Increasing interdisciplinarity and science interest on the undergraduate students: the use of an algorithmic ignitor and low-cost sensors in cloud computing

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Posted on 4 Mar 2024 — CC-BY 4.0 — https://doi.org/10.36227/techrxiv.170956708.87181699/v1 — e-Prints posted on TechRxiv are preliminary reports that are not peer reviewed. They should not b...
Leandro Colevati dos Santos1, Lilian M Silva1, Maria L P Silva1, and Sebasti˜ao G dos
Santos Filho1
1Affiliation not available
March 04, 2024
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Increasing interdisciplinarity and science interest on
the undergraduate students: the use of an algorithmic
ignitor and low-cost sensors in cloud computing
Leandro C. Santos, Lilian M. Silva, Maria L. P. Silva, Sebastião G. dos Santos Filho
Abstract— This work aimed the increase in interdisciplinarity
and in engineering inclusion using algorithms during
undergraduate disciplines and/or scientific initiation.
Methodology follows a case study, focuses two of the largest
educational institutions in Brazil and comprises four Teams,
named as Team USP, Team FATEC SP, Team FATEC FR e Team
FATEC ZL. At the end of a two years period, the work has
involved 4 course instructors and 17 undergraduate students. The
main achievements were: high involvement among the four Team
people; high diversity with multidisciplinary Teams being
spontaneously formed and students approaching subjects that do
not belong to their major area, such as, business school students
developing a meteorological station. Thus, the use of an innovative
algorithm as a basis for the development of extra-class work
and/or scientific initiation is an appropriate stage for increasing
interdisciplinarity.
Keywords—Interdisciplinary, Transdisciplinary, Engineering
Curriculum, Engineering Technology
I. INTRODUCTION
The complexity of the highly connected world turned the
concepts of multidisciplinarity, interdisciplinarity and
transdisciplinarity into a major issue in higher
education[1],[2],[3]. This is especially true for engineering
fields since more and more complex problems should be
addressed by these professionals, even at the beginning of their
careers, which consequently demand for obtaining several
different skills if compared to a few decades ago; for instance,
with the advent of Industry 4.0, the formation of highly
qualified multidisciplinary Teams has become commonplace.
Therefore, some of those required skills were summarized by
Jarosz, Soltysik & Zakrzewska[4] on their review. The authors
state their achievements pointing out that since young people
will deal with “come up with how to do it and learn everything
you need to be able to do it” “such a change means that
emphasis is placed on problem-solving education and
developing self-learning competences.”, i.e. not only
“preparing for lifelong learning becomes one of the most
important competences” but also “continuous training and
development in the areas of advanced web-based applications,
emotional intelligence, flexibility, adaptability to change,
leadership and presentation skills” became essential.
The current interdiscipinarity, highly required and driven by
Industry 4.0, stressed the definition of engineering fields to
much broader concepts. As pointed out by Roy & Roy[5], the
engineering field is much more than its core, “mechanical,
mining/metallurgy, civil, electrical and electronics, and
chemical”, as defined in the 20th century. Nowadays engineers
would be ´Tshaped thinker´, deep in one field, but able to work
across all field and able to communicate well”; therefore,
mixing of engineering and biology/medicine created the
Biomedical Engineering that leads to tissue engineering,
“genetic engineering, neural engineering, pharmaceutical
engineering, and bioinformatics”. Consequently, today
engineers come across with concepts from Medicine, Arts &
Entertainment, Social Science, and Business etc. In other
words, on such context knowledge on science as The
Background, not only for engineers but also for most of the 21st
century citizens, is indispensable.
Nonetheless, graduation in the so-called STEM (science,
technology, engineering, and mathematics) fields are
particularly difficult in Brazil because there is a general lack of
interest from high school students and, among the ones that
decided for these fields, an intense dropout[6], mainly due to
difficulties with Mathematic concepts[7]. Furthermore,
apparently teacher as a role model is a factor as important as a
better high school; nevertheless, women continue to be
underrepresented, being only 20% of overall students. Thus,
countries with such situation tend to emphasize the STEM
concepts in the earlier degrees to increase the undergraduate
student’s population, sometimes approaching professors and
using expensive equipment[8] or inserting industries on such
programs[9].
The Industry 4.0 also requires new employees and
entrepreneurships; thus, engineering education must comply
with and, in order to do so, one important tool is the cloud
services. On this scenario, Verma[10] et al. “recommends the
involvement of IoT sensor-based data acquisition methods, AI-
based data classification and integration methods over fog
nodes”. Thus IoT, Fog, and Cloud, constitute three layers of a
framework: 1)The Physical Layer or IoT Layer; 2)The Fog
Layer and 3)The Cloud Layer. Whereas IoT data Layer is pre-
processed on fog nodes the Fog Layer is responsible for “all
data pre-processing activities, including classification, filtering,
and abstraction”. However, as stated by Rassudov[11] et al, that
implies, aside cybersecurity problems, the dependence of the
customer from the supplier since the supplier can disable the
cloud-based functionalities at any time. These approaches are
consistent with Kipper[12] et al review on Education 4.0 that
proposes Learning Factories as a connection among university-
company-government, creating a simulated real work in an
innovative and learning environment.
