Process Automation and Monitoring Systems Based on IIoT Using

Private LoRaWAN Networks: A Case Study of ArcelorMittal Vega

Facilities

Danilo Farias de Carvalho

and Charles Christian Miers

Graduate Program in Applied Computing (PPGCAP), Santa Catarina State University (UDESC), Brazil

Keywords:

Industry 4.0, IoT, IIoT, Legacy Systems, LoRaWAN, Process Automation.

Abstract:

The Internet of Things (IoT) is increasingly pervasive and ubiquitous in various areas. The industry is incorpo-

rating intelligence into its processes through the Industrial IoT (IIoT). However, availability and performance

issues may limit IoT usage on the shop ﬂoor. Several IoT and IIoT initiatives can be applied on the factory

ﬂoor to improve processes, also allowing the inclusion of less reliable equipment. Thus, there are several

implementation approaches, wireless being the most used one due to its deployment ﬂexibility and centered

management. We analyze a real shop ﬂoor environment, identifying opportunities for using IIoT systems

and equipment such as Long Range Wide Area Network (LoRaWAN) technologies. Our results show the

possibility of improving process automation and monitoring using simple IIoT devices in Small and Medium

Enterprises (SMEs) still far from Industry 4.0 level.

1 INTRODUCTION

The Operational Technology (OT) is an area known

for being conservative (Wollschlaeger et al., 2017),

and often dilatory related to more intensive Informa-

tion Technology (IT) usage on shop ﬂoor. Recently,

IT and OT have been converging in a way they are of-

ten part of the same department, and may share phys-

ical resources, including cyber and physical manufac-

turing spaces (Cheng et al., 2018). The advent of

Industry 4.0 allows for a greater technological inte-

gration between processes, in which it is possible to

have more intelligent sensors and actuators with the

support of Industrial Internet of Things (IIoT) (Sauter

et al., 2011a). Furthermore, ﬁfth-generation Wire-

less (5G) technology adoption allows to intensify new

possibilities of IIoT usage on shop ﬂoor to be eval-

uated and studied. Based on a list of requirements

and a compliance analysis, assessing which technolo-

gies can be used to integrate legacy systems is pos-

sible. Since performing this type of integration can

bring some complexity, depending on how it is re-

alized. Thus, it should consider how the associated

legacy system was designed, what technologies were

used, whether there is adequate documentation, etc.

https://orcid.org/0000-0003-3992-5484

https://orcid.org/0000-0002-1976-0478

Furthermore, it is also necessary to identify the prob-

lem of OT to integrate simple to complex processes,

and there must have a way of communicating to form

systems.

There are several possibilities for using process

automation and monitoring to bring information from

the shop ﬂoor to the computational clouds. There-

fore, searching for case studies showing the feasibil-

ity and beneﬁts of low-cost IIoT equipment with Long

Range Wide Area Network (LoRaWAN) is relevant

to encourage its wide adoption. We emphasized here

that mission-critical equipment that can cause human

harm is not the focus of our study and already has an

extensive literature.

We analyzed a real shop ﬂoor environment, cat-

egorized it, and identiﬁed opportunities to use IIoT

systems and equipment. Moreover, we adopted the

LoRaWAN private networks to identify whether these

are adherent to the scenario of this study.

This work is organized as follows. Section 2

presents the fundamentals. Section 3 enlists key char-

acteristics of industrial automation and Industry 4.0,

IIoT, and LPWAN. Section 4 presents how the in-

dustrial systems are divided based on ISA-95 Model.

Section 5 brieﬂy lists the main reasons for evolution

concerning to automation. Section 6 presents and

evaluates the ArcelorMittal Vega environment, expos-

Farias de Carvalho, D. and Miers, C.

Process Automation and Monitoring Systems Based on IIoT Using Private LoRaWAN Networks: A Case Study of ArcelorMittal Vega Facilities.

DOI: 10.5220/0012039300003482

In Proceedings of the 8th International Conference on Internet of Things, Big Data and Security (IoTBDS 2023), pages 243-254

ISBN: 978-989-758-643-9; ISSN: 2184-4976

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

243

ing our case study details. Section 7 describes our

implementation and identiﬁed results.

2 FUNDAMENTAL CONCEPTS

Automation is a reality being incorporated more and

more, both in homes (also known as domotic automa-

tion or smart house) (Flores et al., 2018), (Giorgetti

et al., 2008), as in industries (Sauter et al., 2011b).

In this context Internet of Things (IoT) emerged as

a way to enable interaction mainly between devices

that until then was essentially between people and

devices (Hassan, 2018). The IoT concept was used

very broadly, with several residential use cases and

cities, but industrial use was slower due to legacy sys-

tems (Choi et al., 2018). Thus, industrial automation

has occurred for several generations and comprises

the types of devices and the networks used to connect

the devices.

The evolution has led the industry to the concept

of IIoT, which is the use of IoT with industrial re-

quirements, including in spite of the shop ﬂoor. Aim-

ing at a parameterization and organization concerning

what is performed by OT, IIoT is organized in several

levels deﬁning fundamental aspects of operation and

their operational requirements.

Both IoT and IIoT have accelerated adoption.

While in some cases, there is the only change from

older legacy systems, there are several new opportu-

nities for new applications and the need to integrate

the new possibilities with the already installed base.

According to estimates from International Data Cor-

poration (IDC) (Reinsel et al., 2018), there will be

41.6 billion “connected things” in 2025. At ﬁrst, IoT

integrates a network of physical objects with a series

of sensors connected with the possibility of exchang-

ing information with other devices using the Inter-

net as a path. The applicability of this type of solu-

tion since domestic applications, through companies

in various segments, medical applications, industrial

applications, etc. For a device to be considered intel-

ligent, it is not enough to be connected to the Internet.

Developing conceptual applications or prototypes on

some device (e.g., Arduino, ESP8266, or ESP32), to

remotely turn on a light, read sensor data or start a

small motor can be considered a good way of learn-

ing, but this does not mean you have a smart device

and it is a simple device connected using the Inter-

net. When some sensors and software interpret this

data and add an ”intelligence” layer to the processing,

there will be a real application of IoT.

Industrial IoT or IIoT refers to the application

of IoT technology in industrial environments, espe-

cially regarding the instrumentation and control of

sensors and devices involving cloud technologies.

Some branches of industry are currently looking for

Machine to Machine (M2M) type communication to

achieve wireless automation and control. The emer-

gence of cloud computing (whether public or private)

and other technologies (e.g., advanced analytics and

Machine Learning (ML)) allow different sectors to

reach a new layer of automation, being able to cre-

ate new revenues and business models. The IIoT also

deﬁnes the set of technologies and services, allowing

devices, computers, and smart objects to be intercon-

nected through the Internet. The IIoT is often asso-

ciated with Industry 4.0. However, a factory or pro-

duction process that only has IIoT cannot be consid-

ered as belonging to Industry 4.0. Besides technolo-

gies such as Big Data, IoT, and cloud computing, it

is possible to reduce costs and make production more

automated and autonomous (Mohamed, 2018), con-

tributing to the overall result of the company. Another

aspect is the industry has speciﬁc requirements that

must be highlighted, such as performance require-

ments in IoT / industrial automation services, which

can be subdivided into:

1. Motion Control: Category that includes continu-

ous processes as in some steel industries, speed

variation of conveyors, painting systems, etc.;

2. Mobile Robots: Robotic control can be static

(e.g., assembly line), or autonomous; as well as

camera systems and cooperative motion systems;

3. Mobile Control Panels with Safety Functions:

Overhead cranes, systems that monitor safety

zones (e.g., NR 12 - Safety at Work in Machinery

and Equipment - Used in Brazil), robotic arms;

and

4. Process Automation / Monitoring: Supervision

and control systems of a process in which sensors

and actuators can be read.

