Recent Advances in Blockchain and Artificial Intelligence Integration: Feasibility Analysis, Research Issues, Applications, Challenges, and Future Work

Abstract

Blockchain constructs a distributed point-to-point system, which is a secure and verifiable mechanism for decentralized transaction validation and is widely used in financial economy, Internet of Things, large data, cloud computing, and edge computing. On the other hand, artificial intelligence technology is gradually promoting the intelligent development of various industries. As two promising technologies today, there is a natural advantage in the convergence between blockchain and artificial intelligence technologies. Blockchain makes artificial intelligence more autonomous and credible, and artificial intelligence can prompt blockchain toward intelligence. In this paper, we analyze the combination of blockchain and artificial intelligence from a more comprehensive and three-dimensional point of view. We first introduce the background of artificial intelligence and the concept, characteristics, and key technologies of blockchain and subsequently analyze the feasibility of combining blockchain with artificial intelligence. Next, we summarize the research work on the convergence of blockchain and artificial intelligence in home and overseas within this category. After that, we list some related application scenarios about the convergence of both technologies and also point out existing problems and challenges. Finally, we discuss the future work.

Full text

Review Article
Recent Advances in Blockchain and Artificial Intelligence
Integration: Feasibility Analysis, Research Issues, Applications,
Challenges, and Future Work
Zhonghua Zhang,
1
Xifei Song ,
2
Lei Liu ,
2
Jie Yin ,
2
Yu Wang ,
3
and Dapeng Lan
4
1
Shenzhen HTI Group Co., Ltd., Shenzhen 518040, China
2
State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an 710071, China
3
Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou 510006, Guangdong, China
4
Department for Informatics, University of Oslo, Postboks 1080 Blindern 0316, Oslo, Norway
Correspondence should be addressed to Xifei Song; cifer.sxf@foxmail.com
Received 19 March 2021; Revised 22 May 2021; Accepted 9 June 2021; Published 25 June 2021
Academic Editor: Neeraj Kumar
Copyright ©2021 Zhonghua Zhang et al. is is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.
Blockchain constructs a distributed point-to-point system, which is a secure and verifiable mechanism for decentralized
transaction validation and is widely used in financial economy, Internet of ings, large data, cloud computing, and edge
computing. On the other hand, artificial intelligence technology is gradually promoting the intelligent development of various
industries. As two promising technologies today, there is a natural advantage in the convergence between blockchain and artificial
intelligence technologies. Blockchain makes artificial intelligence more autonomous and credible, and artificial intelligence can
prompt blockchain toward intelligence. In this paper, we analyze the combination of blockchain and artificial intelligence from a
more comprehensive and three-dimensional point of view. We first introduce the background of artificial intelligence and the
concept, characteristics, and key technologies of blockchain and subsequently analyze the feasibility of combining blockchain with
artificial intelligence. Next, we summarize the research work on the convergence of blockchain and artificial intelligence in home
and overseas within this category. After that, we list some related application scenarios about the convergence of both technologies
and also point out existing problems and challenges. Finally, we discuss the future work.
1. Introduction
As the cutting-edge technologies nowadays, blockchain and
artificial intelligence have attracted increasing attention due to
the irreplaceable role that they play in technological inno-
vation and industrial transformation [1–3]. e concept of
artificial intelligence technology originated from the Dart-
mouth Society in 1956. As an essential branch of computer
science, artificial intelligence technology is dedicated to the
research and development of technical sciences used to
simulate, extend, and expand human intelligence. In recent
years, thanks to the tremendous breakthroughs made in
machine learning (especially deep learning) [4] and the ex-
ponential growth of data, artificial intelligence has ushered in
an explosive period. Due to its advantages in analysis, pre-
diction, judgment, and decision-making, artificial intelligence
can fundamentally empower industries such as security, fi-
nance, retail, transportation, and education [5–8]. Blockchain
technology started relatively late, firstly starting with Bitcoin
proposed by Satoshi Nakamoto in 2008. e blockchain is
essentially a distributed ledger [9, 10]. It can use a decen-
tralized consensus mechanism in an environment where
different entities participate, without the intervention of a
third trusted party. Blockchain also realizes the generation
and verification of transactions in an untrusted distributed
system, building trust at a lower cost [11]. It is precisely
because of this that more and more researchers have con-
centrated on blockchain technology [12, 13].