To illustrate the relevance of multi- and interdisciplinarity
in the study of engineering, a systematic review, as presented in
Table 1, was performed.

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Table 1 - Systematic review on Industry 4.0,
Interdisciplinarity and student skills
Author
Concepts
Year
Chilson & Yeary[15]
Demonstrates a report by the National Research Council
on the need for universities to present possibilities for
new teaching approaches, incorporating the possibility
of multidisciplinary in teaching for students.
2011
Wang et al.[16]
Presents interdisciplinarity as a group of researchers
developing research together, each from their own
perspective, bringing their knowledge to the object to be
researched for a common result.
2016
Chong et al.[17]
Presents as pillars of Industry 4.0 simulations, vertical
and horizontal integration and the large mass of data and
its need for application in computing systems, such as
cloud computing.
2018
Prieto et al.[18]
Presents the mission of universities in engineering
education, focused on the needs of Industry 4.0. Explains
competence as a multidimensional element.
2019
Van de Voorde &
Fitzpatrick[19]
Explain that academic knowledge transcends the concept
of discipline and that the academy needs to organize
itself to provide a network of shared qualifications.
2019
Dambrot[20]
Describes as an important point the junction between
disciplines historically consecrated with success.
2019
McNeese[21]
Explains that the interdisciplinary units have common
goals and must work together, but points out that putting
people with different prior knowledge in the same
process to produce and cooperate is hard work.
2019
Harmuch Neto et
al.[22]
Describes that the application of multidisciplinary
knowledge in a software development environment can
give the professional characteristics such as curiosity,
creativity, self-teaching and communication. With these
skills it is possible to present new skills and abilities
2020
Romero-González &
Bourguet[23]
Demonstrates the partnership between eng ineering areas
to develop a common product. Demonstrates
convergence and development success.
2021
Milici et al.[24]
Describes the teacher as a tutor, that i s, a facilitator of
learning beyond the conceptual.
2021
Pindado Carrion et
al.[25]
Deals with the exponential growth of Information and
Communication Technology elements, using platforms
such as Arduino for teaching in different areas, such as
Control, Automation and Robotics.
2021
Roy & Roy[5]
Demonstrate that the result of the application of
interdisciplinarity can bring evolution in all areas applied
by its combination and the improvement of a certain area
by introducing elements from another area.
2021
Da Silva et al.[26]
Explains that changes in technology are constant and the
integration between Teams, in this case, academia and
industry, is essential to improve the knowledge of
undergraduates.
2022
Verma et al.[10]
Demonstrates that Education 4.0 reproduces the bias of
Industry 4.0 in its challenges and technologies, even
embryonic ones. It considers as applicable technologies,
the concepts of Internet of Things, Fog Computing,
Cloud Computing and Pervasive Computing.
2022
Conversely, considering the term algorithmic “has been
used to mean automated decision-making systems in
general”[13], this work deals with one, but with a particularity
(novelty) that these systems are based on services. Currently,
they became the basis of a complex society and, consequently,
of Industry 4.0. Thus, much research on teaching with such
tools has been carried out[14], but no assessment of the ability
of such systems to increase interdisciplinarity has been
addressed yet.
Therefore, the aim of this work is to increase multi and
interdisciplinarity as well as engineering inclusion during
scientific initiation and undergraduate course completion using
an algorithmic ignitor and low-cost sensors in cloud computing,
as will be detailed in the method.
II. METHOD
This work follows a case study methodology. As pointed
out by Yin[27], case studies have at least three main steps:
design the case, prepare collect and analyze data and finally
conclude. It is also considered that meetings to discuss the case
are needed and should be constant.
1 https://www.cps.sp.gov.br/cursos-oferecidos-pelas-
fatecs/#:~:text=O%20Centro%20Paula%20Souza%20mant%C3%A9m,com%
20tr%C3%AAs%20anos%20de%20dura%C3%A7%C3%A3o.
The design phase considered the definition of research
Team and respective milestones, which also mean the research´
goals had already been defined.
Selection and preparation corresponding to all phases that
requires measurement; thus, that implies that all works
developed by undergraduate students were already defined,
executed and described.
