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# The Duo of Artificial Intelligence and Big Data for Industry 4.0: Review of
Applications, Techniques, Challenges, and Future Research Directions

Senthil Kumar Jagatheesaperumal1, Mohamed Rahouti2, Kashif Ahmad3, Ala Al-
Fuqaha3, Mohsen Guizani4

1 Department of Electronics and Communication Engineering, Mepco Schlenk
Engineering College, Sivakasi, Tamil Nadu, India.
<EMAIL_ADDRESS>2Department of Computer and Information Science,
Fordham University, Bronx, NY, USA.
<EMAIL_ADDRESS>3Information and Computing Technologies (ICT) Division,
College of Science and Engineering (CSE), Hamad Bin Khalifa University, Doha,
Qatar.
{kahmad<EMAIL_ADDRESS>4Computer Science and Engineering Department,
Qatar University, Doha, Qtar.
<EMAIL_ADDRESS>
###### Abstract

The increasing need for economic, safe, and sustainable smart manufacturing
combined with novel technological enablers, has paved the way for Artificial
Intelligence (AI) and Big Data in support of smart manufacturing. This implies
a substantial integration of AI, Industrial Internet of Things (IIoT),
Robotics, Big data, Blockchain, 5G communications, in support of smart
manufacturing and the dynamical processes in modern industries. In this paper,
we provide a comprehensive overview of different aspects of AI and Big Data in
Industry 4.0 with a particular focus on key applications, techniques, the
concepts involved, key enabling technologies, challenges, and research
perspective towards deployment of Industry 5.0. In detail, we highlight and
analyze how the duo of AI and Big Data is helping in different applications of
Industry 4.0. We also highlight key challenges in a successful deployment of
AI and Big Data methods in smart industries with a particular emphasis on
data-related issues, such as availability, bias, auditing, management,
interpretability, communication, and different adversarial attacks and
security issues. In a nutshell, we have explored the significance of AI and
Big data towards Industry 4.0 applications through panoramic reviews and
discussions. We believe, this work will provide a baseline for future research
in the domain.

###### Index Terms:

Industry 4.0, Artificial Intelligence, Big Data, Data Issues, Internet of
Things, 5G Networks, Security.

##  I Introduction

Intelligent manufacturing and smart machines in modern industries are being
evolved with the support of Artificial Intelligence (AI), Machine Learning
(ML), and Big Data approaches. Even though sustainable utilization of these
modern trends is a long way off, the Industry 4.0 revolution with the support
of the Industrial Internet of Things (IIoT) and other supporting technologies
are succeeding in smart factories providing high productivity with intelligent
manufacturing. Manufacturing in smart industries involves data-driven decision
making for improving the continuous production process and also involves
modern robots for the sustainable processing of complex tasks. Driven by the
trends in Industry 4.0, smart factories need to be enabled with high-speed
connectivity between machines, secure devices, and machines with smarter
solutions, to predict machine and product failures and make recommendations to
save cost and time. Imparting AI technology in Industry 4.0 enhances
manufacturing sectors to form a cluster of modern industries with sustainable
vibrant ecosystems.

Figure 1: Timeline of industrial revolution. The red dots represent the
revolution of Industry while the green dots represent different key
developments in Industry 4.0.

We are currently at the fourth industrial revolution (see Figure 1), which is
also known as the smart industry or Industry 4.0. This revolution started in
Germany, which emphasizes the fact that every company needs certain healthy
strategies to start with the smart industry. To this aim, the smart industry
network consortium has developed a framework that gives companies guidance to
determine where they stand, and where there are opportunities to implement
smart industries.

The fourth industrial revolution is about to boost industrial productivity by
at least 30% within a couple of years of full-fledged implementation.
Industries have started using AI to reduce machine failure, improve quality
control, increase productivity, and lower the costs of the products
substantially[1]. AI is equally useful for manufacturers and consumers. On one
side, it makes the customers to be turned out as subscribers for new products
and services. On the other side, it resulted in new opportunities for
manufacturers, such as better customer service, easy maintenance, better
tracking in logistics, and much more sophistication. As a result, industries
are enabled to have smart customer care services, smart dealerships, and smart
experience centers for connecting customers. Moreover, immediate insights into
the manufacturing sector and other services can be easily monitored and
controlled by leveraging AI in industries. AI-based automation in industries
also ensures quality services with the support of advanced robotics and 3D
printing technologies. In short, there are several domains where AI plays a
crucial role in Industry 4.0 as detailed in Section III-A.

Moreover, in modern digital factories all systems, processes, machines, and
products are digitally connected. This digitization of the industries results
in a large collection of data covering different aspects of industries, which
is a highly precious material for smart industries. The expansion and
development of Big Data infrastructures in industries can generate a large
amount of operational information that can no longer be interpreted by humans.
There is already a focus on moving towards data analytic including the
deployment of algorithms able to make autonomous and accurate decisions. In
fact, the factory of the future is expected to consist of intelligent self-
learning robots where the cyber-physical systems can communicate with each
other. Machines themselves can request their demands, raw materials, and
request services whenever needed.

Despite the several advantages, digitization of the industries also brings
several challenges, such as the availability, accessibility, and bias of data,
data storage, management, data auditing, high-speed connectivity, security,
and protection challenges. For instance, AI models in an industrial
environment are at the risk of several security threats, where an adversarial
attack, for example, launched by an attacker may disturb the predictive
capabilities of the model. In addition, to develop stakeholders’ trust in AI
and Big Data techniques, the AI decisions should be interpretable.

Moreover, we believe there is a deep connection between Big data and AI, and
the duo can help industries to develop cognitive abilities. To fully exploit
the potential of the data generated in smart industries, Big Data and AI
should be deployed in an integrated manner. In this paper, we provide a
detailed overview of opportunities and challenges in deploying AI and Big data
in industries and also analyze the connection between the two technologies.

###  I-A Scope of the Survey

The main focus of the paper is on the role played by a duo of technologies
namely AI and Big Data in industry 4.0. The paper pays particular attention to
applications, techniques, and associated data-related, communication, and
security challenges, and opportunities the duo brings in Industry 4.0. In
detail, we provide a detailed overview of a wide range of AI and big data
techniques and explore the Industry 4.0 applications that have benefited from
AI and big data.

The paper also identifies and discusses key technological, data-related, and
security issues and challenges associated with a successful deployment of AI
and Big Data in Industry 4.0. The paper also advises on the current
limitations, pitfalls, and future directions of research in Industry 4.0.

###  I-B Related Surveys

Figure 2: A Roadmap of the paper.

To the best of our knowledge, there is no prior work describing the junction
of Industry 4.0 and the duo of AI and Big Data with particular attention to AI
and Big Data techniques, applications, and associated data-related,
technological and security challenges. However, few works have presented some
common concepts, such as IIoT, Robotics, Big data, security, and other
technologies used in Industry 4.0. For instance, Zhong et al[2] reviewed
several strategic plans for intelligent manufacturing using AI. Some of the
AI-based recent developments in intelligent manufacturing are also addressed
by Preuveneers et al.[3]. On the other hand, the authors in[4], reviewed
several ML and IIoT-based solutions for smart industries. Table I provides a
summary of related works in terms of applications and challenges covered in
each of them.

However, common linkage of AI, smart manufacturing, IIoT, Robotics, Big data,
and more importantly the data, security and communication challenges hindering
a successful deployment of the duo of AI and Big Data technologies in Industry
4.0 are missing in the literature. This paper gives a particular attention to
the linkage of these technologies and tries to fill up the potential research
gap through the compilation of recent literature.

TABLE I: Summary of existing surveys related to artificial intelligence and Industry 4.0. Refs. (Author) |  Year |  AI 4.0
Review |  Smart
Manufa-cturing |  IIoT
Review |  Big Data
Analytics |  Security & Privacy |  Explain-ability |  Adversarial ML |  Datasets |  Big Data Issues |  Overview
---|---|---|---|---|---|---|---|---|---|---|---
Aceto et al.[5] |  2019 |  ✗ |  ✗ |  |  |  |  ✗ |  ✗ |  |  Distributed data management |  Analysis on the integration of key technology enablers for Industry 4.0
Lu et al.[6] |  2017 |  ✗ |  ✗ |  |  ✗ |  |  ✗ |  ✗ |  ✗ |  Interoperability of industrial data |  Examines the critical issues in interoperability and proposed a conceptual framework for Industry 4.0
Zhong et al.[2] |  2017 |  |  |  |  |  |  ✗ |  ✗ |  |  Collection of real-time industrial data |  Strategic plans for intelligent manufacturing are highlighted and analyzed
Oztemel et al.[7] |  2020 |  ✗ |  |  ✗ |  |  ✗ |  ✗ |  ✗ |  |  Data assessment methodologies |  With six design principles the conceptual framework of Industry 4.0 is analyzed
Xu et al.[8] |  2019 |  ✗ |  ✗ |  ✗ |  |  |  ✗ |  ✗ |  |  CPS data management |  Surveys about research direction on complete autonomous operations in Industry 4.0
Kamble et al.[9] |  2018 |  ✗ |  |  |  ✗ |  ✗ |  ✗ |  ✗ |  |  Guaranteed sustainability of data |  Industry 4.0 framework is proposed for sustainable smart manufacturing
Pereira et al.[10] |  2017 |  ✗ |  |  |  ✗ |  |  ✗ |  ✗ |  |  CPS data management |  Drives to develop new business models for Industry 4.0 considering various technological implications
Mittal et al.[11] |  2018 |  ✗ |  |  ✗ |  ✗ |  |  ✗ |  ✗ |  ✗ |  Realistic maturity model |  Impact and contribution of smart manufacturing in medium sized enterprises are summarized with smart manufacturing models
Alladi et al.[12] |  2019 |  ✗ |  ✗ |  |  ✗ |  |  ✗ |  ✗ |  ✗ |  Security threats on industrial data |  Challenges in blockchain implementation for Industry 4.0 in conjugation with IIoT are addressed with the application domains
Kerin et al.[13] |  2019 |  ✗ |  |  |  ✗ |  |  ✗ |  ✗ |  |  Value creating based on industrial data |  Key observations from 29 research topics related to Industry 4.0 are summarized
Osterrieder et al.[14] |  2020 |  ✗ |  |  |  ✗ |  ✗ |  |  ✗ |  |  Low generalizability of data |  research model for smart manufacturing is put forward for future research in this domain
Davies et al.[15] |  2017 |  ✗ |  |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  |  Lean environmental operations |  Importance of socio-technical requirements for lean manufacturing are reviewed
Khan et al.[16] |  2016 |  ✗ |  |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  |  Production level management |  New technology and tools are introduced to support the growth of Industry 4.0
Trappey et al.[17] |  2017 |  ✗ |  |  |  ✗ |  ✗ |  ✗ |  ✗ |  |  Data based product life cycle management |  Impact of IoT standards for enhancing the productivity in smart manufacturing are reviewed with case studies
Yli et al.[18] |  2019 |  ✗ |  |  |  ✗ |  |  ✗ |  ✗ |  |  Matured data sharing architectures |  Communication and networking standards for agile manufacturing are summarized
Sanchez et al.[19] |  2020 |  ✗ |  |  |  ✗ |  ✗ |  ✗ |  ✗ |  |  Data integration issues |  Collaboration, organization and system integration features for Industry 4.0 are discussed
Preuveneers et al.[3] |  2017 |  |  |  ✗ |  |  |  ✗ |  ✗ |  |  Data protection safeguards |  Recent developments and challenges in intelligent manufacturing are addressed
Kim et al.[20] |  2017 |  ✗ |  ✗ |  ✗ |  |  |  ✗ |  ✗ |  |  CPS data management |  Imparting data security in CPS systems for Industries are reviewed
Sony et al.[21] |  2020 |  ✗ |  |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  |  Guranteed sustainability of data |  Critical factors are addressed for sustainable implementation of Industry 4.0
Li et al.[22] |  2017 |  ✗ |  |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  Qualtiy of data among industrial networks |  Design challenges in wireless networking for smart industries are discussed
Souza et al.[23] |  2020 |  ✗ |  ✗ |  |  ✗ |  |  ✗ |  ✗ |  |  Data reliability issues |  From the context of Industry 4.0, how strategic decisions affect the reliability of products are summarized
Leng et al.[24] |  2020 |  ✗ |  |  ✗ |  ✗ |  |  ✗ |  ✗ |  ✗ |  Guaranteed sustainability of data |  Successful empowerment of blockchain technology amidst the challenges in industries are addressed
Nunez et al.[25] |  2020 |  ✗ |  |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  |  Lean supply chain data management |  Impact of digital technology supporting the policymakers for successful implementation of modern trends in industries are reviewed
Calabres et al.[26] |  2020 |  ✗ |  |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  |  Industrial process monitoring |  Evolution and revolution aspects of Industry 4.0 are addressed for foreseeing the business prospects
Angelopoulos et al.[4] |  2020 |  |  ✗ |  |  ✗ |  |  ✗ |  |  |  CPS data management |  ML based closed loop operation of human and machine are analyzed for using IIoT
Nguyen et al.[27] |  2020 |  ✗ |  ✗ |  ✗ |  |  |  ✗ |  ✗ |  |  Data privacy implications |  Handling of Big data effectively in oil & gas industries are addressed with appropriate security measures
ur Rehman et al.[28] |  2019 |  ✗ |  |  |  |  ✗ |  ✗ |  ✗ |  |  Imparting intelligence on IIoT data |  Case studies on IIoT and usage of big data analytics frameworks were explored.
Kumar et al.[29] |  2020 |  ✗ |  ✗ |  ✗ |  ✗ |  |  ✗ |  |  |  Data poisoning issues |  Security enforcement using adversarial ML in smart industries are enumerated.
Guidotti et al.[30] |  2018 |  ✗ |  ✗ |  ✗ |  |  ✗ |  |  ✗ |  |  Trust issues in data |  Provides explainable methods that could be used across multiple industries
Our Survey |  2021 |  |  |  |  |  |  |  |  |  Industrial data related issues are addressed |  Focuses the integration of AI with Smart manufacturing, IIoT, Big data, Security and regulatory standards

###  I-C Contributions

This paper focuses on a duo of modern technologies (AI and Big Data) playing
an important role in industry 4.0. The paper aims to explore the potential
applications, opportunities, and challenges associated with the deployment of
the two technologies in smart industries.