Hindawi
Security and Communication Networks
Volume 2021, Article ID 9991535, 15 pages
https://doi.org/10.1155/2021/9991535

Artificial intelligence and blockchain have their own
advantages, but each one of them also has corresponding
drawbacks. Blockchain has problems regarding energy
consumption, scalability, security, privacy, and efficiency,
while artificial intelligence faces issues such as interpret-
ability and effectiveness. As two different research direc-
tions, they can be related to each other and have the
advantages of natural integration. ese two technologies
have common demands for data analysis, security, and trust,
and they can empower each other. For instance, artificial
intelligence depends on three key elements: algorithms,
computing power, and data, and the blockchain can break
the island of data and realize the flow of algorithms, com-
puting power, and data resources, based on its inherent
characteristics, including decentralization, immutability,
and anonymization. In addition, blockchain can guarantee
the credibility of the original data as well as the audit
credibility and traceability of artificial intelligence. More-
over, blockchain can record the decision-making of artificial
intelligence, which helps to analyze and understand the
behavior of artificial intelligence and ultimately promotes
the decision-making of artificial intelligence, making it more
transparent, explainable, and trustworthy. Artificial intelli-
gence can optimize the construction of the blockchain to
make it more secure, energy-saving, and efficient.
To date, there has been a certain amount of literature to
review the research of artificial intelligence and blockchain.
However, there is still a lack of generalization and sum-
marization of the work on their integration, and the cor-
relation between the two is not yet reflected. Existing
literature shows that researchers pay attention to the
combination of blockchain and artificial intelligence for
application in a variety of vertical fields and business
[14–18]. In contrast, this paper analyzes the feasibility of the
combination of blockchain and artificial intelligence from a
more comprehensive and three-dimensional perspective,
and extensively collect and demonstrate the combination
points of the two in various research fields. e primary
contributions of this paper can be summarized as follows:
(1) We analyze the relationship between blockchain and
artificial intelligence, as well as the feasibility of their
integration.
(2) We make a comprehensive summary from different
classifications, according to the current domestic and
foreign research on the integration of blockchain and
artificial intelligence.
(3) We select various application scenarios and practical
use cases in various fields to discuss how to integrate
the blockchain and artificial intelligence.
(4) We point out the problems and challenges in the
integration of blockchain and artificial intelligence,
and look forward to the research work in the future.
e rest of the paper is organized as follows. Section 2
and Section 3 introduce the necessary foundation and
background knowledge of artificial intelligence and block-
chain technology respectively. Section 4 analyzes the fea-
sibility of the combination of blockchain and artificial
intelligence. Section 5 discusses the current research issues at
home and abroad in detail. Application scenarios in various
fields are categorized in Section 6. Section 7 describes the
existing difficulties and challenges, and Section 8 presents
the future research progress. Section 9 concludes the paper.
Table 1 contains a detailed list of all acronyms used in this
paper.
2. Artificial Intelligence Technology
Artificial intelligence technology started in 1956 and has
experienced three peaks of development from 1956 to 1970,
1980 to 1990, and 2000 to the present. e proposal for
machine learning in 1959 promoted the peak of the first
development. e United States and Japan were dedicated to
artificial intelligence research in the 1980s and 1990s which
promoted the peak of the second development. Benefiting
from the breakthrough of deep learning and reinforcement
learning algorithms, the exponential growth of network data
and the qualitative leap in computing power, artificial in-
telligence has entered the third period of rapid development
[19, 20]. Artificial intelligence includes the following key
technologies: computational vision technology, natural
language processing technology, cross-media reasoning
technology, intelligent adaptive learning technology, swarm
intelligence technology, autonomous drone system tech-
nology, smart chip technology, and brain-computer inter-
face technology, which can be widely used in various
industries, such as healthcare, driverless cars [21], education
development, games, entertainment, Internet of ings
[22, 23], maritime Internet of ings [24, 25], and com-
munication networks [26, 27].
3. Blockchain Technology
3.1. Concept of Blockchain. Blockchain technology is a kind
of distributed ledger technology that stores data in a chain
data structure. It is a new distributed infrastructure and
computing paradigm, which employs the distributed node
consensus algorithm to verify the transaction data and
further synchronize the entire network, as well as uses
cryptography to ensure data security and credibility [28].