Analyze the results requires to interview the undergraduate
students to evaluate the effectiveness of the former phases and
verify documents regarding performance improvement or
course completion, i.e., evaluate student trajectory. The JAD
(Joint Application Development) methodology, as explained by
Picchi Junior[28] (2011), allows the free interaction of
development participants and, at the end, they can answer
questionnaires that define their experience in the process. The
software and the experimental arrangement were presented to
users (Students and Course Instructors) and Information
Technology (IT) Specialist, considering that the system allows
the approach of active users (who propose new functionalities)
and passive users (who access the generated data). The
presentation was individual and informed the following data, in
addition to the presentation of the software and measurement
system:
• IT Specialist: Approximate time of 10 minutes and performance
evaluation;
• Course Instructor User: Approximate learning time, approximately
10 minutes. Possibilities of using the link and discussion of possible
doubts;
• Student User: Discussion of software and hardware. Active and
passive use of the software and discussion of possible doubts.
Finally, using PDCA and DCAP[29] strategies to implant
the methodologies that proved to be useful for teaching and
learning.
A. The Case Study: Places of case study and research Team
This work was developed during effectively three years
period, from 2019 to 2022, with unwanted stop during the
pandemic. It was developed in two distinct, although partners,
institutions: Paula Souza Center and University of São Paulo.
On both institutions, electronic system and IT departments were
involved; thus a brief description of such unities, and respective
researcher Team, is provided.
According to its homepage, Paula Souza Center has 75
Faculties of Technology (FATEC) distributed in 69 cities in São
Paulo. These FATECs attend more than 96 thousand students,
enrolled in 87 technological graduation courses, which have a
workload of 2,400 hours, with duration of three years1. In this
study, three different FATECs are involved: Franco da Rocha2,
São Paulo3 e Zona Leste4, the physical distances between their
localizations are shown in Table 2, which means the average
distance between the study sites of about 35 km, and, despite
the great distance, they are served by efficient means of
transport. Considering the main background formation in each
study place, the students in São Paulo has mainly electronics
formation , Zona Leste, mainly Data Science formation and.
Franco da Rocha. mainly formation in management.
2 http://www.fatecfrancodarocha.edu.br
3 http://wwwfatecsp.br/
4 http://www.fateczl.edu.br

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University of São Paulo is “the major institution of higher
learning and research in Brazil, is responsible for educating a
large part of Brazilian Masters and Ph.D’s.”; furthermore, it
also offers services to the foreign community, such as graduate
and undergraduate programs and internship5. This work
involved School of Engineering6(poli) founded in 1893 it is a
traditional school with internationalization, which allows
students to seek or obtain a double degree on two continents; in
addition, poli has all branches of Engineering.
Table 2 – Distance between study places
Locals
Distance (Km)
Escola Politécnica – FATEC São Paulo
15
FATEC Zona Leste – FATEC São Paulo
24
FATEC Zona Leste – Escola Politécnica
37
Escola Politécnica – FATEC Franco da Rocha
39
FATEC São Paulo – FATEC Franco da Rocha
41
FATEC Zona Leste – FATEC Franco da Rocha
55
The research Team has three course instructors responsible
for different disciplines and presenting distinct backgrounds
with experience in education, as shown in Table 3. These
groups were named as USP Team,FATEC SP Team, FATEC
FR Team and FATEC ZLTeam.
Table 3: Research Team, respective backgrounds and defined
objectives
Person
Abilities
1
IT specialist, 20 years as course instructor and researcher in Data
Science
A group of 7 students in their final year of graduation and two students
developing Course Completion Work in the area of education and IT
2
Experience in Management and Education, 20 years as a course
instructor and research in School-Company Partnership
Advisor of five undergraduate students
3
Experience in Environmental Management, 40 years as a course
instructorand research in this area
Advisor of five undergraduate students
4
Experience in Sensors, Electronic Systems and Integrated Circuit
Manufacturing Processes
Colaboration with all Team
For automating a decision-making system, the use of
sensors and cloud computing based on services are proposed in
order to increase interdisciplinarity and science interest. After
the preparatory meetings, each researcher found undergraduate
students, preferably interested in CI, for research development.
Purposely, the students in question do not need to have a
background in electronics. Each of them was given a task with
the use of Arduino. The choice of this platform meets the
requirements of the maker culture, that is, it facilitates students
to develop small projects in electronics in the do-it-yourself
format. All students were allowed to use a previously developed
system based on fog computing[30],[31].
The students were guided for at least one year and all of
them developed final reports ranging from the defense of
graduation work to the end of the CI period.7. It should be
emphasized that the only restriction to the development
5 https://www5.usp.br/#english
6 https://www.poli.usp.br/
7 It is common for a student to work under the supervision of a teacher. This
process is called IC (Scientific Initiation)
proposed by the students is the use of sensors for the
environmental area.
