**2. (Useful) support to (real) needs of teachers**

Already in 2019 the TALIS [19] survey revealed the strong need for teacher training in the use of information and communication technologies (ICT) - despite 60% of teachers received professional development in ICT, 18% in fact reported a higher need for development in this area. The preparation of teachers to support students' digital learning is not based only on ICT skills [5]: 'technology does not just change methods of teaching and learning, it can also elevate the role of teachers from imparting received knowledge towards working as co-creators of knowledge, as coaches, as mentors and as evaluators' ([5], p. 17).

Teacher training, in general and even more so in the current context, is effective if useful for build pedagogical and technical skills, for integrate digital tools into learning environment [5, 19, 20].

While in the early emergency the first generalized response was to provide tools2 [4] that made it possible to set up a 'field' [21] distance learning, then it was possible to start thinking on the skills needed by the teacher to cope with this situation.

OECD [22] stressed in the second study that 'for educators, the COVID-19 pandemic is a quintessence of the adaptive and transformative challenge, for which there is no pre-configured playbook that can guide appropriate responses' ([22], p. 2) and provides a set of online educational resources to support continuity of teaching and learning:

The second one resources were assessed on the basis of a specific taxonomy for classifying the curriculum and professional development resources [23], see **Table 1**.

Regards resources for professional development suggested by OECD [8], the following table shows the professional skills activated as well as the related taxonomic category (see **Table 2**).

2 Applications, platforms and educational resources for use by parents, teachers, school administrators to support student learning and offer assistance during school closing periods.

#### **Table 1.**

*Taxonomy for the analysis of professional development resources ([22, 23], p. 5).*

The OECD [22] analysis highlights that the resources available to teachers for their effective professional development in a pandemic situation are only partly related to technological skills but invest broader ability, such as redesign of programs, flexible adaptation of strategies, for the effective support for student learning. Such resources must not be limited only to 'technological' (T) skills but even extend beyond the 'techno-pedagogical' (TP) ones, introduced by Mishra & Koehler [24, 25] within the TPCK (Technological Pedagogical Content Knowledge) descriptive model. This is considered as the Shulman's explanatory model integration - 'Pedagogical Content Knowledge' (PCK) - since to the distinction between disciplinary and pedagogical knowledge it adds that of the technological area. Its basic components are: a. technological knowledge ('knowledge of the technologies and skills necessary to operate with them - TK); b. pedagogical knowledge ('teaching/learning processes and practices, methods and approaches' - PK); c. content knowledge ('teachers' understanding of the semantics and syntactic organization of a discipline').

On the other hand, techno-pedagogical (TP) skill - as 'knowing the pedagogical affordances and constraints of a range of technological tools as they relate to disciplinarily and developmentally appropriate pedagogical designs and strategies' ([25], p. 10) - describes relationships and interactions between technological tools and specific pedagogical practices, in other words 'the pedagogical awareness of resources and technological constraints' [26]; on the other hand, pedagogicalcontent (PC) skill describes relationships/interactions between pedagogical practices and specific learning objectives [27]; it is 'the ability to teach content from the students' point of view' [28].

Analyzing carefully, in the construct of 'techno-pedagogical' (TP) skill the relationship with the 'student's point of view' - recognized only in the 'pedagogicalcontent' - would appear to characterize the skills necessary for the teacher to carry out a teaching remote in the pandemic phase.

**23**

*Remote Support through Technologies: A Research-Training on Teachers' 'Sophisticated Knowledge'*

**Resources Professional skill Taxonomic category\***

and collaboration

accessible to all

teachers

students

online

practice

How effectively teach and learn online How to find good sources of educational content

1.1. Processing and cognitive strategies 1.2. Knowledge 2.1. Collaborative group skills 3.1. Intellectual Openness 3.2. Work Ethic, Responsibility 3.3. Self-efficacy

1.1. Processing and cognitive strategies 1.2. Knowledge 2.1. Collaborative group skills 3.1. Intellectual Openness

1.1. Processing and cognitive strategies 3.2. Work Ethic, Responsibility 3.3. Self-efficacy

1.2. Knowledge 2.1. Collaborative group skills 3.2. Work Ethic, Responsibility

1.1. Processing and cognitive strategies 1.2. Knowledge 1.3. Creativity 2.1. Collaborative group skills 2.2. Leadership 3.1. Intellectual Openness 3.2. Work Ethic, Responsibility 3.3. Self-efficacy

How to enable online communication

How to make home teaching decisions with videos, without videos How to make distance learning

How to create and share your educational content online

How to keep students engaged How to keep in touch with other

How to adapt online courses How to manage trauma situations How to practice inclusive education and socio-emotional distance learning

How to find learning resources for

How to track student homework

How to use learning videos and worksheets within digital teaching

How to redesign the teaching-learning path in terms of the pedagogical model and the curriculum

*DOI: http://dx.doi.org/10.5772/intechopen.95949*

Teachercpd.ie (website)

(platform)

(website)

Quipper.com (website)

Knotion.com (platform)

**Table 2.**

Teachfromanywhere.google

Learningpractice.org

**3. Situation and a research-training in Italy**

*Professional development resources: Skills and taxonomic categories ([22], p. 5).*

distance learning - dl n. 18/2020, n. 34/2020.

