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IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS 1
Blockchain-Secured Smart Manufacturing in
Industry 4.0: A Survey
Jiewu Leng , Shide Ye, Man Zhou, J. Leon Zhao, Qiang Liu , Wei Guo, Wei Cao, and Leijie Fu
Abstract—Blockchain is a new generation of secure
information technology that is fueling business and indus-
trial innovation. Many studies on key enabling technologies
for resource organization and system operation of blockchain-
secured smart manufacturing in Industry 4.0 had been con-
ducted. However, the progression and promotion of these
blockchain applications have been fundamentally impeded by
various issues in scalability, flexibility, and cybersecurity. This
survey discusses how blockchain systems can overcome poten-
tial cybersecurity barriers to achieving intelligence in Industry
4.0. In this regard, eight cybersecurity issues (CIs) are iden-
tified in manufacturing systems. Ten metrics for implementing
blockchain applications in the manufacturing system are devised
while surveying research in blockchain-secured smart manufac-
turing. This study reveals how these CIs have been studied in the
literature. Based on insights obtained from this analysis, future
research directions for blockchain-secured smart manufactur-
ing are presented, which potentially guides research on urgent
cybersecurity concerns for achieving intelligence in Industry 4.0.
Index Terms—Blockchain, cybersecurity, Industry 4.0, smart
contract, smart manufacturing.
Manuscript received October 24, 2020; accepted November 23, 2020.
This work was supported in part by the National Key Research and
Development Program of China under Grant 2018AAA0101704 and Grant
2019YFB1706200; in part by the National Natural Science Foundation of
China under Grant 52075107 and Grant 71932002; in part by the Science
and Technology Planning Project of Guangdong Province of China under
Grant 2019A050503010; and in part by the Shenzhen Science and Technology
Innovation Committee under Grant JCYJ20170818100156260. This arti-
cle was recommended by Associate Editor R. Wisniewski. (Corresponding
author: Qiang Liu.)
Jiewu Leng is with the Guangdong Provincial Key Laboratory of Computer
Integrated Manufacturing System, State Key Laboratory of Precision
Electronic Manufacturing Technology and Equipment, Guangdong University
of Technology, Guangzhou 510006, China, also with the Department of
Information Systems, City University of Hong Kong, China, and also with the
State Key Laboratory of Digital Manufacturing Equipment and Technology,
Huazhong University of Science and Technology, Wuhan 430074, China.
Shide Ye, Man Zhou, and Qiang Liu are with the Guangdong Provincial
Key Laboratory of Computer Integrated Manufacturing System, State Key
Laboratory of Precision Electronic Manufacturing Technology and Equipment,
Guangdong University of Technology, Guangzhou 510006, China (e-mail:
liuqiang@gdut.edu.cn).
J. Leon Zhao is with the School of Management and Economics, Chinese
University of Hong Kong (Shenzhen), Shenzhen 518172, China.
Wei Guo is with the State Key Laboratory for Manufacturing Systems
Engineering, Xi’an Jiaotong University, Xi’an 710064, China.
Wei Cao is with the Fujian Provincial Key Laboratory of Special Energy
Manufacturing, Xiamen Key Laboratory of Digital Vision Measurement,
Huaqiao University, Xiamen 361021, China.
Leijie Fu is with the School of Mechanical and Electronical Engineering,
Xi’an Technological University, Xi’an 710023, China.
Color versions of one or more figures in this article are available at
https://doi.org/10.1109/TSMC.2020.3040789.
Digital Object Identifier 10.1109/TSMC.2020.3040789
I. INTRODUCTION
THE DEMANDS for smart, individualized, and sustainable
products lead to the emerging of new smart manufac-
turing paradigms (e.g., cyber-physical production systems,
cloud manufacturing, and social manufacturing) in Industry
4.0 blueprint [1]. In Industry 4.0 vision, machines with a cer-
tain degree of interaction capability will be empowered to
cooperate with each other via the Industrial Internet [2]. Large-
scale manufacturing data will be exchanged all the time.
Machines will be enabled to autonomously make local deci-
sions, which will definitely influence the whole manufacturing
processes [3]. However, these emerging Industry 4.0 manu-
facturing paradigms lack tools to handle security challenges.
Current industrial control systems usually suffer from secu-
rity issues in which manufacturing data could be attacked or
tampered with. For instance, in November 2017, Kobe Steel.,
Ltd., Japan’s third-largest steel company, admitted to tamper-
ing with the performance data onto some of its aluminum
and copper products. These products are unqualified before
delivered, but the strength and size data have been modi-
fied during product inspection. These counterfeiting products
are used in many fields, from national defense to automo-
biles, which may probably cause many unprecedented severe
safety problems. Erroneous and tampered data will lead to
incorrect controls/decisions and pose a significant threat to
interlinked complex manufacturing systems. Current manufac-
turing management usually relies on a centralized platform,
which suffers from inadequate traceability of information and
weak robustness against system failures.
A method to tackle this security issue is to use blockchain,
which is an innovative computing paradigm that is recently
revolutionizing the digital world and bringing a new tool to
the security and efficiency of systems [4]. The blockchain as
a foundation for distributed ledgers offers a transparent and
decentralized mechanism of making transactions (i.e., com-
putational trust) in both business and industry areas [5]. The
inherited characteristics of blockchain enhance trust through
transparency and traceability within transactions [6].
Specific properties of blockchain are showing promising
enhancements for manufacturing systems primarily to guar-
antee cybersecurity. Via smart contracts with inherent and
robust cybersecurity features, blockchain could avoid inter-
vention from third-parties who may not add direct value,
enabling a lower transaction cost [4]. Practitioners are not fully
aware of the advantages of blockchain to disrupt the control
and management of manufacturing systems [7]. Therefore, this
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2IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS
survey discusses the current status of blockchain applications
in manufacturing systems.
The remainder of this article is organized as follows.
Section II introduces the research method. After reviewing
current cybersecurity and trust issues in various smart man-
ufacturing systems in Section III, Section IV surveys the
advantages of implementing blockchain in the manufactur-
ing system. Section V presents the models of blockchain-
secured smart manufacturing. Then, key enabling technologies
of implementing blockchain are analyzed in the follow-
ing section. Challenges and research directions are given in
Sections VII and VIII, respectively. Finally, Section IX draws
the conclusions.
II. RESEARCH METHOD
A literature search was conducted in the Google Scholar
database, where a broad range of literature on blockchain-
secured smart manufacturing can be identified. Articles
retrieved were further refined through a three-step approach.