Most of the meetings were virtual, not only because of the
pandemic but also because the culture of working anywhere at
any time is created, which is consistent with Industry 4.0.
B. Available Tools
To develop the projects each instructor used his personal
quota for research8. In any case, the estimated cost (and the
actual cost) proved to be small, as will be described later.
The project has 4 distinct phases: in the first, a fog
computing tool, named Algorithmic Ignitor in Cloud
Computing, was developed and for now on the acronym AIC2
will be used. Many students, a majority being from IT field,
help on the development of AIC2 and envisage possible uses.
On the second layer (phase 2) AIC2 were disponibilized for
Paula Souza Center instructors’ use; thus, this corresponds to a
pilot initiative. Phase 3 is the expansion on the use/development
and involve more than one institution.
III. THEORETICAL
The following definitions guided this work:
interdisciplinarity, skills, Scientific Initiation & Course
graduation work and the electronic sector.
A. Interdisciplinarity
During the research of several subjects and, at the time of
its deepening, we may be faced with the need to correlate
subjects that are approached in similar disciplines, therefore,
crossing barriers denominated, according to Lélé and
Norgaard[32], horizontal, that means, to bring, for the
environment of a research focused on a certain discipline,
concepts from another discipline, in order to improve the
results.
Brewer [33] explains that the crossing and combination of
concepts between disciplines is called interdisciplinarity,
adding value, aiming that the final result with the applied parts
is much more interesting than partial studies. In this line,
Huutoniemi[34] presents a particular characteristic of
interdisciplinarity, which is that it is not characterized as a
single element, but as a series of different forms of
interconnections between the concepts and methods applied in
the disciplines.
Analysis presented by Chettiparamb[35] presents as
consequences of the application of interdisciplinarity, the
increase of creativity, the contribution given by researchers
from external disciplines are usually valuable, the detection of
errors is more common among researched with knowledge in
multiple disciplines, the emergence of interesting elements that
only appear at the junction of disciplines, everyday problems
require interdisciplinary solutions, researchers gain in scope
flexibility, among others.
For the development of interdisciplinarity, Chettiparam [35]
points out patterns of relationships between disciplines:
8 It is possible for the professor to claim scientific initiation grants and,
eventually, cost help for the development of work.

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a) The development of conceptual links to work between
perspectives;
b) Develop a level of organization with its own processes for dealing
with unresolved problems;
c) Creation or use of research methods to work the theoretical
concept of a concept into another concept;
d) Develop an extension of the theoretical model for application in
another theoretical model;
e) Develop a method of presenting the theoretical concept,
individually, while creating the concept junction model.
A natural consequence of the application of the concept of
interdisciplinarity and, therefore, as demonstrated by Lélé and
Norgaard [32], to break the barriers between these
intercommunications, which will bring greater comfort to the
participating researchers in the sense of communicating their
results, bringing people to its way of working, having a shared
environment where everyone involved behaves as a group.
Lélé and Norgaard[32] presents four main barriers to
interdisciplinarity:
a) The values and styles applied in the research stages;
b) The approach of different disciplines to the same phenomenon;
c) Differences in treatment approaches;
d) The way society interacts with academic negotiations.
When it comes, therefore, to the application of
interdisciplinary research, we have, as detailed by
Huutoniemi[34], the active interaction between the research
elements, namely, concepts, research problems, results
obtained, among others, which leads to the combination of
analysis of the results by several points of analysis. In this way,
we can, in addition to the aforementioned analysis by
Brewer[33] bring the context of interaction between methods
and tools, which transcends the vision of sum of parts to a
whole, for synergistic integration.
Therefore, this work with the broad definition of
interdisciplinarity, one that considers the interaction of
interdisciplinary Teams and different problems linked by
similar tools, in the algorithmic approach for a decision-making
system using end-point sensors and cloud computing based on
services.
B. Skills
To adapt university graduate curricula to this new
millennium some organizations provided competence
requirements lists of skills that education should help to meet.
For OECD (Organization for Economic Co-operation and
Development) these skills are mainly: Interdisciplinary,
Creative, Analytical and technical skills aside with Global
awareness whereas World Economic Forum focuses also on
Complex problem solving, Critical thinking, Judgment and
decision making. This review also carried out an analysis of
trends in Keywords; for Education field they are Educational
intelligence; Maritime education; Education 4.0; Future
education; Workforce education; Online education;
Management education; Lean education and for Engineering
Education comprises Modern engineering education; New
Engineering Education; Control engineering education.
C. Scientific Initiation & Course Graduation Work
According to Fernandez et al.[36], Teamwork and project
management aside a better understanding of interdisciplinarity
subjects can be improved by project-led education, PLE.