As noted by OECD ([29], p. 8), the Italian government already adopted in March, then renewed in May, measures to support distance learning (digital platforms for schools, tools for learning, digital devices for limited means students) and, mainly, a training plan for school staff on methodologies and techniques for

In the 'School Plan 2020–2021' - decree no. 39 of 26 June 2020 - launched in June, in the paragraph on 'Training' for teachers, the 'use of new technologies in relation to the different tasks and professionalism' is encouraged, as regards innovative

*Remote Support through Technologies: A Research-Training on Teachers' 'Sophisticated Knowledge' DOI: http://dx.doi.org/10.5772/intechopen.95949*

**Table 2.**

*Professional development resources: Skills and taxonomic categories ([22], p. 5).*

#### **3. Situation and a research-training in Italy**

As noted by OECD ([29], p. 8), the Italian government already adopted in March, then renewed in May, measures to support distance learning (digital platforms for schools, tools for learning, digital devices for limited means students) and, mainly, a training plan for school staff on methodologies and techniques for distance learning - dl n. 18/2020, n. 34/2020.

In the 'School Plan 2020–2021' - decree no. 39 of 26 June 2020 - launched in June, in the paragraph on 'Training' for teachers, the 'use of new technologies in relation to the different tasks and professionalism' is encouraged, as regards innovative

teaching-learning methodologies, school inclusion, interdisciplinary teaching models, methods and tools for evaluation. The document also proposes an integrated digital teaching solution described in terms of needs analysis, objectives to be pursued, tools to be used, timetable and frequency of lessons, therefore inherent in the redesign of teaching activities, which takes into account the digital potential of the school community, with particular regard to the access and full participation of students with specific needs.

The ministerial note n. 388 of 17 March, formerly, had focused attention on the redesign of the entire teaching activity, on the reshaping of the educational objectives, on the provision of new learning resources and methods of interaction with all pupils, as well as - for students with Special Needs - explanation of new forms of interaction/fruition between student and class, between student and other teachers, between teachers and families, the supply of new personalized material, constant monitoring through periodic feedback based on the established objectives.

The previous National Digital School Plan [30] already favored the modernization of infrastructures and technologies from 2007 to 2015 - see LIM action (2008), Cl@ssi 2.0 action (2009–2011), Scuol@ 2.0 action, Wi-Fi action (2013) - as well as a deep rethinking of teaching based on innovative learning environments - see 'Future Labs' Training Centers Action (since 2015 for the digital training of school representatives) - and has allowed the Italian school not to be completely unprepared for the COVID-19 emergency.

As already noted elsewhere through the metaphor of the 'supply-chain' [30], the digital training of in-service teachers in Italy takes place through integrated governance (see Law no. 107/2015) which holds together the European framework DigCompEdu 2.0 [31, 32], the PNSD and the training needs of territorial 'polo' schools3 .

As known, the European Framework DigCompEdu aimed at describing in six different areas the digital competences of teachers ([31], pp. 33):

Area 1: Professional Engagement - Using digital technologies for communication, collaboration and professional development – i.e. professional interaction with colleagues, students, parents and other parties, for the collective good of the organization;

Area 2: Digital Resources - Sourcing, creating and sharing digital resources;

Area 3: Teaching and Learning - Managing and orchestrating the use of digital technologies in teaching and learning;

Area 4: Assessment - Using digital technologies and strategies to enhance assessment;

Area 5: Empowering Learners - Using digital technologies to enhance inclusion, personalization and learners' active engagement;

Area 6: Facilitating Learners' Digital Competence - Enabling learners to creatively and responsibly use digital technologies for information, communication, content creation, wellbeing and problem-solving.

It provides a general 'reference frame for developers of digital competence models' ([31], p. 9) and it is assumed as a guidance for the design of effective training courses involving teachers [17, 33].

Methodologies and results of a research-intervention, aimed at develop teachers' ability to integrate inclusively remote teaching technologies resources and inspired by the DigCompEdu Framework, are described below.

