Preface

Industry 4.0 involves a high level of digitization, which has been increasing the efficiency and flexibility of various manufacturing and non-manufacturing industries in civil, environmental, mechanical, electrical, petroleum, and medical engineering. The world is changing quickly and the Internet and digitalization are the driving forces of today's trades. In addition, new technologies deliver human-like accuracy and reliability in highly complex tasks. Nowadays, computer-based technologies such as the Internet of Things (IoT), artificial intelligence (AI), machine learning, deep learning, big data, and data mining approaches along with their applications are ubiquitous in automation, intelligence, and sustainability. DigiTech platforms facilitate intelligent tasks and diagnostics in the research and analysis of industries and organizations in predictive policing.

This book presents the key technological developments in Industry 4.0 and explores the potential benefits of using real-world applications. As a roadmap for decisionmakers, it covers several evolutionary advances in Industry 4.0. I hope that readers will find this book useful, and I warmly welcome comments, suggestions, and criticisms.

**Meisam Gordan, Ph.D.**

University College Dublin, Dublin, Ireland

**Dr. Khaled Ghaedi** PASOFAL Engineering Group, Kuala Lumpur, Malaysia

**Dr. Vala Saleh** Shiraz University of Technology, Shiraz, Iran

#### **Chapter 1**

## The Fourth Industrial Revolution: A Technological Wave of Change

*Olasupo Ajayi, Antoine Bagula and Hloniphani Maluleke*

#### **Abstract**

This chapter focuses on the technological wave of change called the fourth industrial revolution (4IR), which is also known as the information age or industry 4.0. It starts off with a brief history of the concept, describing the evolution through the ages, from the age of industrialization to the current technological age. The chapter then presents industry 4.0 through three lenses, which are i) the key enabling technologies that serve as its foundational pillars, such as the Internet and Cloud Computing; ii) technologies and concepts that emanate from 4IR, as well as their applications, which are discussed using use-cases; iii) the impacts of industry 4.0 on the wider society (both positive and negative). Finally, the chapter closes with a discussion on some open challenges that need to be considered in future research works to enhance the widespread adaptation and/or implementation of industry 4.0.

**Keywords:** big data, cloud computing, cyber-physical systems, information and communication technology, industry 4.0, industrial revolution, internet of things

#### **1. Introduction**

The phrase "industrial revolution" (IR) is often associated with progressive transitions in the way things are done, specifically as a result of technological enhancement or enlightenment. Within any given society, IR reshapes the processes therein through several waves of changes that directly impact the people's general way of life. Manual and laborious processes are replaced with automated and mechanized systems, while antique processes are replaced with contemporary solutions.

Till date there have been four (4) industrial revolutions. Though historians refer to the "industrial revolution" as the first industrial revolution (1IR), this chapter makes a distinction between both, by referring to "industrial revolution" as the wave of technological advancements that bring about changes to societies, while 1IR is the first of such revolutions. The four IRs, their respective timelines and impacts are well documented in literature, hence not elaborately repeated in this work, but concisely highlighted on **Table 1**.

The third and fourth industrial revolutions are interwoven, with many of the offerings of the third industrial revolution (3IR), including pervasive computing and


**Table 1.**

*Summary of the four industrial revolutions.*

digitization, still on-going today [7]. The fourth industrial revolution (4IR) can thus be considered a logical extension or continuation of the 3IR, as it builds on many of the same concepts and technologies that enabled the 3IR.

This chapter focuses on the 4IR, which is also known as the information age or industry 4.0. It discusses the 4IR through three lenses, viz., the key enabling technologies that form the foundational pillars of the 4IR, technologies and concepts that emanate from 4IR, and the impacts of 4IR on society, both positive and negative.

#### **2. Enabling technologies of 4IR**

This section would discuss five foundational technologies that enable the 4IR. These are: ICT & Networking, Internet of Things & Sensor Networks, Big Data & Data Analytics, Cloud, Fog & Edge Computing, and Artificial Intelligence & Machine Learning.

#### **2.1 ICT and networking**

#### *2.1.1 Information and communication technology (ICT)*

The phrase Information and Communication Technology (ICT) consists of two (2) parts: Information that refers to processed data and Communication or Telecommunication (Telecoms) and that can be defined as the transmission of information over wired or wireless media, and often over a considerable distance. Communication in itself often connotes the exchange of information between two or more parties; hence, the above definition is apt. Advancements in technology, particularly during the 3IR, led to the rapid growth of ICT. The key enablers of this unprecedented growth were transistor chips and the Internet (and its associated world wide web or www), as shown on **Table 1**. Transistors revolutionized computers, while the Internet "shrunk" the world and made it a global village.

The 4IR is characterized by rapid digitization, growth of pervasive & ubiquitous devices, and prevalence of connected devices – both personal and industrial. A relatable example is the "smartphone". **Figure 1** shows the growth of smartphone usage in the closing 5 years of the 3IR versus the last 5 years of the 4IR. The figure shows a steady rise in smartphone sales between 2007 and 2010, with the numbers escalating from around year 2011. Coincidentally, 2011 is arguably often regarded as the beginning of the fourth industrial revolution (4IR) by some authors. Another example is the Internet, with its increased penetration especially in the global south countries of the world. **Figure 2** shows the global percentage of users with access to the Internet in various regions of the world grouped based on level of development. In the figure, "developed" includes countries of the western world, "developing" includes countries in Africa, Asia, and South America, and "Least developed countries" (LDC) include rural remote regions of the world. Internet accessibility in LDC and developing countries is shown to have more than doubled in 2019 compared to 2009.

#### *2.1.2 Next generation networks*

Over the years communication networks have evolved through five generations. The first generation was mostly analogue based fixed telephone lines supporting voice calls

**Figure 1.** *Comparison of Smartphone sales between 2007 and 2011 and 2017–2021. Adapted from reference [8].*

#### **Figure 2.**

*Global Internet Usage. Adapted from [9].*

only. The second generation introduced digitisation and mobile networks, as well as support for SMS and MMS through GPRS. The third generation introduced better support for multimedia including video streaming and social media, while the fourth brought improved download speeds and reduced latency. The 5th Generation (5G), which is the latest communication standard is still in its deployment stages. It promises several features including up to 10 Gb/s connection speed, lower energy utilization (up to 90% conservation), better availability and coverage, support for a significantly higher number of simultaneous connections, lower network latency (in the order of milliseconds), as well as support for multi-tenancy and modular programmability.

**Figure 3** reveals some key application areas that will be significantly enhanced by the 5G mobile network such as i) residential use, ii) Internet-of-things, iii) infrastructure connection, iv) inter-vehicle connection, and v) augmented and virtual reality. These enhancements would leverage on the increased coverage, high bandwidth, low latency of 5G, coupled with cross-integration of multiple networks including terrestrial networks and aerial networks [10].

#### **2.2 Internet of things and sensor networks**

Internet of Things (IoT) can simply be described as fitting everyday objects with Internet connectivity feature. These objects or things might include TVs, air

**Figure 3.** *5G use cases.*

*The Fourth Industrial Revolution: A Technological Wave of Change DOI: http://dx.doi.org/10.5772/intechopen.106209*

**Figure 4.** *Building blocks of IoT.*

conditioners, doors/windows, vehicles, and heavy machinery etc. and are embedded with uniquely identifiable computing nodes. Key components of this IoT definition are "connectivity", which refers to networking; "embedded", which refers to the infusion of miniaturized devices with built-in sensing and actuation capabilities; and "uniquely identifiable", which implies distinct addresses either via IPv6 or Media Access Control address (MAC). **Figure 4** shows a depiction of the constituent features of IoT.

The simplicity, openness, and the fact that IoT builds upon existing network infrastructure and protocols, accelerates its growth and widespread adoption. It is widely estimated that the number of connected devices would grow exponential to close to 50 billion in the next few years [11]. Though several unique (closed) communication protocols exist for interconnecting IoT "things", recent years have seen a push for openness or interoperability between these protocols. **Table 2** shows the IoT stack (loosely based on [14]) compared to the classic network stack, as well as several IoT specific protocols and their respective operating layer.

#### **2.3 Big data and analytics**

Big data is a term used to describe large volume of data in different formats (variety), generated at a fast pace (velocity), are of good quality (veracity) and holds significant value. Big data emanates from different sources, including social media, streamed media (videos, images and audio), web pages, and IoT telemetry data; and can be structured, semi-structured, quasi-structured, or unstructured. Storage and processing can be challenging, because big data does not conform to the traditional notions of data structure, and often exceeds the storage and processing capacities of conventional computer systems. Federated CPS that relies on a federation of physical systems (such as those proposed in [15, 16]), and/or federation of virtual entities techniques (such as proposed in [17]) could be potential solutions to these issues.


#### **Table 2.**

*Comparison of models.*

Typical big data processing pipeline include i) Data sourcing; ii) Data collection and ingestion; iii) Data storage & warehousing; iv) data preparation, including preprocessing, de-duplication, filtration etc.; v) Data processing and mining, which are the process of discovering patterns, trends, and/or valuable information from large data using statistical and/or machine learning models [18]; vi) Data analytics, vii) Data visualization; viii) result evaluation and application. These processes are summarized in **Figure 5**. By combining qualitative & quantitative analyses, visualization

**Figure 5.** *Big data analysis overview.*

& dash-boarding, with data mining performed on big data, big data analytics can improve processes in diverse domains including agriculture [19], education [20], health [21], etc.

#### **2.4 Cloud, fog and edge computing**

In simple terms and from the end-user's perspective, Cloud computing (CC) is a model that shifts computing from physical devices to a service [22]. It allows users transfer the "headaches" of managing computing infrastructure to a third party (Cloud service provider (CSP)), and instead focus on their core business or goal. On the other hand, the CSPs (with expertise in computing) ensure satisfactory service delivery at agreed price points, using virtual machines [23, 24]. Services offered by CC include but are not limited to storage, high performance computing (HPC), and software/hardware on demand, making CC a key backbone of many of today's disruptive industries.

Cloud computing offers several services, common amongst which are infrastructure as a service (IAAS), wherein HPC are dynamically provisioned for data warehousing, analytics, or machine learning tasks. When developing web, mobile or desktop applications, the platform as a service (PAAS) Cloud service model provides bespoke application development toolkits, which can significantly reduce application development time. Finally, the Software as a service (SAAS), avails Cloud users with ready-made software solutions, thus eliminating the need to install software on personal computer systems. CC can arguably be considered as the foundational enabler of the 4IR. The true power of the Cloud as an enabler of the 4IR comes in form of everything-as-a-service (or XAAS). Where X can be cars, as is the case of on-demand car services provided by the likes of Bolt and Uber; or X = Multimedia or video on demand, such as Netflix and Apple TV; or X = houses - Airbnb; or X = storage - Google Drive and One Drive; or X = Data, which incorporates elements of data analytics, data warehousing, visualization and dash-boarding; or X = productivity/office, where products such as Office 365, Zoho, SAP, and Salesforce, offer remote work and productivity solutions to billions of users globally; and X = Education, with Massive Open Online Courses (MOOC) and Learning Management Systems, such as Sakai, Udemy and EdX which offer remote teaching/learning and education management [25].