A. Publications Identification and Screening
The first step is to obtain quality publications via applica-
ble screening criteria. Working papers and commentaries are
excluded to derive quality publications. Meanwhile, four key-
words, namely, blockchain, smart manufacturing, security, and
Industry 4.0, were identified for searching publications. This
inclusive search yielded 260 publications for further analysis
(up to 16 September 2019).
B. Theoretical Screening Process
To emphasize cybersecurity issues (CIs) in the manufac-
turing system, articles advancing blockchain technology are
included. More specifically, the selection criteria are shown as
follows.
1) Blockchain applications in industry are selected, includ-
ing using blockchain for operation scheduling, resource
planning, and manufacturing management. These stud-
ies highlighted the concepts, technologies, methods,
tools, systems, and empirical cases on blockchain
implementations.
2) Reviews/frameworks on blockchain and smart contracts
were examined to provide a comprehensive overview
of trends, functions, technologies, and issues involved.
The insights gained from these studies will help identify
challenges faced for the implementation of blockchain
in the manufacturing sector.
3) Studies involving decentralized manufacturing concepts
and challenges were examined, even those without men-
tioning blockchain in the title, keywords, or abstract.
This allows the identification of perspectives for new
industrial developments.
The exclusion criteria are 1) research not related to the
blockchain domain or manufacturing management; 2) papers
that were not peer-reviewed; 3) papers not written in English;
and 4) short papers less than four pages.
C. Reference Analysis
In the last step, 126 articles that met the selection crite-
ria were included, and the cited references were further utilized
as a source for literary analysis, resulting in the identifica-
tion of 16 additional articles. For article analysis, six key
enabling technologies of blockchain-secured smart manufac-
turing were identified to form discussion themes and guide the
reading focus. Additionally, 17 supplementary references were
added to the reference section to make the survey concrete.
Therefore, this survey consists of 159 articles in total.
Therefore, this survey discusses the current status of
blockchain applications in manufacturing systems. Eight CIs
are identified in the manufacturing system. This article dis-
cusses how blockchain systems can overcome potential cyber-
security barriers to achieving intelligence in Industry 4.0. To
integrate existing security architectures with blockchain-based
applications regarding technological and organizational issues
in a balanced way, the research direction is highlighted in
a process-data-infrastructure (PDI) perspective.
III. CYBERSECURITY ISSUES IN THE MANUFACTURING
SYSTEM
Industry 4.0 represents a change from the centralized-
scheduled manufacturing to a dynamic and decentralized
one, so as to improve the product quality, individualiza-
tion, and system flexibility [8]. Autonomous manufacturing
and flexible configuration are distinct metrics of Industry
4.0 [9]. The evolution toward Industry 4.0 calls for the
seamless integration of multiple advanced information tech-
niques across all operations of the manufacturing system.
This results in many difficulties when building systems for
achieving the intelligence, traceability, security, and flexibility
of smart manufacturing [10]. For instance, while the digital
thread technology enables the timely sharing of cyber mod-
els together with its physical systems in the configuration and
operation stage, it also becomes vulnerable from malicious
attacks from cyberspace [11]. Manufacturing systems are vul-
nerable under advanced persistent attacks, such as Stuxnet,
Shamoon, BlackEnergy, WannaCry, and TRITON [12]. The
centralized-controlled manufacturing system also may suf-
fer from the device spoofing and false authentication in
information sharing [13]. Also, the heterogeneous nature of
diversified equipment and the individualized service require-
ments make it difficult for peer-to-peer interaction and
interoperating [14].
The critical issues in the control and management of the
distributed manufacturing network are confidentiality and trust
between participants [15]. Moreover, as these security issues
are actually compounded with mass personalization needs of
industrial products across systems, it greatly complicates the
manufacturing and supply activities [16]. A centralized plat-
form cannot protect data privacy from other participants since
it is necessary to know the capabilities and status of each other
to make coordination decisions. Manufacturers also need to
overcome the low robustness to a fault of a single key node in
centralized platforms, resulting in unreliable networking and
data service [17].
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LENG et al.: BLOCKCHAIN-SECURED SMART MANUFACTURING IN INDUSTRY 4.0: A SURVEY 3
TAB LE I
CISINTHEMANUFACTURING SYSTEMS
Table I presents the CIs of the manufacturing sector. All
these CIs may be resolved by existing technologies/tools (e.g.,
RESTful services for handling CI5). Blockchain is a new
tool to solve these CIs, and additional metrics (e.g., enhanced
robustness on fault tolerance and crash tolerance) may also be
achieved by introducing the blockchain technology.
IV. BLOCKCHAIN AND ITS ADVANTAGES IN THE
MANUFACTURING SYSTEM
A. Blockchain Computing
Blockchain is a mixed use of the chain structure, con-
sensus algorithms, cryptography techniques, and automated
smart contracts. It consists of the decentralized updated blocks
of data. Each block of data includes a timestamp together
with a link to a previous block. A blockchain contains com-
plete historical data so that every transaction in the database
could be traced back to the source. Blockchain has been
hailed as a new secured and shareable computing paradigm.
Implementations of blockchain in the manufacturing sector
are usually developed based on the infrastructure provided
by the mainstream blockchain platforms shown in Table II.
Blockchain provides a set of distributed data structures,
interaction mechanisms, and computing paradigms, which
offers an additional security method to exchange information,
service, or product [18].
B. Metrics of Employing Blockchain
Four types of metrics of employing blockchain in the man-
ufacturing systems are overviewed in Table III, including
cybersecurity, decision architecture, system performance, and
trust enhancement. These metrics have been numbered from
M1 to M10.
From the cybersecurity perspective, blockchain provides
a resilient and robust way for managing records distributed
over the Internet [19]. The chain structure links data blocks
sequentially and protects this shared ledger from being tam-
pered with or forged in a cryptographic way (M1). The M1
metric provides manufacturers with secure product designs,
ownership verification, parts validation, and decentralized
decisions free from control by intermediaries. Also, blockchain
enables data provenance (M2) that was conventionally difficult
to achieve, making the system more transparent [20].
From the decision architecture perspective, the critical point
to bringing down the control complexities in the dynamic man-
ufacturing flows is to increase the system flexibility under
TAB L E I I
LIST OF MAINSTREAM BLOCKCHAIN PLATFORMS
disturbances, in which blockchain-based decentralized deci-
sion architecture is a potential solution (M3) [21]. Moreover,
orchestrating smart contracts based on the blockchain could
achieve decentralized decisions and collaborative machine-to-
machine interactions in the distributed manufacturing network.