The contributions of this survey can be summarized as follows:

  * •

We provide a detailed overview of the existing literature on the use of AI and
Big data in industry 4.0 with a particular focus on enabling technologies,
techniques, applications, and associated challenges and opportunities.

  * •

We pay particular attention to different types of challenges associated with
AI and Big data in smart industries including data-related challenges (e.g.,
the availability, accessibility, storage and management, auditing, and
transmission issues), security & privacy, interpretation (explainability), and
adversarial attacks on AI models.

  * •

Being a key component of industry 4.0, we also provide an overview of IIoTs
and analyze how insights are extracted from data generated in IIoTs through AI
and Big data techniques.

  * •

We also discuss and try to explore the connection between the duo of
technologies, and how they are reshaping the industries when integrated.

  * •

We then identify the limitations, pitfalls, and open research challenges in
the domain.

  * •

We also provide an overview of different types of datasets that can support
future research in the domain.

The roadmap of the paper is depicted in Figure 2. Precisely, Section II
discusses the key components and enabling technologies of industry 4.0 and
provides a detailed description of how IIoTs are helping in industries.
Section III provides an overview of the literature on AI in industry 4.0 with
a particular focus on applications and techniques. Section IV focuses on Big
Data for industry 4.0. Section V highlights the key challenges in the
deployment of AI and Big Data techniques in industry 4.0. In Section VI, we
provide key insights and lessons learned from the literature and future
research directions. Finally, Section VII concludes the paper. All the related
acronyms used in this article are listed in Table II.

TABLE II: A Summary of acronyms used in this article. Acronym |  Description
---|---
AI |  Artificial Intelligence
AMCL |  Adaptive Monte-Carlo Localization
ANN |  Artificial Neural Networks
AR |  Augmented Reality
CBRS |  Citizens Broadband Radio Service
C-RAN |  Centralized Radio Access Network
CV |  Computer Vision
DoS |  Denial of Service
DDoS |  Distributed Denial of Service
DIKW |  Data–Information–Knowledge–Wisdom
FiWi |  Fiber-Wireless
GAN |  Generative Adversarial Network
GB |  Gradient Boosting
HMI |  Human Machine Interaction
HTC |  Human Type Communication
IAI |  Industrial Artificial Intelligence
ICA |  Independent Component Analysis
ICT |  Information Communication Technology
IIoT |  Industrial Internet of Things
IMT |  International Mobile Telecommunications
IT |  Information Technology
KNN |  K-Nearest Neighbor
LTE |  Long-Term Evolution
ML |  Machine Learning
DL |  Deep Learning
MLP |  Multi-layer Perceptions
MPC |  Model Predictive Controller
NFV |  Network Function Virtualization
NLP |  Natural Language Processing
PdM |  Predictive Maintenance
PMML |  Predictive Model Markup Language
PCB |  Printed Circuit Board
PSO |  Particle Swarm Optimization
QoS |  Quality of Service
RF |  Random Forest
RFID |  Radio Frequency Identification
SCADA |  Supervisory Control and Data Acquisition
SDN |  Software-Defined Networking
SP |  Service Providers
SVM |  Support Vector Machine
WSN |  Wireless Sensor Network
XAI |  Explainable Artificial Intelligence

##  II Background and Components of Industry 4.0

In this section, we begin with a brief introduction to Industry 4.0,
highlighting the basic components and principles along with the key advantages
that lead to their success. As shown in Figure 3, smart machines and devices
in the industry 4.0 ecosystem fall under five layers of operations, namely (i)
device layer, (ii) edge layer, (iii) cyber layer, (iv) data analytics layer,
and (v) application layer. The device layer includes the machines, robots,
PLC, controllers, and smart wearable devices employed for controlling the
industrial devices. The edge layer stores the data acquired from the
industrial machines through the physical layer communication protocols, such
as ZigBee, wifi, and Bluetooth. The cyber layer manages the supply chain and
handles information from web services. It also engages the networking of
machines with trusted services and solutions. The data analytics layer runs
certain ML algorithms and performs cloud analytics on the IIoT data.

Figure 3: Layers of operations in smart industries with Industry 4.0
framework.

In order to fully exploit the benefits of the smart industries by implementing
the industry 4.0 framework, these layers especially the application layer can
be enabled with several components, such as a dashboard, user visualization
resources, augmented reality, and chat-bots. The following are the vital
components of the Industry 4.0 framework:

  * •

Sensors And Connected Devices: Industrial components with sensors, hardware,
software, and other integrated services are designed to enable the users to
enjoy convenient, sustainable, and secure means of operations.

  * •

Big Data: Smart machines connected in industries are capable of generating a
huge amount of data. In smart industries, data is collected from several
sources, such as smart machines, different plants, and the products
manufactured in the plants. Industrial Big Data could be used separately and
can be linked to other platforms, such as mobile/edge devices, cloud services,
and on-premise systems.

  * •

The Cloud: Cloud is an integral component of Industry 4.0, and connected
industries through cloud services are becoming a key part of the connected
world with the support of Industry 4.0. Every salient feature of smart
industries can be made available to a larger audience by leveraging the
orchestration capabilities of cloud services.

  * •

Augmented Reality: With digitization being the vital driving force for
technological trends and new developments, networking of machines and
gathering information from them including basic and status data are of high
importance. Augmented reality in smart industries utilizes this data and
databases with animations and 3D models on every tool the workspace offers
virtually, allowing the toolmakers to facilitate processes and reduce the
error rate significantly.

  * •

AI: Industrial applications by harnessing the power of AI reduce the machine
failure rate, improve quality control, reduce costs involved, and also
improves productivity. AI also creates new opportunities for the manufactures
in terms of improved customer services, maintenance, marketing, and logistics.

  * •

Digital Twin: digital twin represents a virtual model monitoring an object or
process throughout its life-cycle in smart industries. For enhancing the
business outcomes of smart industries, digital twins could play a crucial
role. They are living models, which acquire environmental as well as
operational data and constantly update themselves by learning from the data.
It can also predict failures, unplanned outages, forecast opportunities, and
also helps to optimize or provide mitigation events.

  * •

Cybersecurity: Privacy, trustworthiness, and other allied security issues of
the industrial data are interlinked entities when considering cyber-physical
industrial systems. They act as a protective shield for the smart machines
connected to the internet from cyberattacks. Overall harm to the machines,
hardware, and software components can be eliminated by deploying robust
cybersecurity systems in smart industries.

  * •

Additive Manufacturing and Digital Scanning: As the end customer preferences
are changing day by day, instead of employing skilled laborers for each taste
of end-users, additive manufacturing provides a lightweight solution. It
provides durable and proven solutions based on the demands of the customers by
replacing metals with 3D products developed using stronger plastic components.
3D printing solutions provide developers to iterate and come up with ideas for
much faster-proven solutions in additive manufacturing.

###  II-A Industrial IoT for Industry 4.0

The automation of industrial systems can be accomplished through smart devices
that are capable of sensing the industrial environment, collecting valuable
information, processing the data, and establishing effective communication
served through IIoT. In[31], IIoT is envisioned as the new domain of IoT
specifically used for industrial automation and also highlighted as one of the
enabling technologies of Industry 4.0. The focus of IIoT is connecting
devices/machines in smart industries to the internet, to each other, and to
the workers. A typical IIoT solution for smart industries includes
devices/machines, internet connectivity, communication setup, data, cloud and
edge deployments, data analytics, and intelligence processing of the data to
take appropriate actions. There are several applications/use-cases of IIoTs in
Industry 4.0.

For instance, real-time status monitoring in production lines of manufacturing
processes is one of the primary requirements for making appropriate decisions
on the quantity and quality of products to be marketed. Such analyses and
decisions are made based on heterogeneous IIoT data of larger volume from
production lines with the support of other technologies, such as wireless
sensor networks and radio frequency identification (RFID) technology[32]. Such
applications/use-cases of IIoTs demonstrate the effectiveness of IIoT-based
solutions for monitoring and analysis of different tasks in the production
line. Similarly, one of the possible mechanisms of better task scheduling for
delay insensitive applications can be achieved using collaborative scheduling
between multiple clusters of tasks[33]. This mechanism can be derived for IIoT
applications with better task scheduling strategy implementation. There are
other several interesting applications of IIoTs in Industry 4.0. Figure 4
summarizes some key applications of IIoTs in Industry 4.0.

Figure 4: Major Applications of IIoT in Industry 4.0 framework.

The integration of Big Data with the IIoT technology is indispensable for
processing a large volume of data. However, they are also prone to challenges
due to the storage, computation, and processing of large amounts of data [28].
A detailed description of different challenges associated with the data
collected through IIoT is provided in Section V.

###  II-B 5G and Wireless Communication for Industry 4.0

5G communication standard not only enables humans to communicate and download
data from the internet faster but also allows devices, such as sensors and
other machines in an IoT environment [34]. The implementation of 5G services
for Industry 4.0 results in highly reliable smart machines, with reduced
latency, improves QoS, easy deployment, and enhanced security as depicted in
Figure 5.

Figure 5: Impact of 5G services in Industry 4.0 applications.

In Smart industries, manufacturing has evolved from simple connections for
communication between industrial machines. They require high-speed
communication to collect and analyze data for driving dynamic intelligent
actions. Quicker analysis of industrial data and accurate decision making
based on the data are made feasible when the 5G communication systems are
being paired with AI and Big Data. Smart factories use Big Data from
interconnected devices driven by AI and 5G communications for establishing
better ecosystems for connected machines in the industries.[35]. The striking
features of 5G overcome the challenges in current communication technologies
and promise to be a key enabler for smart factories[36]. According to the 5G
Alliance for connected industries and automation, 5G communication technology
will uplift the growth of industry 4.0 to the next level. Moreover, though the
mobile technologies and industrial revolutions have happened separately, the
5G factory of the future brings them together to fulfill industrial
requirements that cannot be fulfilled by either of them alone. This vision can
be realized by slicing the physical 5G network into isolated logical networks.
In this regard, Barakabitze et al.[37] reviewed and provided solutions for
softwarization and slicing using Network Function Virtualization (NFV) and
Software-Defined Networking (SDN) respectively in 5G networks.

The Fiber-Wireless (Fi-Wi) network access in 5G communication overcomes the
challenges in human type communication (HTC) by providing high bandwidth
through the use of optical networks[38]. Moreover, the traffic reallocation
problems in the networks are also overcome using virtual network embedding
algorithms and Q-Learning for managing the IIoT traffic.

##  III AI for Industry 4.0

Nowadays, many industries have started harnessing the power of AI to a larger
extent. In the focus of addressing the challenges and transforming towards
Industry 4.0, AI technology is used for reducing the chances of machine
failure, improving the product quality control, increasing productivity in
industries, substantial cost reduction of products and thereby increasing the
potential users of the product in the market. AI provisioning is an important
feature, thanks also to the degree of flexibility provided by the learning
frameworks, as it will help the AI paradigm to set itself with a primary role
in the deployment of future smart industries that deploy Industry 4.0
standards.

This section answers the question, where should AI frameworks be deployed in
smart industries. To answer this question, we cover the following key aspects:
(i) AI-based Industry 4.0 use cases (ii) deployment of AI techniques in
Industry 4.0 applications, and (iii) datasets available for training and
evaluation of AI models in smart industries.

In the next subsection, we discuss some key applications of AI in Industry
4.0.

###  III-A Applications of AI in Industry 4.0

Some key applications of AI in industry 4.0 are provided below.