#### *2.4.1 Fog and edge computing*

Certain Cloud application domains, such as dynamic traffic routing, e-Logistics, ambulance routing, self-driving cars, require fast, on-demand and real-time information from processed data. CC, though capable, is ill-suited for such application areas due of latency emanating from the distance between the Cloud data centre and the data source. Fog and Edge computing have been proposed as potential solutions to this challenge. Fog Computing is a form of distributed CC, where portions of the computational processing that would typically have been done in the Cloud are handled by smaller computing nodes. The Fog layer thus serves as a middle layer between the Cloud layer and the data source. The proximity of the Fog nodes to the data source helps reduce latency emanating from network congestion, bottlenecks, and bandwidth limitations [26]. Being a HPC node, the Fog can perform high intensity computations in real-time and only forwards data meant for long-term storage, batch processing and/or advanced computations to the remote Cloud.

**Figure 6.** *The 4 Layers of a generic cyber-physical network based on ITU architecture [14].*

On the other hand, Edge computing devices are low powered computing nodes placed next to the data source. These devices are responsible to routing, collecting, filtering, and aggregating data collected at the source. They might include network gateways, such as wireless access points, network switches, network routers, single board computers (such as the Raspberry Pi or Asus Tinker board), or microcontrollers (such as the Arduino or ESP32).

**Figure 6** shows a depiction of the Cloud, Fog, and Edge computing layers in a generic Cyber-Physical network. The image shows the Fog and Edge layers being sandwiched in between the physical/device layer (data source) and the Cloud computing layer.

#### **2.5 Artificial intelligence and machine learning**

Artificial Intelligence (AI) is a technique for building systems that mimic human behaviour or decision-making. Machine Learning (ML) is a subset of AI that uses preexisting data to learn and automatically classify or make predictions. There are four main types of ML methods, which are: Supervised ML, which learns by example and yields output based on provided data; Unsupervised ML, which seeks to identifies patterns in raw data without the need for examples; Reinforcement Learning, which learns using reward-based system, in which good decisions are rewarded, while incorrect decisions are penalised; Deep Learning, which is a special subset of ML that relies on multi-layered artificial neural networks to solve complex tasks. AI and ML have been used to identify faces and objects, detect tumours, navigate self-driving cars, and in language processing to analyse, understand, and generate human language, whether written or spoken.

Perhaps the most obvious/real life examples of AI are the ever so popular digital assistants – Amazon's Alexa, Apple's Siri, and Google's Assistant. These assistants are now seemingly commonplace and integrated into numerous "smart" products, including speakers, TVs, watches, and phones. With these assistants, users can order items from stores, control home appliances, pay bills or book flights by simple starting their voice command with "Alexa … " or "Siri … ".

#### **3. Emergent technologies, concepts and applications**

This section discusses several derivative solutions or systems of industry 4.0. The systems are presented as use-cases which showcase the various applications of technologies of the 4IR. For each system, a high-level description of the use-case is presented, followed by a brief discuss of the underlying 4IR technology. Building on **Figures 6** and **7** shows a CPS orchestration framework which encompasses both the physical and cyber worlds, through the integration of various 4IR technologies, particularly IoT, Cloud Computing, data analysis, storage, machine learning, and insights.

Many concepts emerging from industry 4.0 are largely deployed using the orchestration framework shown in **Figure 7**. The physical consists of appliances, machines, or human entities that need to be monitored or tracked. This is achieved using sensors which measure environmental and/or physiological variables such as temperature, humidity, oil level, running time, heart rate, oxygen level etc. These parameters are then collected at the edge, where pre-processing (aggregated and/or filtration) takes place, before being forwarded to the Fog or Cloud for advanced processing, storage, and analysis. The final output is inference, which can be used to make informed decisions and/or make necessary adjustments at the physical level. The entire process can be described as a data pipeline flowing from the physical space to the cyber-space and back to the physical, as described in [27]. **Figure 7** would be used as a guide to discuss the selected use-cases, while **Table 3** summaries the various components of each level.

#### **3.1 Use-case 1: health monitoring system**

This is an application of 4IR wherein wearable devices fitted with sensors are used to actively monitor physiological parameters. These devices collect relevant data and are connected to software applications, through which the wearer or professional, such as healthcare officers (doctors) or fitness coaches, can monitor relevant information. Applications include sports and fitness trackers, cardio and respiratory

**Figure 7.** *CPS orchestration framework.*


**Table 3.**

*CPS* 

*orchestration*

 *framework*

 *components*

 *for each use-case.*

monitors, child minders and infant trackers etc. Several health and fitness monitoring systems were discussed in [15, 21, 28].

In context of the CPS orchestration framework, relevant sensors are used at the physical levels. Protocols, such as the Health Level 7 (HL7) and servers are considered at the Edge and Fog levels respectively, while hospital management systems can be deployed as SAAS solutions in the Cloud. Inference might include health conditions, sleep patterns, emergencies, etc. for which relevant actions, such as doctor's appointments, medical prescriptions, or ambulance could be dispatched. One such system is described in [29], where cyber-healthcare kiosks were proposed to support healthcare systems in developing countries.

#### **3.2 Use-case 2: smart traffic and road systems**

Smart traffic and road systems (STRS) include smart roads, driver assistant, traffic congestion monitoring, smart traffic and streetlights, smart parking, smart transportation etc., some of which are briefly described as follows.

Smart roads and intelligent highways are classic road networks which have sensors installed to monitor various road conditions and report same to commuters. Smart traffic and streetlights are improved versions of their traditional counterparts. Smart traffic lights are fitted with sensors to measure traffic intensity at road intersections and dynamically control traffic flow accordingly [30, 31], while Smart streetlights measure ambient light to autonomously switch themselves on or off. Driver assistant systems provide real time information on traffic situation to drivers and can also include drowsiness detection. Smart parking systems allow communities make optimal use of parking spaces while enabling drivers reserve or locate parking spots [32]. Other road-based solutions that have emerged from 4IR, including driver monitoring, smart mobility & carpooling, Bus Rapid Transport (BRT)), traffic surveillance & license plate detection, electric vehicles, etc. as reviewed in [33]. **Table 3** summaries the technologies at play at each level of the CPS orchestration framework w.r.t. STRS.

#### **3.3 Use-case 3: e-Logistics**

e-Logistics is multi-faceted and incorporates several complementary solutions, including transportation, real-time tracking, geo-location, courier, and cargo delivery services. Delivery services, encompasses the entire process flow required to transport cargo from pickup to delivery points. The technical requirements for each delivery differ and depend on the size, weight, type, and content of the cargo being transported. To instance, high valued items might require real-time tracking using GPS receivers, while sensitive and/or delicate cargo might require maintaining certain ambient conditions such as temperature and humidity.

For the CPS orchestration, the physical layer might require RFID or NFC modules for cargo tagging and identification, as well as sensors to gather data on the cargo and its surrounding environment. The Edge would include data aggregators and network gateways, through which telemetry data are sent to the Cloud, while the Cloud layer could house software for visualization, mapping, and customer engagement.

#### **3.4 Use-case 4: smart factories and manufacturing**

Perhaps the most direct impact of industry 4.0 is the automation of manufacturing processes. Gartner describes smart factories as new forms of efficient and flexible

manufacturing, powered by the interconnection of processes, diverse real-time data sources, and individuals (operators, maintenance officers, etc.) who interact with these systems [34].

Smart factories connect the physical and cyber world together in a bid to monitor (and control) end-to-end manufacturing processes. These processes begin with the procurement of raw materials, tracking their shipment, monitoring parameters from various machines, packaging of finished produce, and the delivery of finished goods. Parameters of interest within the smart manufacturing process might include fuel levels and usage estimation, ambient temperatures, air quality, levels of *CO*<sup>2</sup> and other gases, oil levels etc. These data parameters are then fed into CPS, where ML and data analytics are used to obtain relevant inferences, such as failure metrics (Mean Time Between Failures - MTBF, Mean Time To Repair - MTTR, and Mean Time To Failure - MTTF). With this information, preventive and corrective maintenance can be scheduled, avoiding the need to shut down the factory (stopping production and revenue generation) due to faulty equipment.

#### **3.5 Use-case 5: smart energy and grids**

Traditional electric grids are based on a closed system of production, transmission, distribution, and consumption, with no provision for the exchange, visualization and security of information and energy flows between operators and customers [35, 36]. These classic grids adopted a top-down architecture, with a centralized producer supplying the necessary energy to consumers. Smart grids (SG) in contrast, are made up of decentralized power sources, mostly renewable or "green" energy, which rely on ICT to control the flow of energy and information in real-time to customers. Being made up of several decentralized power sources, SGs employ bi-directional architectures consisting of both the top-down and bottom-up architectures. The bottom-up architecture is one in which the consumers can also produce energy which is fed into the grid, thus, becoming "prosumers". Beyond the grid, sustainable energy usage is an ever-present concern in today's energy market. Several solutions have been proposed including energy efficient appliances and smart appliances, which learn usage patterns through machine learning, and automatically switch themselves on or off [37, 38].

Regarding the CPS orchestration framework, the physical layer might include solar panels, smart meters, adaptive lighting, and motion sensors. Gateway appliances running protocols such as Bluetooth Low Energy (BLE) and ZigBee might be found at the Edge layer, while the Fog and Cloud are merged to provide solutions for remote appliance control and monitoring, as well as billing and metering solutions. Finally, drawn inferences might include energy consumption patterns, while actuations involve remote appliance control.

#### **3.6 Other emergent technologies**

**Digital Twin:** A Digital Twin (DT) is a digital replica of a physical object or concept in the real world. The replica which receives data from the real world is able to mimic and "act" in a manner similar to its real world instance. This ability makes DT technology ideal for prototyping and simulating world events and settings to develop appropriate responses to external stimuli. It is a technology that infuses IoT, AI and Data analytics, as data received from IoT sensors in the real world are fed into AI, mathematical, and/or statistical models from which decisions and useful inferences are obtained.

*The Fourth Industrial Revolution: A Technological Wave of Change DOI: http://dx.doi.org/10.5772/intechopen.106209*

**Blockchain:** A blockchain is a sequence of "blocks", each containing a list of transaction records, stored cryptographically in linked distributed databases (chain) [39]. In essence, Blockchain is an immutable way of storing information. It is characterized by high security, as it uses unique digital signatures and cryptography; and decentralized control, through a peer-to-peer network of consenting users, who control and authorize transactions. It has been applied in numerous fields including finance (cryptocurrency) [40], trading [41], health [42], logistics, construction engineering [43], and in almost any area where secure and accurate record keeping is required.

#### **4. Impacts of the 4IR**

This section discusses some of the direct impacts of Industry 4.0 on the lives of people and societies in general.

#### **4.1 E-commerce**

E-Commerce or electronic commerce is a system of trading carried out via the Internet. The growth of the Internet during the 3IR could be considered one of the catalysts for the wide adoption of e-commerce. This adoption has since risen astronomically, particularly in the 4IR era, with the proliferation of smartphones, tablets, and other mobile devices. Amazon, Alibaba, Best Buy, and eBay are some well-known global online retain stores, most of which accept payment through various means including physical cash, credit/debit cards and NFC-based payment [44] such as Apple Pay, Samsung pay and Google Pay.

The impact of e-Commerce became more apparent during the recent Covid-19 global pandemic, which called for isolation and physical distancing to reduce its spread. People relied heavily on technology to shop for necessities, contact-less deliveries, and payments. **Figure 8** shows the monthly year-on-year growth of

**Figure 8.** *Global growth of e-Commerce in 2020 (based on statistics from www.Bazaarvoice.com).*

e-Commerce in 2019 vs. 2020. As of April 2020, the number of orders placed on e-Commerce platforms had almost doubled the number from the year before at 96% increase.