An industrial plant integrates several automation

systems, e.g., measurement systems, welding ma-

chines, etc. Thus, heterogeneity is part of this reality.

Table 1 lists and categorizes the central industrial au-

tomation systems, taking into account important re-

quirements (Brown et al., 2018), covering the main

systems in any industrial plant.

Table 1 performance criteria (Brown et al., 2018):

1. Service Availability: It is the percentage of avail-

ability of an end-to-end type of communication;

2. Cycle Time and Latency: Refers to the maximum

time allowed in communication, in which the time

of sending a command to the actuator or request-

ing a sensor reading until the return of the execu-

tion conﬁrmation; and

IoTBDS 2023 - 8th International Conference on Internet of Things, Big Data and Security

244

Table 1: Industrial Automation Performance Requirements

/ IIoT Requirements.

Use Case (High Level) Availability

Cycle

Time

Typical

Payload

Size

Number of

Devices

Typical

Service

Area

Motion Control

Printing

Machine

>99,9999% <2ms 20 bytes >100

100m x

100m x 30m

Machine

Tool

>99,9999% <0,5ms 50 bytes ∼20

15m x 15m

x 3m

Packaging

Machine

>99,9999% <1ms 40 bytes ∼50

10m x 5m

x 3m

Mobile Robots

Cooperative

Motion Control

>99,9999% 1ms 40-250 bytes 100 <1 km

Video-Operated

Remote Control

>99,9999% 10-100ms 15-150 bytes 100 <1 km

Mobile Control Panels with

Safety Functions

Assembly Robots or

Milling Machines

>99,9999% 4-8ms 40-250 bytes 4 10m x 10m

Mobile Cranes >99,9999% 12ms 40-250 bytes 2 4m x 60m

Process Automation (Process Monitoring)

>99,99% >50ms several 10.000 devices per km

3. Service Area and Density: Indicates whether the

desired performance has been achieved and the

number of devices within a predeﬁned area.

Compliance with the mentioned criteria will di-

rectly impact ”how” the automation system will be

designed, built, and maintained. The time cycles cri-

terion can determine the level of hardware or redun-

dancy required for the automation system. If the sys-

tem is monitored only in a process in which there is

no risk to human safety, equipment safety, or the envi-

ronment, the level of redundancy may be lower. The

intensiﬁcation of the use of industrial networks, re-

placing the old serial communications (e.g., RS232,

RS422, RS485, etc.), and the need to transfer data

from the lowest levels to the highest levels in con-

vergence to the ISA-95 standard has become even

greater (Hood, 2015).

3 INDUSTRIAL AUTOMATION

Process automation, also known as industrial au-

tomation, is an area known to be conserva-

tive (Wollschlaeger et al., 2017), and sometimes even

reactive in using IT resources. However, conser-

vatism impacts ﬁnances or the necessity of a com-

plex infrastructure. In this context, there is a need

to interconnect devices through communication net-

works that may be similar, or even the same type,

as the networks used in IT. It is an important high-

light among the various possibilities of shareable re-

sources: networks (wired, wireless, etc.), data cen-

ters, servers, databases, monitoring systems, clusters,

storage systems, support teams, etc. The advent of

Industry 4.0 promoted a technological integration be-

tween processes is proposed, in which it is possible

to have improved sensors and actuators supported by

IIoT (Sauter et al., 2011a). Therefore, a fundamental

issue is the role of communication networks, whether

they originate in IT or OT; in the end, these are tech-

nologies, and they must contribute to the evolution of

society. The purpose of technology is not found in

itself but in what it proposes to do with it.

Industry 4.0 has some characteristics and provides

some resources. Still, suppose a company has one of

them. In that case, this does not indicate that such

a company is in this category, as a comprehensive

analysis of the technologies and processes involved

is necessary. Some authors (Mohamed, 2018) have

grouped and highlighted the following characteris-

tics: Autonomous Robots, Simulations, Systems In-

tegration, IoT, Cybersecurity, Cloud Computing, 3D

Printing, Augmented Reality, and Big Data. The ex-

pected beneﬁts of applying the concept of Industry

4.0 are: Cost Reduction, Energy Savings, Increase

in Security, Environmental Conservation, Error Re-

duction, End of Waste, Business Transparency, In-

crease in Quality of Life, Unprecedented Customiza-

tion, and Scale. Even though some essential technolo-

gies emerged during the Industry 3.0 phase (e.g., In-

ternet, mobile telephony, and cloud computing), they

are still part of the Industry 4.0 basis (Colombo et al.,

2021).

Industry 3.0 will continue to be relevant for a long

time, not only because it has elements composing the

Industry 4.0 basis; but also because there is a whole

legacy that cannot be replaced in a short time (Iyer,

2018). The change from Industry 2.0 to Industry 4.0

in a single step is practically unfeasible, being a ”step

by step” approach most suitable, investing in equip-

ment from the beginning will bring a gain in scale

(e.g., robots, etc.), which will allow the company to

enter Industry 3.0, and new or modernized equipment

will also serve at the time of migration to Industry 4.0,

reducing the technological leap (Iyer, 2018).

3.1 IIoT

IIoT technology is an integral part of Industry 4.0 and

can be considered a means for this transition (Pilsan

et al., 2019). Thus, deﬁning the set of technologies

and services allows interconnecting devices, comput-

ers, and smart objects through the internet or indus-

trial network. One of the features inherited from IoT,

and which can be very useful in the industry, is the

possibility of communication between devices and

the collaboration between them M2M (Da Xu et al.,

2014). Several companies in Brazil are still in Indus-

try 1.0 and Industry 2.0, and it will take some time

to replace these legacy systems; in this way, new ap-

proaches arose, and driving characteristics of IIoT can

help in the evolution of these systems. As an example,

some micro-controlled devices serving as gateways or

remotes for Programmable Logic Controller (PLC)s

in the ﬁeld, based on IoT technologies (e.g., ZigBee,

LoRaWAN, ESP32, ESP8266, RaspBerry Pi, among

others). These can also contribute to raising the level

Process Automation and Monitoring Systems Based on IIoT Using Private LoRaWAN Networks: A Case Study of ArcelorMittal Vega

Facilities

245

of monitoring of an industrial process. In companies

where industrial automation is already in Industry 3.0,

the applicability of IIoT can be observed in different

contexts, whether in a traditional automation system

or in the integration with the cloud, Edge Computing,

and Smart Factories, among others. The possibility of

having the processing at the end devices (i.e., in the

sensors themselves) and that these can be connected

in the private enterprise cloud, processes like the eval-

uation of a stock, or production order, can be initiated

or have a certain level of monitoring without human

intervention. An area that can also beneﬁt from IoT

and IIoT is prescriptive maintenance, which is one in

which you can calculate and estimate the ideal time to

be performed with a high level of reliability; being a

smarter approach, it combines the detection of equip-

ment degradation, with statistical models already con-

solidated (Choubey et al., 2019), and later with the in-

troduction of Artiﬁcial Intelligence (AI) and Machine

Learning (ML). Thus, it can also take advantage of

IIoT, with the implementation of sensors and wire-

less networks, using devices with a lower cost than

those existing in traditional architecture with PLCs;

and in this way, generating historical data which are

essential to understand the ”behavior of a production

process”, including in terms of maintenance. A model

reﬁned with such statistical data, and forecasting tech-

niques can provide users with options regarding cor-

rective measures (MATTIOLI et al., 2020).