####  III-A1 Predictive Quality and Yield

In smart industries, AI frameworks aid in predicting the quality of the
products throughout their life-cycles, which ultimately improve the yields to
a larger extent. The quality of the products, which is what the customer
demands, can be analyzed using predictive analytics via improved forecasting
methods on existing products. Quality 4.0, which is a quality management
subsystem in industry, for ensuring quality products in Industry 4.0[39]. In
smart industries, with an abundance of quality data collected from different
smart industrial machines, advanced analytics becomes highly feasible to
effectively predict product quality and yield. Quality prediction of products
at different stages of the life-cycle and real-time monitoring helps in
ensuring improved production yield, better quality management, and improved
customer value.

The quality control loop in smart industries involves the iterative process of
predicting products’ yield. The predictive quality analysis helps in
establishing baseline performance measures, monitoring the actual performance
of smart machines, comparing and analyzing the performance with benchmark
standards, and taking different actions accordingly. Lee et al.[39], presented
an extensive survey on predictive quality management by leveraging AI, Big
Data, and smart sensors in Industry 4.0 applications. Implementation of
predictive quality management requires cooperative efforts from all the
stakeholders of smart industries. They are highly helpful in estimating and
figuring out what kind of quality issues are likely to occur when the product
is out in the market.

####  III-A2 Predictive Maintenance

Predictive maintenance in smart industries is highly beneficial for industrial
business executives, equipment maintenance managers, and the workers employed
in modern industries. It is used to predict the probability of occurrence of a
failure in smart machines and maintenance can be carried out in advance.
Industry 4.0 compliance small-medium enterprises involved in a study of
analyzing the value and cost factors encountered in the CNC machines and their
predictive positive impacts show better economic and life cycle improvements
of the products[40].

The literature shows that managing smart industrial machines in run-time with
the support of cyber-physical systems and IIoT plays a crucial role in
Industry 4.0. Fitting the above-mentioned paradigm, a distributed algorithm is
developed in[41] to plan the maintenance of machines in a decentralized manner
with run-time analysis. The presented framework[41] provides intelligence-
based maintenance scheduling for identically parallel multi-component machines
over a job shop-enabled scenario of manufacturing. The intelligent algorithm
in this framework is integrated within a CPS-IIoT paradigm and has a runtime
scale following the number of deployed machines. The performance efficiency of
this solution was demonstrated over conventional centralized heuristics-
namely, Memetic Algorithm and Particle Swarm Optimization.

In another work by O’Donovan et al.[42], real-time performance monitoring of
the machines and their reliability are compared between conventional cloud
computing and fog computing approaches. It is targeted for Industry 4.0
applications with embedded ML and its latency, failure rates are analyzed
under different scenarios. More remarkably, this research[42] concluded that
while reliability and latency in CPS interfaces designed with
conventional/centralized cloud computing and decentralized computing (i.e.,
fog) technologies to enabled real-time intelligent learning solutions for I
4.0, communication failures rate can reach 6.6% under different levels of
communication stress in the cloud interfaces.

Moreover, delay encountered in predictive maintenance due to migration in an
IIoT scenario is effectively addressed in[43], using a cooperative strategy of
information exchange in the developed cloud computing migration mode.
Precisely, to improve resource utilization, the proposed approach leverages a
partitioning mechanism based on a cyclic fine-grained directed graph and uses
the concept of supply-demand similarity to compute the similarity level
between the amount of computing node resources and requirements of task
computing (at the edge computing platform). In[44], proactive maintenance and
accurate tracking of products during the production stage are implemented
using secure industrial blockchain-based services for effective product life
cycle management. Similarly, in[45], condition monitoring and predictive
maintenance are implemented for the wind turbine system and the analysis of
subsystems are performed using cloud services. The migration process towards
future industries needs to adapt with service-oriented operations utilizing
IIoT, cloud computing, and different levels of automated control of machines
in smart industries[46].

####  III-A3 Smart Manufacturing

The integration of operational and information technologies in modern
industries is achieved through IIoT, AI, data analytics, Industry 4.0, and
smart manufacturing processes. Smart manufacturing tasks allow machines to
monitor physical processes during manufacturing and use the data acquired from
IIoT devices in the factories to make predictive, corrective, and adaptive
decisions with improved operational costs. Industry 4.0 defines smart
manufacturing as fully integrated, collaborative manufacturing systems that
respond in real-time to meet changing demands and conditions in the factory,
supply chain, and customer requirements.

In the literature, several approaches have been proposed for smart
manufacturing. For instance, agent-based architectures for smart manufacturing
include smart agents and cloud-assisted services providing collaborative
negotiation and appropriate decision making[47]. The agents involved in the
architecture reduce the complexity of the framework, thereby increasing the
robustness and adaptiveness while dealing with multiple product manufacturing
processes. Similarly, Digital twin-driven[48, 49] approach automates the
manufacturing process by defining fixed standards, tools with open
architecture providing flexible reconfiguration capability. The Digital Twin
technology drives a smart manufacturing process with minimum overhead on
reconfiguration and quick optimization based on application scenarios.
Additive manufacturing enables the creation of objects by successive addition
of materials layer by layer. Proper utilization of the technology in the
additive manufacturing process with reduced cost factors will be a better
solution for creating quick cost-effective models in smart manufacturing.

Moreover, with the roots of Industry 4.0 originated from Germany, many
countries have developed certain industrial standards for smart manufacturing
using information communication technology (ICT). Li et al.[50] analyzed the
smart manufacturing architectures of different countries and developed a
reference model for smart manufacturing.

####  III-A4 AI in Robotics for Industries

In modern industries, AI and robotics have emerged as a powerful combination
for automating several tasks. According to Kipper et al.[51], different
prospects of Industry 4.0 and beyond its capabilities are possible with an
emphasis on robotic systems in smart industries. The literature already
depicts a particular emphasis and trend of developing specialized robotics for
different tasks in industries. For instance, in[52], the applicability and
importance of robots in production processes in smart industries are analyzed
and discussed in detail. Robots are also used for ubiquitous manufacturing
processes in smart industries[53].

There are several applications of robotics in Industry 4.0 where AI is
deployed, enabling robots to perform different tasks more efficiently. In the
following, we highlight some of the key applications of Industry 4.0 where AI
is deployed in robotics:

  * •

Assembling and Manufacturing : AI has been proved very effective in enhancing
robots’ performance in assembling and manufacturing tasks. AI coupled with
other advanced enabling technologies, such as vision systems, allows more
flexibility by enabling robots to define, learn, and optimize processes in
complex manufacturing sectors, such as aerospace. In such cases, cognitive
robotic systems manage robot automation through Natural Language Processing
(NLP), improved data handling using AI and ML approaches[54]. For instance, Hu
et al.[54] rely on AI in the implementation of an intelligent production line
with robots in a smart factory using cognitive manufacturing for chip
assembly.

  * •

Packaging: In smart industries, robots are used for packaging products. AI can
also help to further enhance robots’ capabilities in packaging tasks, which
could result in a quicker, accurate, and low-cost solution. For instance, Chen
et al.[55] proposed a Particle Swarm Optimization (PSO) based framework for
time-optimal trajectory planning of robots in an AI-enabled intelligent
packaging.

  * •

Production Task Scheduling by Robots: Traditional assembly lines in industries
have fixed tooling and fixed robots for the production process. Enhancement in
the production process in modern industries can be carried out with the usage
of re-configurable tools and mobile robots programmed with inherent task
scheduling mechanisms and intelligent control units with the help of AI.
Parente et al.[56] survey the challenges in Industry 4.0 towards holistic
scheduling of tasks in the industries and the corresponding solutions for
human-robot collaboration and appropriate decision-making tasks. In
literature, several interesting AI-based solutions have been proposed for an
intelligent task scheduling mechanism by robots. For instance, in[57], based
on the demand for resources in the manufacturing process, using a slicing
paradigm and AI, resources are allotted for robotic systems in Industry 4.0
environment. In[58], simultaneous scheduling of tasks in the production sector
for mobile robotic systems is carried out in a job shop environment proving
flexible transfer of materials, providing an optimal benchmark performance.
Customer Service: AI-enabled robots are also used to entertain customers’
queries about products or services, such as retail, in smart industries.
Thanks to other enabling technologies, such as NLP, Computer Vision (CV), AI-
based robots can interact with customers more humanly.

####  III-A5 Generative Design

Thanks to AI and other relevant technologies, such as vision and VR, the
generative design delivers designs of products that we might not even have
imagined or visualized. It helps to provide numerous options during the design
phase in smart industries for a single drawing. All the designs developed
using AI techniques are capable of meeting the specific goals set by the
designers. From those pools of options, we can then select a design meeting
the success criteria and expectations of the designers. The generative design
technology harnesses massive computation power, and helps in creating forms
with precise amounts of materials, only when required by ensuring maximum
performance. In Generative design, the prototypes can be scanned and equipped
with sensors providing real-time performance, which can be looped back into
the design process.

There are already some efforts in this direction. For instance, Yuan et
al.[59] explored the implications of Generative Adversarial Networks (GANs)
based DL models in attribute-aware generative design for generating design
with desired visual attributes. The authors also analyzed how GANs can help
industries to meet the growing diversity in customers’ needs.

####  III-A6 Market Adaption/Supply Chain

Valuable supply chain integration for business planning, design, and
implementation in modern industries largely depends on Industrial AI[60]. Most
of the supply chain paradigms from the perspective of Industry 4.0 are defined
under one of the categories among Green, Resilience, Agile, and Lean[61]. In
this regard, several efforts have been made. For instance, productive increase
in small-scale industries also focused on urban production[62], with a
sustainable production process by implementing Industry 4.0 in their
industrial supply chain. Through IIoT, automation, and predictive analytics,
the maturity level of Industry 4.0 is analyzed through interviews and group
discussion among focus groups[63]. Besides, accounting towards sustainability
levels in the organization are reported along with the future maturity
prospects of Industry 4.0.

Moreover, Inventory management is analyzed from different perspectives of the
customer to business models, combined to form an overall framework for modern
industries[64]. Recommendation system for rescheduling in IIoT edge devices,
particularly in low power wireless personal area networks enhances the
performance in the network[65]. Here, the scheduling is performed using
interference graphs and improved performance is observed compared to the
rescheduling performed randomly. Scheduling jobs in the production line needs
a collaborative approach and management of a cluster of resources efficiently.
Xia et al.[33] developed a collaborative task scheduling strategy for delay
insensitive applications.

###  III-B AI Techniques in Industry 4.0

As discussed earlier, AI has been proven very effective in different
applications of Industry 4.0. It not only enables predictive quality to ensure
quality products but also helps in the optimization of different industrial
operations. To fully explore the potential of AI/ML in industries, several
interesting solutions including supervised, unsupervised, semi-supervised, and
reinforcement learning techniques, have been proposed. In this section, we
provide a detailed overview of AI and ML techniques deployed in Industry 4.0.
For the sake of simplicity, we will divide it into two categories, namely (i)
statistical/traditional methods, and (ii) DL techniques.

####  III-B1 Statistical/Traditional AI/ML

A vast majority of the efforts for AI-based solutions in Industry 4.0 rely on
statistical/traditional techniques[66]. For instance, Penumuru et al.[67] rely
on Computer Vision (CV) and traditional ML methods to embed industrial
machines and robots with capabilities of automatically differentiating in four
different types of materials including aluminum, copper, medium density
fiberboard, and mild steel. To this aim, several classification algorithms
including Support vector machine (SVMs), Decision trees, Random forests (RF),
Logistic regression, and k-Nearest Neighbor (KNN) are trained on color
features extracted from the materials. The experimental results showcase the
validity of the algorithms in terms of their accuracy of classification.
Logistic Regression and RF classifiers are also employed in[68] for automatic
monitoring and analyzing the performance of an HVAC air conditioning system.
The statistical ML techniques are also employed in[69], where several
classification algorithms, such as RF, SVMS, and Logistic Regressions, are
used for the estimation of machine specification data (i.e., machine geometry
and design).

Machines involved in continuous operations in industries are mostly prone to
some unexpected faults, and the subsequent resuming of tasks after carrying
out necessary countermeasures is often time-consuming. Some efforts on fault
detection in smart industries also rely on statistical ML techniques. For
instance, Angelopoulos et al.[1] performed a survey on the detection of faults
by appropriate prediction using ML approaches and providing equivalent
countermeasures in industry 4.0 applications. Moreover, the authors also
addressed the ML-based interactions in the manufacturing process that occurs
between machines and human operators by providing the platform for humans to
be an in-the-loop technique with reduced errors.

Down failure of machines intimation and its availability of carrying out
subsequent manufacturing tasks are recommended features of predictive
maintenance in smart industries. Data acquired from IIoT devices engaged in
event-based monitoring of industrial machines are utilized for training ML
models. Literature also suggests a tendency towards the
statistical/traditional ML techniques, such as SVMs, RF, gradient boosting
(GB), and extreme gradient boosting approaches, for predicting the failure
probabilities of the machine[70]. Similarly, enhancement in production
planning and control tasks is also enabled using ML techniques with modern
tools, data sources, and approaches. For instance, Cadavid et al.[71] provide
a detailed overview of state-of-the-art techniques involved in IIoT devices to
collect data and analyze them using ML methods, including traditional ML and
DL algorithms, used for different tasks involved in intelligent manufacturing
processes. Bajic et al.[72] also evaluated the performance of different ML
algorithms, such as SVM, KNN, and neural networks, in smart manufacturing by
generalizing them for different tasks to provide solutions for enhancing the
decision making challenges existing in industry 4.0 applications.