**25**

**Table 3.**

*Remote Support through Technologies: A Research-Training on Teachers' 'Sophisticated Knowledge'*

To plan for and implement digital devices and resources in the teaching process, so as to enhance the effectiveness of teaching interventions. To appropriately manage and orchestrate digital teaching strategies. To experiment with and develop new formats and pedagogical methods for instruction.

**Area 3: Teaching and Learning Area 5: Empowering**

**3.1 Teaching 3.2 Guidance 5.1 Accessibility and**

To use digital technologies and services to enhance the interaction with learners, individually and collectively, within and outside the learning session. To use digital technologies to offer timely and targeted guidance and assistance. To experiment with and develop new forms and formats for offering guidance and support.

**Learners**

**inclusion**

To ensure accessibility to learning resources and activities, for all learners, including those with special needs. To consider and respond to learners' (digital) expectations, abilities, uses and misconceptions, as well as contextual, physical or cognitive constraints to their use of digital technologies.

**3.1 Technologies for e-inclusion: a research-intervention in the south of Italy**

at developing DigCompEdu teachers' sub-skills (**Table 3**):

*DigCompEdu teachers' sub-skills - 'technologies for e-inclusion' ([31], p. 19–22]).*

explicit by Redecker ([31], pp. 16).

'Technologies for e-Inclusion' intervention-research [34, 35] has been carried out within the second COVID-semester - September–November 2020 - and aimed

These are the sub-competences related to the area of design, rather than intervention towards the skills of the students. The intent was to investigate rather the 'introductory' relationship of the teacher with technologies, rather than the effect on the relationship between students and technologies. More specifically, as made

*'The core of the DigCompEdu framework is defined by Areas 2-5. Together these areas explain educators' digital pedagogic competence, i.e. the digital competences educators need to foster efficient, inclusive and innovative teaching and learning*

*DOI: http://dx.doi.org/10.5772/intechopen.95949*

**Area 2: Digital Resources**

**2.3 Managing, protecting and sharing digital resources**

To organize digital content and make it available to learners, parents and other educators. To effectively protect sensitive digital content. To respect and correctly apply privacy and copyright rules. To understand the use and creation of open licenses and open educational resources, including their proper attribution.

**Area 1: Professional Engagement**

**1.1.**

**Organizational communication**

To use digital technologies to enhance organizational communication with learners, parents and third parties. To contribute to collaboratively developing and improving organizational communication strategies.

3 Schools as territorial centers for teachers training of specific areas: technologies, inclusion etc. – see L. 107/2015.

*Remote Support through Technologies: A Research-Training on Teachers' 'Sophisticated Knowledge' DOI: http://dx.doi.org/10.5772/intechopen.95949*

#### **Table 3.**

*DigCompEdu teachers' sub-skills - 'technologies for e-inclusion' ([31], p. 19–22]).*

#### **3.1 Technologies for e-inclusion: a research-intervention in the south of Italy**

'Technologies for e-Inclusion' intervention-research [34, 35] has been carried out within the second COVID-semester - September–November 2020 - and aimed at developing DigCompEdu teachers' sub-skills (**Table 3**):

These are the sub-competences related to the area of design, rather than intervention towards the skills of the students. The intent was to investigate rather the 'introductory' relationship of the teacher with technologies, rather than the effect on the relationship between students and technologies. More specifically, as made explicit by Redecker ([31], pp. 16).

*'The core of the DigCompEdu framework is defined by Areas 2-5. Together these areas explain educators' digital pedagogic competence, i.e. the digital competences educators need to foster efficient, inclusive and innovative teaching and learning*

*strategies. Areas 1, 2 and 3 are anchored in the stages characteristic of any teaching process, whether supported by technologies or not'.*

The competences listed in the Areas 2 and 3 detail how to make efficient and innovative use of digital technologies when planning and implementing teaching and learning. Instead, the competences listed in the Area 5 concern the potential of digital technologies for learner-centered teaching and learning strategies.

## *3.1.1 Context and population*

The survey was carried out at two High School – A. 'Marone', Vico del Gargano, Puglia and B. 'Alberti', Benevento, Campania - having the following characteristics (**Table 4**):

The survey involved 108 teachers with the following characteristics (**Table 5**):

## *3.1.2 Methodology: object and question of investigation*

Data was collected by administering an 'ad hoc' questionnaire divided into three areas: sociometric-professional data, technological knowledge/skills, inclusive knowledge/skills. The tool wanted to know the perceptions of the teachers involved with respect to the technological tools for distance learning, the devices for setting up e-learning learning environments and the strategies useful for accessing and participating in the teaching process online learning.