3.2 LPWAN

Low Power Wide Area Networks (LPWAN) is a

generic term for a group of technologies allow-

ing long distances communications, low cost, and

reduced energy consumption (Lin et al., 2017).

LPWAN is suitable for IoT applications that need to

transmit small amounts of information over a certain

distance. The IoT market has expanded rapidly, and

technologies based on LPWAN can be used in a wide

variety of scenarios. Several technologies LPWAN

represented in Figure 1 emerged in licensed and unli-

censed markets (e.g., Long Term Evolution (LTE)-M,

SigFox, Long Range (LoRa), and Narrowband Inter-

net of Things (NB-IoT), etc.).

LPWAN can cover distances between 10km and

40km in rural areas and between 1km to 5km in urban

areas (Mekki et al., 2019). An important characteris-

tic of LPWAN is that they work in the Sub-Ghz bands,

directly impacting their ability to overcome obstacles.

As a LPWAN technology, it signiﬁcantly improves

the power consumption of IoT and IIoT devices, with

a battery life of around ten years. It also has spectrum

efﬁciency, especially in indoor coverage, as it belongs

Wifi / Mobile Tecnologies

Short Distance

LPWAN

802.11ac

802.11ad

802.11n

802.11a

802.11b

802.11g

Bluetooth BLE

RFID/NFC

ZigBee 802.15.4

WPAN 802.15.4

WBAN 802.15.6

LoRa

SigFox

NB-IoT

LTE-M

Distance

Required Bandwidth

Figure 1: Bandwidth Required vs. Reached Distance.

to a Sub-Ghz (Mekki et al., 2019) category. Table 2

shows some characteristics of LPWAN technologies

in evidence in IoT and IIoT.

Table 2: IoT Technologies - Characteristics.

Standard 802.11

Bluetooth Low

Energy (BLE)

ZigBee Pro SigFox LoRa LTE-M NB-IoT 5G

3GPP Adherence? No No No No No

Yes

(Release 13)

Yes

(Release 13)

Yes

(Release 15)

Coverage Area

17 - 30

(m)

∼1 - 50

(m)

∼1 a 250

(m)

<12

(km)

<10

(km)

<10

(km)

<15

(km)

<12

(km)

Spectrum /

Bandwidth

2.4 Ghz

(802.11)

2.4 Ghz

(802.15.1)

2.4 Ghz

(802.15.4)

900 Mhz 900 Mhz 7 - 900 Mhz 8 - 900 Mhz

5 - 900Mhz

(entre outras)

Baud Rate

450

(Mbps)

(802.11n)

(Mbps)

250

(kbps)

∼100 - 600

(bps)

∼200 - 50

(kbps)

(Mbps)

<144

(kbps)

∼10

(Gbps)

Cost

4.00 USD

(2016)

4.00 USD

(2016)

3.00 USD

(2016)

4.00 USD

(2015)

2.64 USD

(2020)

4.00 USD

(2015)

2.64 USD

(2020)

5.00 USD

(2015)

3.30 USD

(2020)

4.00 USD

(2015)

2 - 3 USD

(2020)

<2.00 USD

Latency

20 - 40

(ms)

1 - 30

(s)

61 - 371

(ms)

50 - 100

(ms)

1.6 - 10

(s)

5 - 50

(ms)

Security 256 bits

128 bits

AES

128 bits 16 bits

32 bits

AES-128

3GPP

128 - 256 bits

3GPP

128 - 256 bits

3GPP

256 bits

Evaluating the data in the Table 2, when there

are long distances for industrial applications, some

of these can already be discarded (e.g., IEEE 802.11,

BLE and ZigBee) for not ﬁt this requirement.

4 IIoT SCENARIOS, PROBLEMS,

CHALLENGES, AND

OPPORTUNITIES

A traditional representation or classiﬁcation regard-

ing systemic levels within an industry is the ”Automa-

tion Pyramid”, based on the The International Society

for Measurement and Control (ISA) model, the ISA-

95 (Hood, 2015). Each specialty (or level) is repre-

sented by a layer that composes the pyramid. Figure 2

presents the concept of ISA-95 and some processes

exemplifying at which levels they are and how the

levels are divided not only in the view of traditional

automation but also in the so-called informatics. It

is also important to be concerned with issues such as

network segmentation, DMZ, ﬁrewalls, etc.

This representation starts from the sensor/actuator

that is in the base until it reaches the Business Intel-

ligence (BI) systems. At each of these levels, one or

more interfaces may perform the role of process in-

tegrator. Since each system can compose each level,

IoTBDS 2023 - 8th International Conference on Internet of Things, Big Data and Security

246

Sensors Drivers Actuators Robots

Process

Batch

Control

Discrete

Control

Drive

Control

Process

Control

Basic

Control

Safety

Control

Supervisory

Client

Area

Supervisory

Control

Supervisory

Application

Server

PIMS

System

Engineering

Workstation

Remote

Access

Server

Site

Operations

and Control

Level 0

Level 1

Level 2

Level 3

Level 4

Level 5

Site Business Planning and Logistics

Network

Enterprise Network

Cell / Area

Zone

Industrial

Security

Zone

Industrial

DMZ

Industrial

Security

Zone

Supervisory

Client

E-mail, Intranet,

Etc

Operator

Interface

Operator

Interface

Engineering

Workstation

Web

E-Mail

CIP

Application

Mirror

Web

Services

Operations

Application

Server

Remote

Gateway

Services

Patch

Management

Anti-Virus

Server

Figure 2: Automation Levels (Hood, 2015).

which can be customized and heterogeneous, differ-

ent interfaces can be created for this role (Li et al.,

2020).

Table 3 lists and categorizes the hierarchical lev-

els present in the ISA-95 Model, which are present

in medium and large companies / industrial plants.

The difference for smaller companies may be the ab-

sence of a BI system or an Enterprise Resource Plan-

ning (ERP). ISA-95, is a widely known model and

derived from ISA-88. These models are used by

manufacturing companies, providing references and

standards in automation, integration systems between

companies and operations, and Manufacture Opera-

tions Management (MOM) (Hood, 2015). Interna-

tional Electrotechnical Commission (IEC) also has

a widely used IEC62264 standard, which provides

criteria and terminology for batch control systems,

sometimes known as batch processes.

Table 3: Automation Levels - ISA-95 Model (Hood, 2015).

ISA Levels Activities ISA Name Type of System Example

Level 5 Business Intelligence BI BI

Level 4

Business-related activities needed to manage a

manufacturing organization

Enterprise ERP, CRM ERP, CRM, Logistics

Level 3

Activities of the work ﬂows to produce

the desired end products

Site, Area,

Work Centre,

Work Unit

MES. LIMS, CMM

City X Plant,

Cookie Making Area,

Cookie Packaging,

Line Work Center

Level 2

Activities of monitoring and controlling

the physical processes

PLC

HMI, SCADA,

Batch Systems

Process Optimization,

Mathemathical Model

Level 1

Activities and sensors involved in manipulating

the physical process

Device,

Control Model

I/O, Devices, Sensors

Cookie Wrapper Paper,

Tension Measurer

Level 0 Physical Process Equipment Cookie Wrapper

Table 3 not only exempliﬁes how the systemic lev-

els division but also groups types of systems and some

examples. ISA-95 model is the reference to process

automation. This division of levels of industrial infor-

matics includes the segregation of processes and func-

tions that the applications in each level need to have,

as well as an adequate level of segregation. From the

point of view of information security, in some com-

panies, ﬁrewalls are installed between the automation

network and the corporate network, as seen in Fig-

ure 2. For our study, the present work focuses on the

controllers and Human Machine Interfaces (HMIs)

that makeup Level 1, up to the Supervisory Control

And Data Acquisition (SCADA) and process opti-

mization systems, which are part of Level 2. The

challenges of data communication and it is integra-

tion from the shop ﬂoor to the cloud in a production

process are already consolidated in a large company

or process, or even in a smaller process, part of the

same principle; and in this way, even a small process

can take advantage of this integration. This study can

beneﬁt companies before Industry 3.0, and IoT and

IIoT can contribute to this process. Exploring a little

more these factors, regarding the problems and chal-

lenges, essential features can be grouped into 8 groups

((Technologies, 2019), (NetSuite, 2020), (Artemenko,

2021) and (Jayalaxmi et al., 2021): Interoperabil-

ity, Reliability, Security, Performance, Management,

Storage, Scalability, and Change Mindset.