Other coordination challenges, such as order delay, dam-
age to products, and multiple data entry, can also be
reduced by employing blockchain (M4) [22]. Blockchain
could achieve more flexible coordination and broader
data sharing among different manufacturing units/systems
(M5) [23].
From the system performance perspective, the transparency
enabled by blockchain enhances the ability to secure trans-
actions at lower signaling costs. Manufacturers could pri-
marily cut down costs for maintaining partner relations
and make outsourcing decisions more timely via employ-
ing smart contracts (M6) [24]. Moreover, the transparency
furnishes the reputation and competitiveness of manufac-
turers, and thus assures the system sustainability of smart
manufacturing (M7) [25]. Blockchain can also enhance the
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4IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS
TABLE III
METRICS FOR EMPLOYING BLOCKCHAIN IN MANUFACTURING
resilience of the manufacturing network under risks and
uncertainty (M8) [26]. Risk prevention in the manufactur-
ing system is usually protected through inventory, buffer,
and backup. If we can create a record of activities and
data needed for recovery in synchronized contingency plans,
blockchain can reduce these kinds of structural redun-
dancy/inefficiencies based on its decentralized decision princi-
ples. If a disruption happens, blockchain can help us to trace
the roots/propagation of disruptions and to select stabilization
actions based on a clear understanding of what capacities are
available [27]. Based on a comparison of system performance
between blockchain-based ManuChain [14] with other solu-
tions, including digital twin-based centralized system [30]
and agent-based decentralized system [31], the flexibility and
robustness for disturbances/changes [32] makes smart con-
tracts more suitable to the mass individualized manufacturing
paradigm.
From the trust enhancement perspective, trust across
multiple manufacturers is the predominant factor driving the
blockchain implementation, which can offer enhanced disin-
termediation, visibility, and traceability (M9) [28]. Blockchain
provides a decentralized peer-to-peer interaction method to
efficiently disposing of transaction information between partic-
ipants. Thus, it will effectively prevent any single participant
or channel in the network from being damaged by attack-
ers. The transparency achieved by the blockchain system is
a crucial metric to address the trust issues in the decen-
tralized supply chain network [33]. Blockchain can record
all design, manufacturing, maintenance, logistics, capital, and
other information to facilitate supervision and resolve lia-
bility disputes. Manufacturing events could be synchronized
and shared across a community, and the data stored on the
blockchain is immutable. Manufacturers can make their orga-
nization’s data accessible to other nodes on the network to
establish the reputation and hence, partially generate trust
(M10) [29]. With smart contracts, blockchain can provide
massive personalized manufacturing services [34].
V. REFERENCE ARCHITECTURE OF THE
BLOCKCHAIN-SECURED SMART MANUFACTURING
SYSTEM
Recent advances in edge/fog computing and the Internet
of Things provide a new impetus to reforming the manufac-
turing operations. Blockchain is adopted as a decentralized
peer-to-peer communication system to efficiently disposing
information between machines via spreading computation to
network devices and thus significantly reducing the cost of
large data centers. This mechanism could effectively prevent
the whole network from a crash when hackers breach any
single node or transmission channel in the system. Since the
data captured by the blockchain is immutable, securing the
manufacturing data provenance, that was previously difficult or
expensive to achieve, now can be realized. Based on an inter-
nationally recognized standard of the Integration of Enterprise
and Control Systems implemented by the International Society
of Automation (ISA95, www.isa.org), Fig. 1 presents a refer-
ence architecture for mapping the four-layer blockchain com-
puting into the smart manufacturing system. ISA95 standard is
the industrial best practice of manufacturing information tech-
nologies, widely referred to in Industry 4.0, such as RAMI 4.0
(www.plattform-i40.de).
The existing blockchain applications usually include four
components: 1) agent node; 2) machine digital twin; 3) key-
value database; and 4) view manager. At the level of process
perception and controls (Level 2), the time-critical and/or
data-intensive computations are integrated with the physi-
cal manufacturing process on the edge processes of system
automation. The captured manufacturing data from the sen-
sors/controllers are used as the original data source of the
blockchain system. At the level of process monitoring and
operations (Level 3), the system-level operations are mapped
into the smart contracts layer that implements and executes
on a blockchain to flatten the hierarchy automation pyramid.
By means of smart contract-enabled services, edge comput-
ing on devices can synchronize their state, publish locally
scoped information that needs to be aggregated on a global
scope, make local decisions, and thus achieve the flexibility
and resilience of the smart manufacturing system. The dis-
tributed machine tools and machining centers in the workshop
are dynamically organized using smart contracts to finish the
manufacturing tasks. At Level 4, the decentralized applications
(DApps) are integrated with smart contracts for formulating
the manufacturing plans. At Level 5, advanced artificial intelli-
gence algorithms are encapsulated into the DApps for assisting
the upper level decision making in product data management.
Table IV offers an overview of the operation logic,
addressed CIs, and metrics of blockchain-secured smart man-
ufacturing models, such as cyber-physical production systems
and social manufacturing.
The data protection among distributed manufacturer in col-
laborative manufacturing models, such as cloud manufacturing
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LENG et al.: BLOCKCHAIN-SECURED SMART MANUFACTURING IN INDUSTRY 4.0: A SURVEY 5
Fig. 1. Reference architecture of the blockchain-secured smart manufacturing system.
TAB L E I V
OVERVIEW OF BLOCKCHAIN-SECURED ARCHITECTURES FOR VARIOUS SMART MANUFACTURING MODELS
and social manufacturing, is critical. Data security in a dis-
tributed smart manufacturing environment can be categorized
into integrity, confidentiality, and availability, which could be
enhanced by the blockchain and smart contract.
In terms of data confidentiality, access control has become
more complicated in the evolving manufacturing system from
an integrated one to a distributed and cloud-based one.
The variant of the public-key system in blockchain pro-
vides users with privacy. A smart contract can store the
updated ciphertext, express the logic semantics of authoriza-
tion, and flexibly define access control policies for removing
and editing the data. Zero-knowledge proof (ZKP) and secure
multiparty computation provide confidentiality for transac-
tions. Homomorphic encryption-enabled blockchain supports
algebraic calculations executed directly on the ciphertext
instead of plaintext, supporting users’ data without any private
information about their partners.
In terms of data integrity, the group/ring signature ver-
ification algorithm in blockchain ensures the integrity of
transactions. The chain structure ensures the integrity of data.