3.2. Characteristics of Blockchain
3.2.1. Multicenter. e blockchain adopts distributed
decentralized storage, so the distributed recording, storage,
and update of data can be realized without a single central
point. Since there is no centralized hardware or management
organization, any node can operate on the data on the
blockchain according to the established rules.
3.2.2. Transparency. e system data of the blockchain is
open and transparent, and any node can have a general
ledger of the entire network. Except for the private infor-
mation of the directly related parties of the data being
encrypted through asymmetric encryption technology, the
2Security and Communication Networks

blockchain data are open to all nodes, so the entire system
information is highly open and transparent.
3.2.3. Autonomy. e blockchain system has multiple
participants, and they have formulated automatically ne-
gotiated specifications and protocols based on open rules
and algorithms. Each node in the system always follows these
specifications and protocols during operation. is ensures
that every transaction in a trustless environment can
guarantee its correctness and authenticity. e nodes can
securely exchange, record, and update data, and operations
that do not follow the specifications and protocols will not
take effect.
3.2.4. Immutability. After the transaction information of the
blockchain passes the consensus of all nodes and is recorded
in the block, there is a complete backup locally on each node.
At the same time, the correlation between blocks is carried
out by the hash algorithm. If you want to tamper with a piece
of data, you need to modify all subsequent blocks, which is
very costly.
3.2.5. Traceability. Each node in the blockchain saves all the
records in the history. Any piece of data can be found by
traversing the local blockchain data, which makes all the data
on the blockchain chain traceable.
3.2.6. Programmability. e nature of the blockchain pro-
vides a trusted application environment for the execution of
smart contracts, so the blockchain can provide users with
programmable data manipulation capabilities. Users can
customize smart contract rules that meet their needs. At the
same time, due to its open and automatic execution char-
acteristics, it also guarantees the security of assets and data
on the chain.
3.3. Concept of Blockchain. e rich application scenarios of
blockchain are basically based on the four core technologies
of blockchain, namely, consensus mechanism, data struc-
ture, cryptography, and distributed storage. As the key fu-
ture research direction of blockchain technology,
cross-training technology has gradually become one of the
core technologies of blockchain.
3.3.1. Consensus Mechanism. To ensure that nodes are
willing to take the initiative to keep accounts, the blockchain
has formed an important consensus mechanism. Common
consensus mechanisms are as follows: (1) e proof of work
mechanism (PoW) is the original consensus mechanism,
and all participating nodes compete for bookkeeping rights
by comparing computing power. Since everyone partici-
pates, but only one node can be selected, many resources and
time costs will be wasted. (2) For the proof-of-stake (PoS)
mechanism, the longer you hold the digital currency and the
more assets you hold, the more likely this mechanism is to
obtain the right to bookkeeping and rewards, which saves
time but easily causes the Matthew effect. (3) e delegated
proof-of-stake mechanism (DPoS) selects representative
nodes for proxy verification and accounting, which is
simpler and more efficient, but it also sacrifices some de-
centralization to a certain extent.
3.3.2. Data Structure. e blockchain is similar to an iron
chain in form, consisting of one block after another to form a
complete chain. Each block includes a block header and a
block body. e blocks are linked back and forth through the
hash pointer in the block header. e hash value contained
in each block header is similar to a digital fingerprint of all
the data in the previous block, so there is an interlocking
connection between each block. is relationship forms a
chain. When any data in the block are modified, all sub-
sequent hash values will change. Such a structure and
content constitute the entire blockchain.
3.3.3. Cryptography. Blockchain uses killer feature-cryp-
tography. e symmetric encryption is equivalent to using
the same key to open and lock the door. Asymmetric en-
cryption is equivalent to using a pair of different keys to open
and lock the door, namely, public key and private key. If you
use the public-key encryption, you can use the private key to
decrypt; if you use private-key encryption, you can use the
public key to decrypt. ese two keys are generally stored in
the user’s personal wallet. Once the private key is lost, the
assets are gone. It is relatively safe in the blockchain in which
the public key and private key are formed through multiple
transformations, and the characters are relatively long and
complex [29].
3.3.4. Distributed Storage. e most attractive thing about
blockchain is its distributed storage mechanism. e in-
formation record on each block in the blockchain is
recorded by each node participating in the bookkeeping
competition. To prevent some malicious nodes from doing
damage, the new data in the blockchain that adopts the PoW
consensus mechanism need to be unanimously confirmed
and agreed upon by most nodes, and at least 51% of the
nodes must agree. erefore, it is difficult to tamper with
data.