5 PROBLEM DEFINITION

An industrial plant consists of integrating several au-

tomation systems, and heterogeneity is one of its char-

acteristics. Regarding the project, management and

technical/economic feasibility are the same regardless

of whether a project is automated or not. Thus, if the

project is not motivated by legal or safety issues, such

a project needs to be ”attractive”. The automation of a

production process in a traditional way from scratch,

known as (greenﬁeld), or even a reform (brownﬁeld

- also known as revamp), can demand engineering,

in several specialties, such as: electrical, mechanical,

civil, automation, metallic structures, etc. In the case

of a revamp, there is the additional risk that if the ren-

ovation is not carried out within the stipulated dead-

lines, it may cause damage to a production line that

was previously producing (i.e., operational and ﬁnan-

cial stability problems). When an industry decides to

automate it is functions, the motivator is commonly

different from types of processes (Autor, 2015):

1. Operation: Improvement of operating conditions,

which includes possible technical feasibility;

2. Quality: Product quality, that is, manufacturing

in narrower error tolerance bands, using efﬁcient

quality control;

3. Safety: Physical integrity of human beings and/or

equipment;

4. Flexibility: Easily and quickly allow changes in

the parameters of the manufacturing process;

5. Regulatory: When there is a new regulatory stan-

dard or the revision of an existing one;

Process Automation and Monitoring Systems Based on IIoT Using Private LoRaWAN Networks: A Case Study of ArcelorMittal Vega

Facilities

247

6. Productivity: Efﬁcient use of raw materials, ma-

chine time, personal availability, etc.; and

7. Control: Increase the level of process control,

generation of statistical data, reports, Key Perfor-

mance Indicators (KPIs), etc.

However, some questions emerge: (i) What to

do when an automation project is necessary but not

economically viable? (ii) When does a company

of type Small and Medium-sized Enterprises (SME)

have budget constraints? (Powell et al., 2013) state

SMEs face ﬁnancial and resource difﬁculties in ac-

quiring new technologies, one of the reasons why

these companies still behave cautiously in this mat-

ter. The present work aims at a real case study to

analyze how IIoT can contribute to production lines

where there is a low level of automation and may not

have ﬁnancially attractive. This case may be what

happens in SMEs; in this way, our study can be use-

ful for this industrial segment and those companies or

processes before Industry 3.0.

6 PROPOSAL

For the evaluation of a case study and Proof of

Concept (PoC), a real production environment was

chosen at the company ArcelorMittal (https://brasil.

arcelormittal.com/en) Vega located in the city of S

Francisco do Sul - Brazil, one of the most modern

ﬂat steel transformation units in the world at the time

of its startup, but after years it has legacy systems.

Standing a total production capacity of 1.6 million

tons/year of pickled, cold-rolled and hot-dip coated

coils, it mainly serves the automotive, home appli-

ance, pipe production, and civil construction indus-

tries. During the analysis phase, the main automation

systems in all production lines were evaluated, includ-

ing the systems considered as ”auxiliaries”. All data

were tabulated and classiﬁed according to Table 1.

Thus, Table 4 was created, which is a sample. Other

characteristics were also evaluated (e.g., servers, con-

trollers, remotes, actuators, database, etc.).

The grouping represented in Table 4 evidenced the

most predominant characteristic is the ”Automation /

Process Monitoring” systems; therefore, this was cho-

sen for the present analysis. In quantitative terms,

23 different types of automation systems were cata-

loged, and more than one production line can have the

same system. Thus, in this case, this was counted as

just one. Within this category, approaching the vision

of industrial automation, the following characteristics

can be highlighted as essential for the types of indus-

trial processes evaluated: (i) Availability in the order

of 99.99%; (ii) Cycle time (also called ”scan”), on the

Table 4: Case Study: Fragment of Study Environment.

System Description Factory Network Protocol Category Sub-Category

CCK

Energy and utilities

monitoring system

All Ethernet TCP

Process Automation /

Process Monitoring

Eurotherm

Dew point

monitoring system

BAF, SPM

and RCL 1

Ethernet Modbus TCP, OPC

Process Automation /

Process Monitoring

Byond

Zinc ingot

management system

CGL 1 and

CGL 2

802.11

TCP, Zigbee

and MQTT

Process Automation /

Process Monitoring

AMPTEM

Cold strip mill bearing

monitoring system

TCM Ethernet Zigbee

Process Automation /

Process Monitoring

ViWPD

Vision system for online

width measurement

CGL 1 Ethernet TCP, OPC

Process Automation /

Process Monitoring

Thickness Gauge

Online coil thickness

measurement system

CPL,TCM,

CGL 1, CGL 2

and SPM

Ethernet

TCP

(IP and UDP Messages)

Process Automation /

Process Monitoring

Coating Gauge

Online coil coating

measurement system

CGL 1 and

CGL 2

Ethernet

TCP

(IP and UDP Messages)

Process Automation /

Process Monitoring

ASIS

Vision system for

online defect detection

CGL 1 Ethernet

TCP

(IP and UDP Messages)

Process Automation /

Process Monitoring

Dross Robot

Stationary robot for

zinc pot cleaning

CGL 1 and

CGL 2

Ethernet

TCP

(IP and UDP Messages),

OPC

Motion Control Machine Tool

Coil Marker

Coil marker - alphanumeric

texts and barcode

CGL 1 and

CGL 2

Ethernet

TCP

(IP and UDP Messages)

Motion Control Printing Machine

Mobile Cranes

Overhead cranes for

coil handling

PIMS 802.11

OPC,

TCP

(IP and UDP Messages),

Proﬁbus

Mobile Control Panel

with Safety Functions

Mobile Cranes

order of 50ms; and (iii) High number of devices per

. Figure 3 shows the distribution of the categories

we observed within the analyzed industrial park.

86,8% 4,3% 4,3% 4,3%

Process Automation / Process Monitoring Motion Control / Machine - Tool

Mobile Control Panels with Safety Functions - Mobile Cranes

Motion Control / Printing Machine

Figure 3: Observed Distribution.

The data stratiﬁcation presented in Table 4 in it

is a complete version and is also presented in Fig-

ure 3, describing how automation/process monitoring

stands out when compared to the others areas. In addi-

tion, the four areas of Table 1 are represented. Based

on the analyzed data, some existing systems are ad-

herent to be connected in wireless networks on the

shop ﬂoor (IEEE 802.11x), a characteristic observed

in IIoT approaches. A highlight during the data anal-

ysis was that even in a company of this size, there

are non-automated processes; and, thus, are not con-

tained in the tabulated data. Based on the analyzed

items, coil yards (in their various types) are among

the items with no monitoring systems. When analyz-

ing Figure 4, it can also be observed that the yards

have large areas and with movement or occupation of

coils. Figure 4 shows a coil dispatch yard (aka ﬁn-

ished product), which can be found in steel mills, ser-

vice centers (e.g., coil processing), and even in cus-

tomers who purchase such reels.