In smart industries, embedded sensors placed in production lines acquire data
regarding the quality and quantity of tasks performed during manufacturing and
assembling processes. Traditional ML also contributes to task scheduling and
optimization of the scheduling process to improve the efficiency of industrial
machines. The traditional ML techniques are also employed for industrial
prognosis, which deals with the estimation of the lifetime of machines used in
industries due to the aging of machines and extended hours of utilization.
In[73] an overview of different ML techniques along with data fusion
techniques for implementation of prescriptive, predictive, and descriptive
prognostic models for industry 4.0 applications is provided.

Table IV provides a summary of different works relying on
statistical/traditional ML techniques for different applications of Industry
4.0.

TABLE III: Contribution of ML algorithms for Industry 4.0 applications. References |  ML approaches |  Industry 4.0 applications |  Key Inferences
---|---|---|---
Penumuru et al.[67] |  SVM, DT, RF, LR, KNN |  Material classification |  Surface classification of materials Aluminium, Copper, mild steel and fibreboard are done based on the RGB color model.
Dalzochio et al.[66] |  ML algorithms review |  Predictive maintenance |  Using three research questions, the use-cases and implementation aspects of predictive maintenance are carried out.
Angelopoulos et al.[1] |  ML-based human–machine interactions |  Tackling faults |  Fault prediction, detection, and classification with the support of human-in-the-loop techniques.
Odonovan et al.[42] |  Embedded ML approaches |  Cyber-physical interfaces |  Fog computing provides reliable services with secure and consistent support for industrial applications.
Calabrese et al.[70] |  Event-based IIoT and ML approaches |  Predictive maintenance |  Data from the IIoT devices are utilized for effective failure prediction of the smart machines.
Khayyam et al.[74] |  Hybrid ML algorithm |  Big Data modeling |  Over-fitting issues in the industrial Big Data are effectively handled using the hybrid technique.
Cadavid et al.[71] |  ML-PPC |  Production planning and control |  The techniques are used to adapt the system towards changes in the manufacturing sector through modern tools and data sources.
Bajic et al.[72] |  SVM, KNN |  Smart manufacturing |  Enhanced decision making skills are driven in smart manufacturing for generalized tasks.
Diez-olivan et al.[73] |  ML and data fusion |  Industrial prognosis |  Prognostic models are used to predict the status of machines considering its wear and tear conditions.
Mohanta et al.[75] |  ML approaches |  Risk management |  Data and resources are managed effectively by addressing the issues related to uncertainty, volatility, ambiguity and complexity.
Shukla et al.[76] |  ML and NLP |  Aeronautical applications |  CPS works in conjugation with the ML models for better interactions between machines.
Li et al.[77] |  Hybrid metaheuristic |  Production rescheduling |  Tailored delay and frequencies through ML models drives efficient job scheduling.

####  III-B2 Deep Learning

Similar to other domains, DL algorithms have also been widely explored for
different applications of Industry 4.0. The literature shows some interesting
DL-based solutions for different tasks in Industry 4.0. For instance, Miskuf
et al.[78] proposed a DL-based approach to data analytics tasks in Industry
4.0 applications, such as machine failure prediction and recommendation
systems. An open-source framework with H2O ML is used for implementing the DL
model, which is evaluated on a text recognition data set obtained from the UCI
repository. The authors also provide a comparison of the DL method with
traditional ML techniques, such as logistic regression.

In[79], a DL-based framework, relying on a geometric DL architecture, has been
proposed for lean management of cyber-physical networks. In[79], EEG sensors
are configured in conjugation with DL frameworks to assess the behavioral
patterns in lean management. The characterization involves the usage of soft
sensors for the study of the acquired signals for solving the behavioral
patterns. However, this framework is effectively deployed by authorized heads
of smart industries for planning sustainable organizational goals and
providing reliable smart manufacturing systems. DL-based soft sensors are also
used for lean shop floor management by observing the electrical activities of
the human brain using EEG sensors[80]. The analysis is used to provide
effective correlations from the recorded data on the brain’s activity for
assessing the shop floor management and provides better guidance for the
decision-makers of smart industries that implements industry 4.0.

DL techniques have also been employed in other several applications of
Industry 4.0. For instance, Subakti et al.[81] proposed a DL-based framework
for automatic detection of machines and their parts as a key component/module
of their Augmented Reality (AR) based solution for visualization and analysis
of machines and interaction with them in an indoor environment. To this aim,
an existing deep model namely MobileNet[82] has been fine-tuned on machine
images. The selection of MobileNet is motivated by its architecture being
suited for devices with less computational power. Pokhrel et al.[83], on the
other hand, proposed a Deep Q-Network based framework for the implementation
of multi-path communication in an industry 4.0 ecosystem. The authors also
analyzed the challenges rooted due to the convergence of information and
operational technologies with proof of concepts by carefully addressing the
trade-offs in their proposed scheme. In[84], task offloading and multi-channel
access of mobile edge computing operations in industry 4.0 is carried out
using a multi-agent deep reinforcement learning approach. In the developed
scheme, the success rate of the channels is highly improved with a significant
reduction in computation delay as the edge devices involved in the operations
are cooperative.

DL approaches are also effectively employed for the digital transformation of
the construction industry, allowing to carefully and efficiently manage the
complex environments through innovations established among the educational
sectors, public, and government[85]. Table IV provides a summary of some key
works relying on DL methods for different tasks in Industry 4.0.

TABLE IV: Contribution of DL algorithms for Industry 4.0 applications. References |  DL approaches |  Industry 4.0 applications |  Key Inferences
---|---|---|---
Mivskuf et al.[78] |  H2O framework |  Multi-class vs DL |  Data analytics and cloud computing provides foundations for industry 4.0 applications
Villalba et al.[79] |  DL sensor |  Lean management |  EEG sensors are used for assessing the behavioural patterns and it aids in sustainable smart manufacturing systems.
Pokhrel et al.[83] |  Deep Q-Network |  Multipath communication framework |  Information and operational technologies are integrated for implementation of flexible manufacturing in industry 4.0 ecosystem.
Schmidt et al.[80] |  DL sensor |  Shopfloor management |  Brain’s activity are correlated for effective decision making in smart industries
Cao et al.[84] |  Multi-agent DRL |  Mobile edge computing |  Cooperative operation of edge devices significantly reduces the delay and improves the performance of the channels.
Kraus et al.[85] |  DL |  Construction enterprises |  Support for digital transformation through innovations
Villalba et al.[86] |  DNN sensor |  Printing Industry |  Automated quality control with classification accuracy of 98.4%
Elsisi et al.[87] |  YOLOv3 |  Energy management |  Optimizes energy is smart buildings by detecting the number of persons.
Ma et al.[88] |  Multi-source input neural network |  Sea clutter processing |  Sea clutter reflectively prediction through remote sensing provides enhanced accuracy with average prediction error of 1.82 dB.
Francis et al.[89] |  CNN |  Additive manufacturing |  Distortion prediction and geometric inaccuracies in laser-based fabrication process
Demertzis et al.[90] |  Deep Autoencoder |  Anomaly detection |  Smart contracts in Industry 4.0 are secured is association with blockchained network.
Essien et al.[91] |  LSTM, Autoencoders |  Smart Manufacturing |  Data from a metal packaging plant are used for empirical analyses and machine speed prediction.
Leng et al.[92] |  DRL |  PCB manufacturing |  Sustainable and environment friendly decision making on orders are ensured.

###  III-C Datasets for Industry 4.0

We envision that datasets play a crucial role in all categories of learning
frameworks in AI-based industrial applications, where it is one of the vital
ingredients to carry out data analytics. The feasibility of an ML algorithm in
an application is also constrained by the availability of a sufficient amount
of training data. To this aim, in this section, we provide a detailed
description of some of the benchmark datasets available for the training and
evaluation of AI frameworks for Industry 4.0 applications.

####  III-C1 Production datasets

Sustainable and reliable manufacturing processes in the production sectors
mostly demand precision in decision-making through the usage of AI algorithms
trained on appropriate production datasets. Based on the category of
industries, and the products being manufactured these datasets play a
significant role in estimating the prospects of those industries. Honti et
al.[93] collected and used a dataset, namely the I4.0+ dataset, for evaluating
the economic and innovation index with the support of NLP and data mining
principles. In addition, authors in[94], used 200+ use cases of innovation in
the supply chain for customizing the on-demand standard services for
customers. In another work by Romeo et al.[69], using two real-world datasets,
the design support systems in industries are validated using various DL and
traditional ML approaches. Similarly, Elhoone et al.[95] use a parametric
design dataset for additive manufacturing applications using ANN frameworks.
This lays a foundation for carrying out cyber additive manufacturing
processes. In the case of Oil and Gas industry applications, work with massive
seismic datasets was used for evaluating the uncertainties using Type-2 fuzzy
sets.

####  III-C2 Cyber-Physical Systems datasets

The core security-centric operations in Industry 4.0 eco-system could be
strengthened with the AI systems monitoring the functional aspects of the
industry through the aid of CPS. Data accumulated from CPS could be highly
beneficial to train AI frameworks in imparting high-end security solutions.
For instance, CPS datasets are used by Moustafa et al.[96], with the aid of a
mixture-hidden Markov model for addressing the threat issues in industrial
systems. It also ensures an intelligent level of safeguarding the machines
from anomalous activities. Pan et al. [97, 98] proposed and collected a CPS
dataset for training and evaluation of intrusion detection techniques for
power systems. The original dataset is composed of 15 subsets each covering 37
different power system events/scenarios including 8 natural events, 28
attacks, and 1 non-event. Moreover, the dataset supports binary, three class,
and multi-class classification tasks. A similar type of dataset is presented
in [99, 100], where samples of cyber attacks against two different types of
industrial control systems, including a gas pipeline and water storage tank,
are provided. The dataset is intended to help researchers in training and
evaluation of ML algorithms for differentiating in attacks and non-
attacks/normal network behavior. Another dataset from the same authors is
presented in [101], where the authors proposed a dataset for industrial
control system simulation and data logging on intrusion detection. The dataset
provides six different types of features including modbus frame, label, type
of specific attacks, source, destination, and time stamp. In [102], on the
other hand, a dataset is collected for training and evaluation of ML
frameworks for detection of malicious Supervisory Control and Data Acquisition
(SCADA) Communications in gas pipelines. The dataset provides 7 different
types of the data injection attack, namely negative values, burst values, fast
change, slow change, single data injection, value wave, and set point value
injection, each aiming to manipulate the system’s response in a different way.

####  III-C3 Image datasets

The literature also provides several image datasets for training and
evaluation of AI models in different applications of Industry 4.0. For
instance, Penumuru et al. [67] proposed and collected an image dataset to
train and evaluate AI models for the identification and classification of
different types of materials in the context of industry 4.0. The dataset
mainly covers images from four different types of material, namely Aluminium,
Copper, Medium density fibreboard, and Mild steel. In [103], a benchmark image
dataset for industrial tools has been collected. The dataset is composed of
11,000 images from eight different categories of 24 industrial tools, which
are manually analyzed and annotated by experienced workers. All the images are
acquired through a Kinect 2.0 sensor placed at a distance of 1 to 5 meters,
and a resolution $1024X575$ pixels and $512X424$ depth frames. In [104], an
image dataset namely Severstal: Steel Defect Detection has been proposed for
training and evaluation of image-based solution for defect detection in steel.
The dataset provides a total of 18,106 steel images, divided into training and
test sets containing 12,600 and 5,506 images, respectively. All the images are
annotated with either an image with (i) no defects, (ii) a defect of a single
class, or (iii) defects of multiple classes.

Automating the product inspection in smart industries, which is one of the key
applications of Industry 4.0, can be driven through the usage of AI algorithms
trained on an appropriate dataset. Schwebig et al.[105] reported one such
instance for automating the solder joint inspection of Printed Circuit Board
(PCB) assembling. The dataset is composed of a large number of images taken
through optical inspection systems from different manufacturers. Moreover, the
dataset contains both color and gray-scale images. Ntavelis et al.[106], on
the other hand, proposed a GANs-based framework for generating/augmentation of
industrial images utilizing a smaller set of natural images. The synthesized
datasets can then be utilized for training DL algorithms. The method is
evaluated on a dataset of pump parts, which is composed of 3 channel images.
Moreover, the images are acquired under different sources of illumination.

