Industrial internet of things (iiot)

This book discusses how the industrial internet will be augmented through increased network agility, integrated artificial intelligence (AI) and the capacity to deploy, automate, orchestrate, and secure diverse user cases at hyperscale. Since the internet of things (IoT) dominates all sectors of technology, from home to industry, automation through IoT devices is changing the processes of our daily lives. For example, more and more businesses are adopting and accepting industrial automation on a large scale, with the market for industrial robots expected to reach $73.5 billion in 2023. The primary reason for adopting IoT industrial automation in businesses is the benefits it provides, including enhanced efficiency, high accuracy, cost-effectiveness, quick process completion, low power consumption, fewer errors, and ease of control. The 15 chapters in the book showcase industrial automation through the IoT by including case studies in the areas of the IIoT, robotic and intelligent systems, and web-based applications which will be of interest to working professionals and those in education and research involved in a broad cross-section of technical disciplines. The volume will help industry leaders by Advancing hands-on experience working with industrial architecture Demonstrating the potential of cloud-based Industrial IoT platforms, analytics, and protocols Putting forward business models revitalizing the workforce with Industry 4.0.

3 2.3 Security Challenges of IoT

4 2.4 IoT Security Threats

5 2.5 Assaults in IoT Devices

6 2.6 Security Analysis of IoT Platforms

7 2.7 Future Research Approaches

8 References

7 3 Smart Automation, Smart Energy, and Grid Management Challenges

1 3.1 Introduction

2 3.2 Internet of Things and Smart Grids

3 3.3 Conceptual Model of Smart Grid

10 6 Recent Advances in Wearable Antennas: A Survey

2 7.2 Data Center and Internet of Things

3 7.3 Machine Learning Models and IoT

4 7.4 Challenges in Data Center and IoT

3 8.3 Smart Airport Architecture

4 8.4 Barriers to IoT Implementation

5 8.5 Current Technologies in Aviation Industry

6 8.6 IoT Adoption Challenges

7 8.7 Transforming Airline Industry With Internet of Things

8 8.8 Revolution of Change (Paradigm Shift)

9 8.9 The Following Diagram Shows the Design of the Application

10 8.10 Discussion, Limitations, Future Research, and Conclusion

11 8.11 Present and Future Scopes

12 8.12 Conclusion

13 References

13 9 IoT-Based Water Management System for a Healthy Life

1 9.1 Introduction

2 9.2 Water Management Using IoT

3 9.3 IoT Characteristics and Measurement Parameters

4 9.4 Platforms and Configurations

5 9.5 Water Quality Measuring Sensors and Data Analysis

6 9.6 Wastewater and Storm Water Monitoring Using IoT

7 9.7 Sensing and Sampling of Water Treatment Using IoT

3 11.3 Materials and Methods

4 11.4 Results and Discussion

3 12.3 Data Mining Tasks

4 12.4 Feature Selection Techniques in Data Mining

5 12.5 Classification With Neural Predictive Classifier

6 12.6 Conclusions

7 References

17 13 Impact of COVID-19 on IIoT

1 13.1 Introduction

2 13.2 The Benefits of Industrial IoT

3 13.3 The Challenges of Wide-Spread IIoT Implementation

4 13.4 Effects of COVID-19 on Industrial Manufacturing

5 13.5 Winners and Losers—The Impact on IoT/ Connected Applications and Digital Transformation due to COVID-19 Impact

6 13.6 The Impact of COVID-19 on IoT Applications

7 13.7 The Impact of COVID-19 on Technology in General

8 13.8 The Impact of COVID-19 on Specific IoT Technologies

9 13.9 Coronavirus With IoT, Can Coronavirus Be Restrained?