Fernandes et al [36] also reinforce de idea that such projects,
among others active learning strategies, are like a milestone for
Engineering education since it helps “to stimulate students’
motivation for learning, to support the development of technical
and transversal competencies, linking theory to practice
amongst others”. This author describes an attempt to implement
PLE concepts in a semester course as “an important strategy to
avoid dropout and underachievement problems usually faced by
first-year engineering students”; such course was implemented
in the first year of masters´ degree program. Another important
characteristic on that attempt was the coordination Team that
involved tutors, that play a vital role on student support, and
researchers; it is also worth noting that students clearly
identified a high level of interdisciplinary and “benefits of
working with people with different skills, ideas and
perspectives” on such approach. In other words, the study
reinforced the need of interactivity among all players on the
learning process: “students with teachers, students with
students, students with researchers, students with companies”.
D. The Electronic Sector
The electronic production area is the topmost example of
complex environment, mainly due to miniaturization that
impacts not only the electronic devices but also several
unsuspected areas, such as Chemistry and Chemical
Engineering, not to mention Medicine. Therefore,
transdisciplinarity should be a major concern in Electrical
Engineering Education. In fact, transdiciplinarity is pursued by
several means, for instance from graduation projects that also
involve freshmen students[37] or with a interdisciplinarity
approach between traditional Electrical Engineering and
Computer Engineering courses[38] or curriculum[39], real
industrial systems and virtual models for learning technicians
in the field of mechatronics[40] or even Electrical Engineering
and Meteorology, involving both the graduate and
undergraduate levels, for weather radar applications[41].
However, to the best of our knowledge, real or simulated
systems involving miniaturization, sensors etc. are less usual.
Nonetheless, microelectronics, which was responsible for high
miniaturization rate and impact in several technological areas,
is the bases of electronic production area[42].
IV. RESULTS AND DISCUSSION
This section is divided into main parts: first (Results) describes
phases denominated as prepare and collect and then
(Discussion) all the information previously obtained is
analyzed.
A. Results: The Fog Computing Tool (AIC2)
This tool was envisaged for the analysis and selection on a
large amount of data. Furthermore, the need for mobility was
considered, i.e., the possibility of manipulate and/or present
such data on mobile devices, such as tablets and smartphones;
as postulated by Sutherland et al.[43] the development of a
"Mobile-Sensing Technology" and, due to limitation of the cell
phone in processing data, the use of cloud computing
"Computation Offloading", to guarantee cheap, fast software
solutions in the form of applications, that is, as much as possible
in accordance with the practices foreseen on human-computer
interaction[30],[31].

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As the data is generated by different equipment and/or
applications, they are grouped in files of the most diverse
formats, databases with different configurations and,
sometimes made available in portals of difficult access, which
demands a lot of time and effort to create a consistent basis for
doing analyses, generating knowledge and making decisions.
using technologies that make the most of resources, considering
low financial and computational cost, create a system that
allows searching, decoding and persisting related data, which
are spread in different formats, including being able to be
transmitted by microsensors, such as those contained in
smartphones. The system must also allow this data to be
consulted with low resource consumption and low latency.
As shown in Figure 1, the algorithm was developed
considering the characteristics of the most used computing
service, cloud computing, we can understand that, as explained
by Santos, Silva & Santos Filho[30], there are two main
limitations, which are the high latency, that is, the response time
can be higher than desired and the ability to process a diverse
demand. In this case, fog computing, becomes an auxiliary and
complementary technology, as defined by Santos, Silva &
Santos Filho[44], since, running at the edge of the cloud, only
the data that is necessary to supply a request is processed, in
particular, not all data needs to be stored and the response time
decreases, since data transmission is closer to the request layer
of user applications.
The structure, as proposed by Santos, Silva & Santos
Filho[45], is based on a bus of corporate systems. The fog
computing structure is proposed in this innovative format, ESB
based because there is still no standard structure. In this case,
services with specific purposes or as functions are arranged on a
bus, which catalogs and offers these services to third-party
applications that intend to consume one or more services for
their purposes, such as accessing stored data, a certain type of
value conversion, a processing, machine learning analysis,
among others.
As explained in Santos et al.[31] the proposed model
includes data compatibility services, taking data that may come
from sensors or internet scrapings, and both situations were
tested. Compatibility services allow data to be filtered and stored
in relational databases or non-relational databases. It is
important to point out that for non-relational databases,
processing performance improvement services for data access
were also implemented and tested with large masses of data.
Therefore, the use of a part of the proposed structure was
dimensioned, as shown in figure 2 associated with the
communication of the corporate services bus with sensors in
computational nodes that make measurements and forward to
the bus.