Hybrid stores or "Just Walk Out" or "till-free" stores are becoming increasingly popular. As the name implies, a "just walk out" store is one wherein a buyer, after picking any item of interest, simply walks out of the shop without visiting the counter/till to pay. These stores use artificial intelligence, weight sensors on shelves, and cameras to monitor buyers, automatically determine which items were selected and bill the customer. Examples of these stores are Amazon Go and Telesco GetGo stores.

#### **4.2 Remote workers**

An indirect impact of technologies of the 4IR is remote working or tele-working. This is a system wherein employees carry out their tasks or jobs from locations different from the physical building of the employer. Industry 4.0 technologies including high speed Internet (5G), tele-conferencing solutions (such as Zoom, Microsoft Teams), augmented/virtual realities, and collaboration tools (Github, SharePoint), have greatly enabled remote work. The Covid-19 pandemic also popularized remote work as "working from home" became a norm between late 2019 and 2021. These years saw tele-conferencing solutions including Google Meeting, Zoom Microsoft Teams etc. replace in-contact meetings.

#### **4.3 Education**

The education sector has also been greatly impacted by the 4IR. Like with remote workers, the education sector has also seen a surge in the number of remote teaching and learning especially through Massive Open Online Classes (MOOC). In the era of Industry 4.0 the traditional brick and mortar classrooms are either being complemented by or replaced by online alternatives. MOOC, such as Udemy and Coursera, offer teaching and learning solutions that are completely independent of physical classroom environments. In cases where traditional classrooms are being augmented, 4IR offerings, particularly virtual and mixed reality, allow students immerse themselves in a virtual world, giving them first-hand experiences of the concepts being taught. Immersive technologies are commonly used in specialized industries where training equipment are either too expensive or delicate to leave in the hands of trainees. These include the aviation industry, where augmented reality is used to teach pilots and astronauts [45], in medicine to train medical students [46], in agriculture to teach farmers the concept of crop rotations and use of tractors [47], etc.

Though the Internet has been the major catalysts of change, other factors have also played their roles in reshaping the education sector. For instance, smart television and touch screens now enable interactive learning for kids and toddlers, while Podcasts, Webinars and MOOC allow a single lesson to reach billions of globally disperse learners in an on-demand fashion. The authors in [25] discussed several considerations for remote teaching and learning especially from the perspective of developing countries.

#### **4.4 Media and entertainment**

The impact of 4IR has also been felt in media and entertainment. The penetration of smartphones, smart-TVs, and reliable internet has increased the consumption of

*The Fourth Industrial Revolution: A Technological Wave of Change DOI: http://dx.doi.org/10.5772/intechopen.106209*

#### **Figure 9.**

*Year-on-Year growth in Number of Social Media Users [48].*

high-quality, and often bandwidth heavy contents, such as 4 K videos and game streaming. Industry 4.0 has brought about a major yearning for on-demand and ubiquitously accessible media contents. Classic videos on tapes, DVD and Blu rays have been replaced with on-demand streaming from online platforms such as Netflix, YouTube, Apple TV etc. Hard copies of photo albums have been replaced with Instagram and Snapchats, while classic hardware music players have been replaced with streaming services such as Spotify, Deezer and Apple Music. Recent statistics reveal that streamed contents accounted for over 83% of all consumed media, with Spotify accounting for about 33% (180 million subscribers), Apple music with 17.5% (or 90 million subscribers), Amazon music accounting for 14% (77 million), YouTube Premium with 50 million subscribers, and YouTube (free) with over 2 billion users monthly.

Furthermore, 4IR has also changed the way people socialize, with the shift from physical to online socialization. There are now a plethora of social media platforms including Facebook, Twitter, Instagram, Snapchat, WhatsApp etc., with built-in support for direct messages (chats), group messages, and voice & video calls. Using these solutions, friends and families can stay in touch with one another despite being globally disperse. Recent 2022 reports suggest that more than 58% of the world's population (4.6 billion people) use social media, with most users spending an average of about 2.5 hours daily on these platforms [48]. **Figure 9** shows that within the last decade, the number of social media users has tripled from about 1.5 billion in 2012 to over 4.6 in 2022.

#### **4.5 Transportation**

Smart transportation and mobility are another significant impact of the 4IR. Smart transportation encompasses a broad range of concepts including but not limited to vehicle-as-a-service (ride sharing/carpooling, riding hailing), bus rapid transit (BRT), smart roads, autonomous vehicles, electric cars and bicycles, transport monitoring and tracking, and car park management, most of which are accessible through a mobile device [33, 49]. Similarly, several 4IR technologies including IoT, Big data analytics, ML, Fog and Cloud computing are being fused together to achieve

autonomous vehicles navigation. Likewise, IoT, GPS, RFID and NFC are highly influential 4IR technologies in Logistics services and delivery services globally.

Several smart transportation solutions, specifically variants of riding sharing, ride hailing, and courier/logistics services, have been deployed globally. These are mostly due to the increase in Internet and smartphone penetration rate in the last few years. For instance, online ordering and delivery of food, a form of logistics services, has become a norm in recent times [50, 51], while ride sharing services and ride hailing have continued to grow globally, even in developing countries [52, 53]. Leveraging on 4IR offering, insurance firms are able to monitoring driving behaviour [54, 55], while haulage companies can measuring fuel consumption in trucks [56].

#### **5. Open challenges**

The advantages and applications of industry 4.0 are numerous, some of which have been discussed above, however, there are several challenges hampering the widespread deployment of some of these applications. This section briefly discusses some of these open challenges.

#### **5.1 Bandwidth and infrastructure requirement**

With the plethora of social media applications, tele-conferencing solutions (Zoom, Teams), media streaming platforms (Netflix, Hulu), connected devices (smart appliances, connected homes), wearable technologies (smart watches, health monitors), the demand for reliable data access, greater network coverage, and bandwidth has sky-rocketed. Internet service providers (ISP) must be prepared to upgrade or perish. ISPs of today need to be flexible, dynamic, and agile enough to changes their mode of operations to suit dynamic customer demands, as well as, be ready to upgrade or replace ageing infrastructure with modern alternatives. For instance, classic active devices such as routers and switches might need to be replaced with those that support software defined networks, wherein the control and data planes are decoupled, and traffic flows are customised [57]. 5G and 6G are also on the horizon, hence, ISPs must make extensive plans and invest in capacity building. There is also the concern of seamless integration with existing solutions that must be considered, as the transition to 5G/6G would most likely be in phases. Adequately maintaining existing solutions while gradually adopting emerging ones is pivotal for the success of 4IR solutions providers. It is also important to note that Internet penetration in rural and less developed areas is on the rise and must be catered for. The utilization of Unmanned Aerial Vehicles (UAVs) to provide 5G network support in these locations might be viable solutions to consider [10].

#### **5.2 Big data**

By definition, "Big data" should implicitly spell trouble for data centres and ISPs, as they must process enormous volume of data (in petabytes) with minimal delays. Managing, processing, storing, and backing up these enormous amounts of data in batches or in real-time (streaming) can be a major challenge. Building data warehouses and HPC solutions to manage & process petabytes of data can be prohibitively expensive for most organizations. One solution could be cooperative Fog/Cloud federation, whereby small Cloud infrastructures are collaboratively operated, networked, and managed by a group of organizations with common interest [16, 58]. Another alternative could be through partnership with third party solution providers such as Google (Google Cloud Services), Amazon (Amazon Web Services) and Microsoft (Azure).

#### **5.3 Security and privacy concerns**

Preservation of security and privacy is a major concern in today's data-centric world. The IoT, despite its bells and whistles still has several privacy concerns. There is the ever-present threat of unauthorized access to smart systems (homes, buildings, cars) or hackers tapping into feeds from security cameras to spy on people. Moreover, in wearable technology where BLE is prevalent, performance degradation due to electromagnetic and inter-channel inference, specifically for medical devices, is also a major concern [59].

Beyond IoT, issues such as transboundary data ownership and jurisdictions are also major concerns. In many countries, legal, privacy and ethical issues relating to the use and access to sensitive data, such as those on health, judicial, and intellectual properties, remain open challenges, especially in instances where such data are stored on remote Cloud servers located in a different country [60]. Though policies are now in place to address some of these challenges, such as the Protection of Personal Information Act (POPIA) [61], and the General Data Protection Regulation (GDPR) [62], implementation and/or compliance remain a big challenge.

#### **5.4 Interoperability**

There are several protocols upon which 4IR technologies operate. These protocols enable the collection, storage, and exchange of data between various components. They include but are not limited to Li-Fi, Wi-Fi, 3G/4G/5G, ZigBee, Z-Wave, BLE, SigFox, NB-IoT, LTE-M etc. Unfortunately, many of these protocols are developed by different manufacturers and are used on their own appliances, hence, closed off to solutions from different manufacturers. This closed-source ecosystem limits the interplay between equipment and often forces users to be locked into using solutions from specific vendors. Currently, no single vendor can provide equipment to cater for all the phases of an integrated industry 4.0 system, and by operating in closed-source silos, manufacturers increase overall cost of ownership, limit vertical and horizontal scalability, and stifle innovation. Collaboration is thus paramount for scalability and growth. However, multiple studies have shown that proprietary technology, poor coordination, and lack of standards are primary factors limiting inter-operability and collaboration. To combat this, open industrial standards are required which allows for cross-vendor support and global interoperability. By providing APIs, standardized open-source messaging protocols (such as MQTT, HTTP) and RESTful solutions can be deployed to expand the application use-cases.

#### **6. Conclusion**

The fourth industrial revolution (4IR) or Industry 4.0 has indeed brought about a disruption to societal lives and the world in general. The world is now driven by data and the Internet, with some describing data as the new oil of the 21st century. Globally, data intensive activities, such as remote learning, gaming, video streaming,

and video conferencing, have grown dramatically in recent times and would probably keep growing.

This chapter has discussed the technological wave of change called the 4IR. It started off by defining the concept of Industry 4.0, and then its evolution, from the industrial age in the seventeenth century till date, was discussed. The foundational enabling technologies of the 4IR, including ICT, IoT & Big data, Cloud computing, etc. were presented; followed by a discussion on various application use cases using an orchestration framework. Finally, some societal impacts of industry 4.0 were given, including its impact on education and transportation. The chapter then concluded by discussing some open challenges facing the full-scale adoption and/or implementation of industry 4.0, and proposed plausible solutions to them, including cooperative collaborations and the need to embrace open standards.

### **Author details**

Olasupo Ajayi\*†, Antoine Bagula† and Hloniphani Maluleke† Department of Computer Science, University of the Western Cape, Cape Town, South Africa

\*Address all correspondence to: ooajayi@uwc.ac.za; olasupoajayi@gmail.com

† These authors contributed equally.

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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#### **Chapter 2**

## Challenges and Prospective of AI and 5G-Enabled Technologies in Emerging Applications during the Pandemic

*Md. Mijanur Rahman and Fatema Khatun*

#### **Abstract**

5G is being implemented in the Internet of things (IoT) era. This book chapter focuses on 5G technology and the integration of other digital technologies, such as artificial intelligence (AI) and machine learning, IoT, big data analytics, cloud computing, robotics, and other digital platforms into new healthcare applications. Now, the healthcare industry is implementing 5G-enabled technology to improve health services, medical research, quality of life, and medical professionals' and patients' experiences everywhere, at any time. Technology can facilitate faster medical research progress and better clinical and social services management. Furthermore, AI approaches with 5G connectivity may be able to combat the epidemic challenges with minimal resources. This book chapter underlines how 5G technology is growing to address epidemic concerns. The study highlights many technical issues and future developments for creating 5G-powered healthcare solutions. This chapter also addresses the key challenges AI and 5G technology face in emerging healthcare solutions. In addition, this book chapter highlights perspective, policy recommendations, and future research directions of AI and 5G-enabled technologies in confronting future pandemics. More research will be incorporated into future projects, including studies on developing a digital society based on 5G technology in healthcare emergencies.