10 13.10 The Potential of IoT in Coronavirus Like Disease Control

3 14.3 Design of Smart Ambulance Booking System Through App

4 14.4 Smart Ambulance Booking

5 14.5 Result and Discussion

1 Figure 1.1 Big data analytics illustration.

2 Figure 1.2 Big data illustration.

3 Figure 1.3 AI and data science illustration.

4 Figure 1.4 Internet of Things.

5 Figure 1.5 IoT illustration.

6 Figure 1.6 Connection to the car’s illustration.

7 Figure 1.7 IoT devices.

8 Figure 1.8 IoT and blockchain illustration.

9 Figure 1.9 Maintenance IoT vehicle illustration.

2 Chapter 3

1 Figure 3.1 Connections and linked objects of IoT.

2 Figure 3.2 Autonomous robots with Industry 4.0.

3 Figure 3.3 Architecture of SCADA.

4 Figure 3.4 Smart grid conceptual model.

5 Figure 3.5 Smart grid security.

6 Figure 3.6 Classification of IoT-aided smart grid.

7 Figure 3.7 Advanced metering infrastructure.

3 Chapter 4

1 Figure 4.1 Working of IIoT.

2 Figure 4.2 Big data analytics with IoT.

3 Figure 4.3 Types of ML.

4 Figure 4.4 Supervised and unsupervised learning.

5 Figure 4.5 Reinforcement learning.

6 Figure 4.6 Disaster lifecycle.

4 Chapter 5

1 Figure 5.1 Construction of biological neuron.

2 Figure 5.2 McCulloch-Pitts model.

3 Figure 5.3 Multi-layer perceptron structure.

4 Figure 5.4 A two-layered network.

5 Figure 5.5 A recurrent network.

6 Figure 5.6 AND network.

7 Figure 5.7 OR network.

8 Figure 5.8 XOR network.

5 Chapter 6

1 Figure 6.1 Schematic of single patch on textile substrate [34].

2 Figure 6.2 Side view of the five-layer model [34].

3 Figure 6.3 The antenna geometry with MTM cells [36].

4 Figure 6.4 Antenna design with partial ground: (a) front view; (b) back view.

5 Figure 6.5 Logo-based tracking system [38].

6 Figure 6.6 Antenna with EBG structure [41].

7 Figure 6.7 Reconfigurable Antenna of O-shape: (a) OFF; (b) ON [43].

8 Figure 6.8 UHF RFID tag antenna design [45].

9 Figure 6.9 UHF RFID tag read range measurements vs frequency [45].

10 Figure 6.10 Gain, efficiency vs frequency for Zelt antenna [46].

11 Figure 6.11 Wearable fractal antenna with different iterations: (a) 0th; (b) 1st

12 Figure 6.12 Wearable fractal antenna with iterations: (a) 0th; (b) 1st; and (c)

13 Figure 6.13 The embroidered Sierpinski carpet antenna: (a) substrate material; (

6 Chapter 7

1 Figure 7.1 IoT protocol with OSI standard.

2 Figure 7.2 IoT layers and protocols.

3 Figure 7.3 IoT packet headers.

4 Figure 7.4 Confirmable message passing mechanism.

5 Figure 7.5 Non-confirmable message passing mechanism.

6 Figure 7.6 MQTT subscriber-publisher model.

7 Figure 7.7 IoT components.

8 Figure 7.8 IoT communication architecture.

9 Figure 7.9 Data center architecture.

10 Figure 7.10 Fog computing.

11 Figure 7.11 Edge computing.

12 Figure 7.12 Stages of machine learning.

13 Figure 7.13 Classification of machine learning that can be supported in IoT.

14 Figure 7.14 Challenges in IoT and data center.

7 Chapter 8

1 Figure 8.1 Multidrone architecture for autonomous cinematography.

2 Figure 8.2 Current technologies in aviation industry.

8 Chapter 9

1 Figure 9.1 Water quality factors.

2 Figure 9.2 Water quality management framework.

3 Figure 9.3 Intelligent IoT-based water quality management framework.

4 Figure 9.4 Smart wastewater quality monitoring system.

5 Figure 9.5 Containments of sensing in water.

9 Chapter 10

1 Figure 10.1 Aircraft monitoring system.

2 Figure 10.2 IoT information chain to collect aviation data.

3 Figure 10.3 High-end engine in jet plane.

4 Figure 10.4 Fuel emissions from jet airplane.

5 Figure 10.5 IoT real-time connectivity in airline industries.

6 Figure 10.6 Smart luggage IoT connected tracking system.

7 Figure 10.7 Evaluation and performance improvement.

8 Figure 10.8 Steps involved in fuel consumption.

9 Figure 10.9 Optimization of aircraft fuel.

10 Figure 10.10 Overall idea for fuel cost optimization.

11 Figure 10.11 High-end platform in travel route comparison for optimization.

10.Chapter 11

1 Figure 11.1 Flow diagram of the proposed system.

2 Figure 11.2 Architecture of the proposed system.

3 Figure 11.3 YOLO architecture.

4 Figure 11.4 Pages in the Android app.

5 Figure 11.5 Experimental setup of the proposed system.

6 Figure 11.6 (a) Annotating images in VoTT (b) Annotating images in VoTT.

7 Figure 11.7 Object detection using YOLO model trained with 100 (a) vs.

200 (b) v

8 Figure 11.8 Object detection using YOLO model trained with 100 (a) vs.

200 (b) v

9 Figure 11.9 Graph of epoch vs loss during training.

10 Figure 11.10 Output from NodeMCU in serial monitor.

11.Chapter 12

1 Figure 12.1 Knowledge discovery in databases.

2 Figure 12.2 Data mining in Pharmacovigilance.

3 Figure 12.3 Semi-Convergent Matrix construction for improving search accuracy.

4 Figure 12.4 Structure of neural predictive classifier for classification.

5 Figure 12.5 Processing diagram of activation function for classification.

6 Figure 12.6 Neural predictive classifier.

12.Chapter 13

1 Figure 13.1 IIoT infrastructure.

2 Figure 13.2 Applications of IIoT.

3 Figure 13.3 Impact of COVID-19 on the connected applications software market.

4 Figure 13.4 Examples of “winners” in industrial IoT solutions.

5 Figure 13.5 Impact of COVID-19 on IoT.

6 Figure 13.6 Download rank.

7 Figure 13.7 Usage of Librestram’s Onsight.

8 Figure 13.8 Download Rank of the app “Kinsa Health”.

9 Figure 13.9 Impact of COVID-19 on technology.

10 Figure 13.10 Zoom usage analysis.

13.Chapter 14

1 Figure 14.1 Use-case diagram.

2 Figure 14.2 Welcome page.

3 Figure 14.3 Signup page.

4 Figure 14.4 Home page.

5 Figure 14.5 Ambulance section.

6 Figure 14.6 Ambulance selection page.

7 Figure 14.7 Confirmation of booking.

8 Figure 14.8 Ambulance booking app.

9 Figure 14.9 Booking system.

14.Chapter 15

1 Figure 15.1 Schematic representation of the suggested methodology.

2 Figure 15.2 Hardware prototype.

3 Figure 15.3 Health data vs specificity.

4 Figure 15.4 Health data vs sensitivity.

5 Figure 15.5 Health data vs false out.

6 Figure 15.6 Health data vs positive predictive value.

7 Figure 15.7 Health data vs false discovery rate.

8 Figure 15.8 Health data vs miss rate.

9 Figure 15.9 Health data vs F-score.

10 Figure 15.10 Health data vs accuracy.

11 Figure 15.11 Time taken for encryption.

12 Figure 15.12 Time taken for decryption.

List of Tables

1 Chapter 4

1 Table 4.1 Types of assets needs safety [50].

2 Chapter 6

1 Table 6.1 Chronology for the wearable antennas.

2 Table 6.2 SAR values of the antenna with varying distance [35].

3 Table 6.3 Max average SAR values at different distances from human body [36].

4 Table 6.4 Impedance characteristics of proposed denim antenna [37].

5 Table 6.5 Different textile materials with their dielectric constant values.

6 Table 6.6 Description of textile materials.

7 Table 6.7 Comparison of wearable antenna designs.

3 Chapter 7

1 Table 7.1 IoT protocols.

2 Table 7.2 Comparison of CoAP and MQTT.

3 Table 7.3 Pros and cons of fog computing.

4 Table 7.4 Comparison of cloud and fog computing.

4 Chapter 9

1 Table 9.1 Arduino specifications.

2 Table 9.2 Raspberry Pi and Arduino.

3 Table 9.3 Water sensors.

5 Chapter 11

1 Table 11.1 Materials used in the proposed system.

2 Table 11.2 Uses and purpose of sensors.

3 Table 11.3 Analysis of the model (10 testing images - 40 items).

6 Chapter 15

1 Table 15.1 Comparison of encryption time.

2 Table 15.2 Comparison of decryption time.

1School of Electrical and Computer Engineering (FEEC), University of

Campinas – UNICAMP, Av Albert Einstein, Barão Geraldo, Campinas – SP, Brazil

2Faculty of Technology (FT), University of Campinas – UNICAMP, Paschoal

Marmo Street, Jardim Nova Italia, Limeira, Brazil

Abstract

The advent of solutions with AI (Artificial Intelligence) technology means tools and software that integrate resources that automate the process of making algorithmic decisions Simply put, AI consists of systems or machines that mimic human intelligence to perform tasks improving iteratively over time based on the information collected Thus, IoT currently matches a series of hardware that works connected to the internet, from a refrigerator to a wearable watch that measures heart rate and sends this data to an application In this sense, it is possible to interpret what part of these devices uses, even on a small scale, AI technology This technological innovation connects everyday intelligent devices or even intelligent sensors, to the internet, linking the physical world increasingly closer to the digital world In this scenario, the world is experiencing a digital transformation, and related to it, the Industrial Internet of Things (IIoT) aims to connect different devices to collect and transmit data present in an industrial environment Performing this communication through essential industrial variables related to smart devices, effecting communication, data, and data analysis In this sense, this chapter is motivated to provide an updated overview of IoT and IIoT, addressing its evolution along with

AI technology and potential in the industry, approaching its relationship, with a concise bibliographic background, synthesizing the potential of technologies.

Keywords: IoT, IIoT, industrial, IoT applications, sensors

of information technology in the industry [1]

IoT in Industry 4.0 is basically responsible for the integration of all devices inside and outsidethe plant, considering that the concept represents the connection as it is a network of physicaldevices (objects), systems, platforms, and applications with embedded technology tocommunicate, feel or interact with indoor and outdoor environments [1, 2]

Industry 4.0 is the complete transformation of the entire scope of industrial production throughthe fusion of internet and digital technology with traditional industry, being motivated by threemajor changes in the productive industrial world related to the immense amount of digitized

information, exponential advancement of computer capacity, and innovation strategies (people,research, and technology) [2, 3].

When it is said that the internet is in the industry, these changes allow everything inside andaround an operational plant (suppliers, distributors, plants, and even the final product) to bedigitally related and connected, affording a highly incorporated value chain, from the factoryfloor, is important to relate this to an environment where all equipment and machines areconnected in networks and uniquely providing information [3, 4]

For Industry 4.0 to become feasible, it requires the adoption of a technological infrastructuremade up of physical and virtual systems, aiming to create a favorable environment for newtechnologies to be disseminated and incorporated by the industry, with the support of Big DataAnalytics technology (Figure 1.1), automated robots, simulations, advanced manufacturing,augmented reality, and the IoT, employing the monitoring of technological trends, assistingmanagers throughout the entire industrial chain [3, 5]

The Industrial Internet of Things has an IoT and IIoT layer in the industry, provoking aprognostic model, since automation, which in general already exists, answers questions regardingwhat is happening, what happened, and why it happened, considering its network of physicaldevices (objects and things, among others), systems, platforms, systems, and applications withembedded technology in industry sectors, aiming to promote automation of manufacturing and,thus, increase the productivity of production lines, generating greater competitiveness with theinternational industry through intelligent factories (smart manufacturing) [6]

Figure 1.1 Big data analytics illustration

Generating an increasing number of connected devices (in some situations, it even includeunfinished products), since the digitization of data from machines, methods, processes,procedures, and intelligent devices, integrates and complements the operational layer of anindustrial plant, enabling communication and systems integration and controls and allowingresponses and decision-making in real time Thus, IIoT becomes a prerequisite for Industry 4.0[1, 7].

The difference between IoT and IIoT is in the sense that the first relates systems that connectthings, complement information, normally only produce data, and can be used in any sector ofthe industry, transforming the second, to manage assets and analyze maintenance trends [8–10]

IIoT forms a critical layer of the production process and can directly connect a product supplier

in real time on the production line, which analyzes the quality and use of your product, as well asconnecting the input and output logistics chain of materials and control production, in real time,

at the optimum point of operation, becoming an application of production and consumption ofdata, with a critical profile [8–10]

The use of IoT and IIoT proposes the digital factory bringing benefits to productive plants as animprovement in the use of the asset, reduction of operations or asset cycle cost, improvedproduction, reduction of operations or stoppages, improving asset use (performance), increasedspeed in decision-making, allow the sale or purchase of products as a service, generateopportunity for new business, among several others Thus, the premise of digitizing allinformation can lead to a question about the reason and reason for digitizing so much data, sincethis information is all digitized and there are all the means (networks) for them to travel andexchange information with each other, it is expected that decisions can be made not onlybetween operators and machines, but also between machine and machine, this is called M2M,Machine to Machine, which before were not available in real time and are now needed [8–11]

Thus, the architectures of industrial automation systems, which have adherence to Industry 4.0,manage to integrate different devices in favor of industrial evolution, with more and moresensors, cameras, and systems that will be monitoring the entire industrial production process,evaluating and supervising the performance of equipment, and providing, in addition to thealready known layers of operational control and the entire control framework, the IoT and IIoTlayer, where it will converge all this data into a Big Data, delivering operational controlpossibilities (Figure 1.2), with decision-making in prognoses and with the possibility ofautonomous actions [10–12]

Optimizing the production process of the industry is the main reason for the application of IoT inthe production line of the factories, since the IoT technology and its IIoT aspect allows theequipment that makes up the industrial yard of a company today that can be connected in anetwork With the data collected and stored in the cloud, it allows the decision-makers of thecompanies to have quick and easy access to all the information of the company and itscollaborators; in other words, this makes all the industrial machinery work automatically through

of highly programmable intelligent sensors [13, 14]

Figure 1.2 Big data illustration.