Moreover, a highly isomorphic redundancy structure ensures
the integrity of the system. On the other hand, holding com-
plete transparency and control over digital identity will become
far more common. Data provenance is critical to achieving
data accountability. The blockchain transactions could anchor
the provenance records to track data operations. Assembling
an accurate provenance record across the distributed environ-
ment can be realized using blockchain in the manufacturing
system.
In terms of data availability, it is difficult for us to quickly
find the fault when quality problems are found in a dis-
tributed manufacturing environment, since it may be caused
by a single node or cohesion between the nodes. In a decen-
tralized network, each replica node holds the same copy of
blockchain data, thus ensuring the data availability. Moreover,
the consensus mechanism ensures the availability and syn-
chronization of transaction information. Some manufacturing
applications, such as sealed-bid auction across manufacturers,
need trusted computing. Blockchain-based verifiable compu-
tation could improve data security by introducing an incentive
mechanism and offload the computation of some functions
to other untrusted clients while maintaining verifiable results.
A secured searchable data service is essential in a blockchain-
type storage system for the data owner to upload their data in
an encrypted form and enable others to search it.
In general, blockchain makes extensive use of cutting-edge
cryptography technologies, which could provide data security
in a distributed smart manufacturing environment (Table IV).
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6IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS
Fig. 2. Overview of blockchain-secured enabling technologies.
VI. BLOCKCHAIN-SECURED ENABLING TECHNOLOGIES
FOR SMART MANUFACTURING
Smart manufacturing is enabled by a mixed use of tools
and systems, such as the Industrial Internet of Things (IIoT),
digital twin, manufacturing execution system (MES), oper-
ation scheduling, and resource planning. The transparency
provided by blockchain is a solution to address the CIs in
a manufacturing system. Incorporating the blockchain into
these systems could also enhance system performance. Based
on the reference architecture shown in Fig. 1, key enabling
techniques are discussed to guide the implementation and
operation of blockchain-secured smart manufacturing in this
section. Fig. 2 provides an overview of the blockchain-secured
enabling techniques for smart manufacturing.
A. Blockchain-Secured Industrial Internet of Things
The IIoT in the smart manufacturing system is the
interconnection of equipment to collect data and sup-
port operational decisions. The volume and variety of
data obtained in IIoT of smart manufacturing systems are
ever-increasing due to the intensive machine-to-machine
communication. However, IIoT is vulnerable to privacy
information leakage and cyberattacks due to a lack of secu-
rity countermeasures [63]. The conventional centralized IIoT
architecture is less robust to a fault of a single node, result-
ing in unreliable networking and data service. As the number
of nodes becomes substantial, the conventional IIoT architec-
ture can no longer provide sufficient support for handling the
complexity and data scale of such an extensive system.
In this context, blockchain, which enables auditable and
transparent peer-to-peer transactions [64], could help eliminate
the security vulnerabilities of conventional IIoT architecture
and protect data from thefts, corruption, and cyberattacks.
In distributed IIoT enabled by blockchain and smart con-
tract, equipment moves toward greater autonomy in exchang-
ing and analyzing data without intervention. The equipment
could authenticate each other to assure the integrity of trans-
ferred data and prevent malicious users/use. Blockchain could
enhance the IIoT by providing applications with immutabil-
ity, redundancy, transparency, auditability, and operational
resilience [65]. The existing blockchain-secured IIoT research
could be categorized into device authentication and device
networking.
In the device authentication level, in view of the auton-
omy nature of IIoT, the interconnected machines are supposed
to be able to identify and authenticate with each other and
thus guarantee the data integrity of exchanged information.
A decentralized IIoT system named bubbles of trust was
proposed to ensure a reliable recognizing and authentica-
tion of interconnected things, as well as to protect the
data integrity and availability for creating secured virtual
groups where devices could trust partners [66]. An implemen-
tation proved it could satisfy demands on security at a lower
cost. Lin et al. [67] integrated blockchain with attribute sig-
nature and multireceivers encryption to guarantee anonymous
fine-grained mutual authentication. Chen et al. [68] proposed
a cooperative trust evaluation scheme for validating IIoT
devices during the transaction processes of private blockchain
applications. Recently, a self-certified cryptography model
was designed and integrated with Bloom filter-based iden-
tity management to realize the registration and authentication
of blockchain network entities [69]. Generally, the existing
device authentication models in blockchain-secured IIoT could
guarantee the auditability and extensibility, while partially
satisfy privacy protection requirements [70].
In the device networking level, software-defined
networking (SDN) is a potential paradigm to manage
IIoT dynamically. However, one intractable issue is how to
obtain a consensus among distributed IIoT agents/controllers
in a complicated manufacturing context. Qiu et al. [71]
proposed a permissioned blockchain-based consensus algo-
rithm in software-defined IIoT, where blockchain acts as
a trusted system to synchronize data among distributed IIoT
agents/controllers. They formulated a joint optimization
problem of access selection, view change, and resources
allocation, and modeled it as a Markov decision process
for improving the throughput of this blockchain-based
IIoT. Accordingly, they proposed a dueling deep Q-learning
algorithm to solve the joint optimization model effectively.
Lin et al. [72] presented a decentralized and tamper-proofing
blockchain built-in model providing a fundamental tool to
verify transaction data among long-range wide-area network
(LoRaWANs) servers at a specific time. IIoT devices often
suffer from low processing power, limited storage, and poor
manufacturing standards. However, conventional blockchains
using public consensus algorithm and data chain structure are
of low throughput, and thus not suitable for power-sensitive
IIoT network. Huang et al. [73] presented a directed-acyclic-
graph-structured blockchain model with a credit-based
proof-of-work consensus algorithm to guarantee IIoT system
security and transaction efficiency simultaneously. An
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LENG et al.: BLOCKCHAIN-SECURED SMART MANUFACTURING IN INDUSTRY 4.0: A SURVEY 7
implementation of a Raspberry Pi-based smart factory
demonstrates that this system was more efficient than the
Satoshi-style blockchain (using proof-of-work consensus
and data chain structure) in IIoT. Wan et al. [74] presented
a blockchain-based security model to reorganize the orig-
inal IIoT architecture to form a multicenter decentralized
architecture. A case study on automatic production plat-
form shows that this architecture provides better privacy
protection than the traditional architecture. Generally, the
existing industrial blockchain solution for IIoT usually
concentrates on the scalable and robust system rather than
on the self-configuration and optimization mechanism under
a decentralized architecture.