**Keywords:** 5G technology, artificial intelligence, COVID-19 pandemic, deep learning, healthcare, internet of things, machine learning

#### **1. Introduction**

The healthcare industry benefited from the development of a number of digital technologies in 2020. These technologies are used to address issues in conventional healthcare systems and the pandemic, including the "Internet of things (IoT)" with high-speed wireless networks [1], big data [2], "artificial intelligence (AI)" including machine learning and deep learning [3], and blockchain technology [4]. 2019 was the year that witnessed the broad deployment of the latest wireless mobile phone

technology, known as "Fifth Generation (5G)." Even though the 5G network is still in its infancy, some nations have already implemented 5G networks. These nations include China, South Korea, Japan, the United Kingdom, and the United States [5]. 5G home services and some large applications are currently being developed in many cities of the United States [6]. At the "Winter Olympics" in February of 2018, South Korea demonstrated the 5G technology. They have been expanding their 5G networks and anticipate having 5G deployment throughout the nation by 2023. China is extending 5G communication as part of its "Made in China 2025" goal in research and development initiatives. Commercial 5G networks were introduced in China in 2019, and the country is currently expanding 5G communication. In 2020, Japan launched a 5G network for commercial use. Several European countries, like Austria, Spain, and Switzerland, have already launched 5G services and are planning to extend their network capacities. Many other countries have plans to deploy 5G networks by 2025 [7]. By 2025, it is expected that the 5G cloud will support around 1.8 billion connections and cover nearly one-third of the world's population [8, 9].

Compared to current wireless networks, 5G offers fast data rates, reduced latency, and high-volume device connectivity with excellent energy efficiency, high reliability, and support for mobility [10]. In 2019, 204 billion applications were downloaded over the Internet, and 67% of people worldwide had mobile device subscriptions, of which 65% had smartphones [11]. It was anticipated that there would be 3.8 billion people utilizing social media regularly by January 2020 [12]. Despite the constantly increasing number of digital devices connected to 5G, further research is currently being conducted to determine the level of variety in RF exposure.

Meanwhile, the world is facing a public health calamity due to the unique "2019 Coronavirus Disease (COVID-19)" [13]. Many experts researched the genetic code of COVID-19 and attempted to tackle the coronavirus pandemic health emergency when China initially identified the virus in December 2019 [14]. However, the World Health Organization (WHO) identified COVID-19, which was caused by a novel coronavirus named "severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)" in December 2019 in China [15]. On January 30, 2020, the WHO labeled the Chinese COVID-19 outbreak a public health emergency and proclaimed a global pandemic on March 11, 2020, posing a severe threat to public health systems. The COVID-19 pandemic has swept through 228 countries and territories, resulting in almost 6.6 million deaths and 637 million infected cases worldwide, reported by the" Worldometers" on November 4, 2022 (see **Figure 1**) [16]. As of October 2019, 50 cities in China had commercially

*Country-wise coronavirus confirmed cases distribution on November 4, 2022, adapted from Worldometers [16].*

**Figure 1.**

*Challenges and Prospective of AI and 5G-Enabled Technologies in Emerging Applications… DOI: http://dx.doi.org/10.5772/intechopen.109450*

provided 5G wireless networks, and several people claimed ownership of the idea of 5G connectivity with the coronavirus. In December 2018, South Korea was the first to market 5G technology using a mobile hotspot successfully. However, South Korea was not the source of just one coronavirus, which has devastated many countries that do not yet have access to 5G networks. These countries include Malaysia, India, Bangladesh, Iran, France, Singapore, and Nigeria. Thus, the 5G-coronavirus theory makes a misleading claim, and the novel coronavirus has nothing to do with 5G, and there is no scientific evidence [17, 18]. Furthermore, according to several studies, 5G-related telecommunications technologies do not affect the human immune system [19].

Nevertheless, the pandemic has negatively influenced economic, medical, and political situations. Initial identification, isolation, quick management, spread prediction, and contact tracing technologies are all approaches to combat the spread of the coronavirus. The key challenges are virus tests, prescription or pharmaceutical delays, and providing services to critical zones. Modern digital technologies, such as artificial intelligence and 5G-based solutions [20], are essential for health, social, and economic outcomes to combat the coronavirus effectively. The worldwide health catastrophe brought on by this pandemic can be mitigated using these technologies, which can give improved digital solutions. With its potential effects in many industries, the use of 5G-enabled technology is overgrowing, offering more real-time services than anticipated. This study intends to highlight the perspective of AI and 5G-based solutions that can address COVID-19 difficulties in various contexts by concentrating on digital technology and existing socioeconomic issues. The chapter also examines numerous technological challenges and policies in implementing AI and 5G-powered emerging applications for handling post-pandemic issues.

#### **2. Related works**

Individuals and different industries are using multiple types of AI and 5G-powered solutions. The main application categories include diagnosis, patient treatment, administrative tasks, and services. During the global epidemic, numerous studies on AI and 5G-enabled technologies have been conducted, and they suggested many solutions in different sectors. M.M. Rahman et al. [21] aimed to describe the current technical aspects of artificial intelligence and other relevant technologies and their implications for combating COVID-19 and preventing the devastating effects of the pandemic. This study highlighted and categorized AI approaches in tackling the COVID-19 pandemic, including disease detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and predicting outbreaks. An early review paper [22] also discussed the role of AI in the fight against COVID-19 and its current limitations. They identified six critical areas in which AI contributed significantly during the pandemic: (i) early warnings and alerts; (ii) tracking and prediction; (iii) pandemic data dashboards; (iv) diagnosis and prognosis; (v) treatments and medication; and (vi) social control. Fei Jiang et al. [23] looked at how AI is currently being used in healthcare. This survey showed that AI could be used with different kinds of health data (structured and unstructured).

Modern AI techniques like "support vector machine" and "artificial neural network" can be used to learn from structured data. In contrast, advanced deep learning models and natural language processing are used to learn from and understand unstructured data. They talked about how AI could be used in three areas: early detection and diagnosis, treatment, predicting the outcome, and figuring out the

prognosis. In a survey report [4], the authors looked at how blockchain and AI could be used to stop coronavirus outbreaks. First, they introduced a new conceptual architecture that integrates blockchain and AI for COVID-19 fighting. Then, they talked about how blockchain and AI could help fight the COVID-19 outbreak in fundamental ways. They also looked at the most recent research on how blockchain and AI can be used in different ways to fight COVID-19.

Using the geolocation of the patients and massive amounts of data, researchers developed a system capable of detecting and predicting the early spread of an epidemic [24]. A framework [25] enabled by an AI approach was proposed to detect COVID-19 using smartphone sensors. The designed AI-enabled framework can interpret the smartphone sensor's signal readings to predict pneumonia and the disease's outcome. Due to the rapid global spread of coronavirus disease, it is desirable to develop an automatic and accurate detection method for COVID-19 using chest CT. Numerous researchers developed a model based on deep learning to identify COVID-19 on a chest CT scan [26]. Using radiology and chest radiography to screen COVID-19-infected patients effectively is a crucial task [27]. The COVID-Net is a deep neural network-based model designed to detect COVID-19 cases in chest X-ray (CXR) images. In a screening approach [28], the authors sought to develop a deep learningbased early screening model to differentiate COVID-19 pneumonia from Influenza-A viral pneumonia and healthy cases using pulmonary CT images. Deep learning-based methods, like the "Deeper-Feature Convolutional Neural Network (DFCNN)" model [29], can effectively find and rank the interactions between proteins and ligands. The DFCNN can screen people quickly through virtual means. It can discover possible drugs for 2019-nCoV protease by screening drugs against four databases of chemical compounds. Other research used three different convolutional neural network (CNN)-based models (like ResNet50, InceptionV3, and Inception-ResNetV2) to look for patients with coronavirus pneumonia in chest X-rays. In addition, models built on AI were created to enhance the critical care provided to COVID-19 patients [30]. Clinical, paraclinical, personalized medicine, and epidemiological data were included in this model. The healthcare system can use an AI-based decision-making system to defeat COVID-19 and assist in better patient management in the ICU. Seven significant applications of AI for the COVID-19 pandemic were identified by R. Vaishya et al. [31]. By gathering and analyzing historical data, this AI-based solution is crucial in determining the cluster of cases and forecasting virus infection in the future.

Additionally, it is crucial to comprehend and recommend creating a COVID-19 vaccine. Result-driven technology is employed to screen, analyze, predict, and track current and future patients. This technology has already tracked data from confirmed, recovered, and deceased cases. Furthermore, Industry 4.0 can meet the demand for personalized face masks and gloves and gather data for healthcare systems to effectively manage and treat COVID-19 patients [32]. With the proper surveillance systems, it helps to resolve pandemic-related issues and provide a daily update on an infected patient, area, age, and state-wise. The use of various AI-based automated techniques and tools, including "Brain-Computer Interface (BCI)," "Arterial Spin Labeling—Magnetic Resonance Imaging (ASL-MRI)," biomarkers, iT bra, and different machine learning algorithms, aids in reducing errors and controlling disease progression [33]. AI software, expert systems, decision support systems, and computerized diagnosis can help doctors by minimizing intra- and interobserver variability. Deep learning and machine learning methods like artificial neural network (ANN) models can uncover hidden correlations and patterns in medical data, which can be used to create efficient clinical support systems. The IoT era is ushering in the

*Challenges and Prospective of AI and 5G-Enabled Technologies in Emerging Applications… DOI: http://dx.doi.org/10.5772/intechopen.109450*

most recent 5G technology. MM Rahman et al. [20] concentrated on 5G-based solutions that could address COVID-19 problems in various contexts. This study also offered a thorough analysis of 5G technology, incorporating other digital technologies in emerging healthcare applications to address epidemiological challenges. The adoption of 5G-based technologies in healthcare is currently taking place to support better health services, more productive medical research, improved quality of life, and better interactions between medical staff and patients worldwide.