Wherefore, this chapter is motivated and has the purpose to originate an updated overview of IoTand IIoT, addressing its evolution and branch of application potential in the industry,approaching its relationship with current technologies and synthesizing the potential oftechnology with a concise bibliographic background

1.2 Relationship Between Artificial Intelligence and IoT

The emergence of solutions and tools with AI (Artificial Intelligence) technology meanssolutions, tools, and software that have integrated resources that automate the process of makingalgorithmic decisions The technology to be used can be anything from independent databasesemploying Machine Learning to pre-built models that can be employed to a diversity of data sets

to solve paradigms related to image recognition and text analysis Applied in the industry, it canhelp a business achieve a faster time to evaluate, reduce costs, increase productivity, andimprove the relationship with stakeholders and customers [15, 16]

Machine Learning is only part of AI, that is, it is an AI application in which it accesses a largevolume of data and learns from it automatically, without human intervention This is whathappens in the case of recommendations on video streaming platforms and facial recognition inphotos on social media pages AI is a broader concept that, in addition to Machine Learning,includes technologies such as natural language processing, neural networks, inferencealgorithms, and deep learning, in order to achieve reasoning and performance similar to that ofhuman beings [15, 16]

An AI system is not only sufficient and capable of storing, analyze, and manipulating data, butalso of acquiring, representing, and manipulating information and knowledge Including thecharacteristic to infer or even deduce new knowledge, new relationships between data-generatinginformation about facts and concepts, from existing information and knowledge and to usemethods and procedures of representation, statistical analysis, and manipulation to solvecomplex questions that are often incognito and non-quantitative in nature [17]

The increase in mass data collection over the years, related to IoT devices, has boosted AI, giventhat the volume of information produced by people has been growing exponentially But alliedwith Big Data technology to understand this massive set of data, which serves as a basisfor learning the most diverse software, such as Machine Learning This data revolution favoredthe AI scenario, i.e., with more information available, more intelligent, and automated ways toprocess, analyze, and use the data [18, 19].

Big data is the term employed to refer to the enormous amount of data that is produced andstored daily, evaluating that from this abundance of information, there are intelligent systemscreated to organize, analyze, and interpret (that is, process) the data, which are generated bymultiple sources [19, 20], still pondering on predictive analysis as the ability to identify theprobability of future results based on data, statistical algorithms, and machine learningtechniques From Big Data, it is possible to do this type of analysis, identifying trends, predictingbehaviors, and helping to better understand current and future needs and, finally, to qualifydecision-making in machines, equipment, and software, taking technology to a new level AI isimpacting society with machine learning systems, neural networks, voice recognition, predictiveanalysis, and natural language processing (NLP) and continuously remodeling new aspects ofhuman life [19, 20]

Forecasting and adaptation are possible through algorithms that discover programmed datapatterns, the solutions learn and apply their knowledge for future predictions If a sequence ofbits exists, then the AI recognizes the sequence and predicts its continuity This is also able tocorrect spelling errors or predict what a user will type or even estimate time and traffic on certainroutes in transit (autonomous vehicles based on AI) [17]

Decision-making through data analysis, learning, and obtaining new insights is able to predict orconjecture a more detailed and faster decision than a human being But it helps to increasehuman intelligence and people’s productivity Through continuous learning, AI can beconsidered a machine capable of learning from standards [21]

Also related to its characteristics in the ability to build analytical models from algorithms,learning to perform tasks through countless rounds of trial and error In the same sense, NLPprovides machines and computing devices the capability to “read” and even “understand” humanlanguage [22]

1.2.1 AI Concept

Another characteristic of the basic types of AI is purely reactive, since it acts after the perception

of the problem, exemplifying an AI software that identifies the chess pieces on the board andtheir movement, but has no memory of past movements, ignoring everything before the currentmovement, that is, it only reacts to the position of the pieces on the board In the legal field,lawyers focus on more complex aspects of law practice, given the use of text analysis,Jurimetrics, text review, data mining, contract analysis, computational argumentation, and otherpossible AI-derived features [17, 23], still pondering the characteristics of AI-related to itscapacity for intelligent perception, such as visual perception, speech perception, auditoryperception, and processing and learning of perceptual information Reflecting on autonomous

cars and virtual assistants, there is not only a programmed answer to specific questions butanswers that are more personalized [23–25].

Through AI solutions, it is possible to eliminate boring tasks that may be necessary, but withmachine learning, it performs basic tasks, considered human-computer interaction technologies,

or even related to the more robust use found in conversational interfaces that use machinelearning to understand and meet customer needs [23–25]

Even through AI solutions, it is possible to concentrate diffuse problems where data inform alllevels of the operation of a modern company, i.e., it has a lot of material to interpret, so it isnecessary to consume this amount of information at scale Since the extent of the data availabletoday has gone beyond what humans are capable of synthesizing, making it a perfect job formachine learning Through the data, the information is extracted from various sources of publicand private data, still comparing them and making changes when necessary [25]

Through AI solutions, it is possible to distribute data, given that modern cybersecurity leads tothe need to compare terabytes of internal data with a quantity of external data With machinelearning, it can automate the process of detecting attacks as cybersecurity problems change andincrease, vital for dealing with distributed data problems, assessing that humans are unable toinvolve their actions around a distribution so wide of information AI solves dynamic data, which

is a valued characteristic, given the major obstacle related to addressing individual employeecharacteristics, or dynamic problems of human behavior Through AI, it is possible to usedetermining complex patterns to help organizations move more quickly and respond better to thechanging needs of each employee [26]

Or even, through AI, industrial systems integrate robotics powered by AI, 3D printingtechnologies, and human supervision, building interactive robot systems leading by AItechnologies This process not only decreases costs and increases efficiency but also generatesmuch safer industrial environments for human workers The dangerous elements of industrialactivities are surpassed by machines [27, 28]

In simpler terms, AI technologies consist of intelligent systems or intelligent machines thatmimic human intelligence to operate tasks and can improve iteratively supported on theinformation it collects AI technologies manifest itself in various ways in modern contemporarysociety as chatbots to understand customer issues more quickly and provide more efficientresponses or smart assistants to analyze critical data and information from large sets of free-textdata to improve programming, or even at home, through recommendation mechanisms providingintuitive recommendations for TV programs supported on users’ viewing habits However, AItechnologies are not deliberate to replace human beings but aims to substantially improve humanskills and actions, tasks, and even contributions [17, 23, 24]

AI is related to application areas that involve expert systems or systems based on knowledge,natural language comprehension/translation, intelligent systems/learning, speech comprehension/generation, automatic programming, or even image and scene analysis in real time, among manyothers Therefore, it can be evaluated that the technological AI field aims to emulate humanbeings’ capabilities including problem-solving, understanding natural language, computer vision,

and robotics, considering systems for knowledge acquisition, and even knowledge representationmethodologies [15].

To obtain the full value of AI, Data Science is necessary (Figure 1.3), consisting of amultidisciplinary field that employs scientific methods to collect and extract value from data,combining skills such as statistics, probabilities, frequency of occurrence of events, observationalstudies, and computer science, with business knowledge to analyze data gathered from distinctsources [29, 30]

The central principle of AI technologies is to replicate, and then exceed, the processes andconduct humans perceive, notice, see, and react to the world, fueled by several forms of MachineLearning techniques that recognize patterns in data to allow prognosis and predictions Propitiate

a better comprehensive understanding of the wealth of available data, information, andpredictions to automate overly complex or ordinary tasks, improving productivity andperformance, automating tasks or processes that previously demand human energy, and alsomaking sense of the data on a superhuman scale [31]

Data science makes it a priority to add technological value to business intelligence and advancedanalysis as the main technology differential for companies, through the use of demographic andtransactional data to foresee and predict how much certain customers and users will spend overtheir business relationship with a company (or even the customer’s lifetime value), priceoptimization supported on preferences and customer behavior, or even utilizing imagerecognition techniques to analyze X-ray digital images searching for signs of cancer [30]

Figure 1.3 AI and data science illustration.