This chapter focuses on the question no. 12 - Which aspect of your teaching has been most influenced by digital technologies/resources? - related to the perceptions

#### **Table 4.**

*Characteristics of the schools involved.*

**27**

professional positions in the technology area.

*Remote Support through Technologies: A Research-Training on Teachers' 'Sophisticated Knowledge'*

of teachers on the aspect of their teaching practice most influenced by technologies

• synthesis of sociometric-professional data (see 'Sample representativeness') aimed at describing the sample and its representativeness with respect to the

• correlation (see 'Correlation') between answer no. 12 and four factors - qualification, seniority of service, previous professional development training and middle-management assignment - aimed at inferring information about the weight of these factors on the ability to choose and effectively use technologies in inclusion, in an emergency situation such as that determined by the

The group of teachers involved has an high average age (64,8%, over 50 years), a bachelor degree (59,3%), over 10 years of teaching experience (81,5%), previous training in the technological-digital area (40,7%) - as well as in the technologies (39,8%) and design of learning environments (19,4%) - and experience in middle

The following table shows the characteristics of the teachers involved – number and age - in relation to the ones of regional population, i.e. secondary school teachers from southern Italy, retrieved from MIUR (2020) and OCSE (2019). The total number of teachers in Italy is 836,496 for an age of over 50, at 59% - the older

The group of teachers of course A represents 0.097% of the Apulian colleagues with an average age of the rest of the region; the teachers of course B are 0.052% of

Question no. 12 - Which aspect of your teaching has been most influenced by digital technologies/resources? - presented 6 response alternatives (**Table 8**): content clarification, content facilitation, simplification of learning materials, communication effectiveness, class participation, student participation. It sought to investigate in what relationship the teacher places technologies with respect to his teaching practice. The six alternative responses indicated three aspects of teaching

4 Assignment without direct responsibility, unlike the digital animator (A. 3.4%; B. 22.4%) - coordinator of technological inclusion interventions and support of the school principal - and the 'instrumental function' in the technologies area (A. 0.0%; B. 14.3%) - guarantor of operational intervention actions. It should be noted that 61% of the teachers of the A. course declared that they had not held any of the

practice that would be favored by the use of technologies, specifically:

(**Figure 1**).

management, specifically as digital innovation team member (48,15)4

population of teachers in the world and in Europe [29] (**Table 6**).

the Campania colleagues, far beyond the 50-years average.

The aim is to infer useful information to describe areas of possible overlap between technological, pedagogical and disciplinary knowledge and thus reflect on the usefulness of the descriptive models of teachers' technological knowledge/skills.

*DOI: http://dx.doi.org/10.5772/intechopen.95949*

Data were analyzed at two levels:

reference population - see Tabb. 2 and 6;

during the COVI19 emergency.

COVID19 pandemic.

*3.1.4 Sample representativeness*

*3.1.5 Correlation*

*3.1.3 Data analysis*

#### **Table 5.**

*Characteristics of the teachers involved.*

*Remote Support through Technologies: A Research-Training on Teachers' 'Sophisticated Knowledge' DOI: http://dx.doi.org/10.5772/intechopen.95949*

of teachers on the aspect of their teaching practice most influenced by technologies during the COVI19 emergency.

The aim is to infer useful information to describe areas of possible overlap between technological, pedagogical and disciplinary knowledge and thus reflect on the usefulness of the descriptive models of teachers' technological knowledge/skills.

#### *3.1.3 Data analysis*

Data were analyzed at two levels:

#### *3.1.4 Sample representativeness*

The group of teachers involved has an high average age (64,8%, over 50 years), a bachelor degree (59,3%), over 10 years of teaching experience (81,5%), previous training in the technological-digital area (40,7%) - as well as in the technologies (39,8%) and design of learning environments (19,4%) - and experience in middle management, specifically as digital innovation team member (48,15)4 (**Figure 1**).

The following table shows the characteristics of the teachers involved – number and age - in relation to the ones of regional population, i.e. secondary school teachers from southern Italy, retrieved from MIUR (2020) and OCSE (2019). The total number of teachers in Italy is 836,496 for an age of over 50, at 59% - the older population of teachers in the world and in Europe [29] (**Table 6**).

The group of teachers of course A represents 0.097% of the Apulian colleagues with an average age of the rest of the region; the teachers of course B are 0.052% of the Campania colleagues, far beyond the 50-years average.