#### **3. AI and 5G-enabled technologies in real world**

COVID-19 has introduced the capability of digital transformation. Industry 4.0 has the prospects to reshape and restore economic systems in a post-pandemic world via 5G smart infrastructure with IoT and AI, integrated automation, and cloud innovation (see **Figure 2**). All of the available technologies for Industry 4.0 are linked together with the help of 5G connectivity. Medical stakeholders can talk to each other for many different reasons, such as finding and diagnosing COVID-19, supporting healthcare equipment and logistics, remote health monitoring, improving treatment processes and care, controlling and managing COVID-19 patients, lowering the high risk of death, speeding up drug manufacturing and vaccine production, fighting local and global medical emergencies, etc., with less human physical involvement [34]. Using these technologies correctly would help to improve public health education and communication. While the school is on lockdown, these technologies assist in teaching and learning in remote places [35]. These give digital and many different places to find free educational resources. People are working from home and understanding a new office culture, work hours, virtual offices, virtual meetings, and a lot of written communication thanks to Industry 4.0 technologies. Industry 4.0 uses innovative production methods to make essential disposable items in short supply because of the COVID-19 pandemic. Industry 4.0 technologies can help people find better digital solutions during this crisis. Here are some of the ways that Industry 4.0 can help lessen the effects of the COVID-19 pandemic:

#### **Figure 2.**

*The intelligent wireless edge innovation, integrating 5G connectivity with IoT and AI, that brings new and enhanced services.*


Artificial intelligence (AI), including machine learning and deep learning, the Internet of things, big data and e-health, virtual reality (VR) and augmented reality (AR), holography, cloud computing, robots and robotics, 3D scanning and printing, biosensor, blockchain, smart devices/sensors, online digital platforms, are some of Industry 4.0's powerful technologies that could be useful during this pandemic. Digital technology has significantly altered almost every aspect of human life in the last few years, including how we communicate, work, enjoy, travel, bank, and shop. Nowadays, advanced digital technologies allow for the explosive expansion of the potential of diverse diagnostic and therapeutic instruments and systems [36]. Implementing digital medical technologies can improve the general public's healthcare accessibility and adaptability. Digital technology is currently a great way to support teaching and learning processes in institutions like schools and colleges. Therefore, rather than being driven by a particular technology, the effective use of digital technology is determined by learning and teaching goals. It enhances interactions between teachers and students. The COVID-19 pandemic clearly illustrates online education's importance for teaching and learning. Today's communication is entirely dependent on digital technology. Many digital tools facilitate communication between two or more parties. These include email, phone calls, video conferencing, social media, blogs, news portals, forums, and chat and instant messaging via smart devices. It is the most convenient method of communication, as anyone can have a real-time conversation with people from around the world without leaving their desks. The phenomenon of the digital revolution is gaining increasing attention in tourism management. This industry is undergoing digital transformations, including Tourism 4.0 and Smart Tourism [37]. Consequently, the physical structure is labeled "smart" to describe the integration of the physical and digital worlds, such as smartphones, smartcards, smartTV, and smart cities.

Using cutting-edge technologies, media companies can create an efficient end-toend strategy for developing digital platforms for users. With the development of computer-mediated digital technologies, significant portions of the media and entertainment industries can become a reality. Over the past few years, the entertainment

#### *Challenges and Prospective of AI and 5G-Enabled Technologies in Emerging Applications… DOI: http://dx.doi.org/10.5772/intechopen.109450*

sector has undergone significant digital innovations. Future banking will be transformed by digital technology. The rise in AI, blockchain, and IoT demand has promoted the development of modernizing the banking industry. Banking is undergoing technological disruption due to increased competition from fin-tech startups and growing concern about cybersecurity. The digital revolution is a big chance for the agricultural sector to become more productive and advanced. Farmers can use digital technologies to make their farms more productive and develop long-term solutions to climate change. A smart city is a model for urban development that uses digital technologies to make city operations and services more efficient. It improves life for the people who live there and helps the environment [38]. Almost every part of our daily lives is affected by digital technology. In the last few decades, it has given us new devices like smartwatches, tablets, and voice assistants that have changed our world and daily lives. Also, digital technology improves the safety and security of our homes and lifestyles.

#### **3.1 AI approaches**

AI can contribute to the coronavirus pandemic in various ways, including early detection, tracing, forecasting, diagnosis, projection, treatments and pharmaceuticals, and social management and services [22]. In healthcare applications, AI methods can be divided into two primary categories: (i) machine learning and deep learning approaches and (ii) natural language processing approaches. AI approaches, particularly machine learning models, have the potential to benefit human civilizations and healthcare systems in the fight against the worldwide pandemic. In healthcare, machine learning techniques provide enormous prospects. These technologies can be used to develop effective strategies and aid scientists and medical professionals in addressing and resolving the difficulties presented by the coronavirus pandemic crisis. Many companies have recently introduced a range of AI skills, including those for outbreak estimation, coronavirus detection, diagnosis, analysis of data and treatment methods, drug development, research, and future outbreak prediction. Moreover, the term "AI" refers to a collection of technologies [39] that have the potential to significantly advance the field of healthcare (see **Figure 3**).

The three terms "artificial intelligence," "machine learning," and "deep learning" can occasionally be used interchangeably, which frequently causes misunderstanding among nontechnologists [40]. The phrase "artificial intelligence" refers to a vast, established, and highly developed area of computer science study that addresses issues relating to machine intelligence, such as simulating cognitive functions, detecting the environment, and acting independently. Robotics, vision, natural languages, learning, planning, reasoning, and other areas of study are now being studied. Deep learning, or neural network, is a machine learning model used in clinical data analysis and disease identification [41]. Moreover, data mining and statistics are involved in machine learning, where a decision model is learned rather than explicitly programmed by a person. Traditional machine learning methods can handle issues with hundreds or thousands of features, such as decision trees and support vector machines. **Figure 4** illustrates how a machine learning model works in data analysis and prediction. Problems related to computer vision, natural language processing, speech and image recognition, time series analysis, etc. have succeeded when deep learning techniques have been used. With their ability to interpret data effectively, deep learning model can improve their capacity to identify correlations and connections as they analyze additional data, basically learning from prior findings in the healthcare industry [42].

**Figure 3.** *Major AI-related technologies in healthcare applications.*

**Figure 4.** *How does a typical machine learning model work?*

A convolutional neural network (CNN) is one sort of deep learning (see **Figure 5**) that is particularly well suited to interpreting images, such as MRI data and X-rays. This CNN model can assist medical personnel in detecting health issues in their patients more quickly, accurately, and reliably. Furthermore, deep learning models can assess structured and unstructured data in electronic health records, such as clinical notes, laboratory test results, diagnoses, and prescriptions, at high speeds and with high accuracy. During the global outbreak, deep learning models were used by researchers in a variety of applications, including early COVID-19 detection and prediction, assessing chest X-ray or CT images, managing intensive care, risk analysis for COVID-19, and providing essential services.

**Figure 6** illustrates the volume of text data (unstructured and structured) produced by healthcare organizations. Some of it is arranged or organized into particular *Challenges and Prospective of AI and 5G-Enabled Technologies in Emerging Applications… DOI: http://dx.doi.org/10.5772/intechopen.109450*

#### **Figure 5.**

*Basic building blocks of a typical CNN model for interpreting medical image data, adapted from [43].*

**Figure 6.** *Unstructured and structured data generated by healthcare organizations, adapted from [44].*

EHR (electronic health record) fields [45]. With the help of this structure, medical professionals and other software programs may easily find, exchange, analyze, and utilize the data they need. However, a sizable portion of clinical data (about 70–80%) is still retained in narrative reports, patient records, observations, and other textual forms. To find the information they need from textual documents, clinicians must manually go through mountains of paperwork. It causes obstacles in administrative processes and emergencies, resulting in hiccups and delays in medical care. Additionally, EHRs receive a lot of unstructured patient data, making it challenging for a system to assist doctors in gathering this crucial information.

Another AI model, known as "natural language processing (NLP)," helps computers understand and make sense of what people say and write or what it means. NLP can help us do many things, such as extracting information, turning unstructured data into structured data, putting documents into groups, and summarizing

documents [46]. Two main types of algorithms used in NLP: (i) rule-based systems analyze text using pre-established grammatical rules, and (ii) machine learning models employ statistical techniques and acquire knowledge over time by being fed training data. NLP uses free-text medical information to figure out the best ways to treat medical conditions. The use of NLP tools in healthcare offers the ability to accurately give voice to the healthcare industry's unstructured data, yielding considerable insight into comprehending quality, refining methodologies, and improving patient outcomes. Most modern NLP techniques can understand and analyze data with little or no preprocessing [47]. The following are the critical usage cases:


#### **3.2 5G-powered emerging technologies**

The latest 5G mobile networks have excellent technical characteristics, including faster transfer speeds of up to 20 Gbps, ultrareliable low latency (less than a millisecond), enhanced network security, massive machine-to-machine communications, and improved device energy efficiency. The deployment of 5G networks will expand wireless broadband services far beyond mobile Internet to more sophisticated Internet of things systems. These systems have the low latency and high-reliability level required to handle critical applications in all significant industries. The advent of 5G mobile networks will facilitate the development of novel applications in the medical industry [9]. The provision of a platform for inventive uses that enable segmented degrees of latency will be made possible by enhanced broadband experiences, largescale Internet of things networks, and mission-critical services. Even while edge computing can be employed in a 4G context, coupling this with 5G networks and AI is

#### *Challenges and Prospective of AI and 5G-Enabled Technologies in Emerging Applications… DOI: http://dx.doi.org/10.5772/intechopen.109450*

likely to open up new possibilities in accelerating the adoption of Industry 4.0. The deployment of 5G networks makes it feasible to construct "smart factories" and reap the benefits of technologies such as automation and robotics, artificial intelligence, computer vision, augmented reality, and the Internet of things in various disciplines and applications.

In addition, it is projected that the 5G technology would connect billions of devices while improving their functionality. Applications that are supported by 5G have the potential to deliver transformative impacts in a variety of industries, including healthcare, education, resource management, transportation, agriculture, and other sectors, to address the challenges brought about by the current pandemic [48]. **Figure 7** depicts the industries that make the most use of 5G-powered emerging technologies and provides an estimate for the amount of income that digital markets will generate in the year 2026 [49]. Since the year 2020, the entire world has been experiencing a health disaster. The use of 5G in conjunction with other sophisticated digital technologies is an assistance in the fight against the issues posed by the coronavirus in many countries [50]. This cutting-edge 5G technology will revolutionize fast connection, storage in the cloud, billions of intelligent gadgets, and improved medical services in the healthcare field. As a result, 5G will revolutionize the healthcare industry and add more than 1.1 trillion USD to the global economy by 2035 [51].

5G technology has the potential to assist in medical research, diagnosis, and treatment, and improving healthcare services for both medical professionals and patients remotely [52]. **Figure 8** depicts a straightforward 5G-based health platform that can be useful to patients and medical practitioners. Since 5G promises superspeed with large data bandwidth and low latency (around 100 Mbs), AI technologies deployed in 5G networks can enable intelligent and autonomous functionality to control the coronavirus outbreak. According to a report by IHS Market Ltd. [53], 5G would make it possible for the global healthcare industry to sell more than one trillion dollars worth of goods and services by 2020. In addition, it is anticipated that the 5G network will accommodate approximately 212 billion sensors and about 50 billion smart devices [54]. These health gadgets, medical wearables, and remote sensors in 5G networks all efficiently contribute to healthcare to assist the health emergency difficulties that the

#### **Figure 7.**

*Industries that make the most use of 5G-powered emerging technologies, adapted from [49].*

**Figure 8.**

COVID-19 outbreak has produced. Now, the healthcare industry is implementing digital technologies with 5G connectivity that can provide health services and improve the quality of life and the experiences of medical personnel and patients. It is anticipated that the expansion of this technology will achieve a compound annual growth rate of 16.5% from 2019 until 2023 [55].

5G connectivity is improving healthcare services in various ways [56], including facilitating home healthcare, digitizing pathological analysis, managing patient information files, robotic surgery and medications, training, and therapeutics, securing staffpatient communication and management, etc. The favorable characteristics of 5G also significantly impact future healthcare research and the advancement of treatment. In today's world, cutting-edge digital technologies are transforming the healthcare industry. The promising digital technologies powered by the 5G standard have aided the public health schemes to fix the shortcomings in healthcare services and to confront the coronavirus epidemic [57]. **Figure 9** illustrates some of the characteristics of the 5G technology that can bring about significant breakthroughs in the medical field [58]. The following paragraphs provide further explanations of these aspects.

A. Telemedicine: It demands a network connection that is dependable as well as speedier, and it must be able to provide high-quality video and real-time conversation. 5G standards make it possible to create a suitable telemedicine environment, enhancing online health consultancy [59]. The market for telemedicine in the healthcare sector is anticipated to expand at a rate of 16.5% each year from 2017 to 2023 [55].