Three elements are leading the development of AI technologies across all sectors, which are thecomputational high-performance, affordable, and even processing capacity available, assessingthe abundance of computing power in the cloud technologies allowing easy access to affordableand high-performance computing power Large volumes of data available for conduct training,given that AI, require to be trained on a lot of data available to generate the correct predictions,also relating the emergence of distinct tools for labeling data, in addition to the ease andaccessibility of storing and processing structured and unstructured data, to train AI algorithms[31]

The benefits of operationalizing AI are related to the cognitive interactions of machine learningtechniques with conventional business applications, methods, and processes that can greatlyincrease productivity and user experience, or even considering AI as a strategic method andcompetitive advantage related to greater efficiency in processes, doing more in less time, andincreasing customer loyalty, creating customized and attractive customer (user) experiences, andpredicting commercial results to generate greater profitability [23, 24, 32]

AI applications in people’s daily lives are based on an app that recognizes the content of imagesand allows a search by typing the name of an object or action, or streaming platformstranscribing audio and generating subtitles for videos, or in an email offering automaticresponses smart; or even with regard to online translators who translate texts from signs, labels,and menus with the cell phone camera; or even pondering about streaming platforms that use AI

to understand users’ preferences and recommend music and movies, respectively, still relatingautonomous cars that drive alone, or even in medicine, advancing cancer studies [26]

The application of AI is present in various segments of the economy; in industry, automation is akeynote for machines that keep getting smarter With AI, the equipment manufactures andchecks the products without having to be operated by a human, that is, it performs repetitivework and has no limitations for their use Through the GPS (Global Positioning System), theroutes suggested by online applications, generally, point out the best path, considering that the

AI interprets data provided automatically by other users about the traffic on the roads Onlineretailers, using online store algorithms, recognize user purchasing patterns to present offersaccording to their preferences Financial institutions use AI algorithms to analyze market data,manage finances, and relate to their customers [33]

Thus, the first industrial revolutions created equipment that replaced manual labor, carrying outthe work of many men with greater efficiency and less cost Currently, in several cases, throughthe AI employee in tasks, they have been previously seen as “intellectuals” In any case, theimportant thing is that AI theater is a reality In this regard, the understanding of its mechanismsand the understanding of the possibilities that this provides must be expanded The concept of AIrefers to the creation of machines, not necessarily with physical bodies (software that canabstract, create, deduce, and learn ideas), with the ability to think and act like human beings andaim to facilitate everyday tasks [7, 34]

1.2.2 IoT Concept

IoT in the early days corresponded to the connection via the internet in physical objects, such as

a toaster, especially sensors Over the years, the concept of connecting the physical materialworld with the virtual world has evolved into a technological revolution in order to connect allthe objects that people use on a daily basis to the internet (Figure 1.4), describing a scenario inwhich several things are connected and communicate, through technologies like Wi-Fi Theresult is a smarter and more responsive planet [35, 36]

Figure 1.4 Internet of Things.

Thus, IoT currently matches a series of hardware that works connected to the internet, from asmart TV to a running watch that measures heart rate and sends this data to an application.However, it is possible to interpret what part of these devices uses, even on a small scale, AI.This technological innovation connects everyday items (smart devices), or smart sensors, to theinternet, making the physical world increasingly closer to the digital Thus, the technologydescribes the physical objects (things) connected and communicating (transmitting) with eachother and with the user, transmitting data (information) to a network, as if it were a broad digitalnervous system, i.e., a structure that allows the exchange of information (data) between two ormore points [17, 18, 37]

Still pondering that every day, more appliances, watches, means of transport, and accessories areconnected to the Internet and other devices, such as smartphones, tablets, and mobile devices that

transmit signals and appear to each other Still pondering that through a connected network, thesedevices can be connected via the internet with cars, refrigerators, microwaves, trains, airplanes,among other thousands of artifacts (Figure 1.5) [18].

The field of IoT practices has been diversified over time, and currently, the field of applicabilityand use of IoT is very broad, reflecting on numerous technological resources that have been used

to provide connection of devices Like Bluetooth technology, communication by proximity field(short-range wireless technology, which allows the exchange of information between deviceswith enabled and compatible NFC) is also a feature used in IoT Making the devices “talkdigitally” to each other, generating more productivity, comfort, information, knowledge, andpracticality in general, and their uses and application can include health monitoring or leadingreal-time information about city traffic, or yet the number of parking lots available in parking,even indicating activities, reminders, or even content on their connected intelligent devices [38].Nowadays, everyday “things” become intelligent and have their functions and role expanded bycrossing data (information), seeing a virtual assistant crossing data from connected intelligentdevices to inform, even if not requested, the time (travel duration) it will take to get to workwhen leaving the house, also relating the interconnectivity of smart IoT devices around theenvironment and making a digital assistant learn a user’s routine, their times, their location viaGPS connection, the connection (link) to the car’s Bluetooth at a singular time (Figure 1.6), andthe circumstance that this context has been repeated many times [18]

Figure 1.5 IoT illustration.

The IoT exchanges information is essentially derived from three elements that require to beassociated with an application to work which are the intelligent devices, the network (structure),and a digital control system The intelligent devices are all those imaginable equipped withsensors and antennas, among others, providing communication with the other elements such aslamps, bedside lamps, refrigerators, microwaves, cars, coffee makers, and watches, television,among others (Figure 1.7) The network is the means of communication such as Wi-Fi,Bluetooth, mobile data, and fiber optics, among others The control system causes all data(information) captured from the devices (things) to be processed and then sent (transmitted) to adigital system that controls each aspect analyzed and evaluated [36, 39]

Big Data is the driving technology of IoT, related to data are currently the great creators anddestroyers of business value Since the IoT devices connected to the network are constantlysending, receiving, exchanging, and crossing data, i.e., constantly producing data As a result, theaccumulation, analysis, and use of Big Data are more significant, especially for companies,which have the most expressive production of data with IoT, as it has a large number of objectsthat can be connected or already connected In addition, with data and information in hand,companies make fewer mistakes, produce more, and win more customers To make sense (means

of storing, tracking, analyzing, and making use of this large amount) of all this data(information), Big Data analysis has a fundamental role, which is critical for companies of allsizes [19, 40]

Figure 1.6 Connection to the car’s illustration.

Figure 1.7 IoT devices.

Still pondering the seven main attributes that define and differentiate a normal object or devicefrom an item that is part of the great mass of IoT connectivity, these devices and systems includesensors that track and measure activity worldwide Internet connectivity will be in the item itself(thing/device), probably collecting information over time through sensors, exchanging messages,and files with a Cloud platform Like any computer, the devices will have some built-inprocessing power, even if only to analyze and transmit data Although many of the IoT devicesare not yet equipped with special features to become really powerful in processing [41–43]

Efficient energy consumption is related to these devices being able to operate for a certain time

or more on their own, using stored energy or staying connected only while used Cost x benefitratio is linked to the premise that several objects with sensors (must be relatively inexpensive topurchase and implant) distributed on a large scale to be really efficient, as in the case of foodproducts in supermarkets that must have an indication of validity Quality and reliability arerelated that many of the devices must operate exposed to harsh climates for long periods of time[41–43]

Figure 1.8 IoT and blockchain illustration.

Security is given that IoT machines and devices transmit private and detailed information, such

as that related to the user’s health, still reflecting that the change from previously inert objects to

a reality based on connectivity transforms businesses, products, and workflows to suit consumertrends and needs In this respect, blockchain technology can promote more digital security(Figure 1.8), so that objects connected to networks are not hacked [41–43]

However, the main potential of IoT is to carry out communication between objects, and peopleare given the practical nature, via the internet, “things” exchange signals with each other, i.e.,mobile and fixed objects gain autonomy to interact with each other and with users One of thegreatest examples of this digital transformation in recent years is the increased use of IoT inhomes and work relationships Another technology that enhances the growth of IoT is AI,guaranteeing more autonomy and learning for objects connected to the internet [44]

1.3 IoT Ecosystem

IoT is basically things, i.e., it is all types of equipment/device/sensor that can be connected indifferent ways, from a truck to monitor the displacement of product transport fleets, use ofsensors in tractors that measure the soil situation and send data to systems responsible forprocessing this information, and make suggestions for the best areas or times for planting, aboiler temperature sensor in a factory, or the adoption of devices at home, such as thermometers,energy consumption regulators, or home appliance managers, who allow the householder tocontrol this equipment remotely, or even microsensors that monitor the status of patientsremotely in hospitals or outside them [45]