One of the characteristics of the highlighted pro-

cesses that do not have any level of automation is that

they are slow processes and occupy large areas. In this

way, the LPWAN technology can contribute to this

automation, as it can cover a large area when choosing

to use LoRaWAN type networks. A process, as pre-

sented in Figure 4, has low systemic integration and

high monitoring potential, including communication

with existing PLC and with the Manufacturing Ex-

ecution System (MES) and Warehouse Management

System (WMS) system, which are systems of Level

3 systems, according to the ISA-95 model. To carry

out our Proof of Concept (PoC), the smallest infras-

IoTBDS 2023 - 8th International Conference on Internet of Things, Big Data and Security

248

Figure 4: ArcelorMittal Vega Coils Warehouse.

tructure for the point-to-point connection in a wireless

network may be the most suitable. In addition, there

are System on a Chip (SoC) type microcontrollers,

which already have the LoRa network built in, along

with the antenna and place for installing a recharge-

able battery. In terms of infrastructure for automating

this process in a traditional model, there is a need for

passing cables, assembling metallic structures and/or

mechanical structures (e.g., trays), a need to remove

interference (e.g., when it is necessary to build some-

thing of civil engineering, but cables or pipes are pass-

ing through the same place), among others. Figure 5

shows our process proposal for the PoC.

Process Diagram

Microcontroler /

Microprocessor

IIoT

Field Device

Read

(Sensors)

Field Device

Write

(Actuators)

Use Use

«Transmitter»

e.g. Lora

«Receiver»

e.g. Lora

PLC

Field Device

Read

(Sensors)

Field Device

Write

(Actuators)

Use Use

Data Exchange

OPC Server

Data Exchange Data Exchange

OPC Client

(SCADA - Runtime)

Data Exchange

Operator

(Production)

Historian

(e.g., BD,

PIMS, etc)

Data Exchange

User

(Office)

Figure 5: PoC - Process procedures - Static Vision.

Figure 5, shows a PoC using a microcontroller to

receive/send data and integration with an automation

system. The architecture includes important systems

and communication protocols such as OLE for Pro-

cess Control (OPC) Server, OPC Client, Historians,

Database, Fieldbus, etc. The use of OPC communi-

cation as middleware also opens up the possibility for

data to be sent to any part of the internal or exter-

nal network (e.g., cloud computing), as well as com-

munication to any other automation system, such as

Plant Information Management System (PIMS), Su-

pervisory, SCADA, etc. OPC communication is one

of the most used forms of communication in indus-

trial automation, but this is not the only one. For the

same proposed scenario for PoC, a socket commu-

nication (TCP or UDP), IPC, HTTP, REST, XML ,

among others, could also be used without the need for

changes to the architecture. The process proposed in

Figure 5 is the representation of the area of interest to

be developed in PoC, and which is highlighted in Fig-

ure 6. This area of interest (Section 5), in which the

focus is on communication and processing at Level

1, Level 2, and communication with Level 3 systems

according to the ISA-95 model (Table 3).

Batch

Control

Discrete

Control

Drive

Control

Process

Control

Basic

Control

Safety

Control

Supervisory

Client

Area

Supervisory

Control

Supervisory

Application

Server

PIMS

System

Engineering

Workstation

Remote

Access

Server

Site

Operations

and Control

Level 0

Level 1

Level 2

Level 3

Cell / Area

Zone

Industrial

Security

Zone

Supervisory

Client

Operator

Interface

Operator

Interface

Engineering

Workstation

Figure 6: PoC - Process architecture.

Figure 7 graphically exempliﬁes the sequence of

events in a process like this and the behavior of a func-

tionality, considering the interaction between all soft-

ware components and procedures related to their use.

For better visualization, each process macro is repre-

sented by the color that each ”System Level” received

in Figure 2.

It can be seen that both ﬁgures (Figure 5 and Fig-

ure 7) are adherent to the current automation pro-

cesses and that it is part of the work of (Koziolek,

2018), notations like Uniﬁed Modeling Language

(UML) are used in process automation. These repre-

sentations help to bring software developers closer to

professionals in the business areas, with the objective

of a clear understanding of the behavior that the soft-

ware and the process to which it is automated must

have.

Process Automation and Monitoring Systems Based on IIoT Using Private LoRaWAN Networks: A Case Study of ArcelorMittal Vega

Facilities

249

Loop

Shop Floor

Data is Acquired using

PLC, Fieldbus, Remote

Sensors, Traditional

Sensors and Actuators,

etc.

Level 1

Level 2

Level 3

:Device

Transmitter

(e.g. Lora)

:Device

Communication

Channel

:Device

Receiver

(e.g. Lora)

:Device

Management

:Device

OPC Server or

TCP Server

:Device

OPC Client /

SCADA

Fieldbus

Communication

is Required for

Sensors and

Actuators

scan()

:addresses

getDeviceId()

:deviceId

retrieveIIoTData()

:IIoTData

Loop

scan()

:addresses

getDeviceId()

:deviceId

retrieveIIoTData()

:IIoTData

OPC Server

or TCP Server Can Be Used as

a "Middleware" to Integrate

Upper Levels.

(e.g. Data Lake, Big Data, PIMS,

etc)

retrievePlcData()

:PlcData

Loop

scan()

:addresses

getDeviceId()

:deviceId

Loop

retrieveOPCData()

:OPCData

Figure 7: PoC - Process arquitecture - Dinamic Vision.

7 IMPLEMENTATION, RESULTS

AND ANALYSIS

7.1 Proof of Concept

To perform the PoC, the LPWAN environment was

set up using two approaches. The ﬁrst concerns the

use of commercial gateways and sensors developed

by companies that integrate solutions and specialize

in IoT; and the second concerns the use of microcon-

trollers of the type SoC ESP32 models with built-in

radio LoRa that the authors of this work developed.

This gateway has access to the LoRaWAN network

of the KORE Wireless company using the platform

called KORA and access to the Microsoft Azure cor-

porate environment. To carry out this PoC Arcelor-

Mittal Vega provided the necessary equipment and re-

sources (e.g., LoRa gateway, network installation ser-

vices, etc.). The gateway was installed above the line

Continuos Galvanizing Line 1 (CGL1) at the height of

65m. The gateway received a ﬁnal installation since

ArcelorMittal Vega is interested in using this tech-

nology once in another unit belonging to the same

group; this one had excellent results. This model is

connected in an automation network using Ethernet

(RJ45) cable and integration with internal antennas

GPS, LTE/4G, and LoRa Radio. If necessary, the

communication LTE/4G is used as redundancy. The

gateway is involved in a case with Ingress Protec-

tion (IP67) protection. Concerning the technical spec-

iﬁcation for LoRaWAN communication, this gateway

has 8ch RX (125kHz, multi Spreading Factor) + 1ch

RX (250KHz or 500kHz, mono Spreading Factor) +

1ch RX (FSK) to get 10ch RX + 1ch TX.