#### *3.1.5 Correlation*

Question no. 12 - Which aspect of your teaching has been most influenced by digital technologies/resources? - presented 6 response alternatives (**Table 8**): content clarification, content facilitation, simplification of learning materials, communication effectiveness, class participation, student participation. It sought to investigate in what relationship the teacher places technologies with respect to his teaching practice. The six alternative responses indicated three aspects of teaching practice that would be favored by the use of technologies, specifically:

4 Assignment without direct responsibility, unlike the digital animator (A. 3.4%; B. 22.4%) - coordinator of technological inclusion interventions and support of the school principal - and the 'instrumental function' in the technologies area (A. 0.0%; B. 14.3%) - guarantor of operational intervention actions. It should be noted that 61% of the teachers of the A. course declared that they had not held any of the professional positions in the technology area.

#### **Figure 1.**

*(a) Age. (b) % of qualification. (c) % of PD training. (d) % middle-management assignment.*

#### **Table 6.**

*Sample representativeness.*

These three aspects - *what*, *how* and *to whom* - were assumed as criteria for aggregating the data in the analysis process (see **Table 7**, **Figure 2**): they make it possible to better highlight where teachers' opinions and representations tend towards technologies: on aspects of content (*what*), of method (*how*) or with respect to students (*to whom*).

The following table shows absolute and percentages values of the six response alternatives (**Table 7**).

**29**

**Table 8.**

*Answers to question no. 12.*

**Figure 2.**

**Table 7.**

*Type of answers to question no. 12 in %.*

*Aspects of teaching influenced by technologies.*

*Remote Support through Technologies: A Research-Training on Teachers' 'Sophisticated Knowledge'*

*To whom* 31 52,5% 27 55,1% 58 53,7% *How* 20 33,9% 12 24,5% 32 29,6% *What* 8 13,6% 7 14,3% 15 13,9% No answer 0 0,0% 3 6,1% 3 2,8% Tot. 59 100,0% 49 100,0% 108 100,0%

**A B Tot. n. % n. % n. %**

**A B Tot.**

**Response alternatives n. % n. % n. %** Student participation 21 35,6% 18 36,7% 39 36,1% Communication effectiveness 19 32,2% 11 22,4% 30 27,8% Class participation 10 16,9% 9 18,4% 19 17,6% Content clarification 8 13,6 7 14,3% 15 13,9% No answer 0 0% 3 6,1% 3 2,8% Simplification of learning materials 1 1,7% 1 2,0% 2 1,9% Content facilitation 0 0% 0 0% 0 0,0% Tot. 59 100% 49 100% 108 100%

*DOI: http://dx.doi.org/10.5772/intechopen.95949*

*Remote Support through Technologies: A Research-Training on Teachers' 'Sophisticated Knowledge' DOI: http://dx.doi.org/10.5772/intechopen.95949*

#### **Table 7.**

*Type of answers to question no. 12 in %.*

#### **Figure 2.**

*Aspects of teaching influenced by technologies.*

#### **Table 8.** *Answers to question no. 12.*

**Table 7** shows the same answers grouped by type (**Figure 2**):

The prevalence of responses relating to the student-area (*to whom*) is highlighted, followed by the intervention methodologies/strategies (*how*) and teaching content (*what*) ones. The previous data are now correlated with the characteristics of the group of teachers involved (see **Table 2**):

#### **Table 9.**

*Impact of technologies/age.*

**31**

**Figure 5.**

*Impact of technologies/seniority.*

**Table 10.**

*Impact of technologies/qualification.*

*Remote Support through Technologies: A Research-Training on Teachers' 'Sophisticated Knowledge'*

In all age groups, consideration of the impact of technologies on students prevails, with the exception of the 40–50 range where the influence on teaching methods is higher. It should be noted that within the younger classes - 22-30 years; 31–40 years there is a complete lack of references to content - cf. **Table 9** - **Figures 3** and **4**.

Influence of technologies on students (*to whom*) prevails in all qualification grades (see **Table 10** - **Figure 5**). A slight increase compared to the methodologies

Influence of technologies on students (*to whom*) prevails also within the seniority of service clusters (see **Table 11** – **Figure 6**). It peaks in the younger age group.

Influence of technologies on students (*to whom*) prevails also within the previous PD training clusters (see **Table 12** – **Figure 6**). It peaks in the 'design of learning

Same result for what concerns the characteristic of middle-management assignment (see **Table 13** - **Figure 7**). Influence of technologies on students (*to whom*)

To whom 47,1% 56,3% 51,9% 53,7% How 29,4% 25,0% 40,7% 29,6% What 23,5% 14,1% 7,4% 13,9% No answer 0,0% 4,7% 0,0% 2,8% Tot. 100,0% 100,0% 100,0% 100,0%

**Diploma B. Degree Master's Degree/Ph.D. Total**

environment', while it stabilizes for 'technology' and 'digital-technology'.