Table 1: Abbreviations and their corresponding meaning.
Acronym Meaning
AI Artificial intelligence
ML Machine learning
DRL Deep reinforcement learning
DNN Deep neural networks
PoW Proof of work
PoS Proof of stack
DPos Delegated proof of stake
D2D Device-to-device
BaaS Blockchain as a service
MEC Mobile edge computing
DApp Decentralized application
Security and Communication Networks 3

3.3.5. Cross-Chain Technology. Cross-chain technology is an
important technical means for blockchain to realize inter-
connection and improve scalability. In terms of network
morphology, blockchain is different from the Internet. e
latter supports one network to connect to global nodes, while
the former forms multiple isolated parallel networks. In
addition to the extensive coexistence of public chains, pri-
vate chains and consortium chains allow different organi-
zations to have their own blockchains and even allow
multiple blockchains to run simultaneously within the same
organization. e number of global blockchains is in-
creasing, and the isolation of different blockchain networks
makes it impossible to effectively carry out operations, such
as digital asset transfer and cross-chain communication
between chains. In the cross-chain process, the two most
important things are: e first is to recognize atomicity, that
is, cross-chain transactions either happen or do not happen,
so that honest nodes will not be damaged; the second is to
ensure that the total assets on each chain will not decrease.
4. Feasibility Analysis of the Integration of
Blockchain and Artificial Intelligence
e combination of artificial intelligence and blockchain is
complementary. Blockchain provides a trustworthy foun-
dation for artificial intelligence, and artificial intelligence
provides the landing conditions for blockchain.
4.1. Blockchain Empowers Artificial Intelligence
4.1.1. Transparent and Reliable Data Sources. To more se-
curely share data among multiple organizations, it is par-
ticularly important to ensure the transparency and reliability
of data sources. Smart blockchain technology ensures the
transparency of data on the chain through the synchroni-
zation of the full ledger of the nodes and ensures the
traceability of data through transaction signatures and time
stamps and so on after certificate authentication. A trans-
parent and reliable information-sharing channel has been
constructed among multiple participants.
4.1.2. Strong Fairness Guarantee. e traditional blockchain
system rewards miners who work hard for the normal
operation of the system through tokens and promotes the
good operation of the system by ensuring fairness. e party
who misbehaves in multiple parties will be punished eco-
nomically, and the honest party will be compensated ac-
cordingly. Smart blockchain technology ensures that system
participants can obtain corresponding rewards as long as
they honestly abide by the agreement through automatically
executed preset smart contract codes. At the same time, the
condition-triggered automatic transfer mechanism is used to
distribute the rewards to the corresponding participants,
which provides a strong fairness guarantee for the intelligent
scene of multiparty participation.
4.1.3. Efficient Autonomy. As a distributed ledger technol-
ogy, the main feature of blockchain is decentralization.
Decentralization means that there is no authoritative center
or server to manage the entire system, so the blockchain
system will not be controlled by a single organization. Using
the automatic execution characteristics of smart contracts,
predefining management rules in smart contracts can reduce
the uncertainty and possible attacks brought about by the
human operation process [30, 31].
4.1.4. Privacy Protection. As increasingly more data content
is shared on the chain, the privacy of users may be directly or
indirectly leaked [32]. Traditional blockchain systems use
pseudonyms, shuffling, and other methods to protect users’
privacy, but malicious attackers can nevertheless steal users’
private information through data mining and analysis. In the
new smart blockchain system, some cryptographic tech-
nologies with excellent security performance are used to
protect users’ data privacy. Li et al. [33] proposed a privacy
protection scheme based on ring signatures using an
anonymous signature method based on an elliptic curve
encryption algorithm to protect privacy. Cai et al. [34]
provided a privacy protection scheme based on Pedersen’s
commitment for the deletable blockchain system, which can
hold users accountable when necessary while protecting
privacy. e Prada-Delgado team [35] used zero-knowledge
proof technology to identify the Internet of things devices in
the smart blockchain system, which can protect the data
privacy of lightweight devices efficiently and at low cost.