####  III-C4 Text datasets

In the modern world, businesses rely on AI tools to automatically analyze and
extract people’s sentiment towards their products, businesses, or a service
[107, 108]. In smart industries, sentiment analysis can help businesses to
improve the quality of their products by analyzing customers’ responses to
their products. There are already some efforts in this regard. For instance,
in [109], sentiment analysis of customers’ feedback on different products has
been carried out to analyze the drawbacks of the product, and improve the
products’ quality accordingly. To this aim, a dataset composed of customers’
reviews on a product from the e-commerce website Amazon has been used to
evaluate the performance of the proposed sentiment analysis framework. A
similar type of dataset is collected in [110], where sentiment analysis has
been carried out on a large collection of customers’ reviews on Amazone
products. A customers’ reviews dataset,composed of 26,993 positive and 13,203
negative reviews, is also collected in [111]. The positive and negative
reviews are then processed by some NLP techniques to extract and classify
product design attributes. In [112], on the other hand, a Twitter has been
crawled to analyze people’s response to Industry 4.0. To this aim, a dataset
composed of Industry 4.0 related tweets is collected and used for assessing
and classification of sentiments’ polarity towards Industry 4.0 into positive,
negative, and neutral. The analysis was carried out using text mining to
assess the impact of the words related to Industry 4.0.

Besides sentiment analysis, text-based datasets are also used for other
applications/tasks in Industry 4.0. For instance, In [113], a text mining
dataset, composed of Industry 4.0 job advertisements, has been proposed and
collected. To collect the dataset, different online sources/websites of job
advertisements are crawled. One of the main motivations of the dataset is to
collect relevant information on the knowledge and skills required for
industries.

####  III-C5 Survey datasets

In smart industries, surveys are key to extract meaningful insights from the
data collected from the workgroups in smart industries through means of
feedback and surveys. Several such datasets have been used in different
applications of Industry 4.0. For instance, Frank et al.[114] collected a
survey dataset from 92 different industries and assessed the implementation
patterns of smart industries through cluster analysis. The dataset is used for
evaluating the systematic adoption of base supportive technologies for modern
industries. In another work by Medić et al.[115], the authors collected a
European manufacturing survey dataset for assessing the technology for
carrying out strategic decisions through the usage of Fuzzy based analytics.

TABLE V: A summary of some of the benchmark datasets for training and evaluation of AI techniques in Industry 4.0 applications. Dataset categories |  References |  Datasets |  Industry 4.0 applications |  AI framework |  Key Inferences
---|---|---|---|---|---
Production datasets |  Honti et al.[93] |  I4.0+ |  Industry 4.0 readiness measures |  NLP and data mining |  Economic and innovation index are evaluated
Hahn et al.[94] |  200 + company use cases |  Supply chain innovation |  Structured content analysis |  Customized and on demand standard services for customers are suggested.
Romeo et al.[69] |  2 real use datasets |  Design Support System |  Different ML and DL approaches |  Validation of support system are assessed by comparing accuracy, speed and
Elhoone et al.[95] |  Parametric design |  Additive manufacturing |  ANN |  Lays foundation for cyber additive manufacturing by dynamically alloting digital resources.
Villalonga et al.[116] |  On-production |  Data driven reasoning |  Reinforcement learning |  Surface roughness quality is ensured in the milling process.
CPS datasets |  Moustafa et al.[96] |  CPS dataset and UNSW-NB15 |  Intelligent safeguarding of industrial systems |  Mixture-hidden markov model |  The threat intelligence scheme evaluates the anomalous activities in industrial systems.
Pan et al.[97] |  Power system events |  Intrusion detection systems |  Path mining |  Disturbances, control and cyber attacks are classified.
Morris et al.[99] |  Network traffic |  Cyber attacks in SCADA systems |  ML techniques |  Traffic attacks on the SCADA systems are analyzed
Morris et al.[101] |  Industrial control system |  Intrusion Detection System |  ML techniques |  Gas pipeline network data log was trained and tested.
Beaver et al.[102] |  Remote Terminal Unit communications |  Malicious SCADA Communications |  ML techniques |  Normal and data injection attacks are assessed under various scenarios.
Li et al.[117] |  CPS dataset |  Predictive maintenance |  ANN |  Fault diagnosis and prognosis are carried out for systematic maintenance of smart machines.
Image datasets |  Penumuru et al.[67] |  Industrial materials |  Industrial materials |  ML techniques |  For various camera orientations, illumination and focal length of image data, the robustness of the techniques are evaluated.
Luo et al.[103] |  Industrial tools |  Benchmark standards |  Mean averaging |  Provides ground truth images for evaluating industrial systems.
Kaggle [104] |  Steel |  Defect identification |  ML techniques |  Helps to localize and classify surface defects on steel sheets.
Schwebig et al.[105] |  Images of PCBs |  Automatic optical inspection |  CNN |  Automated inspection of solder joints in assembling are assessed without human interference
Ntavelis et al.[106] |  Industrial images |  Augmentation of industrial images |  GAN |  With handful of industrial images, augmentation is carried out using DL.
Ozdaugouglu et al.[118] |  Bibliometric dataset |  Text classification |  Fuzzy based multi-criteria technique |  The computing techniques reveals the mainstream research direction towards Industry 4.0
Ntavelis et al.[106] |  Industrial images |  Augmentation of industrial images |  GAN |  With handful of industrial images, augmentation is carried out using DL.
|  Nguyen et al.[27] |  Massive seismic |  Oil and Gas industry |  Type-2 fuzzy sets |  Evaluates various aspects of using Big Data with their benefits and uncertainties involved.
Text datasets |  Hassan et al.[107] |  Customers’ responses |  Sentimental analysis |  Text mining |  Social media feedback helps to estimate people’s emotions, feelings and opinions.
Ireland et al.[109] |  Customers’ reviews on Amazon products |  Sentimental analysis |  Statistical approach |  Suggests creative and logical design aspects on the products.
Syamala et al.[110] |  Customers’ reviews on Amazon products |  E-commerce |  Decision Tree |  Decision making towards better product recommendation.
Pasmawati et al.[111] |  Product reviews |  Customer online reviews |  ML techniques |  User experience are evaluated for providing better business strategies and product design.
Yilmaz et al.[112] |  Tweets dataset |  Sentimental analysis |  Text mining |  Positive, Negative and Neutral aspects of words related to Industry 4.0 is analyzed.
Pejic et al.[113] |  Job advertisements |  Knowledge requirement for smart industries |  Text mining |  Helps to chose aspiring and potential candidates for Industry 4.0 organizations.
Survey datasets |  Frank et al.[114] |  Survey dataset from 92 companies |  Implementation patterns |  cluster analysis |  Systematic adoption of base supporting technologies are evaluated.
Medić et al.[115] |  European manufacturing survey dataset |  Technology assessment in manufacturing |  Fuzzy analytic hierarchy |  Assists manufactures to provide strategic decision making.

##  IV Big Data Analytics in Industry 4.0

Big Data is one of the ten technologies driving the smart industries[5]. More
recently, several works emphasized the importance of Big Data methods for
extracting meaningful insights on different aspects of smart industries. For
instance, Cui et al.[119] provide a detailed analysis of how Big Data can help
in different applications of smart industries. The authors also highlight the
usage of appropriate hardware, sharing of resources, integration of
subsystems, data prediction, and sustainability as core enablers for Big Data
applications. Similarly, Belhadi et al.[120] analyzed the strategic
implications of Big Data in smart manufacturing processes by showcasing
several use-cases. The Big Data solutions in Industry 4.0 are centralized
providing new answers to new questions as visually depicted in Figure 6 along
with the 8V’s of Big Data. These 8V’s used in literature to define Big Data
include Volume, Variety, Value, Velocity, Veracity, Variability, validity, and
visualization. Moreover, the complexity of the methods is directly
proportional to the level of analytics, where the complexity of the methods
increases as we go for advanced analytics to find newer and detailed answers.

In the next subsections, we discuss the applications and most commonly used
Big Data techniques in Industry 4.0 applications.

Figure 6: Answers to the questions based on the analytics on Industrial Big
data.

###  IV-A Big Data Applications in Industry 4.0

####  IV-A1 Big Data in Smart Manufacturing

Sustainability in smart manufacturing is supported when the smart machines and
robotic systems employed in industrial sectors learn from data. Big Data and
the real-time analytics performed on the data are widely used for ubiquitous
manufacturing[53, 121]. To assess and evaluate Big Data solutions in smart
manufacturing, Nagadi et al.[122] proposed a hybrid simulation tool composed
of several modeling techniques. The tool allows mapping characteristic data of
different machines, modeling tools, and other captured plans into an object-
oriented simulation. In[123], Big Data applications in smart industries are
assessed using a lean six sigma process for identifying the gaps and
opportunities for development in the manufacturing sector and Big Data-related
research for sustainable manufacturing processes.

Early fault detection and diagnosis in smart manufacturing is one of the key
applications relying on Big Data analytics. To this aim, several interesting
solutions have been proposed. For instance, Yu et al.[124] proposed a
distributed PCA-based solution namely the DPCA model for fault detection and
diagnosis in smart manufacturing. The simplicity and ease of implementation
make it a suitable solution for real-time processing, which is one of the key
requirements of Industry 4.0 applications. In[125], Big Data is employed in a
smart factory for candy packing where the semantic data and ontology knowledge
are combined for enabling personalized packaging.

####  IV-A2 Additive manufacturing

Additive manufacturing allows industries to develop low-cost personalized and
on-demand products or complex parts of a machine in a short time, which
results in low energy consumption and fewer waste materials. In additive
manufacturing, cost prediction of a product (i.e., machine or a part of a
machine) is an important factor that has a direct impact on the production and
is a very challenging process. In smart industries, thanks to ML and Big Data
techniques, the cost of different products can be accurately predicted.
However, accurate predictions require a sufficient amount of manufacturing
knowledge/information, which could be obtained from the raw data produced
during the manufacturing and supply chain process.

In literature, several interesting solutions have been proposed for analyzing
different aspects of additive manufacturing. In[126], usage of digital data
model thread for additive manufacturing is presented with the solutions for
data management to meet the requirements of modern industries. In[127], smart
logistics are implemented for Industry 4.0 using a warehouse management system
driven by IoT devices employed for collecting real-time data. Majeed et
al.[128] on the other hand propose a Big Data-based analytical framework to
analyze and optimize the production performance of additive manufacturing
processes. The evaluations of the proposed framework are carried out on real-
life industrial use-cases and are found useful for all stakeholders. Francis
et al.[89] employed a DL-based framework for the analyses of industrial Big
Data acquired through several sensors to deal with the inaccuracy of the
fabricated parts due to distortion. The DL models are used to accurately
predict the distortion in the fabricated parts.

####  IV-A3 Product/machine design

Big Data along with AI and ML techniques are also contributing to
product/machine designs[129]. In modern industries, the designing job is
shifting towards AI and Big Data, and the role of the human designers could be
reduced to directions/supervision only. The majority of the design aspects
will be covered by AI with the help of Big Data techniques to extract key
insights from historical data. For instance, AI algorithms can help the human
designers in finding alternatives, risk assessment, and incorporating sensor-
based data, generating design precursors, and in the optimization of supply-
chain processes[129]. There are already some efforts in this direction. For
instance, Yu et al.[130] propose a data-driven design for a product. The
authors also analyze the connection between data and a product design, and how
Big Data can help in the process.

####  IV-A4 Critical Analysis in Smart Industries

Big Data enables industries to conduct a critical analysis of different
aspects of a business. To this aim, various frameworks have been proposed in
the literature. For instance, Rehman et al.[28] provide a detailed analysis of
various analytical frameworks for Big Data processing with the help of
appropriate use-cases. In[131], a hybrid two-stage model supported by
Artificial Neural Networks (ANN) is developed to explore sustainability in the
performance of a business. Ren et al.[121] also found Big Data analytics very
useful in sustainable smart manufacturing. Belhadi et al.[120] on the other
hand applied Big Data analytics in multiple case studies on manufacturing
during the manufacturing process in industries.

Since the data collected from industrial appliances are highly prone to
different types of attacks as detailed in Section V, Big Data analytics are
also used to guard against different attacks on data, such as privacy and
illegal access, and use to data. For instance, Keshk et al.[132] analyzed the
role of Big Data analytics in ensuring data privacy and security of sensitive
data. To this aim, an independent component analysis (ICA) based framework has
been proposed to transform raw Cyber-physical systems information into a new
shape. The proposed technique is evaluated against several state-of-the-art
methods on real Cyber-physical systems data, where significantly better
results are observed for the proposed method.

####  IV-A5 Advanced Information Management in Industries

Transformation towards smart manufacturing is driven by effective information
management analyzed and learned from industrial data. Dynamic demands in the
industries are learned from the data and analyzed with the aid of an
autonomous model[133]. Management of information from the smart machines is
performed through humans in the loop interactions carried out by learning from
the industrial data and by the adoption of AI in the process[134]. Data
regarding appropriate usage of the machines, resource sharing, sustainability
predictions in industries are key information extracted from the real-time
analytics of Big Data in smart industries[119]. Moreover, the information
gained from machines has transformed knowledge-based systems into smart and
sustainable manufacturing systems and provides a platform for cyber-physical
production processes[135].