*DOI: http://dx.doi.org/10.5772/intechopen.95949*

*3.1.7 Impact of technologies and qualification*

*3.1.8 Impact of technologies and seniority*

(*how*) is found in the higher level of qualification.

*3.1.9 Impact of technologies and previous PD training*

*3.1.10 Impact of technologies and MM assignment*

*3.1.6 Impact of technologies and age*

**Figure 4.** *Impact of technologies/qualification.*

*Remote Support through Technologies: A Research-Training on Teachers' 'Sophisticated Knowledge' DOI: http://dx.doi.org/10.5772/intechopen.95949*


4.1.4. Back-End and Front-End

A web interface was designed and implemented to show historical values (i.e., a graph of the glucose levels) in order to ease medical supervision. Moreover, notifications are sent to the medical services whenever anomalous values of glucose are detected.

The required systems are deployed in a cloud machine that is also running an OrbitDB node and a service that exposes a REST API that obtains the data from the database, thus allowing other systems to access the decentralized data in a traditional way.

The visualization panel consists of a web interface designed as a Single Site Application using the Vue.js framework [68]. The application loads into the user's web browser and makes HTTP requests to the REST API to show patients' glucose historical data.

Along with the web application (showed in Figure 4, on the right) it is executed an event notification service. Such a service is triggered every time a user accesses the visualization interface and processes the data, thus detecting whether the glucose values are or not within certain pre-established thresholds. If the collected value is above or below such thresholds, an alert is generated and an SMS or a Telegram message [69] is sent to the medical services to warn them. For example, such messages can serve as a reminder for a nurse that a specific patient has an incoming hypoglycemia. Specifically, the notification service uses Twilio's API [70] to send SMS, while a Telegram bot was developed to warn remote users through Telegram messages. As an example, Figure 5 shows SMS and Telegram messages sent to notify hypoglycemia and hyperglycemia alerts.

**Figure 4.** Screenshot of the smartphone app (**A**) and of the web application (**B**).

**Figure 5.** Event notifications received through a Twilio's SMS (**A**) and a Telegram message (**B**).

### 4.1.5. Distributed Ledger

Ethereum was chosen as blockchain since the distributed ledger required by the proposed architecture has to be able to run smart contracts. Although the developed system can be deployed in the public Ethereum blockchain, for the experiments performed in this article, it was run on two testnets (i.e., Ethereum test networks): Rinkeby [71] and Ropsten [72]. Rinkeby and Ropsten differ in their consensus algorithm: while Rinkeby uses Proof-of-Authority (PoA) (with Clique PoA as consensus protocol), Ropsten uses Proof-of-Work (PoW). This means that, in the case of Rinkeby, there is a subset of authorized signers that assume block minting and Ether cannot be mined (it is requested through a faucet [73]). In contrast, in Ropsten Ether is mined, although it can be also requested from a faucet [74]. In practice, Rinkeby is able to create blocks on an average of 15 s, while Ropsten usually requires up to 30 s.

Smart contracts can be executed both in Rinkeby and Ropsten. For this article, the smart contracts were compiled and deployed by using Truffle [75]. The main smart contract used by the proposed system was developed to implement the meta-coin incentive mechanism detailed in Section 3.2, which initializes user account balance for the participating parties and manages the performed GlucoCoin payments. User wallets increase their balance with a fee when the owner of the wallet sends data to OrbitDB. If the user needs to buy a new glucose sensor, the account balance is decreased. The mechanism that performs the sensor purchase is implemented outside the blockchain (i.e., it is handled by an external provider). Since the expiration information is stored in OrbitDB, it can be used to trigger automatically the purchase and payment of the new sensor.

### 4.1.6. Fog Gateway

The fog gateway of the cture may be implemented on most current SBCs, like Raspberry Pi 3 [76], Banan Pi [77] or Odroid-XU4 [78]. Among such devices it was selected the Orange Pi Zero Plus [79], since it provides a good trade-off between features and cost (it can be currently purchased for less than \$20). The Orange Pi Zero Plus features an Allwinner H2 System on Chip (SoC) that embeds a quad-core ARM Cortex-A7 microcontroller. The selected SBC also includes 256 MB of DDR RAM and includes interfaces for USB 2.0, 10/100 M Ethernet and WiFi (IEEE 802.11 b/g/n). In addition, it is worth noting that the SBC is really small (48 mm × 46 mm) and lightweight (26 g).