4.1.5. Distributed Computing Power. Artificial intelligence is
usually provided by a single unit of computing capacity or
computing platform. With the rapid increase in the amount
of data and the obvious increase in computational com-
plexity, it is difficult for traditional computing platforms to
independently provide the computing capacity required for
artificial intelligence, and the hardware costs and mainte-
nance costs of enterprises are also rising [36]. Blockchain
realizes the decentralization of computing capacity with its
distributed nature, which is helpful to realize the operation
of artificial intelligence models on global mass decentralized
nodes and realize decentralized computing. Lin et al. [37]
propose a new wireless edge intelligence framework to
achieve stable and robust edge intelligence through energy
collection methods on a permissioned edge blockchain, and
design the optimal edge learning strategy to maximize the
efficiency of edge intelligence.
4.2. Artificial Intelligence Empowers Blockchain. e design
and operation of blockchain involves thousands of pa-
rameters, as well as the trade-off of security, throughput,
decentralization, and other parameters. Artificial intelli-
gence technology can simplify these decisions and optimize
the blockchain to achieve higher performance and better
governance. Moreover, artificial intelligence can also im-
prove the intelligence of blockchain applications and reduce
errors caused by human influence.
4Security and Communication Networks

4.2.1. Security. As we all know, unless the adversary owns
the majority of mining rights, blockchain is almost im-
possible to hack. Unfortunately, the programs and functions
of decentralized applications built on the blockchain plat-
form are not so secure. For example, in the DAO incident
[38], hackers took advantage of loopholes in smart contracts
to repeatedly withdraw funds, resulting in a loss of $50
million. Artificial intelligence technology provides new
development opportunities for the intelligentization of
blockchain system security protection and can provide se-
curity and technical support for the entire life-cycle of
blockchain transactions. As far as the security of smart
contracts is concerned, some work has been done. Raja et al.
[39] have used artificial intelligence technology to auto-
matically generate smart contracts, so as to reduce the
vulnerability of smart contracts as much as possible. Fur-
thermore, data mining and other technologies are used to
analyze the vulnerabilities of the smart contract, and big data
analysis is used to check malicious vulnerabilities to avoid
economic losses caused by hackers. e involvement of
artificial intelligence in the blockchain can make smart
contracts more intelligent and efficient, allowing them to
form a more complete code through continuous learning
and practice and reshape the capabilities of blockchain smart
contracts.
4.2.2. Efficiency. In the industrial sector, a large number of
mature blockchain systems have been put into practical
application, and more enterprises are increasing investment
in the application of blockchain. However, due to the
limitation of data storage mode, the blockchain system
generally faces serious problems of simple query function
and low query performance. e reason is that the under-
lying data storage systems of most blockchain systems use
levelDB, a data storage system designed for write-intensive
applications. At the cost of data reading performance,
writing performance has improved. With the increase of
data and the expansion of applications in blockchain sys-
tems, it is often necessary to deal with frequent queries. e
underlying storage system has excessive writing perfor-
mance but insufficient reading performance, which has
become the main bottleneck limiting the query performance.
e data-storing methods of blockchain can likewise be
enhanced with the assistance of AI algorithms. Gawas et al.
[40] propose an AI-based novel TTA-CB protocol to es-
tablish a secure and distributed blockchain for data man-
agement in VECONs and utilize a PSO algorithm to solve
the optimal data provider selection problem. Artificial in-
telligence technology has brought new opportunities for the
development of blockchain. rough continuous learning
and practice, it has significantly improved the speed of data
query and the efficiency of blockchain applications.
5. Research Issues of Blockchain and Artificial
Intelligence Integration
In this section, we classify and summarize the applications of
blockchain and artificial intelligence integration.
5.1. Sharing Applications. e information age has ushered
in an explosive growth of data, and the value of data lies in
circulation. However, the existing data trust system is not
perfect, which restricts the secure circulation of data and
affects the development of the industry. Blockchain tech-
nology can provide new technical means for data sharing
due to its inherent characteristics, such as immutability,
decentralization, and traceability. However, in general data-
sharing applications, blockchain is often only used as a
secure and reliable distributed database. Because of missing
data analysis capability, the practicality of blockchain is
greatly reduced. For this reason, artificial intelligence
technology can be used to compensate for the deficiencies of
blockchain and enhance the value of its applications.