#### **Figure 9.**

*Few aspects of 5G interconnected technologies in healthcare to tackle the pandemic, adapted from [20].*

*Challenges and Prospective of AI and 5G-Enabled Technologies in Emerging Applications… DOI: http://dx.doi.org/10.5772/intechopen.109450*


#### **4. Challenges and prospective**

AI technologies are growing as emerging digital innovations in the healthcare industry. In addition, the 5G network's real-time superspeed and extremely low latency offer a variety of new prospects to serve developing healthcare applications. In the context of healthcare services during the current pandemic, the following subsections discuss the principal difficulties and opportunities presented by AI and 5G-enabled technologies.

#### **4.1 Key challenges faced by AI**

Though AI, including machine learning, has fantastic capabilities in healthcare in fighting against the epidemic, this field also has a few limitations or challenges in improving the current healthcare systems. Therefore, this study also addresses some challenges faced by AI in healthcare that are listed below:

A. Require a high volume of relevant data for AI: Finding rich health data is one of the biggest challenges of using AI in healthcare. AI algorithms cannot be fully trusted until they are built and trained on a large amount of relevant data in healthcare applications. Thus, AI depends on various data gathered from millions of people who have suffered from similar conditions. It must require

sufficient data on a particular group of such patients in AI databases to make the correct comparison. But enough data on patients is often challenging to find from a specific background. Moreover, medical data has a sensitive nature and ethical constraints that make it challenging to collect. In this case, AI will make an inaccurate diagnosis with insufficient data, and doctors might make a mistake in taking proper treatment.


*Challenges and Prospective of AI and 5G-Enabled Technologies in Emerging Applications… DOI: http://dx.doi.org/10.5772/intechopen.109450*

always want to use it. For example, in the early stages of the pandemic, patients did not feel comfortable with online checkups. So, it needs to teach patients about the benefits of AI in healthcare to help them feel more comfortable with it.

#### **4.2 Key challenges faced by 5G technology**

As we move toward a 5G world, we'll have to deal with many problems. Compared to older wireless technologies, 5G needs a new standard to provide customers with high-speed, low latency, reliable, and safe services. Because of this, the design, development, and implementation of 5G networks are full of big problems. Here are some other issues that have been found in the literature:


#### **4.3 Prospective of AI and 5G-enabled technologies**

AI and 5G-enabled technologies are concurrently expanding and enhancing efforts to improve global healthcare. Patients throughout the world benefit from more advanced healthcare systems that include intelligence and 5G standard in their practices. Thus, the fundamental aspects of healthcare could be entirely reimagined by the capabilities of 5G. 5G-powered technologies may prove helpful in many facets of today's healthcare, such as telehealth, remote surgery, the transfer of substantial medical records, tracking patient activities and real-time monitoring, and providing patients with proper treatment and support. This technology can provide vital services on a massive scale that are precise, efficient, convenient, and cost-effective. The following are many significant prospects that explain why technology enabled by 5G ought to be a component of every healthcare system across the globe.


*Challenges and Prospective of AI and 5G-Enabled Technologies in Emerging Applications… DOI: http://dx.doi.org/10.5772/intechopen.109450*

procedure while documenting medical data. Making available critical healthcare facilities and equipment, like operating rooms and electrocardiogram (ECG) monitors, improves intervention management. These invaluable resources aid in the administration of government operations and guarantee their security and efficacy.

F. Improving Accessibility of Healthcare Worldwide: The World Bank and the WHO have released reports indicating that at least half of the world population does not have access to elementary healthcare services. In addition, people living in rural areas of countries that lack a developed healthcare infrastructure do not have access to healthcare facilities. Many organizations utilize cutting-edge technology powered by 5G to provide medical treatment to underserved communities. These solutions are both cost-free and applicable even in rural areas for serving medical treatment.

#### **5. Policy recommendations to the states**

Universal accessibility of 5G-enabled technologies depends on the state's positive measures and various factors (such as socioeconomic, geographic location, and digital ecosystem). Currently, a number of organizations are creating digital frameworks and other ideas to bridge the digital gap. For considering the post-pandemic, we are suggested a few recommendations to the states, listed below.


D.Ensuring permanent and sustainable accessibility: Since 2019, the pandemic has highlighted the significance of digital technologies during a crisis. The internet platform has enabled millions of individuals to work and study remotely. We may emerge from this crisis with the knowledge that appropriate digital policies can promote global economic recovery and ensure that no one is left behind. States and officials must ensure that access is permanent and enduring, eliminate obstacles to community-driven connectivity, and make it easy for all groups to access resources.

#### **6. Conclusion**

In healthcare, using 5G networks to integrate other digital technologies (such as AI and machine learning, IoT, big data analytics, and cloud computing) is now a reality. The results of this study are summed up, and a deep connection with 5G-enabled technologies, especially artificial intelligence and machine learning. This study aims to find out the existing technological facets of AI strategies that can be used in healthcare to deal with the pandemic. This book chapter addresses several challenges faced by implementing AI and 5G-enabled technologies in medical services and highlights the prospects of emerging technologies. AI has played a significant role in combating the coronavirus pandemic and assisting researchers in developing systems to limit human interaction in afflicted areas, provide services, and manage health emergencies. In addition, they can help with the legal and ethical difficulties associated with producing medications in response to public health emergencies.

Future pandemic concerns and public health issues will necessitate the most effective and convincing AI methods, AI-based searching strategies, probabilistic models, and supervised learning. Thus, professionals must thoroughly understand the system they are utilizing and be aware of its security measures. Even if artificial intelligence and 5G-enabled technologies have many benefits for healthcare, AI will not replace doctors or other professionals; instead, it will improve their performance. Additionally, 5G-enabled digital technologies have been utilized to control the COVID-19 outbreak and enhance public health plans in 2020. Some advanced technology leaders are studying 5G-related applications to tackle the health hazards associated with undesired diseases. The 5G network will give a comprehensive road to a smart society with numerous potentially beneficial applications in the field of healthcare when combined with the latest technology advancements.

When deploying the 5G network in healthcare, some issues need to be considered since it is a new field of research. These issues include the development of infrastructure, the establishment of technical standards, the implementation of efficient regulations and policies, the safeguarding of personal information, and the accessibility of research data. More studies need to be done on how to expand a digital society based on 5G while addressing some challenges such as safety, security, privacy, availability, accessibility, and integrity, and improving resilience to future health crises, which lead to the following research directions in fighting against future pandemics:


*Challenges and Prospective of AI and 5G-Enabled Technologies in Emerging Applications… DOI: http://dx.doi.org/10.5772/intechopen.109450*

therapy, administrative automation, and storing patient information in private clouds.


### **Acknowledgements**

The authors wish to acknowledge the research and extension cell of Jatiya Kabi Kazi Nazrul Islam University, Bangladesh, for their support and cooperation in conducting the research.

#### **Conflict of interest**

The authors declare no conflict of interest.

### **Author details**

Md. Mijanur Rahman<sup>1</sup> \*† and Fatema Khatun2†

1 Department of Computer Science and Engineering, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, Bangladesh

2 Department of Electrical and Electronic Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh

\*Address all correspondence to: mijan@jkkniu.edu.bd

† These authors contributed equally.

© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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#### **Chapter 3**

## Critical Review on Internet of Things (IoT): Evolution and Components Perspectives

*Benjamin Appiah Osei and Emmanuela Kwao-Boateng*

#### **Abstract**

Technological advancement in recent years has transformed the internet to a network where everything is linked, and everyday objects can be recognised and controlled. This interconnection is popularly termed as the Internet of Things (IoT). Although, IoT remains popular in academic literature, limited studies have focused on its evolution, components, and implications for industries. Hence, the focus of this book chapter is to explore these dimensions, and their implications for industries. The study adopted the critical review method, to address these gaps in the IoT literature for service and manufacturing industries. Furthermore, the relevance for IoT for service and manufacturing industries were also discussed. While the impact of IoT in the next five years is expected to be high by industry practitioners, experts consider the current degree of its implementation across industry to be on the average. This critical review contributes theoretically to the literature on IoT. In effect, the intense implementation of the IoT, IIoT and IoS will go a long way in ensuring improvements in various industries that would in the long run positively impact the general livelihood of people as well as the way of doing things. Practical implications and suggestions for future studies have been discussed.

**Keywords:** internet of things, evolution, components, internet of services, industrial internet of things, fourth industrial revolution

#### **1. Introduction**

In the words of Schwab [1], "Internet of Things (IoT) is one of the main bridges between the physical and digital applications enabled by the fourth industrial revolution". The concept of IoT is more focused on enabling and accelerating the adoption of Internetconnected technologies across industries, both manufacturing and non-manufacturing [2]. Also known as the Internet of all things; it is a promising direction in productions systems and expected to bring to bear the full potential of the fourth industrial revolution [3]. Likewise Cyber Physical Systems (CPS), most researchers and scholars have attributed IoT as the key enabler or initiator of the fourth industrial revolution [4–6].

In this sense, Lee et al. [7] opined that, "all the items that can be imagined in terms of the Fourth Industrial Revolution have their basis on all the technologies required

for manufacturing and implementation of the IoT evolution". The researchers further explained that, unless all the IoT-related technologies are developed and implemented, all the possibilities mentioned and discussed regarding the fourth industrial revolution cannot be realised. IoT enables objects to be sensed and/or controlled remotely across existing network infrastructure, creating opportunities for more direct integration of the physical world into based systems [8].

Sharma, Shamkuwar and Singh [9] elucidated that, the technological advancement in recent years has transformed the internet to the network where everything is linked, and everyday objects can be recognised and controlled via Radio Frequency Identification (RFID) tags, sensors and smart phones. This interconnection is made possible with a combination of software, sensor, processor, and communication technologies. Kamble et al. [10] also explained the role and/or relationship between CPS and IoT in the fourth industrial revolution. They posited that, the IoT is connected alongside Cyber-Physical Systems in such a way that the system develops the potential to generate and feed information, adding value to the manufacturing and service process. This project the relevance of the IoT for manufacturing and service industries.

Nevertheless, limited studies have focused their argument on the evolution and components of this disruptive technology in our industries. Additionally, there exist non-consensual agreement among researchers on the evolution of the IoT scholars [9, 11]. Furthermore, there is non-consensual theorisation on the IoT technological concept among scholars [12–14]. Also, there is differing information on the components of IoT in the literature [2, 10]. Hence, there is the need to understand the etymology, evolution, and the components of this interesting technology. Therefore, the objective of this review is to explore the evolution, components, benefits as well as the implications of the IoT for manufacturing and service industries.

#### **2. Literature review**

#### **2.1 Evolution of internet of things (IoT)**

The term IoT was first coined by British entrepreneur, Kevin Ashton in 1999, to highlight the power of connecting Radio Frequency Identification tags globally to the internet, in the context or domain of supply chain management [12, 15]. According to Zhong et al. [11], the concept of IoT first came from RFID (Radio Frequency Identification) fields; stating that it is the information network constructed by the radio frequency identification technology and communication technology. **Figure 1** illustrates the chronology of IoT evolution from 1969 till date.

Although the term IoT have been attributed to the works of Ashton in 1999, Sharma et al. [9] also elucidated that that technologies behind IoT had already existed and were under development many years ago. The researchers highlighted the evolution of IoT and its supporting or associated technologies in chronological order from 1969 to the 2000s. In 1969, the Internet, which is the main technology behind IoT emerged as Advanced Research Project Agency Network (ARPANET). It was mainly used by academic and research fraternity to share research work, to develop new interconnection techniques and to link computers to many general-purpose computer centres of the United States Defence Department and in public and private sectors.