7.2 Site Survey

To determine the coverage area of ArcelorMittal

Vega, a coverage site survey procedure was carried

out with measuring devices that indicate the qual-

ity of data transmission from a device to the gate-

way. Three ESP32 micro-controllers with LoRa an-

tenna were used, one of which also had a Global Posi-

tioning System (GPS) receiver. All micro-controllers

transmitted a counter, and the one with GPS also sent

the location. The data was received by the gate-

IoTBDS 2023 - 8th International Conference on Internet of Things, Big Data and Security

250

way and transmitted to Databricks in the Microsoft

Azure environment. Once the data is stored, Mi-

crosoft PowerBI allows the creation of a map with

the entire scope and coverage capacity of the antenna

and gateway installed. To carry out this site survey,

all factories and ofﬁces were visited, including build-

ings and the underground of some factories. There

were locations where the GPS signal was lost de-

pending on the location (e.g., underground), but there

was LoRa transmission. The measurement was con-

sidered valid in these cases, and the nearest GPS lo-

cation was considered. The amount of four ESP32

micro-controllers from three different manufacturers

was used (e.g., Heltec, TTGO, and Robocore), but all

of them had a LoRa radio/shield. In addition to pre-

senting the identiﬁcation of each device, information

related to Received Signal Strength Indicator (RSSI)

is also presented, which represents the quality of the

received signal (Industries, 2022), which also indi-

cates the level of power received after any possible

loss of antenna and cable, and is represented by Deci-

bel Milliwatts (dBm). The higher the RSSI value

indicates the signal strength. Information about the

Signal-to-Noise Ratio (SNR) is also presented (Indus-

tries, 2022), which is an existing relationship between

the received signal and the noise that accompanies

this signal and which is represented by values in Deci-

bels Relative to Isotropic (dBi). The lower the SNR,

the worse the communication. An important indicator

that should also be considered is the Spreading Factor

(SF). This information determines the amount of data

that can be transmitted, the period that information

will be ”over the air”, the distance reached, and others

(Pham et al., 2020). The speciﬁcation of LoRaWAN

protocol includes the Adaptive Data Rate (ADR) tech-

nology. The main objective is to adjust the SF and

Transmission Power (TP) variables to balance each

device’s consumption and efﬁciency and include the

control of used radio channels. Table 5 shows some

characteristics of LoRaWAN technology.

Table 5: Relationship: SF, SNR, RSSI, and Payload.

Required SNR

(dB)

RSSI

(min)

RSSI

(max)

Data Rate

(kbps)

Transmission

Duration

(sec)

User Payload

(Bytes)

7 -7 0 -110 5.47 0.036 230

8 -10 -110 -113 3.13 0.064 230

9 -12.5 -113 -116 1.76 0.113 123

10 -15 -116 -119 0.98 0.204 59

11 -17.5 -119 -120 0.54 0.365 59

12 -20 -120 -123 0.29 0.682 59

7.3 Scenario

The area chosen to carry out the PoC was the pro-

duction line shed called Recoiling Line (RCL)#1,

which shares the space with the ﬁnished product stor-

age yards, coil packaging, and shipping. We deﬁned

six possible scenarios, in which several aspects were

taken into account, such as applicability, cost, ﬁnan-

cial return, security, scope, replicability, and transfor-

mation into a product after PoC, among others. The

scenario chosen can contribute to ArcelorMittal Vega

as they fully meet the company’s needs. This scenario

concerns the control of the environmental monitoring

of the coil yard because it has low automation and

because the monitored variables directly contribute to

the product’s ﬁnal quality.

7.4 Scenario - Environmental

Monitoring

This scenario is for implementing the environmental

monitoring project (temperature, humidity, status of

gates, and vibration) of the coil yard shed, also called

ﬁnished product, packaging, and dispatch of coils.

The shed environment directly inﬂuences the possi-

bility of oxidation in steel coils. Thus, monitoring

and calculating the so-called ”dew point” is essential

for the product’s ﬁnal quality. The coil and packaging

yard shed has four dehumidiﬁers to control the am-

bient humidity. However, these are turned on manu-

ally and are not monitored (only lights on the control

panel). Depending on the ambient temperature and

climatic conditions of the industrial plant, operators

decide whether to turn the dehumidiﬁers on/off. As

there is no online monitoring for these conditions, the

equipment can be turned on late or remain turned on

even when they need to be in operation. The mon-

itoring of temperature and humidity is done through

manual devices. Even if they go through a certiﬁca-

tion and validation process of the Measurement Sys-

tem Analysis (MSA) type, which is a method that has

the objective of evaluating the validity of a measure-

ment system and minimizing external factors to the

equipment that can interfere with the quality of a mea-

surement and even the human factors; yet it is a man-

ual process and therefore depends on the measure-

ments being carried out at the appropriate frequen-

cies. Another aspect is that no documentary record

indicates whether the measurements were carried out

and which values were obtained. The shed gates are

another variable that can inﬂuence the environment,

and in this way, this monitoring is also essential; be-

cause with this opening, there is the entrance of tem-

perature and humidity which takes the atmospheric

balance of the internal environment. The opening and

closing of the gates occur automatically when a ve-

hicle is 1 meter away, both in the entry and exit di-

rections. Eventually, a gate may remain open due to

some problem or other need, inﬂuencing the balance.

Process Automation and Monitoring Systems Based on IIoT Using Private LoRaWAN Networks: A Case Study of ArcelorMittal Vega

Facilities

251

Crane

Transfer Coil

Goneville

Datacenter

Babitonga

Datacenter

Automation / Electrical Room

PLC

Dev

HMI.

Dev.

PLC 1 PLC 2 PLC 3

Firewall

10Gbps 10Gbps

Coil Package Zone

Uncoiler

Recoiler

Entry Shear

Exit Shear

Weld Machine

Side Trimmer

Oiler

Quality Inspection

SW. L2

PLC Weld

Machine

Gate

Office

Field Devices

(Motors, Sensors, Actuators, etc)

Field Devices

(Motors, Sensors, Actuators, etc)

Corporative

Station

Corporative

Stations

Coil

Label

Printer

Sample

Label

Printer

Weight

Scale

APP

Server

Gate

SW. L2

Legacy

Fieldbus

SCADA /

Level 2

Corporative

Station

SW. L2

PLC

Remote

PLC

Remote

Exit

Section

(External)

Entry

Section

(External)

Process

Section

(Pulpit)

Crane

PLC

Remote

PLC

Remote

SCADA /

Level 2

SCADA /

Level 2

PIMS

Server

PIMS

Server

Weld

Macheine

SW. L2

SW. L3

Scrap

Bucket

Scrap

Bucket

Scrap

Bucket

Scrap

Bucket

Legend

LoRaWan Gateway

LoRa End Point

Status

Temperature

Humity

Temperature

Humity

Temperature

Humity

Temperature

Humity

Status

Temperature

Humity

Status

Temperature

Humity

Status

Production Flow

CT07-W3-TMP1

CT07-W3-HUM1

CT07-W3-STS4

CT05-W2-TMP1

CT05-W2-HUM1

CT05-W2-STS1

CT03-W1-TMP1

CT03-W1-HUM1

CT03-W1-STS1

CT02-W1-TMP1

CT02-W1-HUM1

CT04-W2-TMP1

CT04-W2-HUM1

CT01-W1-TMP1

CT04-W1-HUM1

CT01-W1-STS1

CT01-W2-STS1

CT06-W3-STS1

GEN-GTW1

Dehumidifier 3 Dehumidifier 4Dehumidifier 1 Dehumidifier 2

Temperature

Vibration

Status

CT10-W1-TMP1

CT10-W1-VIBR1

CT10-W1-STS1

Temperature

Vibration

Status

CT09-W1-TMP1

CT09-W1-VIBR1

CT09-W1-STS1

Temperature

Vibration

Status

CT08-W1-TMP1

CT08-W1-VIBR1

CT08-W1-STS1

Temperature

Vibration

Status

CT11-W1-TMP1

CT11-W1-VIBR1

CT11-W1-STS1

Warehouse 1 - W1

Warehouse 2 - W2

Warehouse 3 - W3

Figure 8: Coil Warehouse, Package Zone, and RCL#1 - ArcelorMittal Vega.