####  IV-A6 Risk Analysis using Big Data

Risk management, such as reputational risk management, can enable the
industrial sector to deploy its massive datasets in an efficient and optimized
manner while extracting accurate and precise information. Moreover, real-time
implementations can further improve risk management and render it more precise
and timely in risk capturing, evaluation, and mitigation in addition to the
identification of hidden values from the data via advanced measures of data
quality monitoring. It is essential to note that such data measures can be
useful for non-quantifiable risk typologies. While the complexity and
dimensions of data collected by smart manufactures’ sensors (from the huge
amount of daily inputs in the manufactures’ operating systems) are
dramatically increasing, it presents a great opportunity for AI-based risk
extraction and assessment while processing and extracting values efficiently
and timely form Big Data. As a result, useful information for risk assessment
can be returned in real time[136].

Although the integration of Big Data in risk assessment can notably provide a
vital advantage for real-time data quality monitoring in I 4.0, the real-time
processing of quietly variable amount of collected data demands not only new
mechanisms and tools, but also the broadening of mathematical and statistical
information, and AI innovations, primarily oriented to both quantitative and
qualitative analysis of data. As a consequence, data interpretation and
transformation into a high added-value become notably more efficient which
currently requires elaboration and design of new skills and knowledge for the
transformation of data into advanced industrial strategic resources and
simulations of industrial supply chain[137].

###  IV-B Big Data Methods in Industry 4.0

Streaming Big Data in Industry 4.0 applications could be processed either at
local devices, edge devices, or remote machines. Attributes of industrial Big
Data, such as speed, bandwidth, access mechanisms, and storage aspects, play a
crucial role in the decision-making of choice of computation resources. In
addition, data analysis and visualization of the data help to gain meaningful
insights out of them. Following are the most prominent analytical methods
carried out for industrial Big Data.

####  IV-B1 Descriptive Analytics

Descriptive analytics, which refers to the interpretation and analysis of
historical data of business, allows businesses to better analyze and
understand the changes and their causes over a while. Such analysis plays a
critical role in the decision-making process by identifying the strength and
weaknesses of a business or a particular strategy adopted and practiced over a
while. There are two main data collection techniques, namely (i) data
aggregation, and (ii) data mining, involved in collecting data for descriptive
analysis[138]. Since in the industries data is collected from several sources,
before processing the data for meaningful insights, data is firstly collected,
aggregated, and then analyzed to obtain manageable information. Moreover, the
Majority of the business/financial metrics, such as year-over-year cost/growth
changes, number of customers, etc, are the product of descriptive
analysis[138].

In smart industries, descriptive analytics is carried out regularly to see
what went wrong and where the business excelled. The literature also suggests
the importance of descriptive analytics in Industry 4.0 applications, and
several interesting solutions are proposed to this aim. For instance, Ciano et
al.[139] used the industrial data to carry out descriptive analytics for
improving the lean production techniques and integrating them with Industry
4.0 technologies. To this aim, the authors analyzed in several case studies.
Descriptive analytics also helps to provide smart process planning by
characterizing the use of data and providing analysis on what happened in the
industry[138].

####  IV-B2 Predictive Analytics

Predictive analytics allows industries, businesses, and organizations to be
proactive by predicting future events based on historical data. Future event
detection in an industrial environment is very critical in several
applications of Industry 4.0. Some key applications include predictive
maintenance, product cost, lifetime, and failure prediction. Using statistical
modeling, ML, and other sophisticated predictive analytic tools, industries
could use the past and current data to reliably forecast trends and behaviors
of machines. Moreover, it helps the industrial control group to predict the
inventory requirements, manage shipping schedules and configure store layouts
to maximize the productivity and sales of the products.

The literature shows that predictive analytics has been widely deployed in
Industry 4.0. For instance, Zonta et al.[140] utilize predictive analytics for
predictive maintenance in smart industries based on historical industrial Big
Data. The authors also presented time-based schemes for addressing the
challenges associated with predictive maintenance in the manufacturing
process. In addition, the impact of Big Data is well addressed with a taxonomy
of monitoring tasks in industries by carrying out predictive analytics.
Similarly, in[141], a predictive maintenance management architecture is
modeled by applying system engineering methods on existing maintenance
management systems. In addition, with the support of predictive analytics,
establishing AI-based maintenance management provides a reference
architecture. In[142], on the other hand, a wide range of applications of
predictive analytics is explored in energy and transportation industries along
with open source tools used for deployment. According to Tiwari et al.[143],
high levels of maturity in smart industries could be achieved through
predictive analytics and AI for achieving sustainability in industrial
operations.

####  IV-B3 Prescriptive Analytics

Prescriptive analytics allows industries, businesses, and organizations to
make better decisions based on historical data. It is important to mention
that prescriptive and predictive analysis are closely linked where the former
makes use of the latter in defining a course of action based on predictions
made by predictive analytics methods.

Big Data analytics in modern industries is moving from predictive to
prescriptive analytics[144]. It not only predicts what will happen but also
defines a course of actions that can be taken to benefit from these relations.
Prescriptive analytics in industries could also express the impact of each
alternative, and helps to avoid the problems by choosing alternate solutions.
Prescriptive analytics in industries helps to achieve optimal decisions with
adaptive and supports automation of tasks by driving them to operate in a
time-constrained manner. According to Bousdeki et al.[145] prescriptive
analytics of time-dependent parameters makes the decision process much easier
by providing reliable and realistic outcomes.

Table VI summarizes some key works employing different methods of Big Data for
Industry 4.0 applications.

TABLE VI: Contribution of Big Data approaches for Industry 4.0 applications. Big Data approaches |  References |  Industry 4.0 applications |  Key Inferences
---|---|---|---
Descriptive Analytics |  Lade et al. [146] |  Industrial defects identification |  Linear and nonlinear relationships among parts, machineries, and production lines are realised with root cause of defect.
Vernadat et al. [135] |  Cyber-physical production |  Enables flexible manufacturing processes with modern tools and enhances automation processes.
Araz et al.[138] |  Smart planning of industrial processes |  Descriptions on business metrics in terms of customer count, growth impact, financial states are aggregated and analyzed.
Ciano et al.[139] |  Lean production |  Integration of lean production techniques with Industry 4.0 helps to reduce the cost in the production process.
|  Marzouk et al.[147] |  Building information modeling |  Performance of construction projects are analyzed and improves the communication with better visualizations.
|  Hofmann et al.[148] |  Automotive industry |  Enhanced customer focus, with better efficiency in product development process.
Predictive Analytics |  Dicuonzo et al.[136] |  Risk assessment |  AI-based risk extraction and assessment are carried out and provides real-time assistance.
Zonta et al.[140] |  Predictive maintenance |  Time-based schemes used on the industrial Big Data for monitoring industrial tasks and carrying out predictive maintenance.
Nordal et al.[141] |  Reference architecture for predictive maintenance |  System engineering methods are applied on the architecture developed for the management of industrial machines using predictive maintenance.
Sahal et al.[142] |  Analytics in energy and transportation industries |  Support of open source tools for carrying out analytics ease up the process and provides customized solutions.
Tiwari et al.[143] |  Sustainable industrial operations |  Provides mature framework for ensuring sustainability in production process.
Ngo et al.[149] |  Construction industries |  Capability assessment tool predicts the weakness, strength and capability of materials.
|  Dinis et al.[150] |  Aircraft maintenance |  Bayesian networks are used for forecasting maintenance works based on historical data.
|  Schmidt et al.[151] |  Condition monitoring |  Decision-making process are carried out using the data from machining operations.
Prescriptive Analytics |  Vieira et al.[137] |  Supply chain management |  Provides added-value through transformation of industrial Big Data towards acquiring new skills and strategic resources.
Groger et al.[144] |  Automation of tasks |  Helps to achieve optimal decisions and supports automation with strict time constraints.
Bousdekis et al.[145] |  Reliable decision process |  The time dependent parameters, helps to make the decision process much easier with realistic outcomes.
Lepenioti et al.[152] |  Business performance improvement |  Optimizes decision making in industry realm using prominent methods.

###  IV-C Relationship between AI and Big Data in Industry 4.0

AI and Big Data are among the key modern technologies that have revolutionized
different application domains in the modern world. Both the technologies,
though are operationally different from each other, have formed an amazing
pair providing a diversified set of applications[153]. The main reason behind
the connection of both technologies is the data requirements of data-driven AI
algorithms. The performance and feasibility of AI algorithms in an application
are big-time constrained by the availability of quality data. Thanks to Big
Data, it is possible to structure, integrate and identify useful patterns in
data, which is then used to train AI/ML models providing outstanding
prediction and decision-making capabilities. In the industry 4.0 framework,
all industrial machines and devices in smart industries are connected, and
they interact, communicate and learn from each other. As a result, large
volumes of unstructured and heterogeneous data. Big Data
techniques/technologies help to analyze, process and extract and identify key
patterns in large volumes of data, which is then used to train AI models. To
fully exploit the potential of the data generated in smart industries, Big
Data and AI should be deployed in an integrated manner.

##  V Challenges of AI and Big Data in Industry 4.0

Despite the outstanding performance of AI and Big Data, several challenges are
hindering their way towards a successful deployment in Industry 4.0. In the
following subsections, we highlight some key data-related challenges,
explainability and interpretation issues, and adversarial and other security
attacks on AI in Industry 4.0.

###  V-A Data Related Issues

Data lies at the heart of AI systems. They permanently affect the way the AI
and Big Data methods respond to different applications. Some of the key data-
related challenges to the AI and Big Data methods in Industry 4.0 are
described below.

  * •

Data Availability: In modern industries, super-computing resources and cloud
platforms along with AI models allow us to make sense of raw data in real-
time. However, valid and meaningful insights from AI and Big Data frameworks
used in Industry 4.0 applications require a sufficient amount of quality data.
The industry knows what the customer demands from the voicing gathered from
customers through social media and other public sources. These queries,
complaints, and opinions are useful information that could be fed into AI
models employed in smart industries along with the data available from the
machines and IIoT devices employed. However, gathering various modalities of
data and channelizing them for AI systems is very challenging. For instance,
the collection of such heterogeneous data from different sources/machines in
smart industries requires intensive inter-connectivity with the computational
platform[154].

The literature shows that improving the information assets has not been
generally the priority of a majority of companies. However, better
performances have been observed for data-driven companies compared to
others[155]. In other words, better industrial productivity could be achieved
through quality data and improved AI and Big Data frameworks by enabling
accurate decision-making processes. Auditing on data availability ensures
concrete policies on data sharing among authorized individuals and allows
proper usage of it in industrial applications. Such instances are also carried
out in pharmaceutical industries[156].

  * •

Data Accessibility: Accessibility of data in a prescribed format for AI models
not only helps to make accurate decisions but also helps to forecast the
requirement and prospects of smart industries. However, streaming the data
from the appropriate source, and easy accessibility of the data to the systems
without any hindrance is not straightforward. To solve the issue, several
attempts have been made in the literature. For instance, Gaba et al.[157]
developed a lightweight key exchange mechanism for smooth and secure
industrial data access. It was tested to ensure the trust on the IIoT nodes,
by preventing the nodes from unauthorized attacks. However, the computation of
elliptic cryptographic complexities and its impact on industrial data and
accessibility need to be considered for ensuring the legitimization of the
IIoT nodes in the industrial network of smart devices.

  * •

Data Quality: In learning the right patterns and extracting meaningful
insights from the data, the quality of the gathered data plays a crucial role.
The higher the quality of industrial data acquired from different smart
machines and other industrial sources the better and accurate are the
predictions made by an AI algorithm. Restrictions in access to quality data
force businesses and policymakers in smart industries to create substandard AI
systems. According to Williams et al.[158], data quality management is
mandatory while integrating Industry 4.0 into lean, powering up smart
factories using CPS, IIoT asset management, and enhancing the global supply
chain. Transparency in product quality can be supervised using ML approaches
to detect the outliers in the data for ensuring its quality[159].

  * •

Data Auditing: Data auditing is one of the key processes to be carried out in
data analytics, where data is analyzed for a particular application. In
detail, during the process data collected in an environment for predictive
analysis is analyzed whether the available data is suitable for the
application or not? Moreover, the potential risks associated with the data are
identified[160]. In smart industries, industrial data is collected from
different types of machines, which results in a large volume of unstructured
and heterogeneous data. To fully exploit the potential of the data in
different applications of industry 4.0, data auditing is essential to analyze
and assess the quality of data to be used for training AI/ML models.

The literature already suggests some interesting efforts in this regard. For
instance, Fan et al.[161] proposed a decentralized auditing scheme namely
Dreads for auditing data collected in an industrial environment. The proposed
method ensures three different types of data auditing namely public, dynamic,
and batch auditing, and brings several benefits compared to the traditional
data auditing schemes. Wu et al.[162] addressed a critical infrastructure in
Industry 4.0 while auditing the industrial data. The authors also provide a
detailed analysis of the convergence of edge computing and blockchain
technology for scalable and secure data management frameworks.