In 1973, another essential technology for IoT called Radio-Frequency Identification (RFID) resurfaced, although the roots of RFID can be traced back to the second world war. For instance, the developments associated with RFID continued through 1950s and *Critical Review on Internet of Things (IoT): Evolution and Components Perspectives DOI: http://dx.doi.org/10.5772/intechopen.109283*


**Figure 1.** *Chronology of IoT evolution.*

1960s, but the first U.S. patent for RFID tag with rewritable memory was received by Mario W. Cardullo in 1973. In the same year, Charles Walton, a California based entrepreneur, also received a patent for passive transponder to unlock the door remotely. In the year 1974, embedded computer system, which is also another important technology for IoT was invented. These systems are implemented using single board computers and microcontrollers and are embedded in the bigger system to form its integral part.

IoT was earlier used in 1984 without it being christened. Sharma et al. [9] explained that a coke machine was connected to internet to report the availability and temperature of the drink. During the year 1990, there was a proliferation of internet in business and consumer markets. Howbeit, its use was still limited due to low performance of network connectivity. In 1991, Mark Weiser proposed the concept of ubiquitous computing, another essential technological component for IoT. Weiser's ubiquitous computing made use of advanced embedded computing as a computer to be present in everything, yet invisible. It later became known as pervasive computing.

In the mid-90s, sensor nodes were developed to sense the data from uniquely identified embedded devices and seamlessly exchange the information to realise the basic idea of IoT [15]. Bill Joy in 1999 introduced device to device communication in his taxonomy of internet and the term 'IoT' was used for the first time [12]. During that same time, the RFID technology was boosted by an establishment of the Auto-ID Center at the Massachusetts Institute of Technology (MIT) to produce an inexpensive chip which can store information and can be used to link objects to the internet [9].

From the year 2000, because of digitalization, internet connectivity became the sine qua non for many applications because of digitalisation and automation. Most

businesses and products were expected to have their presence on the internet and provide information and services online [16]. Since then, the true potential of IoT begun; with imperceptible technology being operated behind the scenes and dynamically responding to our expectations for the "things" to act and behave.

Following the pronouncement of the "IoT" in 1999, its connotation has been in continuous development and expansion. The connotation of IoT has been continuously enriched. The ideal goal of IoT is that any person, any physical object, any transaction, or any process can communicate with each other by using any network at any time in anywhere [11, 15]. In other words, making a computer sense information without the aid of human intervention.

Gubbi et al. [15] writes a beautiful explication of the fast-rising industry-changing technology (i.e. IoT). They write, "a radical evolution of the current Internet into a network of interconnected objects that not only harvests information from the environment (sensing) and interacts with the physical world (actuation/command/ control), but also uses existing Internet standards to provide services for information transfer, analytics, applications, and communications". Contemporarily, IoT has outgrown its infancy and is transforming the current state of the internet into the inclusive internet of the future, covering wide range of systems in industries like transport, healthcare, logistics, etc.

#### **3. Methodology**

Based on the literature gaps highlighted, and the relative novelty of the areas considered for this study, the researchers adopted the critical review method [13, 16]. With this, the study broadened its search strategies, to unearth seminal papers on IoT by different scholars from diverse academic fields. Particular interest and attention were also given to the evolution and the concept of the IoT, as well as the components and relevance of IoT for industries. Tables were prepared to summarise the areas the study captured and their supportive references.

Specifically, these include the evolution, components and further developments of the IoT (i.e. IIoT and IoS). After this, an exegesis was done on IoT implications for manufacturing and service industries. Additionally, a table was prepared and organised to show the major findings or references that were identified for the conceptualisation of IoT. Finally, practical implications of the IoT for the industry practitioners and suggestions for future research were also discussed.

#### **4. Discussion**

#### **4.1 Conceptualising IoT**

Since the introduction of the concept of IoT in academic literature, it has received various definitions from different scholars and researchers. Most definitions of IoT emerged in the past ten years based on the latest technology and applications in existence at that time. These definitions depended on the way the researchers conceived and perceived the potency of IoT. Nonetheless, there is yet no universally agreed definition of IoT in the academic literature. Sharma et al. [9] clarified that, "different researchers, scientists define the term in their own way; some focus more objects, devices, Internet Protocols and Internet, while others focus on the communication

*Critical Review on Internet of Things (IoT): Evolution and Components Perspectives DOI: http://dx.doi.org/10.5772/intechopen.109283*


#### **Table 1.**

*Definitions of IoT by authors in their publications.*

processes involved". **Table 1** summarises some notable definitions of IoT by scholars and researchers in their papers.

For the purpose of this study, IoT is conclusively theorised based on the varying cues of definitions as "*the network of physical and virtual objects (things) connected with sensors, RFID and actuators, for the purpose of collecting, sharing and/or exchanging information through a unified platform over the internet which enables automated solutions to multiple problem sets*".

#### **4.2 Components of IoT**

Gubbi et al. [15] presented a taxonomy that aided in defining the components required for the IoT from a high-level perspective. According to the researchers, there are three IoT components which enables seamless ubiquitous computing. These are Hardware, Middleware and Presentation. They classified hardware component of IoT as those objects made up of sensors, actuators, and embedded communication hardware. This level also includes central units. Leloglu [19] described the central unit as a source of centralised services in IoTs; and has a capability of storing, processing, and delivering data to users.

Middleware component of IoT comprises on-demand storage and computing tools for data analytics [9, 15]. An example of middleware is cloud computing. This style of computing relies on sharing of resources are provided as a service over the Internet to achieve coherence and economy of scale [20]. Also, Presentation component includes new and easy-to-understand visualisation and interpretation tools which can be widely accessed on different platforms, and which can be designed for different applications.

Some enabling technologies in these categories that make up the three components given above include RFID, wireless sensor networks, cloud computing, addressing schemes, data storage & analytics, visualisation [4, 19]. These technologies help in realising the fruitful operations of the entire IoT network. The development of IoT ecosystem or network enables the object to be uniquely identified and be able to connect and communicate with other objects anytime and anywhere. According to Sharma et al. [9], the two main components of an "IoT object" are its ability to capture data via sensors and transmit data via the Internet. This internet connectivity allows object to have their own identities as well as receive and send valuable communication making them smart.

Again, these objects are embedded with electronics (Microcontrollers and transceivers), software, sensors, actuators, and network connectivity that enables them to collect and exchange the data using various protocols. In other words, the IoT allows "'things' and 'objects', such as RFID, sensors, actuators, mobile phones, which, through unique addressing schemas, interact with each other and cooperate with their neighbouring 'smart' components, to reach common goals [4]. IoT technology is purposely utilised for collecting, analysing, controlling, and managing data in manufacturing systems [20]. Hence, IoT offers connectivity of devices, systems, and services; and caters to variety of application in different domains. Kamble et al. [10] elucidated that IoT products are allotted unique identifiers and are intricately linked to information about their provenance, use, and destination.

Leloglu [19] also proposed four layers of a secured IoT architecture to guide theoretical research. The scholar referenced that the architecture of IoT should be an open architecture, using open protocols to support a variety of existing network

#### *Critical Review on Internet of Things (IoT): Evolution and Components Perspectives DOI: http://dx.doi.org/10.5772/intechopen.109283*

applications; additionally, incorporating security, adaptability, and semantic representation middleware to promote data world integration with Internet. The four layers proposed by the researcher are Perception, Network, Support and Application layers. Perception Layer consists of the sensor technology, intelligence embedded technology, nano technology and tagging technology. He explained that the main purpose of this layer is the identification of unique objects and the collection of information from the physical world with the help of its sensors.

Additionally, network layer contains Wireless Sensor Networks (WSN), optical fibre communication networks, broad television networks, 2G/3G communications networks, fixed telephone networks and closed IP data networks for each carrier. The researcher further added that the responsibility of this layer also includes the transfer of collected information from sensors, devices, etc., to an information processing system. Thirdly, the support layer involves information processing systems which takes information in one form and processes (transforms) it into another form. This processed data is stored in a database and will be available when there is a demand. According to Leloglu [19], this layer works very closely with applications. Last but not least, application layer harbours practical and useful applications which are developed based on user requirements or industry specifications such as smart traffic, precise agriculture, smart home, mining monitor, etc.

Another indispensable part of IoT worth noting is, smart connectivity with existing networks and context-aware computation using network resources. Gubbi et al. [15] highlighted three IoT demands that will allow technology to disappear from the consciousness of the user; and evolve into connecting everyday existing objects and embedding intelligence into our environment. First, a shared understanding of the situation of its users and their appliances. Secondly, software architectures and pervasive communication networks to process and convey the contextual information to where it is relevant. Thirdly, the analytics tools in the IoT that aim for autonomous and smart behaviour. With these three fundamental grounds in place, smart connectivity and context-aware computation can be accomplished.


**Table 2.** *Summary of the components of IoT.*

**Table 2** below summarises the various components of IoT that are discussed in this paper and their associated references.

#### **4.3 Further developments of the IoT**

#### *4.3.1 Industrial internet of things (IIoT)*

IoT serve as a foundation that allows objects to interact and communicate with each other in order to collect and share information among themselves. To this end, an industry-hardened IoT was needed to provide the reliability and security that were required by industry for manufacturing applications. An industry consortium initiated by General Electric (GE) is developing Internet technology for industry, resulting in a special IoT system for industrial application called the Industrial Internet of Things (IIoT) [22]. Lampropoulos et al. [23] opined that, "IoT is well aligned with the architecture of intelligent manufacturing industries, therefore IoT includes a specific category focusing on its applications and use cases in modern industries and manufacturing, named IIoT".

Just as IoT, the IIoT do not have a universally agreed definition in the academic literature. IIoT have been used along with other technological terms like IoT, CPS, Industry 4.0, Big Data, Machine Learning, Machine-to-Machine Communication [2, 22, 23]. However different researchers have given different definitions for IIoT. For instance, Chen [22] postulated that, "the IIoT refers to the integration and connectivity of complex physical machines and devices, humans, and resources through networked sensors and software for the purposes of industry production and operations".

Later, Ardito, Petruzzelli, Panniello and Garavelli [21] also defined Industrial IoT as the use of IoT technologies in demand-focussed and supply-focussed process; which favours the interoperability between devices and machines that use different protocols and have different architectures, thus allowing to have real-time data across the value-chain. Turcu and Turcu [2] also added their voice and defined the IIoT as, "a universe of intelligent industrial products, processes and services that communicate with each other and with people over a global network. It is a distributed network of smart sensors that enables precise control and monitoring of complex processes over arbitrary distances". IIoT is considered to be a complex system of many independent systems. It combines several contemporary key technologies to produce a system which functions more efficiently than the sum of its parts; and focuses on automation, services, cloud computing, big data, CPS and people [23].

The term IIoT was first introduced by Frost and Sullivan around the turn of the 21st century [21]. According to Chen [22], the Industrial Internet, which is the fundamental tool for the implementation of IIoT, was used as a collective toolset for a digital enterprise transformation at that time. Other than regular Internet applications, such as office automation, the Industrial Internet requires conditions such as a hardened environment on the factory floor and extreme dependence on its reliability. Furthermore, IIoT provides functions that help develop insight and improve the ability to monitor and control company processes and assets through the use of appropriate services, networking technologies, applications, sensors, software, middleware and storage systems [23].