In the industrial IoTdriven by mobile crowd sensing, Liu
et al. [41] combined Ethereum and deep reinforcement
learning to propose a joint framework to ensure effective
data collection and secure data sharing. To achieve maxi-
mum data collection, minimum energy expenditure, and
regional fairness, the authors used a distributed deep re-
inforcement learning mechanism to help smart mobile
terminals perceive nearby points of interest and then used
blockchain technology to ensure the security and reliability
of data sharing.
To ensure flexible and secure resource sharing, Dai et al.
[42] made full use of the advantages of blockchain and
artificial intelligence to construct a secure and intelligent
network architecture for the next generation of wireless
networks, as shown in Figure 1. Blockchain technology was
used to establish a secure and distributed resource-sharing
environment, while artificial intelligence technology was
used to solve the problems of uncertainty, time variation,
and complexity in wireless systems. In particular, the au-
thors used the consortium blockchain to establish a secure
content-caching environment and used deep-reinforcement
learning to design a caching mechanism to maximize the use
of cache resources.
To reduce the burden of transmission and address
privacy issues, Lu et al. [43] established a network archi-
tecture based on federated learning. e authors ensured the
security and stability of model parameters through a hybrid
blockchain architecture that integrated consortium block-
chains and local directed acyclic graphs and then designed
an asynchronous federated learning mechanism empowered
by deep-reinforcement learning to improve model-learning
efficiency. e model was integrated into the blockchain,
and two-phase verification was performed to ensure the
reliability of the shared data.
Using video analysis technology based on artificial in-
telligence, the current intelligent surveillance system can
provide more diversified services. However, there are still
security and privacy issues caused by malicious attackers and
untrusted third parties. To solve this problem, the block-
chain technology developed by Lee et al. [44] was used to
ensure the integrity and security of cloud-based intelligent
monitoring systems. e proposed Merkle-Tree method can
promote the effective transmission of video data, help reduce
the bandwidth required for transmission and the overhead
of redundant data storage, and realize the secure
Security and Communication Networks 5

synchronization of video data without exposing the privacy
of the target.
At present, the wearable device market is growing,
storing a large amount of personal health data, which can be
used to implement various health-related applications.
Blockchain technology transparently records these massive
amounts of health data, which can provide support to some
researchers and commercial companies while also protecting
the privacy of data providers. Bagchi et al. used neural
networks to process different types of cardiovascular clinical
data and integrated them into the main cardiovascular
output [45]. ese outputs were shared with patients and
doctors through the designed blockchain mechanism. To
deal with low data quality in data sharing, Zheng et al.
proposed a data quality check module based on machine
learning. When integrated into the system, the module can
analyze the high-quality data required by related applica-
tions [46].
5.2. Security Applications. With the continuous develop-
ment of the blockchain system, the perfection of smart
contracts and incentive mechanisms will depend on the
occurrence of malicious behavior in the system. On the one
hand, these malicious behaviors pose huge challenges to the
security of the blockchain system. On the other hand, the
large amount of data generated by the blockchain will in-
crease the difficulty of reviewing and detecting malicious
behavior. e integration of blockchain and artificial in-
telligence is conducive to enhancing the existing blockchain
system.
Smart contracts may contain wrong codes and loopholes,
which can easily cause huge financial losses. e current
smart contract vulnerability detection methods mainly focus
on symbolic execution and dynamic execution methods with
low accuracy. Liao et al. proposed a smart contract vul-
nerability detection method [47], namely, SoliAudit. is
method used both static and dynamic testing technologies
and enhanced smart contract vulnerability detection capa-
bilities through machine learning and dynamic fuzzers. e
SoliAudit method achieved up to 90% vulnerability iden-
tification accuracy on 17,979 samples and can still quickly
adapt to new unknown weaknesses without expert knowl-
edge and predefined features. Zhuang et al. also proposed a
vulnerability detection method fused with machine learning,
using a graph neural network method to detect smart
contracts from another perspective [48]. Aiming at the
syntactic and semantic structure of the smart contract
function, the authors constructed a contract graph and
designed an elimination phase to normalize the graph and
highlight the main nodes. Furthermore, a nondegree graph
convolutional neural network and a novel time information
dissemination network were proposed to learn from the
normalized graph and detect smart contract vulnerabilities.
e incentive mechanism is the core of the public chain.