  * •

Data Transmission (Platforms, lack of universal standards, etc.,):
Interoperability of data transmission across a diversified range of
communication sources remains highly challenging due to the lack of universal
standards and common communication platforms. Sharing the data among smart
industrial subsystems in meaningful ways through the available
infrastructures, such as Ethernet, fiber, or wireless means for the end
receiver could be able to interpret the transmitted data and gain meaningful
insights from it.

To deal with such issues, Cotet et al.[163] presented a secure, quick, and
efficient means of transferring data from the smart industrial sensors to the
cloud services. Based on the data collected from waste management systems in
industries, it helps to de-clutter the waste from smart factories in a much
more effective way. Silvestri et al.[164], on the other hand, provides a
detailed overview of data transmission schemes for remote maintenance in
Industry 4.0. As remote maintenance becomes a serious concern due to the bloom
of smart industrial machines and augmented reality, the proposed schemes help
in the self-maintenance of smart factories by minimizing the operators’
management tasks.

###  V-B Explainability and Intrepretability

Despite having a slight difference in the concept, explainability and
interpretability are used interchangeably in the literature. Interpretability
represents the ability of an AI model to link a cause/reason with a decision
while explainability on the other hand shows the ability of a model to justify
its decision. Both the concepts aim to make AI decisions understandable for
humans, which ultimately helps in developing stakeholders’ trust in
predictions/decisions made by AI in different applications domains[160]. The
interpretation of AI decisions is generally not desirable in low-risk
applications. For instance, low risk is associated with an error in movie
recommendations. However, interpretations are highly desirable in high-risk
applications, such as predictive policing and AI-based recruitment, etc.,
where critical decisions are made based on insights provided by an AI model.

As detailed in the earlier sections, in Industry 4.0 AI models are used for
several tasks, such as predictive maintenance, and predictive quality and
yield, etc., where the models need to take critical decisions. In such high-
risk applications, interpretability and explainability are highly desirable to
reduce the associated risks and increase users’ trust in the models. The
literature already reports some efforts in this direction. For instance, Rehse
et al.[165] provides a detailed analysis of the challenges, opportunities, and
potential applications of explainable AI in Industry 4.0. Moreover, the
authors also illustrate the potential use of explainable AI in process
prediction through experimentation in the DFKI-Smart-Lego-Factory prototype.
Galanti et al.[166] also proposed an explainable AL solution for process
monitoring in the Industry 4.0 setup. The proposed solution is evaluated on
real industry benchmark datasets. The traditional black-box AI algorithms
could be made explainable either by providing explanations of the provided
results or developing explainable and interpretable AI solutions for Industry
4.0 applications. However, there is a trade-off between accuracy and
explanations, which is a challenging task to keep a balance between
explanations and accuracy of a model[30].

###  V-C Adversarial ML

In 2019, Gartner, a leading industry market research firm, published its first
report on adversarial ML advising that application leaders must anticipate and
prepare to mitigate potential risks of data corruption, model theft, and
adversarial samples[29]. Figure 7 depicts a high-level overview of common
adversarial ML attacks affecting industrial organizations and production
control.

A list of adversarial ML attacks is outlined in[167], from which the top four
attacks with the highest impact from an industrial perspective can be outlined
as follows.

  * •

Poisoning attacks: In these attack, adversaries aim to share manipulated
labels such that the ML model would consider the re-training process to
degrade the performance of the implemented model (assuming that adversaries
either have complete control over the training dataset or can contribute to
the training dataset[168].

  * •

Evasion attacks: Unlike poisoning attacks, evasion attacks may occur after the
model training phase, where the adversary may not know about the necessary
data manipulation to attack the ML model. In such an attack scenario,
adversaries can aim to continuously query the models based on a trial and
error fashion, and as result, they can sense how to elaborate the
corresponding inputs to pass the ML model. As a consequence, such conduct can
lead to resource overhead and impact the system availability and power
consumption (especially when devices of limited energy are deployed)[160].

  * •

Trojan attacks: In these attacks, adversaries aim to adjust the ML model
weights, while maintaining its structure. Although such attacks operate
efficiently on normal samples, a trojan target label can further be predicted
if a trojan trigger is activated for an input sample[169].

  * •

Model extraction/stealing attacks: In these attacks, adversaries intend to
reconstruct or clone the targeted model to compromise the properties and
nature of the training dataset[170]. Unlike previous adversarial attacks,
these attacks do not require any knowledge about the ML model’s architecture
or the training dataset. However, adversaries herein are assumed to have
access along with its submitted queries’ replies[171, 172].

Moreover, a mismatch between Industry 4.0 expectations and existing solutions
remains significant when it comes to adversarial ML. Industrial organizations
greatly rely on the assumption that solutions available in widely-used
platforms (e.g., TensorFlow, PyTorch, Keras, etc.) are inherently robust
against adversarial manipulation attacks that have been battle-examined
against these attacks. Therefore, organizations typically deploy ML as a
service (ML as a Service such as Microsoft Cognitive API), while pushing the
security upstream to Service Providers (SP)[29].

Adversarial ML attacks can lead to severe effects over industrial production
control[173]. Although various solutions feeding on texts, images, and videos
have been proposed for the smart waste and smart agriculture industries, other
smart industrial sectors remain susceptible to a broad range of unintentional
ML threats[160].

Figure 7: Adversarial ML attacks affecting industrial organizations.

###  V-D Security and Privacy in Industry 4.0

(a) Common security threats of IIoT devices.

(b) General security challenges of IIoT environments in Industry 4.0.

Figure 8: A Taxonomic overview of key security challenges in IIoT.

As depicted in Figure 8(b), security in IIoT systems poses several issues
including, but not limited to, the lack of cyber risk mitigation, lack of
endpoint visibility, security updates, protocols vulnerability, the
vulnerability of multiple industrial components when shifting from closed to
open networks[5].

####  V-D1 Security Solutions in IIoT Ecosystem

IIoT ecosystems exploit IoT communication paradigms in various industrial
applications, converging toward interoperability and reliability within
connected machines [174]. The amount of devices (i.e., objects) connected to
the Internet has been dramatically rising, and thus IIoT becomes a dynamic
network of networks. Various challenges include but are not limited to
resiliency, reliability, and security, rendering services in IIoT ecosystems
inaccurate or incorrect. These challenges are critical in a broad range of
IIoT applications of industry [174]. Enabling real-time AI-based solutions for
knowledge extraction can reduce cyber risks IIoT systems and control data
sharing across the industrial application.

Furthermore, as IIoT ecosystems keep growing by diverse usage of at-scale
measurement devices, IIoT-enabled predictive maintenance (PdM) has become
critically necessary in smart manufacturing. Industries have significantly
invested in the PdM to improve both the machine parts and the uptime of
equipment while optimizing maintenance and security costs[175]. However, fault
detection remains a prominent challenge in PdM in IIoT-enabled smart
manufacturing. Reliable monitoring of such faults requires the involvement of
the entire ecosystem from data collection and storage to results delivery (to
target applications), rather than ML being solely deployed in the prediction
phase[124]. Injection procedures can be integrated with data lake structure in
real-time IIoT-based smart manufacturing to enhance fault detection in PdM.

Wireless sensor network (WSN) has also emerged as a substantial component of
the contemporary IIoT ecosystems[176]. Typically, sensor nodes try to sense
phenomena and report findings in their environments to a central base station.
These sensor activities enable the core platform for heterogeneous service-
centric industrial and business applications[36]. Yet, security
vulnerabilities and threats to sensor nodes in IIoT ecosystems remain
considerable. Namely, denial of service (DoS) and distributed denial of
service (DDoS) with a significantly large number of networking packets are
considered sophisticated attacks against sensor nodes in the IIoT
network[177]. Smart detection and prevention mechanisms can be elaborated with
proper ML techniques[178]. To strengthen the threat detection accuracy, it is
recommended to test the framework over real-world IIoT attack scenarios before
deployment[177].

Furthermore, with the increasing number of connected devices, IIoT ecosystems
are expected to have more demands for resilient and reliable communication
infrastructures[176]. Besides being able to learn how to autonomously retrieve
spectrum knowledge on-the-fly, IIoT devices must also utilize this knowledge
to securely adjust wireless parameters synchronously to achieve the optimal
functionality and operation of the IIoT underlying network using ML
mechanisms[178]. Lastly, Industrial cyber-physical and IIoT systems are the
primary enablers of data-driven paradigms and manufacturing intelligence in
Industry 4.0. To attain reliable real-time pervasive operational data streams
and networks, these industry data-driven facilities are better assessed with
ML models, e.g., Predictive Model Markup Language (PMML) encoded ML models in
factory operations to assess security and privacy[179].

####  V-D2 Privacy in IIoT Cloud Services

By leveraging ML and DL techniques, industrial artificial intelligence (IAI)
has mainly been deployed to address a broad range of industrial limitations
and challenges, such as privacy and data sharing, in IIoT-enabled cloud
services in Industry 4.0[180]. Traditional centralized knowledge training is
often inefficient for critical data-driven scenarios, e.g., the case of
healthcare[181].

Federated learning, an ML mechanism that has recently started emerging and
drawn significant attention [182]. It trains the prediction model over
multiple distributed edge entities carrying unexchangeable local samples of
data while allowing sharers to impart a shared model in real-time without the
need for revealing their local data samples. Nevertheless, the shared
parameters among the local participants can be exploited to compromise
applications and cloud services in IIoT platforms (e.g., healthcare data,
decision-making-based industrial robots, and navigation services) [183]. A
federated learning-based privacy-preserving solution can be designed for IAI
to restrain private and sensitive data from leakage and local participants
from collisions[180].

Although service-based architecture and cloud computing services have been
proven to be a vital integration with cyber-physical and IIoT systems, they
are operating based upon information technology perspectives. This raises
concerns related to security, privacy, and real-time performance. Therefore,
centralized cloud computing implementations can be integrated with
decentralized fog computing paradigms to provide privacy-enhanced, real-time,
and dynamic ML applications for Industry 4.0[42].

The integration of blockchain technology and AI has recently received strong
attention for adoption in IIoT applications and services[184]. However,
designing secure and resilient smart contracts for applications remains a
grand challenge because of these contracts’ complexity[185]. Smart contracts
as self-executable and verifiable can enhance the privacy and security of
Industry 4.0 while eliminating the need for a third party implementation[186].
Although such smart contracts yield reforming Industry 4.0, challenges in
security and privacy remain significant[184]. Various privacy vulnerabilities
within the software code may be exploited by an adversary to compromise the
underlying IIoT networks, whereas developing complex smart contract paradigms
cannot guarantee a secure and privacy-enhanced IIoT system[187].

Privacy and security of smart contracts can be enhanced by splitting them into
off-chain and on-chain contracts, such that contracts whose operation requires
high computation cost and data sensitivity are integrated as off-chain and
executed and signed only by the participating entities[188]. Furthermore,
assessing the influential factors in building an efficient IIoT environment is
crucial from the Industry 4.0 perspective. A rule-based decision making
approach can be elaborated to better assess the system influential
factors[189]. As security and connectivity characteristics are key tangible
influential factors and more significant than intangible factors, it is
essential to incorporate proper security schemes with these influential
factors while building an enterprise IIoT system[189].

####  V-D3 Blockchain security solutions

Blockchain is a disruptive technology that provides secure, peer-to-peer
connectivity and eliminates the need for a central authority. Blockchains
provide cheap and faster means of business transactions and eliminate the need
for third-party intervention. Blockchain technology enables records to store
all transactions in a decentralized ledger. Blockchain safeguards smart
industrial machines from cyberattacks and supports effective supply chain
management. Also, Blockchain technology is playing a leading role in the
healthcare sector, maintaining government records and insurance records with
high-end security features.

Edge intelligence IIoT applications empowered with blockchain support are
integrated with cloud services for distributed sharing of industrial resources
with reduced cost[190]. The most relevant roles of blockchain along with the
support of IIoT devices are summarized in Figure 9. By taking a closer look at
the presented figure, it is clear that there is an indisputable impact of
blockchain in supply chain management with the phases of evidence from
intelligent manufacturing, asset management, provision of smart contracts and
imparting trusted customer relationships.

Figure 9: Role of Blockchain with the support of IIoT in Industry 4.0 for
Digital supplychain.

####  V-D4 Supply chain security solutions

Data analytics on IIoT data through the support of ICT enhances the smartness
of IIoT devices providing promising enhancements in smartness and intelligence
of the systems[191]. This framework enables a secured supply chain and
providing real-time analytics by leveraging ICT.

Digitization of the food supply chain in industries is carried out based on
demand for resources with a secured framework integrated with the IIoT
devices[192]. Impact of Industry 4.0 with enhancement in the agriculture
sector is adapted as agriculture 4.0 by Lezoche et al.[193]. The authors
reviewed and analyzed the secured supply chain methods incorporated by
considering several key aspects in the domain of agriculture.

Based on the cascaded review performed by Parente et al.[56], the authors
focused their discussions on the scheduling of resources in Industry 4.0
frameworks. Moreover, the authors highlighted the importance of holistic
scheduling mechanisms and the flexibility imparted with the aid of appropriate
scheduling strategies.