B. Blockchain-Secured Manufacturing Execution System
The function of the MES for smart manufacturing is the
online orchestration of local operations. Blockchain provides
a unified model for computing and networking, enabling
data to be locally processed close to end devices in a timely
and efficient manner.
Several solutions on blockchain-secured MES of smart
manufacturing systems have been proposed. Stanciu [75] inte-
grated the blockchain, smart contracts, and microservices
technologies for developing the distributed control system. In
the lower executive level, function blocks for actual produc-
tion process control are implemented based on microservices
in Docker containers of the edge nodes. In an upper super-
visor level, smart contracts are deployed for coordinating the
execution of microservices in containers across the network.
Blockchain could be modeled for the configuration and oper-
ation of the physical Internet in a decentralized networking
approach [76].
Furthermore, transactions between agents of machines
could be autonomously executed via the support of smart-
contracts [77]. Bose et al. [78] proposed a composite con-
sensus protocol integrated with identity-based encryption and
physically unclonable models to protect the proactive and
passive integrated circuits transactions, which suffer from
malicious practices such as harmful design modification to
obtain an unfair benefit. Adhikari and Winslett [79] integrated
blockchains with cloud storage to develop a manufacturing
information framework to capture, preprocess, and securely
share process data to the data storage systems across a dis-
tributed manufacturing network. Lopes et al. [80] proposed
an architecture that uses blockchain as a ledger and smart-
contract technology for robotic control to process data. They
integrated a smart contract with external artificial intelligence
algorithms for image analysis.
However, in these blockchain-secured MES solutions, the
edge computing algorithms/services deployed in distributed
machines result in more friction because the local operation
logic distributed in the edge side needs to comply with the
holistic management goal of the smart manufacturing system.
A mechanism that coordinates the holistic optimization and
distributed self-organization is needed.
Fig. 3. Synchronization of blockchain with other cyber-physical
systems [14].
C. Twining Between Blockchain With Cyber-Physical
Systems
In the Industry 4.0 vision, the physical attributes of the man-
ufacturing process were mapped into a digital model, which
finally forms the digital twin of manufacturing systems [81].
Digital twin allows displaying the instant status of the machin-
ing equipment, as well as predicting its performance based on
analyzing the manufacturing context by learning and utilizing
behavior patterns. The communication of digital twins via the
Industrial Internet suffers from cybersecurity and trust issues.
One solution to such issues is introducing blockchain as a new
cyber system of the digital twin system to make the controls
and instructions more secure. Each cyber system has unique
advantages in terms of the management and control of the
physical manufacturing system. For example, the blockchain
system could be acted as an indexing server for tracking the
manufacturing activities and product quality, while the MES is
an essential tool for timely scheduling executing of upper level
order planning. Furthermore, synchronization among different
cyber and physical systems is necessary for a management
coordination purpose (Fig. 3).
A key enabling technology in the twinning among the manu-
facturing blockchain, multiple cyber systems, and the physical
system is the instant synchronization of manufacturing data.
It could be divided into two steps: 1) establish the instant
communication among machines through industrial Internet,
such as industrial Ethernet and logical programmable logic
controller (PLC) and 2) hash manufacturing data collected
from the distributed machine into the blockchain. As shown
in Fig. 3, instant data synchronization between the physical
machine and cyber system (including blockchain, MES, and
SCADA) was achieved [14]. It is a hybrid model by integrat-
ing multiple protocols, such as industrial Ethernet and remote
procedure call (RPC) into both key-value and entity-relation
database. Drivers are programmed to enable collecting con-
text data from the control agents of the smart machine to the
digital twin models. This multientity synchronization mecha-
nism connects different functional cyber systems in a unified
architecture that incorporates both group self-organization
intelligence and global optimization intelligence. The twinning
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8IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS
between blockchain with a conventional E-R database could
protect the manufacturing data from being tampered with, and
thus decreasing the chance of Byzantine or crash faults.
D. Smart Contract-Enabled Operation Scheduling
The operation scheduling in a smart manufacturing system
is a decision process characterized via a set of decision
variables, prespecified parameters, learned knowledge, objec-
tives under a different context, and individualized preferences.
It is a complicated task due to the multisource decision
information inclusion from the production equipment and
resource planning systems. The operation scheduling deci-
sions usually are made in both strategic and operational
situations related to multistages of the manufacturing pro-
cess. A proactive self-organized and decentralized operation
scheduling based on a multiagent system (MAS) will be bet-
ter. MAS is characterized as a set of active decision agents
(embedded in machines or gateways) interacting and negoti-
ating with each other to coordinate a set of manufacturing
tasks in a dynamic and unpredictable context. Based on the
decentralization of functions enabled by distributed active
decision agents, MAS is of high flexibility, scalability, auton-
omy, adaptability, concurrency, and openness. MAS could be
useful in operation scheduling in smart manufacturing with
uncertain and distributed features. Blockchain could be incor-
porated into a multiagent model to aid the decision-making
processes in a smart manufacturing system [82]. The smart
contract allowed the modeling of the interactions and reason-
ing of a scheduling decision. Kapitonov et al. [83] described
a blockchain-based model for organizing the communication
protocols, enabling autonomous agents–robots or smart things
of the MAS to make proactive scheduling decisions in the
manufacturing processes under an unstable and unknown envi-
ronment. Sikorski et al. [84] utilized the blockchain to enable
machine-to-machine interactions. The products made will be
customized with increased process flexibility and optimized
efficacy, as well as decreased costs.
The growing individualized requirements of products call
for process flexibility and system robustness in managing
the unpredictable disturbances and order changes. The smart
contract could be used for supporting the self-organizing of
the manufacturing process and thus accelerating the holistic
optimization of massive individualized manufacturing tasks.
Leng et al. [14] proposed a bilevel ManuChain system
that integrates blockchain with a digital twin-based holistic
optimization algorithm to eliminate the unbalance between
manufacturing planning and execution (Fig. 4). In the lower-
level of the ManuChain system, smart contracts embedded
in the smart gateways coordinate execution of individualized
manufacturing tasks among groups of machines, and upload
the decision and execution results to the upper level digi-
tal twin for adjusting the coarse-grained holistic planning.
In the lower level self-organizing of the ManuChain system,
the smart gateway allows machines to interact with work-
pieces, and each machining task is proactively matched to
one suitable machine based on a smart contract [85]. The
Fig. 4. Integrating the blockchain with a digital twin for scheduling [14].
reason for using the ManuChain system as a case for dis-
cussing the smart contract-enabled operation scheduling lies
in its innovative proposal of the smart contract tree concept,
which we believe that this kind of modularized design is crit-
ical to enhancing the efficiency of configuring and operating
of a smart contract system. This work also contributed to
a proof-of-concept implementation and application landscape
of blockchain concerning Industry 4.0.