The dew point is the temperature at which the wa-

ter vapor in the ambient air changes to a liquid state

in the form of small drops by condensation, which is

called dew (Lawrence, 2005). Integrating all the data

mentioned will contribute to better control of this yard

and reduce the possibility of generating defect arising

from oxidation that brings ﬁnancial damage and the

company’s image with customers and the market. In

Figure 8, it’s possible to observe the distribution of

the gateway LoRa of the controllers and of the vari-

ous sensors that were spread out according to the ini-

tial and ﬁnal distance of the yards where the stock and

packing. Figure 8 lists all the necessary hardware for

the full automation of this environment using devices

like the ones tested in PoC.

The sensors used are for measuring temperature,

humidity, the state of the gates, as well as the state

of the dehumidiﬁers. Each device receives an iden-

tiﬁcation (tag), the same as existing in the systems

that will use this information. ArcelorMittal Vega also

acquired market sensors for monitoring dehumidiﬁer

motors. The model chosen is from Advantech, and the

sensors are from the WISE-2410 family operating on

the LoRaWAN network. The data can be visualized

in a supervisory system (AVEVA InTouch) generat-

ing animation alarms and alerts to RCL#1 operators

through the PIMS tool as well as in a corporate net-

work through tools such as Databricks and PowerBI.

Table 6 relates all devices involved in this PoC are

shown and its tagnames.

Table 6: Scenario - Monitoring of Ambiental Enviroment.

Tagname Type Location Controler Family

GEN-GTW1 General Gateway Out of Warehouses Gateway Kerlink

CT01-W1-TMP1 Temperature Warehouse 1 CT1 ESP32

CT01-W1-HUM1 Humidity Warehouse 1 CT1 ESP32

CT01-W1-STS1 Status Warehouse 1 CT1 ESP32

CT01-W2-STS1 Status Warehouse 2 CT1 ESP32

CT02-W1-TMP1 Temperature Warehouse 1 CT2 ESP32

CT02-W1-HUM1 Humidity Warehouse 1 CT2 ESP32

CT03-W1-TMP1 Temperature Warehouse 1 CT3 ESP32

CT03-W1-HUM1 Humidity Warehouse 1 CT3 ESP32

CT03-W1-STS1 Status Warehouse 1 CT3 ESP32

CT04-W1-TMP1 Temperature Warehouse 1 CT4 ESP32

CT04-W1-HUM1 Humidity Warehouse 1 CT4 ESP32

CT05-W2-TMP1 Temperature Warehouse 2 CT5 ESP32

CT05-W2-HUM1 Humidity Warehouse 2 CT5 ESP32

CT05-W2-STS1 Status Warehouse 2 CT5 ESP32

CT06-W3-STS1 Status Warehouse 3 CT6 ESP32

CT07-W3-TMP1 Temperature Warehouse 3 CT7 ESP32

CT07-W3-HUM1 Humidity Warehouse 3 CT7 ESP32

CT07-W3-STS4 Status Warehouse 3 CT7 ESP32

CT08-W1-TMP1 Temperature Warehouse 1 CT8 Advantech

CT08-W1-HUM1 Vibration Warehouse 1 CT8 Advantech

CT08-W1-STS1 Status Warehouse 1 CT8 Advantech

CT09-W1-TMP1 Temperature Warehouse 1 CT9 Advantech

CT09-W1-VIBR1 Vibration Warehouse 1 CT9 Advantech

CT09-W1-STS1 Status Warehouse 1 CT9 Advantech

CT10-W1-TMP1 Temperature Warehouse 1 CT10 Advantech

CT10-W1-VIBR1 Vibration Warehouse 1 CT10 Advantech

CT10-W1-STS1 Status Warehouse 1 CT10 Advantech

CT11-W1-TMP1 Temperature Warehouse 1 CT11 Advantech

CT11-W1-VIBR1 Vibration Warehouse 1 CT11 Advantech

CT11-W1-STS1 Status Warehouse 1 CT11 Advantech

IoTBDS 2023 - 8th International Conference on Internet of Things, Big Data and Security

252

Table 6 is vital for the areas that maintain the

system and the electrical maintenance of the indus-

trial condominium, which will be responsible for re-

placing components in case of defects, predictive in-

spection, etc. After the PoC phase, a drawing of the

electrical interconnections must be generated and in-

cluded in this company’s archive department. Fig-

ure 9 shows the sequence diagram of this scenario

and how it relates to all actors, such as sensors, con-

trollers, cloud systems, etc.

:Gateway

GEN-GTW1

:Sensors :Controlers

Loop

:Azure

(Databriks,

BigData, etc)

:sendRawData()

receiveResult

scan(Status)

:resultStatus

scan(Temperature)

:resultTemperature

receiveResult

:sendTelegram()

CT01 to CT07

scan(Humity)

:resultHumity

Figure 9: Sequence Diagram - Scenario.

Figure 9 allows us to observe the integration from

the shop ﬂoor, the interconnection to the general gate-

way (GEN-GTW1), the interconnection to the corpo-

rate ﬁrewall, and the connection to Azure Data Bricks,

which is hosted in Microsoft’s computing cloud.

Table 7: Results per Family Devices.

Device Case

Sensor to Gateway

(ms)

Gateway to Cloud

(ms)

Total Latency

(ms)

Required Cycle

(ms)

Total Bytes

Advantech WISE-2410 Average 8000 3000 11000 3600000

68 + 20

2 msg

Best 5000 2000 7000 3600000

68 + 20

2 msg

Worse 22000 13000 24000 3600000

68 + 20

2 msg

ESP32 Devices Average 2000 2000 4000 600000 20

Best 1000 2000 3000 600000 20

Worse 4000 3000 7000 600000 20

Table 7 shows the values obtained in the exper-

iment in which the times were classiﬁed as mean,

best case, and worst case. For Advantech sensors,

two messages are sent, one complementing the other.

Thus, the message can only be decoded after receiv-

ing the second message. The differences in times

between Advantech and ESP32 devices can be ex-

plained precisely by the size and need of the comple-

ment message. The monitored processes are consid-

ered slow; therefore, the total latency is perfectly ac-

cepted since the sending frequencies are in the order

of 10 minutes (temperature and humidity) and 01 hour

(vibration). The status of the gates occurs by even-

tual opening, and there is no real-time requirement

but storage. It can be concluded that all times ob-

tained were satisfactory for the experiment and con-

ﬁrms the data of Table 1 for the category ”Process

Monitoring” (Brown et al., 2018).

8 CONSIDERATIONS & FUTURE

WORK

One of the challenges for implementing systems

based on IoT and IIoT on the shop ﬂoor is to identify

the ideal technology for each organization’s require-

ments. Thus, this implies elaborating a detailed analy-

sis of the cost-beneﬁt ratio compared to the objectives

to be achieved. Therefore, a clear speciﬁcation ad-

dresses the life cycle of the data from the generation

on the shop ﬂoor (e.g., sensors, actuators, etc.) to the

level of relevance of the information generated, with

the desired degree of reliability. The use of LPWAN

technologies, such as LoRaWAN, create new possi-

bilities for an organization to modernize its processes

and allowing to ingress under the Industry 4.0 level.