In IoT, it is consistent with an environment whose rules deal with both connection and intelligentdata collection and processing, since applications allow the coordinated and intelligent use ofdevices to control various activities, from monitoring with cameras and sensors to managingspaces and of productive processes The IoT ecosystem is a system composed of a digital space

of interaction including digital tools related to data analysis and modeling, as well as digitalelements that integrate and interact within it It is through these interactions and the exchange ofinformation that AI allows these elements to work in an integrated manner, composing anintelligence potential far superior to what each of its elements has separately The IoT ecosysteminvolves different agents and processes, such as smart objects [sensors, appliances, cars (Figure1.9), and factory automation equipment], smart modules (processors and memories), connectivityservices (access to the internet or private networks that connect these devices), integrators(systems that combine applications, processes, and devices), enablers (control systems,collection, and processing of data and commands involving objects), and even providers of IoTservices [45, 46]

Figure 1.9 Maintenance IoT vehicle illustration

Within an IoT ecosystem, applications that integrate IoT technologies with Big Datatechnologies are operated, enabling the collection and analysis in real time of large data sets,allowing the development of predictive models for a variety of situations, from consumerbehavior to the prevention of factory failures, and optimizing activities on the most varied fronts

of activity IoT technology brings changes both in the development of more pervasiveconnectivity and in the increase of data processing, derived from the refinement of sensors thatallow data collection in different environments All of this is associated with some practicalsolution allowing for increased efficiency, reduced human intervention, or even new businessmodels [45], still evaluating that the AI generates a layer to enhance the value generated by theanalysis of the different information captured and combined; allowing the automation of thedecision-making process and actions in specific situations; bringing significant benefits to the

increase in the speed of processes, reduction of the error rate due to human interference, andreduction of costs per transaction, in addition to the possibility of greater absorption of insights ateach interaction that feedback and “teach” the AI algorithms (Machine Learning as an example);and making this incrementally more efficient [31].

In the digital transformation of the industry (relating the advent of the Fourth Industrialrevolution), AI associates IoT with the combination of the ecosystem for data transmissionbetween devices and the technology for analyzing this information independently, stillconceptualizing the emergence of Artificial Intelligence of Things (AIoT) Considering that theIoT concept is related to the various IoT devices that collect data and create a network fortransmitting critical information to administrators, on the other hand, AIoT data is processed byresources that analyze the standards providing only the information necessary for making adecision and can even make the necessary decisions without human involvement [17]

Pondering on AI, this uses algorithms to analyze data and resources through aspects such asMachine Learning by automating processes without manual intervention, incorporating with IoTgaining connectivity and capacity for data exchange The great advantage of the IoT concept is inthe various solutions involving machine-to-machine communication, integrated into a singlenetwork, where it publish and consume information Thus, it is through the integration of IoT,with the analysis of broad data sets (Big Data Analytics), and with the performance inecosystems using AIoT that it is possible to exceed the limits that each of these technologies hasindividually, developing an advanced solution to support operational management, offeringpredictive maintenance, and consequently increasing control, quality, and efficiency in businessoperations [35]

IoT in Industry 4.0 is basically responsible for the integration of all devices inside and outsidethe industrial plant, relating the digital transformation and the function of the IIoT, together withdevelopments in mechanics, engineering, and manufacturing [2]

Consider that the IoT is a network of physical objects, platforms, systems, and applications withincorporated technology to communicate, feel, or interact digitally with internal and externalenvironments The IoT on the shop floor is related to an environment where all equipment andmachines are connected in networks and providing information in a unique way; therefore,different industrial cells have different purposes, having different functions and applicabilities,but they are united under the same network Thus, IIoT is a subcategory of IoT, which alsocomprises user-oriented applications, such as usable devices, machine devices, and infrastructurewith integrated sensors that transmit data (collected information) via the internet and which aremanaged by software, technology for smart homes, and even cars autonomous [3]

However, this industrial revolution is not yet a reality, even so, it is being motivated by threemajor changes in the productive industrial world related to the exponential advance of thecapacity of computers, the immense amount of digitized information, and also new innovationstrategies in relation to research and technology [4].

The connections generated by IoT in the industry generate opportunities create a large circle ofadded value to products and services as integrated monitoring, generating data that communicate

in real time through what can be considered a large unified database or even scheduledmaintenance stop on the production line before this is intensified From this generated database,automatic decisions are made through online communication between interconnected devicescorrelated to event monitoring Based on the decisions taken through the global view, theproduction process becomes more efficient, reducing negative impacts and maximizing the valuechain of a given industrial sector [5]

The benefits of IoT in Industry 4.0 for industrial plants can be understood in the followingaspects related to operational efficiency and maximizing profits by introducing more flexibleautomation, connectivity, and production techniques In addition, scalability, time, and costsavings help to maximize profits for industrial organizations Pondering about the aspects thatincrease the operational efficiency of a plant is reducing production stops, reducing the cost ofthe asset cycle, improving the use of the asset, and even improving the production [46]

Even listing the benefits of new services and business models given that IoT in Industry 4.0allows the creation of new sources of revenue by creating new connected services Hybridbusiness models allow both digital products and services to be used In an applicable context, avehicle manufacturer can take advantage of the raw data obtained to provide car conditionservice in real time as a source for preventive maintenance This use of digital services alsoimproves the relationship with the customer, since it allows different points of contact thatgenerate valuable information for the customer, creating a relationship of trust and loyalty [47].Even the benefits related to greater knowledge for decision-making arising from the analysis ofindustrial data, allowing and facilitating the making of better decisions due to a more accurateview of the industry’s performance To top it off, IIoT’s network of smart devices allowsindustrial organizations to connect all of their employees, data, and processes from the shop floor

to executives and managers, further assisting the productivity of department leaders anddecision-making [48]

It is important to emphasize that more than facilitating decision making, Industry 4.0 aims topromote that these decisions are made automatically by intelligent techniques, toward anautonomous reaction of the machines From the point of view of systems and equipment, thesesteps correspond, respectively, to a vision of what is happening (data), to know why it ishappening (analysis, knowledge), and to predict what will happen (based on standards and AI).After that, analyze the implementation of a strategic plan, requiring a clear roadmap in relation tothe processes, security, and necessary technologies [7]

1.3.2 Industrial Internet of Things

The world is experiencing a digital transformation and the IIoT aims to connect different devices

to collect and transmit data in an industrial environment Performing this communication throughessential variables related to the devices, the communication between the devices, the data, andthe data analysis The concept is the same as the IoT used for home appliances; however, forIIoT, the connection is between industrial machines, legacy systems, and other devices related tothe world of production This can be applied in sectors such as facility management, supplychain monitoring, healthcare, and retail, among others [8]

The application of IIoT is through a network of devices and intelligent objects that collect,through sensors, and share large amounts of data This forms a technological layer that candirectly connect a product supplier in real time on the production line, which analyzes the qualityand use of your product This through intelligent data consumption creating a critical profile canconnect the logistic chain of input and output of materials and control production, in real time, atthe optimum point of operation [9]

The main challenges of IIoT are interoperability, security, and a high volume of data exchange.Interoperability is the ability of different systems and organizations to work together, consideringthe difficulty on the appearance of devices from different brands is a challenge and it isnecessary to develop technological initiatives to unify these systems Security is a challengebecause companies need to know that their data is safe, and it is necessary to guarantee thenecessary infrastructure for an exponential explosion of data [10]

Thus, IIoT comprises of machines connected to the internet and advanced analytics platforms(digital structure) that process the data produced, and IIoT devices range from complex industrialrobots to tiny environmental sensors; however, the technology also includes agriculture, financialservices, healthcare, retail, and advertising, among others To get the most out of the benefits ofIIoT, three technological capabilities related to sensor-oriented computing, industrial analytics,and the application of intelligent machines are needed [49]

IIoT technology can be applied in various sectors such as production where most of thetechnology is being implemented and employed, derived from machines that can autonomouslymonitor, analyze, and predict potential problems, meaning less downtime and more efficiency ingeneral, or even simpler and safer facility management with sensor-driven climate controls Inaddition, intelligent devices that monitor facility entry points and react quickly to potentialthreats improve facility security, or even supply chain with sensor-managed inventory takingcare of supplies orders before stocks run out This reduces waste, while keeping the necessarygoods in stock and freeing workers to focus on other more specific tasks [49–51]

This large industrial data generation machine will be an opportunity to explore capacities related

to sensor-driven computing, thus enabling the measurement of temperature, pressure, speed, andseveral other parameters Given that all this information is valuable to innovate in services, it isusually data that customers do not have access to [51]

With regard to Industrial Analytics, the data generated through the sensors allows the industrialanalysis to transform this data into valuable insights, managing to extract all the informationfrom the thousands of data generated daily and then serving for decision-making and action

plans, as alarms that constantly signal for abnormalities of processes Still evaluating that the rawdata are transformed into valuable insights into the conditions of the industrial plant, this willallow it to control the plant with greater precision, increasing productivity and decreasing losses[50, 51]; or even applying intelligent machines, i.e., machines that do not have only mechanicalfunctions, considering that this will be the driving force for the generation of new revenuestreams, reinforcing the concept of a hybrid business model; or even, the advancement oftechnology is making it possible to compose physical intelligent devices and their monitoringsoftware with third-party services [52, 53].