ML and DL models are proposed by Gupta et al.[194] to identify unknown attacks
on Big Data. The developed threat model analyzes various vital parameters such
as reliability, accuracy, latency, and efficiency. This secure data analytics
model favors securing the supply chain from threats.

####  V-D5 Integrated security for maturity models

In augmentation with human intelligence, collaborative cognition has been
adapted and applied to the developed models for appropriate decision making in
industrial applications[195]. The maturity model includes cyber-physical-human
analysis using novel strategies with an enhanced level of trust and an
optimized level of human interactions and automation.

For driving the smart agricultural industry in terms of enhancement in food
security, autonomous pollination is developed for farming[55], by utilizing
robotic systems that act as micro air vehicle pollinators. With the support of
AI and humans in the loop, the automated systems provide a matured model with
enhanced security features.

CPS-based automation for Industry 4.0 along with IIoT devices are utilized for
smart transportation systems that integrate AI, ML, and human-machine
interaction support[196]. Various bus scenarios are considered to test the
modular features of the framework under traffic conditions in a simulation
environment.

Smart-troubleshooting is introduced for collecting the failure status of IIoT
devices connected for the implementation of Industry 4.0[197]. Further, it is
also involved in prognostics and diagnostics of the devices integrated through
the smart infrastructure that are manufactured by different vendors. Also,
product information, faults encountered and other security threats are also
intimated by the smart troubleshooting framework.

Effective decision making with the aid of data acquired from IIoT devices is
carried out using the Maximum Mean De-Entropy technique considering a specific
threshold[198]. Also, they are used in Interpretive Structural Modelling
considering the barriers and challenges faced by the manufacturing industry.

With the application of the Industry 4.0 maturity model[199], maturity levels
of various manufacturing industries are validated. Moreover, the model was
also used to observe the maturity score for self-assessment and real-life
implementation in the Industry 4.0 based supply chain operations.

Maturity levels are assessed from 12 different defense sector-related
manufacturing firms that have implemented Industry 4.0 frameworks by Bibby et
al.[200]. The maturity levels are observed by considering eight different
attributes of smart manufacturing industries.

Policies put forward for implementation of Industry 4.0 are addressed by Sung
et al.[201], by considering the stability, security, and reliability issues in
the machine to machine communication. The authors summarized the detailed
action plans for making innovative changes and providing necessary
sophisticated infrastructure for the initiatives towards successful
implementation of Industry 4.0 solutions.

####  V-D6 Securing communication between smart industrial devices

Security threats such as DoS attacks against IIoT devices (i.e., target a node
of the industrial network) are still prevalent and can cause catastrophic
effects while emergency and crucial operations are carried out. An intelligent
DoS detection framework is proposed by Baig et al.[177], which was
experimented with actual IoT attacking scenarios providing promising accuracy
in the classification of DoS attacks. Apart from the adoption of blockchain
for transport and logistics, there is also the importance of tracking the
mobility of goods through a seamless flow of uninterrupted communication. The
role of 5G communications plays a significant part along with blockchain and
even supports in establishing secure communication across borders[202].

##  VI Insights, Future Research Directions and Open Issues

###  VI-A Future aspects of Industry (i.e., beyond 4.0 )

This section focuses on key innovations and implications in modern smart
industries that go beyond Industry 4.0 towards Industry 5.0. One of the
important key features of adopting Industry 4.0 is the internal cultural
transformation in the industry. This requires strong leadership with a
commitment to change the management process, invest in the necessary
technology enablers and education to successfully adopt smart manufacturing
practices. All these tend to be huge investments from the perspective of
owners and managers, but the significant operational cost improvements pay
them back with huge benefits within a couple of years. With the deployment of
Industry 4.0 practices in an Industry, they can compete with anyone in
acquiring and retaining potential customers with enhanced operational
efficiency.

Smart manufacturing drives the competitiveness of manufacturing enterprises.
Competitiveness is a critical factor, where the manufacturers around the world
seek a real-time understanding of the operational status of every machine that
is producing any part or component ending up into finished manufactured
products.

####  VI-A1 Industrial Innovation and Infrastructure

As trust in the automated decision making by smart industrial machines using
AI is getting a serious concern, the demand for trustworthy machines is
gaining importance. Explainable artificial intelligence (XAI) has emerged as a
transparent mechanism for predicting the requirements in the automated
industry with complete exploitation of Industry 4.0[165].

For context-aware and ubiquitous computing through the data collected from
IIoT devices or sensor nodes, usage of cloud and fog computing helps towards
the sustainable transformation of present smart industries[203].

The impact of IIoT will be significant for critical industrial sectors that
address manufacturing, power, and chemicals as the smart industries get their
transformation towards Industry 5.0 and beyond. IIoT devices also improve
production efficiency, reduce the cost for manufacturing, provide better
status monitoring and security-enhanced solutions. Moreover, the blockchain
solutions also detect authenticated usage of software and its update
management in the IIoT devices[204].

The miniaturization of modern devices, wearable electronics, high-speed
computing, efficient data analytics has opened up avenues for policymakers for
transforming the existing industrial standards. The business management,
researchers, and technical experts have started visualizing the transformation
from technical to socio-technical perspective for efficient and smart
manufacturing in future industries[205].

Figure 10: Impact of CPS integrating the physical and virtual world in
Industry 4.0 applications.

Figure 10 illustrates the smart manufacturing process through the integration
of the physical and virtual world with the aid of CPS. Robotic systems
involved in the production process in Industry 4.0 enhances flexibility,
productivity thereby reducing the risks of fatigue and injury for humans.
Certain key aspects of the CPS is modern industries are pushed to the
simulation part and carried out through virtual systems. IIoT devices and
smart machines are integrated with cloud services, Big Data, and additive
manufacturing providing enhanced services to the end-users through smart
devices and augmented reality. Minimization of interaction forces between
humans and robots optimizes the production process and improves the
qualitative performance using robust controllers[206].

Deployment of high-performance robotic systems in Industry 4.0, enables the
smart industries transformation towards Industry 5.0. Involved production
process and service roles of the industrial systems are personalized towards
customer specification smart production processes. However, those smart
industries also address the organizational analysis through socio-technical
approaches[207].

Transparent architecture with user-friendly and readable coding processes
makes novice programmers program and control industrial devices and control
them. Such illustrations of the conceptual model drive smart industrial
objects to adapt to the modern industrial environment and paves way for
effective means of industrial transformation[208].

####  VI-A2 Sustainability in Industrial Revolution

The research community, academicians, and business experts have defined many
road maps for sustainable implementation of Industry 4.0 considering the
societal and economic progress through a systematic approach[209].

Robotics for domestic services and industrial automation tasks along with AI
and image vision have enough expertise towards the adaptation of Industry 5.0.
Moreover, the enhancement in terms of production quality, improved efficiency,
and reduced cost benefits can be addressed with auto-adaptation for providing
quality services[210].

Over digital networks, automation built using robotic systems for connecting
the urban sector through the industrial revolution is addressed through the
socio-spatial perspective through innovation and speculative modernization and
transformation of smart industries[211].

Automation of smart industrial objects through IIoT along with the enabling
technologies and challenges faced are highlighted to envision the revolution
of industries with substantial development towards Industry 5.0[31].

Sustainable Development Goals at the international level are well established
by addressing the economy and ecology for the industrial revolution with
sustainable growth in production and automation through innovative
technologies. The economic y transformation process is hindered through the
technology towards the successful deployment of the smart factories for
sustainable development[212].

####  VI-A3 Re-configurable factories

Manufacturing processes can be made adaptive and reconfigurable based on
consumer demands and expectations. The product cycle of goods is tailored to
the requirements of customers with improved serviceability and scalability
with affordable cost for the consumers[213].

####  VI-A4 5G Private Networks for Industry 4.0

Nowadays, the usage of 5G technology, including its private networks, is
considerably increasing [214]. As a result, manufacturers will have to ensure
their network technologies are functioning at maximum capacity while
leveraging the 5G private networks into their AI-enabled production systems
and applications. Notably, various integrated solutions can adopt the
capabilities of 5G technology to bridge the noticeable gap between the
manufacturing networks and the private Long-Term Evolution (LTE) [215]. The
private 5G networks here can supplant the deployment of WiFi in industrial
production settings (and applications) through the Citizens Broadband Radio
Service (CBRS). Such a novel and adaptive wireless connectivity feature can
fit the various AI and big data-based industrial applications, which require
real-time and uninterrupted co-operation/interoperability to perform decision-
making, e.g., IIoT devices that require ultra-reliable networks for an
efficient time-sensitive data collection and transfer.

Furthermore, private 5G networks can provide the desired latency for future
AI-enabled smart manufacturing applications in Industry 4.0, which is a
critical factor for automation processes over big data management and
analytics [216]. Lastly, private mobile networks can be regarded as remarkable
and new networking architecture extensions to bridge the current gap between
AI, smart manufacturing systems, and edge computing. Information applications
of Industry 4.0 and big data analytics still reside within the onsite data
center or the cloud; however, the adoption of private 5G networks can
potentially extend the functionality of AI applications to the manufacturer’s
floor for better automation of industrial settings and systems, providing
reliable and predictable inter-communication between guided vehicles, system
administrators, and AI-based decision-making applications.

###  VI-B Open Issues

This sub-section provides sample instances of a few potential research
directions for the researchers working on AI and Big Data and their
implications towards Industry 4.0. Some key potential future research
directions in AI and Big Data-based solutions for Industry 4.0 are :

  * •

Energy constraints: Energy and hardware constraints are among the significant
and crucial challenges hindering the deployment of ML in Industry 4.0. The
research contribution is therefore needed to enhance and optimize energy
consumption and conservation in IIoT devices[174]. Real-time ML, a research
trend that has recently emerged with the hope of addressing these challenges
through online or incremental learning, could be a potential direction of
future research. Incremental learning is perceived to replace the traditional
data training module with incremental-based training, improving both energy
and hardware (e.g., memory) consumption.

  * •

Scalability issues: Scalability is another significant challenge that may also
be considered for future research. The number of IoT devices is expected to
reach several trillion in the next few years, and thus, performance and
information monitoring challenges will increase remarkably[174]. Besides, as
the number of IIoT devices vastly increases, privacy and security must be
reconsidered. Namely, ML and/or real-time ML solutions can be developed to
thwart run-time attacks and identify various security threats at both the user
and network levels. Besides, management operations such as device discovery,
enumeration, and firmware updates must be taken into account while designing
and placing any security solution over IIoT environments. Lastly, the
legitimacy of geolocation data shared by IIoT devices is an additional
research challenge. A few studies in the past aimed to resolve this challenge
using covert channels (e.g.,[217]), but the challenge of trust of affected
third parties and privacy of direct users remains an open research problem as
a trade-off between security and privacy-preserving geolocation-enabled IIoT
devices.

  * •

Cloud Computing: Cloud computing and performance is an additional vital
component in Industry 4.0-enabled cloud multi-faced paradigms. Although this
provides a baseline mechanism for handling the on-demand metered services,
cloud computing, as well as communication protocols performance and
efficiency, pose limitations for Industry 4.0 objectiveness. Such limitations
include, but are not limited to, the criticality level of the underlying
communication technology linking the cloud with clients under dynamic and
unpredictable end-to-end delay conditions. As a result, an open research
problem herein is designing AI-based sustainable solutions to assist in
predicting cloud-user communication latency while preserving energy
consumption and recommended QoS. Besides, the design of efficient ML-based
monitoring mechanisms for latency-sensitive applications, such as emergency
response systems and virtual reality, requires consideration of the large and
pervasive amount of information generated by the connected devices[218, 219].

  * •

Edge Computing: Edge computing is the other important aspect and enabling
technology that is expected to fully explore the potential of AI in Industry
4.0 [220, 221]. Edge computing enables to process data locally (i.e. near to
the collecting devices) resulting in a significant reduction in bandwidth and
latency. However, there are several challenges associated with edge computing
hindering the successful deployment of AI algorithms in Industry 4.0. For
instance, the computational and storage resources scarcity of edge devices is
the biggest challenge to successfully deploy DL techniques at the edge. One of
the potential solutions is the design of computation and memory-efficient DL
architectures. Network pruning and distillation are the other potential
solutions to cope with the computational and memory issues in deploying DL
algorithms for Industry 4.0 applications at the edge.

  * •

Robotics: Recently, several tentative robotics-based application proposals to
new industrial scenarios and services lead to newly evolving research
problems, e.g., DARPA robot challenges[222]. Precisely, emerging robotics-
based scenarios in Industry 4.0 create novel and critical challenges related
to efficiency, resiliency, and security. These challenges are due to
teleoperation and/or human-based cooperation, traffic monitoring,
hardware/software maintenance (e.g., firmware update), and real-time framework
performance. By leveraging advanced APIs and real-time performance
specifications, AI techniques can be of great interest in employment for
addressing these emerging robotics-based framework challenges in Industry 4.0.
Although human-robot interaction is still regarded as one of the major