The service that will be activated first must be the format
compatibility service, as the sensor can send data in different
formats, but, in the end, it will be converted into JSON format,
a format accepted in the most diverse programming languages
and in many applications. the 3rd. The service to be called next
will depend on the data format that will be received from the
sensor. If it is a sequence of semi-structured data, the data
storage service in a non-relational database will be called,
however, if it is structured data, the data storage service in a
relational database will be requested. Once the service is
defined and the data is compatible, the data collected by the
sensing nodes is stored and, immediately, this data is available
for third-party applications to access.
Figure 1 – Algorithm developed (AIC2)
Figure 2 - Forwarding of ESB services
Finally, the bus is available for the introduction of control
services where, as already demonstrated, in addition to data
storage, rapid processing can be performed and an immediate

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response can be provided on the data that has just been
measured.
Based on this scenario, some possibilities for applying the
fog computing structure were tested in situations that favor the
development not only of teaching-learning methodologies in
different areas of knowledge, thus taking advantage of
interdisciplinarity, but also of service to emergencies, such as
the pandemic. For instance, relating to pandemic status, AIC2
was used to evaluate correlations between diabetes and COVID
infection, using FAPESP's Shared Database. The analysis
sorted 6 million clinical exams in less than 1 second and
surprisingly reveals no direct connection[46], which indicates
that several other factors should also be consider. In fact, as
stated by Banerjee et al.[47] in their meta-analysis, COVID
leads to diabetes; although some other researchers still connect
COVID risk and diabetes[48].
The structure was, therefore, made available for
measurements to be made with sensors, allowing the data to be
available, with low latency, for any possible decision-making.
B. Results: The Pilot Initiative
As explained on Method section, on this second layer (phase
2) AIC2 tool was made available for use by instructors at the
institution (Paula Souza Center). They offered the possibility of
applying the AIC2 in their studies and two different approaches
were revealed. whereas FATEC FR decided by the
development of control in weather stations FATEC SP focused
on mini and microreactors.
The Weather Station
The first action of the FATEC FR Team was to look for a
relevant topic in energy management that would generate a
considerable amount of data. The group, formed by 5 students
with basic training in administration, opted for the creation of a
meteorological mini station, with local data storage. The station
measures Temperature, Humidity, Atmospheric Pressure,
Altitude, Wind Speed, Wind Direction and Rainfall. To perform
these measurements, sensors were placed, BME280
(Temperature, Humidity, Atmospheric Pressure and Altitude),
SV-10 (Wind Speed), BR-1 (Wind Direction) and PL 1
(Rainfall), associated with a Arduino UNO microcontroller
with an SD card reader for storing measurement data. The
station structure was developed as shown in figure 3.
After tests on the weather station FATEC FR Team and
FATEC ZL Team tried to interact, for that matter, many
students, a majority being from IT field, help to envisage
possible uses. The interaction between the Teams took place as
follows. Part of the data generated by the FATEC FR Team was
sent to the FATEC ZL Team in a non-integrated way, that is,
the SD card was removed from the station, the data was
downloaded onto a computer and sent by email to the FATEC
ZL Team who, in turn, did a data analysis, recreated a part of
the mini station with the BME280 sensor, as shown in Figure
3b.
The structure of the bus was presented to at least 7 students,
who made up the FATEC ZL Team, all of them students in the
course completion stage in Information Technology and it was
proposed to them that they develop third-party software that,
connected to the fog structure, made it possible to perform data
analysis, extraction and display of reports in real time. This
action was proposed by the professor to demonstrate the
applicability of developing systems based on data sources
whose origin was not local, but derived from computing
services, such as a cloud computing service.
Figure 3 – Schematic Representation of the Ministation and
partial reproduction
Adapted from Alves et al.[49]
The Mini and Micro Reactors
Team FATEC SP defines as main project goal the design,
manufacturing and tests of mini/microreactors and respective
automatization. Deign step was fully carried out by three
students with electronic background[50],[51], however, due the
pandemic status and consequent lockdown, manufacturing was
postponed. After the return of activities, a professor from the
mechanical department of FATEC SP campus manufactured
the correspondent mini reactor and a fourth student, electronic
background, tested it and at this moment, data is being
collected. Moreover, two other instructors, with electronic and
chemistry background, tried to miniaturize even further the
respective reactor and a student, electronic background, was
addressed to the task. Surprisingly, the student changed most of
the formerly defined parameters, therefore, he intended to use
additive manufactured (3D Printer) and tested it on School of
Engineering - USP, associated school of FATEC SP, however,
he also observed the high level of noising on such places and,
again, changed the subject (for his own decision).
C. Results: The Expansion
The expansion on Phase 3 is on the use/development and
involve more than one institution and also the ability of a senior
professor.
This subsection is further described to show the integration
capabilities of AIC2, enabled maker culture and do-it-yourself
method.