With IIoT technology, the production process is differentiated, that is, there is greatercommunication between what is produced and the machine, aiming that any inconsistency can bedetected during the production process, thus greater quality control Inventory control is alsomore efficient with the use of IoT sensors, which can verify the need for parts replacement.Thus, in addition to accurate inventories, there is a streamlining of processes and savings, bothfor employee time in controlling inventory and to avoid wasting unnecessary purchases [52–54]

1.4 Discussion

The application of AI in the industry has been increasingly optimizing its results, in an attempt toreach its maximum degree of efficiency AI advent is the arrangement of several technologies,which allow machines digitally to perceive, understand, act, and learn on their own actions orcomplement human activities, which has become a broad technology used for machine learning,predictive analytics, augmented reality, robotics, performance diagnostic software, and manyothers

With entire procedures performed by machines capable of making decisions based on data,agility and increased productivity are natural consequences Through AI, industrial productionhas become faster and more effective compared to human labor Still considering the possibility

of the machines performing tasks that a person would not be able to do, as is the case withdangerous raw materials or microscopic components

AI works through the integration of factors such as the use of IoT sensors, Cloud Computing,and other technologies present in Industry 4.0, working in sync, devices equipped with AI createcomplex systems, which correlate the information collected and, with this, seek the best ways tocarry out the activities for which they were scheduled These new technologies are developed towork using the least amount of resources possible, whether in terms of raw material or energyconsumption, still relating the point of cost reduction, the mitigation of errors, and waste of theoperation

When addressing AI applications, it is worth mentioning IIoT as a critical technological layeradded to the production chain, which allows even the connection with suppliers and the analysis

of the performance of its raw materials, still pondering the potential of AI in relation to securityalerts, which point to the need for maintenance and performance reports in real time, indicatingthe best measures to be taken

Still pondering the aspect in which machines can withstand extreme conditions that would beharmful even if perceived only in the long term for the health of the employees of industry, such

as cooling cameras, chemical processes, and management of explosive materials, among others,that can be carried out almost entirely through automation

The aspects in Industry 4.0 in relation to the digitization processes that guarantee the collection

of data that were previously lost, the mitigation of risks in decision making, the optimization ofoperations, and the gain of agility, among others, are also mentioned The implementation ofcomplex AI algorithms has been enabling industries to assess and enable problem-solving anddecision-making in a more complex and secure way

Assessing that each sector of the industry receives contributions from AI in a different way, as alogistics and inventory structure can benefit from technology for identification and control ofdemand, for example; or industries with production chains that have different machinery, as isthe case with the automotive industry, since with the use of predictive analysis, they can identifythe need for maintenance on their machines

The benefits are not the only ones since the industry receives an extremely positive impact on theuse of AI in its processes Given that it is possible to point out an increase in the quality ofproducts and services, since AI reduces execution errors and subsequently uses operation data toanalyze performance and make improvements; or even more effective new products andservices, since the development can also be supported by AI to evaluate the proposed designs,identifying the material variables, the weaknesses to be improved, and the possibility of usingaugmented reality to make tests before actually putting it into production; or even through dataanalysis, it is possible to get an agile response to new market demands, considering that theneeds and interests of consumers are changing with great velocity

AI brings great advantages to the industry related to the reduction of errors, because after beingtrained, intelligent algorithms are able to perform very well tasks that are susceptible to errors inprocesses executed by humans The reduction of costs since e-commerce stores or banks userobots (chatbot) for customer service, this allows employees to be allocated in more strategicareas, which can increase profit So, with fewer errors and employees focused on more importantprocesses, the company will have more time to think about the business and leave other tasks toAI

Thus, AI through an automated process uses large volumes of data to make decisions, dispensingwith human intervention and increasing productivity in different activities

1.5 Trends

Adaptive Intelligence is about helping to generate better business decisions by integrating thecomputational power of internal and external data in real time with the computing infrastructureand highly scalable decision science In this type of systems, relating the adaptive learning, thecharacteristics are monitored so that there is an adjustment in order to improve the process Theefficiency of these systems depends on methodologies adopted to collect and diagnoseinformation related to needs and characteristics, in relation to how this information is processed

to develop an adaptive context These applications essentially make businesses smarter, allowingthem to provide customers with better products, recommendations, and digital services, all ofwhich generating better business results [55].

Digital twins are related to the practice of creating a computer model of an object, such as amachine or even a human organ, or yet a process like a climate By studying the behavior of thedigital twin, it is possible to analyze, understand, and predict the behavior of its counterpart inthe real world and to solve issues before they occur However, to take full advantage of thedigital twin’s potential, real systems need not only be networked with each other but also need todevelop the ability to “think” and act autonomously [56]

This development tends toward AI, from simple mutual perception and interaction toindependent communication and optimization, also requiring integrated information systems thatallow a continuous exchange of information, still demanding powerful software systems that canimplement them along the entire value chain, and planning and designing products, machines,and plants, in addition to operating products and production systems The technology of digitaltwins allows users to act in a much more flexible and efficient way, as well as personalize theirmanufacturing [57]

Intelligent Edge refers to the place where data is digitally generated, interpreted, analyzed, andtreated, i.e., the use of this technology means that analyses can be managed more quickly andthat the probability of data being unduly intercepted or leaked is considerably less Thistechnology refers to the analysis of data and the development of solutions in the place where thedata is generated, reducing latency, costs, and security risks, making associated businesses moreefficient, still pondering that the three largest categories of Intelligent Edge are the edges ofoperational technologies, IoT edges, and IT edges [58]

The use of Intelligent Edge technology helps to maximize business efficiency, since instead ofsending data to a data center or even to a third party to perform processing, the analysis isperformed at the location where the data is generated This means not only that the analysis can

be performed more quickly, but it also means that companies are much more self-sufficient and

do not depend on potentially flawed network connections to do their job [58]

Predictive maintenance is one of the most promising branches for industrial applications based

on the use of data received from the factory to avoid production failures This type of systemeliminates unnecessary maintenance and increases the probability of avoiding failures, whichinvolves a machine or even a component with sensors capable to collect and transmit data andthen analyze it, and perform storage in a database Then, this database offers comparison pointsfor events, as they occur [59, 60]

The predictive maintenance model aims to periodically monitor the operation of machinery,equipment, and parts in a factory, in order to detect failures before they occur and preventinterruptions in the production line, relating IoT and AI in order to assist in the survey andmanagement of data from all sectors of production, integrating the company’s departments,performing analyzes to take advantage of the useful life of industrial equipment, indicating thereal conditions of its operation, detecting possible deterioration of parts and components, and

ensuring the reliability and availability of services This information obtained is used to supportdecisions and present suggestions for actions and interventions, generating better results thanwith the use of raw data [59, 60].

1.6 Conclusions

IoT refers to the network of intelligent devices that are concerned with issues of connectivity,competition, and protocols, among other aspects Relating the respective AI to the branch ofcognitive computing caring for principles of data analysis, statistics, and other aspects.Considering that when applied together, it brings results related to the data generated by the IoTand can be processed by an AI software, which will optimize decision-making and contribute tothe increase in the agility of the processes

From the historical point of view, objects (things), people, and even nature, emitted a hugeamount of data; however, humanity just could not to perceive, i.e., see, hear, or make sense ofthem However, through the IoT and the data collected, humanity began to see, understand, anduse it to its advantage with technological advances in practically all sectors of society It is in thisaspect that the IoT came to change the reality of the contemporary and modern world,considering that everything around the environment has intelligence and is interconnected, sothat through this technology, it is possible to have access to data, or better, information Havingaccess to this sea of data, which through the technological potential brought by AI is able to putdigital intelligence and transform them into information, i.e., knowledge, and finally, intowisdom

Starting from the premise that it is possible to perceive the patterns of all these data, society willbecome more efficient, effective, increasing productivity, enhancing the quality of life of people,and the planet itself Reflecting on the possibility of generating new insights, new activitiespromoting even more technological innovation In this respect, the bridge between datacollection (information) and the suitable sharing of that data, with safety and protection digitalfor all parties, abides the key in technological evolution

Reflecting on the industrial sector, it is possible to identify a behavioral trend and anticipate theapplication of a new idea, and this premise shows that the world is heading toward the FourthIndustrial Revolution This represents the introduction of information technology in industries,correlating a hidden potential that is the use of data, since the good use of this data increasesoperational efficiency, better decision-making, and even creates new business models