The fog gateway runs on ARMbian [80] and includes the following main software to implement the proposed architecture:

Finally, it is worth pointing out that every fog gateway makes use of two WiFi interfaces simultaneously. One of them is the embedded interface, while an external WiFi adapter is plugged into the Orange Pi Zero Plus to provide the second one. The reason for the use of both interfaces is the need for connecting wirelessly to the Internet through one interface, while the second one is setup in Access Point mode to broadcast a Service Set IDentifier (SSID) and a MAC address that can be identified easily by the smartphones of patients in order to connect to them fast.


*Article* **Communication and Control of an Assembly, Disassembly and Repair Flexible Manufacturing Technology on a Mechatronics Line Assisted by an Autonomous Robotic System**

**Dan Ionescu 1,2,\*, Adrian Filipescu 1,2, Georgian Simion 1,2, Eugenia Mincă 2,3, Daniela Cernega 1, Răzvan S, olea 1 and Adriana Filipescu 1**

**Abstract:** This paper aims to describe modeling and control in what concerns advanced manufacturing technology running on a flexible assembly, disassembly and repair on a mechatronic line (A/D/RML) assisted by an Autonomous Robotic System (ARS), two robotic manipulators (RM) and visual servoing system (VSS). The A/D/RML consists of a six workstations (WS) mechatronics line (ML) connected to a flexible cell (FC) equipped with a 6-DOF ABB industrial robotic manipulator (IRM) and an ARS used for manipulation and transport. A hybrid communication and control based on programmable logic controller (PLC) architecture is used, which consists of two interconnected systems that feature both distributed and centralized topology, with specific tasks for all the manufacturing stages. Profinet communication link is used to interconnect and control FC and A/D/RML. The paper also discusses how to synchronize data between different field equipment used in the industry and the control systems. Synchronization signals between the master PLC and ARS is performed by means of Modbus TCP protocol and OPC UA. The structure of the ARS consists of a wheeled mobile robot (WMR) with two driving wheels and one free wheel (2DW/1FW) equipped with a 7-DOF RM. Trajectory tracking sliding-mode control (TTSMC) is used to control WMR. The end effector of the ARS RM is equipped with a mobile eye-in-hand VSS technology for the precise positioning of RM to pick and place the workparts in the desired location. Technology operates synchronously with signals from sensors and from the VSS HD camera. If the workpiece does not pass the quality test, the process handles it by transporting back from the end storage unit to the flexible cell where it will be considered for reprocessing, repair or disassembling with the recovery of the dismantled parts. The recovered or replaced components are taken over by the ARS from disassembling location and transported back to the dedicated storage warehouses to be reused in the further assembly processes.

**Keywords:** programmable logic controller; modbus TCP; open platform communications; visual servoing system; wheeled mobile robot; industrial robotic manipulator

### **1. Introduction**

The continuous development of software and automation in industrial environments brings new concepts for communication, design and control for manufacturing technology. There is a growing need for high-speed robotic assembly and transport of small parts, which often means higher throughput and greater precision than can be achieved using human labor [1].

**Citation:** Ionescu, D.; Filipescu, A.; Simion, G.; Minc ˘a, E.; Cernega, D.; S, olea, R.; Filipescu, A. Communication and Control of an Assembly, Disassembly and Repair Flexible Manufacturing Technology on a Mechatronics Line Assisted by an Autonomous Robotic System. *Inventions* **2022**, *7*, 43. https://

doi.org/10.3390/inventions7020043 Academic Editor: Luigi Fortuna

Received: 15 May 2022 Accepted: 10 June 2022 Published: 15 June 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

This study focused on the implementation, simulation and system design of the hybrid communication and control for the advanced flexible manufacturing technology presented on a laboratory system that integrates several subsystems and different field equipment and autonomous robotic systems (ARSs) [2]. A fully automated assembly line assisted by mobile robots is still in its early stages and is not yet widely used. ARSs are extremely flexible because once the facility map is built, they can travel from one destination to the next, autonomously avoiding obstacles along the way, unlike conveyor systems that have limited flexibility, and are quite expensive and time-consuming to reconfigure [3].

The objective of this research is to introduce a new perspective upon the framework of manufacturing technology design where implementation and setup was based more on engineering experience and less on simulations, investigation and validation methods to increase efficiency and to evaluate the performance of the manufacturing lines assisted by ARS [4].