The USP Team is formed not only by a researcher from the
institution itself, from the environmental area, but also by
students from FATEC SP, from the electronics area. We
consider, at this point, therefore, that the need was originally
born by researchers from FATEC SP, which in this work is the
FATEC SP Team and is carried out at the Polytechnic School
of the University of São Paulo.
The group's first action was a previous visit to verify the
sensing needs of the laboratory under analysis. The existence
and need for several emission and affluent sensors was

7
expected, but such requirements were not observed because it
is a closed-loop system.
However, an external researcher observed background noise
continuity in all environments. Thus, an exploratory
measurement was performed that indicated minimum and
maximum noise values of 43 dB and 75 dB, respectively, with
an average of 57 +/- 11 dB. Due to such dispersion, the
maximum points were sought and the activities carried out in
these places were verified. In this analysis, it was verified that
the critical points were less dependent on machinery and more
on air conditioning outputs. In addition, full-time work is
carried out at 2 different points (approximately 8h/day); in this
context, the group began to study the influence of noise on daily
work activities. To carry out this study, a KY-037 sound sensor
associated with an ESP-8266 board was used. All
measurements were sent immediately to the bus service. Figure
4b shows a real-time reading taken by the bus and plotted in a
third-party tool, in this case, an electronic spreadsheet.
With the identification of the data, in real time, the first action
regarding noise was to carry out a semi-structured interview
with the employees allocated in the noisiest room (minimum of
65 dB), which is an office occupied by 2 employees. Figure 4a
shows the layout of the evaluated office.
Figure 4 – Room under study (a), experimental arrangement
applied disassembly (b), KY-037 connections on ESP8266
The interview indicated quite different behavior between
the 2 collaborators, with the collaborator who is more constant
in the room reporting problems with concentration and the use
of music to drown out the noise. In this context, an experimental
arrangement presented in figure 4b was developed, whose
electrical scheme is shown in figure 4c.
The data obtained, as shown in figure 5, show considerable
noise and, for comparison, a new sensing was carried out, in an
area outside the building, a path like that used for walking in
physical exercise by the university community. The sensor
structure remains connected to the Corporate Services Bus and,
although there are some louder peaks, such as approximately 70
dB in a crowded restaurant, on average, the values obtained
externally were much lower than those measured in the room.
The verification of the behavior of the occupants of the
room (monitoring for eight consecutive hours) indicated
sporadic exits (on average every 2 to 3 hours) that may be due
to the background noise present in the environment. At this
moment, the knowledge acquired from the noise measurement
encouraged one of the instructors (Team FATEC FR) to adapt
the instrument to a room specifically developed for autistic
students. Moreover, an undergraduate student (Team FATEC
SP) already volunteers for the task (as later explained in
Figure 6).
Figure 5 - Noise sampling in the room and outside the
building
D. Discussion: The Weather Station
The students of the FATEC ZL Team received a semi-
structured questionnaire about the complexity of
communication with the corporate systems bus, as well as about
the increase in their training from the use of elements from
another area (electronics) associated with the knowledge of
their basic training, Technology of Information.
The students considered the use of the low-complexity
system, which allowed them to generate various software
artifacts, and said that this type of approach increased their
training, as it presented forms of communication with
complementary data to conventional methods. In this case, we
can see the Corporate Services Bus as a facilitator of
interdisciplinarity because, once third-party applications are
developed, without major difficulties, the data can be monitored
by professionals in the Energy Management area, as in the case
of the FATEC FR Team without the need to be physically at the
mini station to retrieve them.
E. Discussion: The Expansion
After the evaluation of the results by USP Team, a non-
structured interview with the students who monitored the
system was carried out.
Therefore, the capacity and application of the proposed Fog
Computing structure was demonstrated, as an interdisciplinary
element for work in the environmental and electronics area,
since environmental data could be measured and an analysis of
the behavior of those involved in the environment could be
carried out, as well as how to create an electronic structure
capable of measuring and sending data to the bus, allowing the
electronics specialist to study a specific sensor.
There was a tendency not to question the complexity of the
system and the possibility of its interdisciplinary use, thus, the
non-specialist instructor thought of doing other tests in the
environmental area and the student with knowledge in the area
thought of developing measures for additive manufacturing in
microreactors. The IT specialist considered a structure of high
applicability and low learning curve, as well as the student who,
in addition to checking possibilities of applications in
disciplines he studies and are not specifically electronics,
therefore, improving training, understands it to be possible to
integrate the disciplines.

8
Figure 6 – Conceptual map of the interrelationships between
institutions, teachers, students
In addition to the integration of knowledge into the student's
training, integration among the Teams was observed and,
equally important, the background of the student or the teacher
- the exception for the development of AIC2 - was not relevant
to define the problems, objectives or solutions, that is,
interdisciplinarity was enriched. Figure 6 presents a graphical
representation of these interrelationships.