E. Blockchain-Secured Enterprise Resource Planning
Enterprise resource planning (ERP) can detail the demand,
resources, supply, manufacturing, logistics, and through-
put of manufacturers [86]. Blockchain-secured ERP may
be developed across departments of manufacturers with
decentralized-shared and secured ledgers with consensus algo-
rithms to validate and record decisions. It is a powerful
system to improve plans and operations in transparency, effi-
ciency, and cost reduction [87]. Banerjee [88] illustrated the
use of blockchain for enhancing transparency in ERP and
supply chain applications. Wang and Kogan [89] presented
a blockchain-enabled transaction processing system to support
the monitoring of enterprise information. Using homomorphic
encryption and ZKP, they developed and evaluated the com-
putational performance of the transaction processing system
prototype to demonstrate the functionality in constant account-
ing, monitoring, and fraud prevention. Dolgui et al. [90]
developed and tested a new model for smart contract design
with multiple logistics service providers. The execution of
physical operations was modeled as a virtual operation inside
the start and completion of cyber information services. The
constructed model constituted an event-driven dynamic dis-
patching approach [30] of service composition when designing
the smart contract. The use of state control variables allowed
for operations status updates in the blockchain that, in turn,
feeds computerized information feedbacks, disruption detec-
tion, and control of contract execution.
The benefit of blockchain for data analytics lies in providing
high-quality data (i.e., validated and standardized data) [91],
which could be used in the optimization algorithms to enhance
the efficiency of blockchain-secured ERP [92].
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LENG et al.: BLOCKCHAIN-SECURED SMART MANUFACTURING IN INDUSTRY 4.0: A SURVEY 9
TAB L E V
OVERVIEW OF BLOCKCHAIN-SECURED MODELS FOR PRODUCT DATA MANAGEMENT
F. Blockchain-Secured Product Data Management
The distributed manufacturing network driven by con-
sumers’ personalized requirements makes every participant
able to join in the design, development, and manufactur-
ing of products, which subverts the conventional manu-
facturing paradigm. The new paradigm dominated product
design, development, and manufacturing change from produc-
ers gradually to prosumers. Table V offers an overview of
blockchain-secured models in product data management, such
as computer-aided design and knowledge sharing.
In a distributed blockchain network, manufacturers could
intensively collaborate in product development processes to
continuously extend the value-chain boundary. Blockchain
formed in a smart manufacturing system could retrieve prod-
uct provenance, secure product data management, and increase
system transparency. However, in the blockchain-secured prod-
uct data management, manufacturers collect and share massive
amounts of product data while they cannot eliminate users’
and partners’ privacy concerns. Although manufacturers try
to address these concerns with some risk reduction strategies,
few of them can guarantee cryptographic secureness. A secure
multiparty computation system [107] that allows manufactur-
ers to collaborate with each other without disclosing their
privacy is critical. If the privacy is protected, a reduction of
fraud/misuse and a higher willingness to share information
could be expected.
VII. CHALLENGES AND BARRIERS
The transformation of the blockchain-secured smart man-
ufacturing paradigm is still in the early stage [142]. The
dilemma in the technology challenges and standard barriers
impede progress [143].
A. Technology Challenges
Although the research of blockchain-secured smart manu-
facturing in Industry 4.0 has achieved significant progress, it
is still in an early stage toward the productization of these pro-
totype systems (e.g., ManuChain [14] and Makerchain [44]),
which are usually developed based on the infrastructure pro-
vided by the mainstream blockchain platforms shown in
Table II. Deploying a practical blockchain-secured platform in
the smart manufacturing system faces several technical chal-
lenges, including consensus algorithms, signature algorithms,
data mutability, and privacy protection mechanisms.
In the running time of blockchain, designing a scalable
consensus algorithm for distributed data synchronizing is crit-
ical to facilitate coordination in each smart manufacturing
system [144]. Besides meeting the manufacturers’ person-
alized requirements for functions and performance of the
blockchain system, it is necessary to balance the implement-
ing cost, security, and system complexity. The most significant
bottleneck to achieving the scalability of blockchain appli-
cations is the transaction processing capability per unit time
(consensus efficiency). Also, the data analysis capability of
the existing blockchain systems is weak [145]. The ever-
increasing size and number of encrypted data blocks are
a computing dilemma for timely data analysis.
In view of the rapid development of quantum computing,
current blockchain platforms, which rely on group signatures
and hash algorithms, are vulnerable to quantum attacks. The
efficient and robust anti-quantum-attack blockchain system is
in urgent need [146]. Driving blockchain applications requires
significant development to enable interaction with physi-
cal machines and associated cyber systems in a decentral-
ized manner. Besides, solutions may have to be designed
and implemented via multiple blockchains simultaneously.
Multichain synchronization, such as side-chain technology, is
in urgent need.
Although the information immutability is an essential fea-
ture of blockchain that prevents falsifying and adulteration
of data without consensus, manufacturers still concentrated
on applying blockchain with the risk of recording erro-
neous data. However, the incorrect record data could not be
deleted in the blockchain by the principal owners. Moreover,
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10 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS
blockchain’s immutability conflicts with the right to be
forgotten (RtbF) provision in the General Data Protection
Regulation (GDPR). As the RtbF obliges data to be muta-
ble if requested, blockchains compliance with the GDPR is
still in infant stages. To resolve this contradiction, many
editable/redactable/mutable blockchain solutions on condition-
ally removing immutability have been proposed, such as
chameleon hash, meta-transactions, consensus-based voting,
omit content of transactions, self-destruct/suicide functions,
and block matrix [147]. Removing blockchain’s immutabil-
ity brings the question by whom should be edited and
under which circumstances. Nonetheless, some circumventing
methods, such as blockchain pruning, off-chain storage, and
encryption in blockchain, are proposed to bypass blockchain’s
immutability.
Moreover, privacy-preserving concerns are challenging in
implementing blockchain. For instance, the GDPR identifies
privacy protection demands for any organization that can reach
the market. It is beyond the data owner’s capability to per-
ceive whether a service provider continuously complies with
the GDPR and effectively protects her data. Truong et al. [148]
developed a GDPR-compliant permissioned blockchain to pro-
vide decentralized mechanisms to both service providers and
data owners for processing personal data. However, in the
public blockchain, ZKP is needed to realize the privacy pro-
tection, in which the user can prove to another user that
the statement is correct with high probability without reveal-
ing any information except the veracity of the statement.