Finally, IIoT brings together different technologies correlating the Information Technology (IT)initiative for resource management, planning, and decision support systems, OperationsTechnology (TO) that monitors, analyzes, and controls field equipment, manufacturing, andproduction procedure, through AI One of the applications of this is predictive analysis, whichmakes it possible to forecast a given situation in the future based on information from the pastand probability From this, it is possible to get an AI to perform a certain action corresponding to

a specific sensor in the IoT network indicating a specific state of the shop floor, optimizing thisactivity with increased precision

Still reflecting on the digitization of processes and the entire production chain of the industry, it

is the basis of Industry 4.0, with the layers of IoT and IIoT enabling the planning, control, andeven tracking of production, both by digital simulation and virtualization, winning decision-making time and cost reduction Thus, AI and IoT are tools that drive business and guarantee acompetitive advantage with the possibility of generating automated and more agile services,consequently impacting the final consumer

2

Analysis on Security in IoT Devices—An Overview

T Nalini 1 * and T Murali Krishna † 2

1Dept of CSE, Dr M.G.R Educational and Research Institute, Chennai, India 2Dept of CSE, Srinivasa Ramanujan Institute of Technology,

Ananthapuramu, India

Abstract

Internet of Things (IoT) is becoming an evolving technology being a part of day-to-day activities

of human life The number of IoT devices is expected to rise up to 30–35 billion by 2022 As the connectivity to World Wide Web is highly available at affordable price, which leads to more number of internet users Therefore, an enormous number of electronic gadgets that are connected to Internet are producing huge amount of data This creates a biggest challenge in IoT sector such as securing the IoT devices and data that is been exchanged over the network The user’s private information is transferred among the gadgets, and several security challenges such as privacy, confidentiality, integrity, and reliability issues need to be addressed Several industries are manufacturing different IoT devices at various standards Incorrectly configured IoT device (faulty apps on mobile device) can cause excessive data traffic over Internet Protocol and device batteries are getting drained faster This research is mainly focused on different issues such as analysis of present research in IoT security, and this analyzes the communications behavior of IoT devices and mobile apps, security threats on IoT technology, various IoT tools, IoT manufacturers, and the simulators that are currently used.

Keywords: IoT technology, authenticity, confidentiality, privacy, simulation

2.1 Introduction

The promising IoT connects various kinds of devices through the Internet so as to reap dataformulated by sensor(s), end devices connected at longer distance, buildings, vehicles, etc [1] Inthe last year, IoT devices have radically increased in number with variations and nearly 50billion of them being connected to web by end of 2020 [2] IoT devices are distributed across allenvironments and several kinds like “smart cities, grids, health, retail, watches, supply chain,farming, TVs, and so forth” While designing IoT, important aspects to consider are security and

privacy services Unfortunately, there is a chance for the IoT devices to be inadequate ordeficiently structured security systems Moreover, security assaults can infiltrate into IoT devicesand destroy the communications; hence, such security dangers must be aware of in IoT network.

In order to evade cyber assaults, during IoT devices design, security must be looked upon as avital segment [3] But, the various types of IoT constituents interrupt unfolding of wellrecognized techniques for reassuring Security in IoT systems [4, 5] The foremost challengingaspects of IoT devices are quantification, energy, storage, and communication capabilities Justabout, it is very delicate to build cyber-security among the IoT end users and manufacturers.Incidentally, most of the IoT equipment companies hold lowered cost for actuator and sensors inthe market Such devices were primarily intended to operate in disconnected networks, where thesecurity threats are substantially less prevalent

As a consequence, some of the designer are not capable enough on cyber security and might beignorant of the security dangers relevant to their real world devices Hence, with the need tolower expenses and the time on advertisements related to IoT networks commercialization,security is overlooked [6]

The objective of this paper is therefore to focus on current IoT cyber security issues and getfamiliarized with the dangers posed by IoT devices The paper discusses about the characteristics

of such dangers and the possible infringements The issues recognized with IoT related cybersecurity have been presented in various works in the literature as in, e.g., [7–9] As compared tosuch papers, here, we address the theme from an increasingly down to earth viewpoint.Commonly, “Zigbee, 6Lo-WPAN, LoRa-WAN, and Bluetooth Low Energy” are eventuallyutilized as communication protocols in IoT devices

We quickly review the security methods supported by every protocol and, consequently,investigate the attack surface, additionally revealing a progression of genuine assaults againsteminent business IoT devices as instances of the dangers related with inadequately plannedsecurity components Moreover, we depict the “formulating units, communication protocol, andcryptographic equipment, and programming” utilized around business arrangements, to bestowpreparatory processes as of now embraced in the market This examination would then be able to

be valuable to readers and specialists intrigued to get a handle on the more functionalramifications of IoT security

2.2 Security Properties

Security is an inevitable issue that must be addressed in anything we do, anyplace we do, andwhenever we do There is a digital information about individuals and about what individuals do,what individuals talk, and where they go, and details about their arrangements and so on and soforth What is more, with a plan to go ahead with IoT, the aggregates of this information will beaugmented comprising sensitive data about user’s conducts and behaviors So, it might lead toundesirable outcomes on account of unprotected information

For data protection, the major concept is that of security policy—it combines several serviceslike confidentiality, integrity, as well as accessibility These notions collect the elementary

security objectives for both data and computational services Furthermore, authenticity, reputation, and then privacy are security services, too [10].

non-1 Confidentiality: This denotes protecting exchanged content acquired by IoT devices.

2 Integrity: When anticipated recipients must be able to verify if the exchanged things have been modified or not within themselves.

3 Availability: The data must be available to authorized parties at all point of time Partial resources, functionalities, or other services produced or attained

by the network may be endangered and it is not accessible within the peers

of the network.

4 Authenticity: This indicates that the system is not accessed by unauthorized users Authentication mechanism helps establishing proof of identities without which fabrication is possible.

5 Non-repudiation: It does not permit the sender of a specific message to refute the claim of not directing that message.

2.3 Security Challenges of IoT

There are three classes of IoT related risks encompassing the risks that are as follows:

1 Characteristic to any web oriented system

2 Pertaining to devices dedicated to IoT systems

3 Critical to implement safety such that no danger is posed by misusing devices, for example, industrial actuators.

Customary ways such as securing of open port(s) on units fit in the first group The second typecomprises of issues particularly relevant to IoT computer hardware Also, any scheme that canlink to Internet holds an operating system—embedded positioned in respective firmware andmost of these are not intended with security as their main concern

Although the IoT presents features that are already present in other computer networkingparadigms, we strongly believe that the IoT presents a completely different scenario and thusnovel research challenges, especially as far as the security field is concerned We believe thefollowing points summarize the main reasons that should spur novel and transformative IoTsecurity research in the near future

1 Size of Device and Network: Management of absolute size of the IoT is a main issue based on security view, as it is prevailing security conventions and tools were not built to scale up higher Besides, the rigorous budget constrictions

of IoT companies enact restricted memory as well as power of computing Most significantly, as replacing battery can be very difficult or incredible, such processes turn out to be greatly exclusive and time overwhelming Therefore, augmenting energy depletion gets basic To reword, the utter volume of devices together with the confines in energy, computation, and memory competences intensely stimulate the necessity for design and implementation of fresh security tools skilled with offering their features

without stately extreme computing or loading problem on the devices but again intended to be exceedingly scalable.

2 Manual components: Unified machine-human communication is one of the most troublesome aspects of IoT Very small sensor devices are able to flawlessly supply medications and acquire biometric details remotely, additionally providing medical specialists with a thorough view of health related conditions Also, the data exchange would be shared and interweaved On the contrary, sharing data about everyone, either home or occupational grounds, may transform as a responsibility accessible by mean users—third parties Hence, control of access and privacy convert as basic feature in IoT Another problem exists where human beings are major actors

of the detecting systems in IoT But, there is no warranty that they will create not information unreliably, for instance, since they do not wish to or not be able to To handle this major issue, different faith and reputation means are needed, with a scale up to huge population.

3 Diversity: IoT is a complicated ecosystem interrelating smart gadgets people and routine entities into a larger-scaled interrelated network Due to this broad variety of components, a superfluity of various IoT conventions, methods, and standards may essentially co-occur, specifically in the networking field While some industrialists adopt IoT standards that are open these days, most of IoT is on basis of legacy-oriented systems that depend on exclusive technology, eventually leading to anti-model concept called as Intranet of Things Additionally, most of prevailing researches assume that existence of fixed association among IoT and resources along with the environmental entities In contrast, the IoT setup is extremely varied and vigorous and IoT devices might undergo erratic mobility, resulting in rapid dissimilarities in communication aptitudes and positions with time Such a setup resolves for accessible IoT devices which is a challenging job.

In this section, the paper projects the varied security challenges with respect to IoT domains Theusual attack method includes negotiating original IoT devices and perform counterfeit activitiestoward some another network [11] A broad overview of classification of security levels and IoTlayered architecture are discussed in detail as below

2.3.1 Classification of Security Levels

This fragment presents a classification of requirements related to IoT system security based onoperational levels, namely, at the levels of Information, Access, as well as Functional [12]

2.3.1.1 At Information Level

The following security requirements should warrant in this level:

 Integrity: During data transmission, the received data should not have been altered.