IIoT is of great importance and has a lot of benefits for industries that attend to its use. Turcu and Turcu [2] gave some key benefits of IIoT in an industrial context. They highlighted, "monitoring production flow and inventory; enhancing automation,

#### *Critical Review on Internet of Things (IoT): Evolution and Components Perspectives DOI: http://dx.doi.org/10.5772/intechopen.109283*

productivity, industrial safety, efficiency, security and quality control; enabling easy maintenance, inventory management, products tracking and tracing, development of new business models, services and/or products; optimisation of packaging, logistics and supply chain; reduction of human errors and manual labour, and of costs (both in terms of time and money)", as some key benefits of IIoT ([2]; p. 57). In addition, Lampropoulos et al. [23] elaborated that, IIoT offers enormous potential for unprecedented levels of economic growth and productivity efficiency in the years ahead. They further added that, IIoT will attract the interest both of businesses and governments as well as researchers and academics who will have to collaborate closely and feverishly in order to harness and exploit this huge opportunity.

#### *4.3.2 Internet of services (IoS)*

Another technological concept, Internet of Services (IoS), is emerging which is similar to IoT. This is as a result of the world being classified as a "service society" and the idea that services are made easily available through web technologies than physically. Internet of Services is allowing companies and private users to combine, create and offer new kind of value- added services via the internet [5]. Internet of Services can be simply defined as platforms that allows internet users to provide services via the internet [24]. Chen [22] also defined Internet of Services as the connection of non-physical systems (service or social elements) to the internet through embedded systems, sensors, software, and network devices.

Internet of Services are characterised by participants, infrastructure services, business models and the services themselves. The services are offered and merged into value-added services from different vendors, and communications via various communication channels. This approach allows different variants of distribution in the value chain. Hofmann and Rüsch [5] agreed with Barros and Oberle, with regards to their proposed definition of the term service, which reads "a commercial transaction where one party grants temporary access to the resources of another party in order to perform a prescribed function and a related benefit. Resources may be human workforce and skills, technical systems, information, consumables, land and others".

The main goal or destination of Internet of Services is to enable service providers to offer services via the Internet. Contreras et al. [24] elaborated that, the CPS, the hardware and software are represented as services. They further added that, this way of conceiving the elements as services, allows a new form of dynamic variation distribution in individual activities of the value chain [24]. Using the IoS during the fourth industrial revolution implies that, the elements of the value chain adopt a service-oriented architecture (SOA); which requires a platform for networking and a series of layers in each element than can be accessed from other elements as services [5, 24]. From a pure technological perspective, concepts such as Service-Oriented Architecture (SOA), Software as a Service (SaaS) or Business Process Outsourcing (BPO) are closely related to the IoS. It is quite promising and prospective that that internet-based market places of services are playing and will continue to play a key role in future industrial operations.

Penultimately, while the impact of IoT in the next five years is considered to be high by business leaders, experts consider the current degree of implementation of IoT applications across businesses and organisations to be on the average [17, 18]. Some developed countries such as France and developing countries such as China and India are working collaboratively to employ the IoT for specific projects. These collaborations not only enhance the development of IoT technologies, but also address global issues, since it is necessary for countries and districts to work collaboratively, especially when adopting a cutting-edge technology such as the IoT [11].

#### **5. Conclusion**

IoT technologies have been widely used in industrial fields such as smart cities, manufacturing, and healthcare. To achieve improvements, specific applications of IoT is employed. Sensors and numerous other means of connecting things in the physical world to virtual networks are proliferating at an astounding pace. Smaller, cheaper, and smarter sensors are being installed in homes, clothes and accessories, cities, transport and energy networks, as well as manufacturing processes. Today, there are billions of devices around the world such as smart phones, tablets and computers that are connected to the internet. Their numbers are expected to increase dramatically over the next few years, with wider application in Agriculture, healthcare, manufacturing as well as the tourism and services provision.

Penultimately, while the impact of IoT in the next five years is considered to be high by business leaders, experts consider the current degree of implementation of IoT applications across businesses and organisations to be on the average. Some developed countries such as France and developing countries such as China and India are working collaboratively to employ the IoT for specific projects. The IoT application even though not so advanced in Africa, a lot of effort are being made to ensure the very good use of IoT, IIoT and IoS to achieve massive developmental changes in the African continent. The intense implementation of the IoT, IIoT and IoS will go a long way in ensuring improvements in various industries that would in the long run positively impact the general livelihood of people as well as the way of doing things. Minimization of human errors and process down times due to human interventions and errors could be readily achieved.

#### **5.1 Practical implications of IoT for industries**

The IoT Technology, as identified in academic literature, is impacting every aspect of our daily lives as well as the way we work [11, 20]. This implies that a large number of traditional areas with regards to our daily lives and living, will be affected by IoT technology. The quality of life is undergoing fast transformation and will be improved drastically in future. The IoT is expected to open up numerous economic opportunities and is considered one of the most promising technologies with a huge disruptive potential [5]. Sharma et al. [9] opined that, governments are believed to be the second-largest adopter of such technological solutions; and will also take a keen interest in such technologies to improve the quality of life of their people. The prospects of IoT will dramatically improve security, energy efficiency, education, health, and many other aspects of daily life for consumers, through amazing solutions. The ecosystem or network of connected devices has great benefit in all industries and fields, including energy, safety and security, industry, manufacturing, retail, healthcare, independence of elderly persons, people with reduced mobility, environment, transport, smart cities, entertainment, etc. [10, 22].

Again, IoT offers a lot of prospects or benefits for business and enterprises that utilise its technology. The business intelligence sector will adopt IoT solutions at a bit faster rate than other sectors. These business sectors are expected to increase their

#### *Critical Review on Internet of Things (IoT): Evolution and Components Perspectives DOI: http://dx.doi.org/10.5772/intechopen.109283*

productivity, have a higher growth of profit as well as lower their costs of operations, with the adoption of IoT technology. This will be possible as a result of IoT technology enhancing their operational efficiency, decreasing their product time-to-market by reducing unplanned downtime, and optimising their overall operational efficiency. IoT also improve decision-making and productivity of businesses in retail, supply chain management, manufacturing, agriculture, and other sectors by reinforcing solutions. Enterprises can further enhance overall availability and maintainability thanks to the vital solutions for more effective scheduling, planning, and controlling of manufacturing operations and systems that IoT provides [23].

Interestingly, the IoT Technology provides a unique and much-needed foundation that is capable of connecting all the elements of a manufacturing system together [22]. Smart-connected products offer exponentially expanding opportunities for new functionality, far greater reliability, much higher product utilisation, and capabilities that cut across and transcend traditional product boundaries [5]. In this way, not only can the efficiency of data collection be improved, but the quality of the data can also be significantly improved. The IoT also enables network control and the management of manufacturing equipment, assets, and information flow.

In this line of thought, Kamble et al. [10] explained the use of the IoT in helping the effective co-ordination and synchronisation of product, and information flows. The researchers postulated that, "the CPSs based on IoT technology find applications in smart manufacturing to achieve intelligent perception and access to various manufacturing resources, to connect multiple parties using social networks to facilitate open innovations, for process control using RFID to provide more flexibility to the manufacturing process, to improve the productivity of the microdevices assembly, and to manage dynamics in production logistics processes".

IoT is radically transforming the way supply chains are managed for businesses and customers as well. It is enabling businesses to monitor and optimise assets and activities to the very granular level. This transformative impact on supply chains will also cut across all industries in its process, from manufacturing to infrastructure to healthcare. Also, 1oT systems like the RFID allows a company to track its products as they move through the supply chain. A widespread application of the IoT that makes this possible is termed as remote monitoring [1]. With remote monitoring, any package, container or pallet can now be equipped with a sensor, transmitter or radio frequency identification (RFID) tag that allows it to be tracked, know how it is performing, and how it is being used. Similarly, IoT also allows customers to practically and continuously track the progress and location of their product or package they are expecting in real time.

Furthermore, IoT does not only ensure effective collection and gathering of data but also acquisition of real-time data for effective decision-making and data analytics. Connected devices ensure the availability of real-time data, enable the geographic distribution of operations and manufacturing, and result in improvements in operational efficiency, processing time and operating and management costs [17, 18]. Ardito et al. [21] highlighted three ways IoT benefit in the acquisition of real-time data for marketing and supply chain functions. They include real-time acquisition of market data (customer data and product-customer interactions); real-time acquisition of operational data (e.g. products life-cycle and material flow); and possibility to elaborate and integrate both market and operational data. The IoT provides real-time sensing/actuating ability and fast transmission capability of data/information, so that the remote operation of manufacturing activities and efficient collaboration among stakeholders are greatly facilitated.

For instance, the RFID technology provides one such example; and this influence most of industries, especially manufacturing sectors. Zhong et al. [11] explained that, "RFID technology has been used for identifying various objects in warehouses, production shop floors, logistics companies, distribution centres, retailers, and disposal/ recycle stages. After identification, such objects have smart sensing abilities so that they can connect and interact with each other through specific forms of interconnectivity, which may create a huge amount of data from their movements or sensing behaviours. The interconnectivity between smart objects is predefined; such objects are given specific applications or logics, such as manufacturing procedures, that they follow after being equipped with RFID readers and tags. RFID facilities not only help end-users to fulfil their daily operations, but also capture data related to these operations so that production management is achieved on a real-time basis".

In addition, the application of IoT, especially in industry, results into the creation of vast amounts of heterogeneous information that needs special manipulation and analysis to perform meaningful reasoning and extract the actual value. The extraction of the knowledge from the data collected in all levels of manufacturing systems can create autonomous smart manufacturing system. Oztemel & Gursev [8] elucidated that, IoT manufacturing systems make decisions that are quick, more optimistic, and faster than those of others. However, this depends upon the architecture and related intelligence embedded into the system. Moreover, the information networks that are based on the IoT application also create new business models, improve business processes, and reduce costs and risks [20].

There are a lot of independent technologies that come together or involved in the IoT eco-system. They include RFID, cloud computing, communication technologies, sensor technologies, advanced analytics, Big Data, machine learning [2, 19]. However, they are also prone to cyber risk, which exerts pressure on both stakeholders (government and business) to implement appropriate security and privacy policies across organisations, manufacturing networks and supply chains [17, 18].

#### **5.2 Limitations and suggestions for future research**

Notably, this review serves as a theoretical foundation for further studies. Future studies should empirically evaluate the IoT components that are in use at manufacturing and service firms. Furthermore, studies can also explore the prospects of the use of the IoT systems for industries using pragmatic methods. Also, other studies can also focus on the extent to which IoT systems of the Fourth Industrial Revolution apply to industries. Again, the impact of IoT operations on employee productivity, organisational performance and customer satisfaction can also be investigated. Additionally, an empirical study could be done to understand the challenges associated with the implementation of IoT and solutions that could be used to address these challenges. Finally, factors that would enhance the adoption of IoT systems in the face of the incoming technology revolution, could be the focus of future studies.

*Critical Review on Internet of Things (IoT): Evolution and Components Perspectives DOI: http://dx.doi.org/10.5772/intechopen.109283*

#### **Author details**

Benjamin Appiah Osei1 \* and Emmanuela Kwao-Boateng2

1 Department of Human Resource and Organisational Development, Kwame Nkrumah University of Science and Technology, Ghana

2 Department of Chemical Engineering, Kwame Nkrumah University of Science and Technology, Ghana

\*Address all correspondence to: oseibenjaminappiah@gmail.com

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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#### **Chapter 4**