Figure 6 shows a dashed line in the relationship between
students from the FATEC FR and FATEC SP Teams due to a
new study that emerged from the results presented in the
expansion of this study. Use the structure to work noise levels
with people belonging to the autistic spectrum. This study is in
the preliminary phase.
Table 3 in introduction presents a comparison with the
existing interdisciplinary and engineering teaching showing
that several achievements were fulfilled and all of them find
some result in this paper.
V. CONCLUSIONS
This work presented the Fog Computing structure, arranged
in a Corporate Services Bus, and demonstrated its use as a
central structure for the promotion of interdisciplinarity.
The advantages of using the AIC2 are undeniable as it
favors the interrelationships between institutions, students,
instructors and different backgrounds.
A significant result is the greater independence of students
in decision-making, in line with the maker culture.
The use of a new algorithm as a basis for the development
of extra-class work and/or scientific initiation was shown an
appropriate stage for increasing interdisciplinarity.
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Leandro C. dos Santos. Professor at Fatec
Z/L, working in several disciplines in the
Systems Analysis and Development course,
who was Department´s Head for two years
(2011 – 2013).
I am graduated in Technology in Materials,
Processes and Electronic Components from
the Faculty of Technology of São Paulo
(2003) and a master's degree in Electrical Engineering,
Microelectronics area, from the Escola Politecnica at the
University of São Paulo (2006), PhD student in Electrical
Engineering, Microelectronics area , from the Polytechnic
School at the University of São Paulo. He has several national
and international articles published in the areas of Distributed
Computing (Fog Computing) and Data Sciences.
Lilian M. Silva, PhD in Sciences from the Polytechnic School
of the University of São Paulo (2010), Master in Electrical
Engineering from the Polytechnic School of the University of
São Paulo (2005), Graduate in Materials, Processes and
Electronic Components from the Faculty of Technology of São
Paulo (2002), Graduated in Pedagogy from Faculdade
Alfamérica (2019), Bachelor in Administration from Faculdade
Facese (2020), Graduated in Systems Analysis and
Development from Unibf (2023), Postgraduate in Special
Education and Early Childhood Education (Specialization)
from Faculdade Alfamérica (2019 ), Postgraduate in Integrated
School Management with an emphasis on Administration,
Supervision, Guidance and School Inspection (Specialization)
from Faculdade Famart (2021) and Postgraduate in Digital
Marketing (Specialization) from Faculdade Faveni (2022),
Postgraduate in Big Data (MBA) from Unibf (2023).
I am a Professor of Higher Education III, in the Higher Course
in Technology in Land Transport, in the Higher Course in
Technology in Foreign Trade and in the Higher Course in
Technology in Digital Media Design, at the Faculty of
Technology from Barueri and Professor of Higher Education
III, in the Higher Education Technology course in Energy
Management and Energy Efficiency, at the Faculty of
Technology of Franco da Rocha..

10
Maria L. P. Silva. Obtained her degree in chemistry in 1980,
and physical chemistry degrees, M. Sc in 1989 and PhD in
1995, all in University of São Paulo. She has been a researcher
at the Laboratório de Sistemas Integráveis of the Engineering
School— University of São Paulo since 1985 and a professor at
the Faculty of Technology of São Paulo since 1992. She is a
member of several organizations that support affirmative
action, women in science and STEM. Her research is focused
on the development of industrial ecology and clean
technologies for microelectronics devices; in addition she also
develops active methodologies for STEM teaching.
Sebastião G. dos Santos Filho. Full Professor in the
Department of Electronic Systems Engineering at EPUSP since
2008. Electrical Engineer in 1984, Master in 1988, Doctor in
1996 and Associate Professor in 1999, all from the USP
Polytechnic School. He was coordinator of the Postgraduate
Program in Electrical Engineering (PPGEE) at EPUSP in the
period 2011-2013 in which the CAPES concept rose to 6. He
was deputy coordinator of PPGEE in the period 2014-2016,
Deputy Head of the Department of Systems Engineering
Electronics (PSI-EPUSP) from 10/2015 to 09/2017 and Head
of PSI-EPUSP from 10/2017 to 09/2021. He works in the areas
of microelectronics and nanoelectronics, having developed
R&D in manufacturing processes for MOS integrated circuits
and modeling MOS devices.
Currently, my topics of greatest interest are: nano-sensors,
nano-systems, chemical sensors, R&D in chemical cleaning
processes for silicon wafers, ultra-thin MOS gate dielectrics,
electrochemical deposition of metals and techniques for
characterizing surfaces and interfaces and nanostructures. He is
the author/co-author of more than 250 articles published in
national and international conferences and technical journals.