The main elements of originality and contributions are concentrated in the following areas: task scheduling and assigning; planning and synchronization of A/D/RML assisted by ARS, RMs and VSS; Petri Nets modeling; hardware architecture design of the entire system to allow flexible manufacturing, communication concepts, supervisory control and data acquisition (SCADA) [5]; implementation and network topology, synchronization of signals from sensors and between subsystems, distributed control and image processing for precise positioning; VSS and real-time control for implementation of a fully automated manufacturing technology; improving the automation level; security; and increasing the efficiency by using the ARS IRM with VSS technology [6,7]. The presented flexible manufacturing concept allows the assembly of two different products and complete disassembly or repair of the products depending on the quality test. Disassembled components from the rejected products are recovered by ARS and placed back in the designated storage compartments. The recovery process implementation allows the reuse of the products subcomponents through reprocessing, technology that works automatically, completely independent without the operator intervention, increasing efficiency, productivity and safety [8].

The presented technology for flexible assembly, disassembly and repair with components recovery, consists of an assembly/disassembly mechatronics line (A/D/ML), a flexible cell (FC), which is an assembly/disassembly station with an integrated 6-DOF industrial robotic manipulator (IRM), and an ARS which is a WMR equipped with a 7-DOF RM and an eye-in-hand visual servoing system (VSS) [1,2]. Along with the communication concept and real-time implementation, several aspects will be discussed regarding the design of the flexible manufacturing technology, such as: task planning, hybrid modeling, simulation, sensors and actuators, interoperability between field level devices, synchronization, data acquisition, remote monitoring and control [9].

An assembly/disassembly and repair flexible manufacturing line (A/D/R/ML), consists of the following subsystems: IRMs, WMRs, workstations and manufacturing cells, component storage units, transporting system (conveyor belts) and monitoring, control and data acquisition systems, able to perform specific tasks for manufacturing technology such as product assembly, quality check and repair or disassembly operations with components recovery, including a reconfigurable manner that confers reversibility, repeatability, and last but not least, flexibility [3]. The main idea of flexibility added to a manufacturing line, a FML (Flexible Manufacturing Line), means a technology capable to automatically manufacture different products, in small or medium batches, without adding hardware changes or the complete redesign of the system.

The automatic control of all system components and automatic supervision, controls and diagnosis is performed with the help of two PLCs in a hybrid hardware architecture for controlling all the subsystems of the complete A/D/RML and managing the process and operation facilities, thereby coordinating control tasks as well as synchronizing the operations of the ARS with process timings [10]. On top of that, for controlling assembly/disassembly and repair for the flexible manufacturing line, the algorithm architecture is agent-based

control, in which the PLC from the FC station acts as a main control unit, or "master PLC", for centrally managing both subsystems of the complete A/D/RML by means of synchronization and confirmation signals [11]. Therefore, master PLC synchronizes with subsystems PLCs to automate their respective areas and for operating and controlling locally their components, after confirmation from the main control unit is applied.

The presented hardware structure includes two Human-Machine Interfaces (HMI) as operator control panels for both major subsystems (A/D/ML and FC) and a SCADA application running on the Remote PC as the main visualization, control and data acquisition system. The information to perform the flexible manufacturing process tasks is obtained from the system using IO Field Devices such as sensors, cameras, measuring devices and transducers and is processed by the PLCs and interfaced via a communication link with Remote PC or SCADA [5].

Industrial development has been evolving rapidly, bringing new smart technologies to automation systems and becoming more dynamic and adaptable production systems. Visual servoing is a commonly used technology in combination with RM and works by processing and implementing the results obtained from several research fields such as real-time image analysis and processing, robotics, control theory and systems and real-time application design. Therefore, a visual sensor—an HD camera—is connected on the end effector, "the eye" of the RM, which allows the visual inspection and investigation of the working environment without contact with its elements. VSS behavior is mainly influenced by the type of visual features used to generate control law [12]. There are several VSS control architectures corresponding to the servoing systems; in this approach, the Hybrid Visual Servoing (HVS) architecture is used for driving the mobile VSS mounted on the ARS robot manipulator [13,14].

The rest of the paper is organized as follows: the proposed hardware technology of the A/D/RML assisted by ARS is presented in Section 2.1 describing FC, ARS and eye-in-hand VSS control architectures; in Section 2.2, Petri Nets modeling is presented and also task planning and scheduling for each of the flexible manufacturing operation; in Section 2.3, the communication concept of the A/D/RML assisted by ARS is described; real-time control results for assembly, disassembly and repair operations are shown in Section 3; Section 4 provides a vision of the experimental laboratory level A/D/RML assisted by ARS, discusses the real-time control results and highlights the laboratory tests limitations of the study and Section 5 is reserved for final conclusions of the approach from this research paper, draws the main research findings and gives an insight to future directions for research/recommendations.