A ZKP satisfies properties of completeness, soundness, and
zero knowledge. Existing ZKP suffers from many security
issues on scalability, computation cost, as well as imple-
menting complexity. For instance, the security of zkSNARKs
(a technology which provides ZKP using elliptic curves [149])
used in Zerocash has not yet been proved in theory.
B. Standard Barriers
The regulatory works promote future innovations of
blockchain. Some organizations are exploring global standards
for blockchain technology, such as ISO (e.g., ISO/TC 307,
ISO/AWI 22739, ISO/CD TR 23455, and ISO TR 23246),
IEEE (e.g., P2418), International Telecommunications Union
(ITU, e.g., SG 16, SG 17, and SG 20), World Wide Web
Consortium (W3C), and Society for Worldwide Interbank
Financial Telecommunications (SWIFT). Most of these stan-
dards are related to primary taxonomy, ontology, reference
architecture, and smart contracts compliance.
However, there lacks an industrial blockchain standard
for manufacturing applications. Practical implementation of
blockchain for transforming the conventional manufacturing
system into a novel decentralized architecture faces a variety of
policy challenges, regulatory recognition, and interoperability
issues, which calls for a high-level policy standard, environ-
mental regulations, and regulatory compatibility to coordinate
issues. The lack of consensus on definitions, modeling, imple-
menting, and coding is the driver of standardization in the
manufacturing industry. Also, adopting the blockchain in
manufacturing processes demands new expertise to make
ends meet various facets [142]. Standardization in 1) man-
ufacturing event data models for blockchain; 2) industrial
consensus protocols; 3) interplay protocols; 4) signature algo-
rithms; and 5) Web-based access protocols is essential for
enabling blockchain systems to be interoperable in manufac-
turing system [150]. Strong bidirectional interaction between
manufacturer communities and standard-developing organiza-
tions is crucial. Particularly, standards are necessary for the
redactable and editable blockchain to remove and rewrite
inappropriate data.
VIII. RESEARCH DIRECTIONS
Despite the critical advantages of visibility, transparency,
and resiliency, the disadvantages of employing blockchain
are lack of privacy, standardization, black box effect, and
inefficiency. To integrate existing security architectures (e.g.,
ISO/IEC 27000) with blockchain-based applications regard-
ing technological and organizational issues in a balanced way,
this survey concluded the CIs of blockchain in a PDI perspec-
tive (Fig. 5). The PDI model was first appeared in [151]. The
process level includes CIs on the implementation scenarios,
standards and regulations, fraud detection and risk manage-
ment, and smart contracts. The process level could be mapped
into the application and contract layer of the blockchain
(Fig. 1). The data level is composed of CIs on consensus algo-
rithms, access control and public-key cryptography, encryption
(including computing and retrieval), signature scheme, and pri-
vacy protection. The data level could be mapped into the
consensus and data layer of the blockchain (Fig. 1). The
infrastructure level includes CIs on the private key man-
agement, terminal and network, and compliance enablement.
Corresponding to the PDI model of blockchain, future research
directions for blockchain-secured smart manufacturing in
Industry 4.0 are listed in Fig. 5.
A. Evaluation of Blockchain Cybersecurity
Before we remount a digital system on the blockchain
entirely, it is wise to take stock of what makes the blockchain
unique and what costs are associated with it [152]. The techni-
cal idea is to develop the evaluation model for understanding
the impact of blockchain on reliability and performance in
a manufacturing system. Performance, security, and efficient
communication of blockchain applications should be measured
and assured at the target level of acceptability [12]. How to
continually monitor the operations in blockchain to ensure that
it achieves the desired benefits is also critical.
Security issues in blockchain applications continue to
impose a great challenge for executives and professionals.
For instance, in the proof-of-work consensus protocol of
blockchain, although a single attacker cannot possess 51% of
the total computing resources, it is possible to make a potential
joint attack by aggregating computing power from multiple
nodes. The security of the blockchain applications includes
three elements, namely, confidentiality,integrity, and avail-
ability. The hacker may take advantage of the programming
vulnerability existing in the smart contracts. Standards to
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LENG et al.: BLOCKCHAIN-SECURED SMART MANUFACTURING IN INDUSTRY 4.0: A SURVEY 11
Fig. 5. Future research directions for blockchain-secured smart manufacturing in Industry 4.0.
facilitate the evaluation of blockchain cybersecurity in smart
manufacturing systems should be developed.
B. Highly Efficient Consensus Algorithm and Smart Contract
Consensus algorithms are critical to achieving data consis-
tency among all distributed participants in a system, which
is a process of data synchronization on the network status to
ensure it is secured and tamper-proofing. As the blockchain
is not only designed for serving a few manufacturers, the
tendency to centralization should be stopped. A lightweight
and highly efficient consensus on recording the manufacturing
events into the blockchain is desired [153].
Interactions among smart contracts are constrained within
a prespecified set of precise and predictable situations in
the highly constrained virtual machine. The ordering and
service relationship in manufacturing operations could be
reflected via the connection among smart contracts. Through
the calling relationship, a variety of concise and highly effi-
cient manufacturing smart contracts could be engineered to
form new products and services. Besides the factors that
come from blockchain and virtual machine runtime, the effi-
ciency of smart contracts mainly depends on whether the
contract code is efficient or not. Based on the smart contracts
designed to build the mapping mechanism between individu-
alized requirements [154] and a smart manufacturing system,
various DApps are also crucial in unlocking the value of
blockchain [155].
C. Blockchain Data Mining and Privacy Protection
Transforming toward smart manufacturing services calls for
different computational informatics and data analytics as pre-
requisites to achieve higher efficiency and intelligence [156].
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12 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS
The successful implementations of blockchain-secured
processes have created a large volume of high quantity
and multidimensional manufacturing data [157]. Since
data uploaded into the blockchain are verified by the dis-
tribute node and thus of high quality, data analytics could
be encoded into blockchain nodes. If designed well, the big
data-based decentralized intelligence may result in a smart
and sustainable system [158].
However, manufacturers are usually sensitive to the shar-
ing of manufacturing information. Due to a subjective selfish
tendency of the manufacturer, the information asymmetry phe-
nomenon is evident in manufacturing services trading. The
service provider cannot grasp the individual needs of con-
sumers, while the service demander is not clear about the
provider’s ability and cost function. Taking unilateral or chain
structure as the primary form of the service coordination
lacks practical significance and does not conform to the group
decision-making principle based on the distributed consensus.
