the 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), Paris, France, 23-26April 2019, pp. 976-982. DOI: 10.1109/CoDIT.2019.8820591.
19. Popovic B., Popovic N., Mijic D., Stankovski S., Ostojic G. (2013) Remote control of laboratory equipment for basic electronics courses: A LabVIEW-based implementation. Computer Applications in Engineering Education, vol. 21, no E1, pp. E110-E120. DOI: 10.1002/cae.20531.
20. ISO (2018) ISO/IEC 20924:2018. Information technology - Internet ofThing (IoT) - Vocabulary. Available at: https://www.iso.org/standard/69470.html (accessed 15 October 2020).
21. ISO (2018) ISO/IEC 30141:2018. Internet ofThing (IoT) - Reference Architecture. Available at: https://www.iso.org/standard/65695.html (accessed 15 October 2020).
22. Kucuk K. (2018) RTWiFi-Lab: A real-time Wi-Fi laboratory platform on USRP and LabVIEW for wireless communications education and research. Computer Applications in Engineering Education, vol. 26, no 1, pp. 111-124. DOI: 10.1002/cae.21865.
23. Bykhovsky D. (2020) Teaching wireless channel modeling with software-defined radio. Computer Applications in Engineering Education, vol. 28, no 2, pp. 314-323. DOI: 10.1002/cae.22195.
24. Toyoda Y., Koike N., Li Y. (2016) An FPGA-based remote laboratory: Implementing semi-automatic experiments in the hybrid cloud. Proceedings of the 201613th International Conference on Remote Engineering and Virtual Instrumentation (REV), Madrid, Spain, 24-26 February 2016, pp. 24-29. DOI: 10.1109/REV.2016.7444435.
25. Sklar B. (2003) Digital communication. Theoretical bases and practical application. Moscow: Williams (in Russian).
26. Odeh S. (2014) A web-based remote lab platform with reusability for electronic experiments in engineering education. International Journal of Online and Biomedical Engineering, vol. 10, no 4, pp. 40-45. DOI: 10.3991/ijoe.v10i4.3729.
27. Sierra-Fernandez J.M., Florencias-Oliveros O., Espinosa-Gavira M.J., Palomares-Salas J.C., Aguera-Perez A., Gonzalez-de-la-Rosa J.J. (2020) Reconfigurable web-interface remote lab for instrumentation and electronic learning. Proceedings of the 2020 IEEE Global Engineering Education Conference (EDUCON), Porto, Portugal, 27—30April 2020, pp. 713—717.
DOI: 10.1109/EDUCON45650.2020.9125380.
28. Kirsanov A.Yu., Salakhova A.Sh. (2009) Experimental study of the statistical dynamics of a remote control system for an experiment in multiuser mode. Non-Linear World, vol. 7, no 5, pp. 405—409 (in Russian).
29. Standatdinform (2018) GOSTR 54088-2017. Integrated logistic support. Operating and maintenance documentation in interactive electronic technical manuals format. General provisions and general requirements. Moscow: Standatdinform (in Russian).
30. Garcia-Loro F., Baizan P., Blazquez-Merino M., Plaza P., Aroca A.M., Orduna P., Cristobal E.S., Castro M. (2019) Spreading remote laboratory scope through a federation of nodes: VISIR case.
IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, vol. 14, no 4, pp. 107—116. DOI: 10.1109/RITA.2019.2950131.
About the authors
Vladimir A. Komarov
Cand. Sci. (Tech.), Associate Professor;
Head of the Complex Modeling Space Systems Group, Department of Design of Space Systems and Communication Complexes, Information Relaying and Special Purpose, JSC "Academician M.F. Reshetnev Information Satellite Systems", 52, Lenin Street, Zheleznogorsk 662972, Russia; Associate Professor, Basic Department of Radio-Electronic Equipment of Information Systems, Siberian Federal University, 79, Svobodny Prospect, Krasnoyarsk 660041, Russia; E-mail: [email protected] ORCID: 0000-0001-9210-9908
Albert V. Sarafanov
Dr. Sci. (Tech.), Professor;
Business Development Director, Department of Enterprise Management Systems, Vitte Consulting Co. (I-Teco Group), 15, Kedrova Street, Moscow 117036, Russia;
E-mail: [email protected]
ORCID: 0000-0003-4264-9388