Moreover, it can mean lowering the cost of automat-

ing processes in companies without a minimally mod-

ern or technological park, especially in cases where

speed is not a strong technical requirement. Geo-

graphically distant areas are also strong candidates for

adopting an LPWAN technology. It may also mean

greater adoption of Software as a Service (SaaS) tech-

nologies, as small IoT and IIoT devices can integrate

from the shop ﬂoor to a system hosted in some cloud,

e.g., Amazon AWS, Microsoft Azure, Google, etc.

Future works, complains a project for the adop-

tion/conversion of the PoC to industrialization us-

ing more robust hardware and system entry into pro-

duction. Despite this, the potential we observed is

promising, and a PoC in a real environment such as

the one proposed can contribute to a greater adhesion

of IoT technologies on the shop ﬂoor for the category

of ”Automation / Monitoring of Processes”. Another

future work comprises three more scenarios using the

same infrastructure, intended to evaluate the opera-

tional limits and performances related to the number

of sensors, transmission rates, and density per gate-

way and antenna.

ACKNOWLEDGEMENTS

This work was supported by FAPESC,

LabP2D/UDESC, and ArcelorMittal Vega.

This work received ﬁnancial support from the Co-

ordination for the Improvement of Higher Education

Personnel - CAPES - Brazil (PROAP-AUXPE/PDPG

CONSOLIDACAO 3-4).

Process Automation and Monitoring Systems Based on IIoT Using Private LoRaWAN Networks: A Case Study of ArcelorMittal Vega

Facilities

253

REFERENCES

Artemenko, A. (2021). Keynote: Advances and challenges

of industrial iot. In 2021 IEEE International Con-

ference on Pervasive Computing and Communications

Workshops and other Afﬁliated Events (PerCom Work-

shops), page 526–526.

Autor, D. H. (2015). Why are there still so many jobs? the

history and future of workplace automation. Journal

of Economic Perspectives, 29(3):3–30.

Brown, G. et al. (2018). Ultra-reliable low-latency 5G

for industrial automation. Technol. Rep. Qualcomm,

2:52065394.

Cheng, J., Chen, W., Tao, F., and Lin, C.-L. (2018). In-

dustrial iot in 5g environment towards smart manufac-

turing. Journal of Industrial Information Integration,

10:10–19.

Choi, H., Song, J., and Yi, K. (2018). Brightics-iot: To-

wards effective industrial iot platforms for connected

smart factories. In 2018 IEEE International Confer-

ence on Industrial Internet (ICII), page 146–152.

Choubey, S., Benton, R., and Johnsten, T. (2019). Pre-

scriptive equipment maintenance: A framework. In

2019 IEEE International Conference on Big Data (Big

Data), page 4366–4374.

Colombo, A. W., Karnouskos, S., Yu, X., Kaynak, O.,

Luo, R. C., Shi, Y., Leitao, P., Ribeiro, L., and

Haase, J. (2021). A 70-year industrial electronics

society evolution through industrial revolutions: The

rise and ﬂourishing of information and communica-

tion technologies. IEEE Industrial Electronics Maga-

zine, 15(1):115–126.

Da Xu, L., He, W., and Li, S. (2014). Internet of things in

industries: A survey. IEEE Transactions on industrial

informatics, 10(4):2233–2243.

Flores, P., Moscoso-Martinez, M., Minchala, L. I.,

Vazquez-Rodas, A., and Astudillo-Salinas, D. (2018).

A simple reliable wireless bus design for home au-

tomation systems. In 2018 IEEE XXV International

Conference on Electronics, Electrical Engineering

and Computing (INTERCON), page 1–4.

Giorgetti, G., Gambi, E., Spinsante, S., Baldi, M.,

Morichetti, S., and Magniﬁco, I. (2008). An integrated

solution for home automation. In 2008 IEEE Inter-

national Symposium on Consumer Electronics, page

1–4.

Hassan, Q. F. (2018). Environment, People, and Time as

Factors in the Internet of Things Technical Revolution,

page 51–76. IEEE.

Hood, G. W. (2015). Industry standard models for de-

sign/discussion of manufacturing systems. In 2015 5th

Australian Control Conference (AUCC), page 12–15.

Industries, T. T. (2022). Rssi and snr.

Iyer, A. (2018). Moving from industry 2.0 to industry 4.0:

A case study from india on leapfrogging in smart man-

ufacturing. Procedia Manufacturing, 21:663–670.

Jayalaxmi, P., Saha, R., Kumar, G., Kumar, N., and Kim,

T.-H. (2021). A taxonomy of security issues in in-

dustrial internet-of-things: Scoping review for exist-

ing solutions, future implications, and research chal-

lenges. IEEE Access, 9:25344–25359.

Koziolek, H. (2018). Software architecture in process au-

tomation: Uml & the “smart factory”.

Lawrence, M. G. (2005). The relationship between relative

humidity and the dewpoint temperature in moist air:

A simple conversion and applications. Bulletin of the

American Meteorological Society, 86(2):225–234.

Li, C., Mantravadi, S., and Møller, C. (2020). Aau open

source mes architecture for smart factories – exploit-

ing isa 95. In 2020 IEEE 18th International Con-

ference on Industrial Informatics (INDIN), volume 1,

page 369–373.

Lin, J., Shen, Z., and Miao, C. (2017). Using blockchain

technology to build trust in sharing LoRaWAN IoT.

In Proceedings of the 2nd International Conference

on Crowd Science and Engineering, pages 38–43.

MATTIOLI, J., PERICO, P., and ROBIC, P.-O. (2020). Im-

prove total production maintenance with artiﬁcial in-

telligence. In 2020 Third International Conference

on Artiﬁcial Intelligence for Industries (AI4I), page

56–59.

Mekki, K., Bajic, E., Chaxel, F., and Meyer, F. (2019). A

comparative study of LPWAN technologies for large-

scale iot deployment. ICT express, 5(1):1–7.

Mohamed, M. (2018). Challenges and beneﬁts of industry

4.0: An overview. International Journal of Supply and

Operations Management, 5(3):256–265.

NetSuite (2020). Iiot & manufacturing.

Pham, C., Bounceur, A., Clavier, L., Noreen, U., and Ehsan,

M. (2020). Radio channel access challenges in lora

low-power wide-area networks. In LPWAN Technolo-

gies for IoT and M2M Applications, pages 65–102.

Elsevier.

Pilsan, H. O., Amann, R., and Gerstenberg, M. (2019). Re-

alization of a small iiot node: A greenﬁeld approach.

In 2019 20th International Conference on Research

and Education in Mechatronics (REM), page 1–5.

Powell, D., Riezebos, J., and Strandhagen, J. O. (2013).

Lean production and erp systems in small- and

medium-sized enterprises: Erp support for pull pro-

duction. International Journal of Production Re-

search, 51(2):395–409.

Reinsel, D., Gantz, J., and Rydning, J. (2018). The digiti-

zation of the world from edge to core.

Sauter, T., Soucek, S., Kastner, W., and Dietrich, D.

(2011a). The evolution of factory and building au-

tomation. IEEE Industrial Electronics Magazine,

5(3):35–48.

Sauter, T., Soucek, S., Kastner, W., and Dietrich, D.

(2011b). The evolution of factory and building au-

tomation. IEEE Industrial Electronics Magazine,

5(3):35–48.

Technologies, M. (2019). 6 challenges for industrial iot.

Wollschlaeger, M., Sauter, T., and Jasperneite, J. (2017).

The future of industrial communication: Automation

networks in the era of the internet of things and in-

dustry 4.0. IEEE industrial electronics magazine,

11(1):17–27.

IoTBDS 2023 - 8th International Conference on Internet of Things, Big Data and Security

254