Therefore, it is of considerable importance to eliminate bilat-
eral asymmetric information problems between supply and
demand in the manufacturing network. Secure multiparty com-
puting, homomorphic encryption, and searchable encryption
are potential solutions. Further, developing federated learning
algorithms on/across the blockchain promises to unfold the
value of high quality and confidential data cooperatively based
on computing on joint inputs of distributed participants while
making those inputs private.
As information is visible across the entire public blockchain
network, transactions based on the exposed data could be
tracked by users, and thereby, individuals may not be able to
obtain privacy protection under this public consensus mech-
anism. Thus, to avoid any node participants tracking trade
through the available information in the public blockchain, pri-
vacy protection methods such as ZKP and ring/group signature
techniques need to be explored to prevent privacy exposure.
The existing way of broadcasting bookkeeping over the whole
network could be improved so that the upload and verifica-
tion of transaction data can only be carried out at the relevant
nodes in the transaction process.
D. Middleware for Integrating Blockchain Services
Industry 4.0 demands the seamless integration of operations
across all components. The manufacturing events managed
in the distributed shared ledger should be coordinated and
reconciled among various nodes on the network [48]. The
middleware is critical for incorporating blockchain services
to provide secure, traceable, and autonomous manufacturing
implementations among involved nodes [10]. It is essential to
identify the interaction mechanism of manufacturers driven by
blockchain, to model the operation mechanism of manufactur-
ing alliance blockchain explicitly, and to establish adaptive
blockchain logical architecture for manufacturing service. To
empower secure interaction among various nodes of a dif-
ferent place, new algorithms on private-key management,
terminal and network protection, and compliance enable-
ment are needed to authenticate things on the manufacturing
network.
A middleware interface of blockchain security is also
essential to encapsulate the tools supporting the application
development, such as governance and auditing, identity and
access management system, and data connection service. To
facilitate the efficient service request and access of each partic-
ipant in the blockchain P2P network, corresponding interfaces
could be designed and encapsulated for different underlying
blockchain platforms and users of different roles.
IX. CONCLUSION
This article surveys the research progress of blockchain-
secured smart manufacturing in Industry 4.0 vision. To the best
of our knowledge, this article is the first survey of how to use
blockchain to address CIs in the smart manufacturing system.
Eight CIs (CI1–CI8 in Table I) are identified in manufactur-
ing systems. Ten metrics (M1–M10 in Table III) for employing
blockchain in manufacturing systems have been devised. This
study reveals how these CIs have been studied in the litera-
ture. Based on insights obtained from the analysis of research
issues, technical challenges, and standards for compliance,
future research directions for blockchain-secured smart manu-
facturing in Industry 4.0 are outlined, namely: 1) middleware
for integrating blockchain services; 2) highly efficient consen-
sus algorithms and smart contracts; 3) blockchain data mining
and privacy protection; and 4) evaluation of blockchain cyber-
security. We hope that the survey lays a foundation for
making blockchain-secured smart manufacturing a new venue
of research innovation.
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Jiewu Leng received the Ph.D. degree in mechanical
engineering from Xi’an Jiaotong University, Xi’an,
China, in 2016.
He is an Associate Professor with the State Key
Laboratory of Precision Electronic Manufacturing
Technology and Equipment, Guangdong University
of Technology, Guangzhou, China. He has been
a Postdoctoral Fellow with the City University of
Hong Kong, Hong Kong, under the support of the
“Hong Kong Scholars” program since 2018. He
has published 40 papers on IEEE TRANSACTIONS
ON SYSTEMS,MAN,AND CYBERNETICS:SYSTEMS. His current research
interests include blockchain and digital twin.
Shide Ye received the B.S. degree in mechanical engineering from Guangzhou
University, Guangzhou, China, in 2018. He is a postgraduate with the
Guangdong University of Technology, Guangzhou.
His research interests include blockchain and cyber-physical systems.
Man Zhou received the B.S. degree in mechanical engineering from Guangxi
University, Nanning, China, in 2020. He is a postgraduate with the Guangdong
University of Technology, Guangzhou.
His research interests include blockchain and cyber-physical systems.
J. Leon Zhao received the Ph.D. degree in
information systems from the Haas School of
Business, University of California at Berkeley,
Berkeley, CA, USA, in 1992.
He is a Professor of Information Systems with
the School of Management and Economics, Chinese
University of Hong Kong (Shenzhen), Shenzhen,
China. He was the Chair Professor and the former
Head of the Department of Information Systems,
City University of Hong Kong, Hong Kong, from
2009 to 2015. He was an Interim Head and the Eller
Professor of MIS with the University of Arizona, Tucson, AZ, USA. His
research is on information technology and management, including blockchain
and FinTech.
Dr. Zhao received the IBM Faculty Award in 2005. He has been
an Associate Editor of ACM Transactions on Management Information
Systems,Information Systems Research, IEEE TRANSACTIONS ON SERVICES
COMPUTING,andDecision Support Systems. He has co-edited over ten special
issues in various Information System journals.
Qiang Liu received the M.S. degree in com-
puter science from the Guangdong University
of Technology, Guangzhou, China, in 2003, and
the Ph.D. degree in computer science from Sun
Yat-sen University, Guangzhou, in 2009.
He is currently a Professor and the Vice Director
of the State Key Laboratory of Precision Electronic
Manufacturing Technology and Equipment,
Guangdong University of Technology, where he is
also the Director of the Guangdong Provincial Key
Laboratory of Computer Integrated Manufacturing
System. His research interests include intelligent manufacturing and
digital twin.
Wei Guo received the M.S. degree in mechanical engineering from the
Taiyuan University of Technology, Taiyuan, China, and the Ph.D. degree from
Xi’an Jiaotong University, Xi’an, China, in 2019.
He is doing postdoctoral research with Xi’an Jiaotong University.
His current research interests include product–service systems and social
manufacturing.
Wei Cao received the Ph.D. degree in mechanical engineering from Xi’an
Jiaotong University, Xi’an, China, in 2013.
He is an Associate Professor with the Huaqiao University of Technology,
Xiamen, China. His current research interests manufacturing IoT and 3-D
printing.
Leijie Fu received the Ph.D. degree in mechanical engineering from Xi’an
Jiaotong University, Xi’an, China, in 2015.
He is a Lecturer with Xi’an Technological University. His current research
interests include knowledge-based engineering and collaborative design.
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