 Anonymity: Hide the data source’s identity from the nonmember parties.

 Confidentiality: To exchange protected information, a straight forward association has been imposed among the gadgetry to avert third parties from fetching confidential data.

 Privacy: During data transmission, sensitive information about the users should not be revealed.

2.3.1.2 At Access Level

This specifies security methodologies to control the access to the network

Some of the functional abilities of Access level listed below:

 Access control: Access control grants permission only for authorized users to access the IoT devices and the various network tasks.

 Authentication: Authentication mechanism helps launch right identities in the IoT network This is an important aspect in IoT network in order to cooperate with other devices [13] The devices need to be provided with validation systems to avoid security dangers For instance, for all the IoT gadgets from similar manufacturers that are configured with analogous confirmation accreditations, the hacking of one gadget may lead to violating security at the data level.

 Authorization: Only authorized IoT devices can hold the right to use the network services or resources.

2.3.1.3 At Functional Level

It describes security requirements in terms of the following features:

 Resilience: Resilience provides IoT security during assaults and failures due

to the provided network capabilities.

 Self-association: It indicates the system’s ability to adapt unaided to be viable while there is a failure of certain parts of the systems due to intermittent break down or malicious assaults.

2.3.2 Classification of IoT Layered Architecture

Other than the above mentioned security stages, it is indispensable to focus on the vulnerabilitiesand assaults for varied modes of communication As discussed in [14], the IoT communicationarchitecture can be categorized as (i) Edge-Layer, (ii) Access-Layer, and (iii) Application-Layers

2.3.2.1 Edge Layer

It pertains to side channel assaults [15] The objective of assaults is to reveal details of thescrutiny of adverse events like consumption of power, discharges pertinent to electricity, andtransmittance time, with nodal points effectuating encryption policies The consumptive power

of the units is one of the major susceptibilities among easy guesses to decrypt secret keys Here,assaults force IoT devices deplete battery or jam the communications

2.3.2.2 Access Layer

Eavesdropping, dishonest packets injections, and conversations that are not authorized are some

of the major weaknesses Based on the routing assaults, an assailant can try spoofing, redirecting,misdirecting, or drop packets

 i Functionality-Ignoring: Assaults ability is to associate with the web to exploit vulnerability For instance, IoT devices can be utilized to make to enter into the sufferer’s network and then pollute users PCs.

 ii Functionality-Reducing: the assailant attempts to reduce objectives of the device, so as to disturb the person or to make breakdowns the entire coordination For instance, the mode of attack is coordinated to workings like smart TVs and refrigerators, with the intention to stall their working devices

so as to extort currency after the sufferer for reestablishing their regular conduct.

 iii Functionality-Abuse: IoT elements are meant to be convenient to administrator For instance, an assailant might alter “Heating, Ventilation, and AC control” and spoil the domain by unnecessarily diminishing the temperature In the same way, the attacker takes overall control of the smart devices and overwriting the victims’ orders.

 iv Functionality-Extending: The IoT device is taken for service to accomplish all kinds of functionalities For instance, in living environment, an alarm signal may be utilized for watching the site of the sufferer even when the alarm is off.

2.4 IoT Security Threats

By way of consistent refinement of speculative familiarity and growth of every day applicableconditions, security concerning issues uncovered using IoT innovation seem to be increasinglyunambiguous The threat of IoT security has continuously drawn in exploration and examination

by researchers widely In the midst three-layer IoT design, few researchers suggested every layerassociating with conventional three-layer assembly relating with most threats The physical layerincludes IoT terminal, WSN, and RFID security [18] The above supposed classes take not onlyphysical but also network relevant concerns of security Issues with network layer are rooted in

“security and authentication”, while privacy and reliability pose problems in application layer[19]

Unsurprisingly, the various leveled investigation method for IoT security threats as disclosed bythe conventional design has lost its real-world importance This strategy cannot wholly sum upthe IoT security threats experienced in the disaster stage Hence, quests at this stage just viewthis order as a characterization strategy In [20] ordering of security intimidations by “active andpassive” assaults, methods for labels that are inhibited, distorted, shaped, replayed, and captured.Anyhow, this grouping plan just comprises of data security in the IoT domain.

As of now, a few have proposed security threats for edge processing [21], and a fewcharacterized them as designated by definite attributes of IoT structure For example, as shown

by the decent variety of IoT, it is separated into two types of threats in IoT security [22].Classification is done as per multiplicity and interoperability [23] These have brought in aperfect classification of particular dangers in systems; these are explicit and not all factors areinclusive to a specific component classification It will describe three aspects of IoT securitythreats, namely, “physical device, network communication, and finally data threats”

2.4.1 Physical Device Threats

Conservative digital security risks incorporate mask, prohibited association, unapproved access,denial of service, withdrawal, see page of information, analysis traffic, and data destruction Themajor IoT and conventional network security has huge issues with IoT devices

2.4.1.1 Device-Threats

An end-point device plays a major role at the time of data gathering In IoT network, identity issubstantial between devices to secure devices from several kinds of attacks [21] In IoT network,security is enhance do wing to various trending technologies like cryptography mechanism.Despite, IoT devices and sensors are impacted by the numerous threats Normally, RFID hasvulnerability to physical assaults, along with the damage of the node by itself RF tags areattacked by Assailants order for altering the tag contents and communication channels blocked[24] In appalling cases, the whole network will be in a damaged condition Besides, in thenetwork holding wireless sensors, the individual nodes have limitations with respect to battery aswell as storage

2.4.1.2 Resource Led Constraints

Devices being attacked portray [21] that IoT devices hold resources limitations This resourcelimitation will compel the quantification of nodes, not being able to perform complicatedquantifications, and thereby, finally, it leads to threatening the entire technology This form ofrestriction mainly dominates the analyses of edges, restricting the system refinement

2.4.2 Network-Oriented Communication Assaults

In IoT security formation, physical threats form part and parcel of the IoT security.Fundamentally, the IoT has qualities of “interoperability and operability”; nevertheless, itexposes all weaknesses of “controllability and heterogeneity” While designing IoT systems,

communication among network elements transmit save as well as prepare the data communicatedthrough the hidden layer.

2.4.2.1 Structure

Primarily, the greatest differences spanning IoT network and the conventional one lie in thedetails where previous one has traits of sensibility and powerless controllability This has carriedextraordinary complications to the advancement of the IoT and it should be connected with theInternet The specialized strategy in the three layers of the IoT [18] is not just wiredcommunication but it is remotely connected association and via Bluetooth, Wi-Fi, Ethernet,ZigBee, etc IoT has a bonding with a massive number of varied intense components Butlooking at the other side, this diversity marks network management mechanism for incrediblycomplicated equipment [25]

Other side, conservatively the three layer system, namely, the hidden WSN exhibits weakcontrollability “Controllability and manageability” aims to accomplish the “dissemination andcontent of information” Considering the standard type of propagation and proliferation contentobservation, the most ordinary model is the hosting of password strategy Here, the encryptionalgorithm is stringent in accordance with the necessities about controllability The softwareoutlined networking application in IoT’s security [26] is the arrangement that emulates handling

of the IoTs

Howsoever, this mechanism has not been wholly advanced for current situations The importanttest is facing of threats In IoT, centralized control frequently turns into its confinement, andagain in the most cynical scenario, it might turn into the tailback of the whole network Itscontrol node is immobilized against any harm When the control hub is negotiated, a corruptednode can exploit this vulnerability to attack the network Examples of such assaults are “DoSassaults, alteration of data, black hole assaults, and side channel assaults” [27]

2.4.2.2 Protocol

Every time IoT data is available over the network, it should be “transmitted, prepared, as well ashave them stored” Innumerable procedures are applied for the interactions They arecharacterized as “transmission and communication protocols” “REST/HTTP, MQTT, CoAP,DDS, AMQP, XMPP, and JMS” are the some of the foremost protocols in addition “MQTT,AMQP, and XMPP” are cloud servers under communication, many types of IoT communicationprotocols acknowledged in MQTT protocol, etc [28] MQTT is a M2M light weightedconvention and it will work on minimum-bandwidth approach CoAP enables an assailant totransmit a small UDP packet to a CoAP user and gets a bigger packet as response In thismanner, it is powerless against DDoS assaults The cause is that the protocol itself does eliminatesession management and encryption processing requirements Both the “AMQP protocol and theXMPP protocol” hold read object spoofing weaknesses

2.4.3 Data-Based Threats

Data securing methods consists of five qualities in terms of “confidentiality, integrity,availability, controllability, and non-repudiation”