It encourages participants to run and ensure the security of
the underlying consensus protocol. However, it is very
difficult to design an incentive mechanism compatible with
incentives. Hou et al. proposed a framework based on deep
learning to detect vulnerabilities in the blockchain incentive
mechanism-SquirRL [49], as shown in Figure 2. Developers
of the protocol can use SquirRL as a general method to test
the deficiencies of the incentive mechanism. SquirRL does
not provide theoretical guarantees, but its instantiation is
very effective in checking adversarial strategies, which can be
used to show that an incentive mechanism is insecure.
e blockchain system will generate a large amount of
transaction data, which brings certain challenges to the
review and detection of malicious behavior. e authors in
[50] proposed a method of using data mining and machine
learning to detect and capture Ponzi schemes that occurred
in Ethereum. is method first extracted features from user
accounts and operating codes of smart contracts and then
built a classification model to detect potential Ponzi
Verifier
1. Send caching request
Verifier
6. Verify the created block
and add it into blockchain
Leader
2. Make caching pairs based on DRL
3. Deliver content and pay the reward
4. Record D2D caching transactions and broadcast them 5. Create a new block and broadcast it
Figure 1: Secure content storage based on consortium blockchain.
6Security and Communication Networks

schemes. DOORChain was proposed in [51] for the mali-
cious behavior of blockchain. It combined three powerful
intrusion and malicious detection methods, namely, deep
learning, ontology, and operations research. is method
utilized the constraints of operations research to formalize
and detect malicious behaviors on the network, especially
using ontology to detect behavioral malicious behavior, and
then used feedback from this formalization of deep learning
to check whether transactions in the blockchain were
malicious.
5.3. Transaction Applications. Blockchain has great advan-
tages in protecting data, while artificial intelligence is good at
analysis, prediction, and judgment. e combination of the
two can be used for related research, such as price prediction
and transaction analysis.
McNally et al. applied machine learning technology to
predict the price trend of Bitcoin and completed this task by
Bayesian optimization of recurrent neural networks and
long short-term memory networks [52]. McNally et al. also
compared the experimental results with the results predicted
by the popular ARIMA model and found that the effect of
nonlinear deep learning methods was better than that of
ARIMA prediction. Users in the Bitcoin system used
pseudonymous Bitcoin addresses as transaction accounts,
making Bitcoin address correlation analysis a challenging
task. In this case, a new Bitcoin address association scheme
was proposed in [53], making it possible to track addresses in
the Bitcoin system. After extracting the Bitcoin addresses,
the authors converted the address clustering problem into a
binary classification problem to reduce the computational
complexity. en, the system analyzed whether the two
Bitcoin addresses belong to the same user by constructing a
two-layer model. Finally, the system clustered addresses
belonging to the same user. Shao et al. proposed a deep
learning method to implement address-user mapping [54],
which makes it possible to realize user identification in the
Bitcoin system. Shao et al. mapped the representation of the
address to the Euclidean space and used a deep neural
network to embed transaction behavior, thereby obtaining
the feature vector of each address. Finally, the owner of the
address is identified through address verification, identifi-
cation, and clustering.
5.4. Deposit Applications. Blockchain can guarantee the
authenticity, integrity, and credibility of stored digital in-
formation due to its inherent characteristics. Artificial in-
telligence can assist in data analysis and processing as well as
the natural evolution and dynamic adjustment of smart
contracts. Combining blockchain and artificial intelligence
technology can provide a wide range of application scenarios
for data storage, retrieval, and inspection services.
Immunization is an indispensable mechanism for pre-
venting infectious diseases in modern society. Vaccine safety
is closely related to public health and national security.
However, issues such as vaccine expiration and vaccine
record fraud are still common in the vaccine supply chain.
erefore, there is an urgent need to establish an effective
vaccine regulatory system. To this end, Yong et al. developed
a vaccine blockchain system based on blockchain and ma-
chine learning technology [55]. Blockchain technology aims
to change the current information management method and
establish a new trust mechanism, while machine learning
technology provides an additional method for data analysis
in the information management system. e vaccine
blockchain system was designed with a smart contract based
on Ethereum to query personal vaccination records and
vaccine circulation for consumers and to track vaccine
operation records for vaccine institutions and governments.
If the vaccine has liability issues, it can be solved through the
vaccine blockchain system.
For environmental protection considerations, electric
cars are regarded as an important tool for green city projects.
As the demand for electric cars increases, it is not easy for
users to find suitable charging facilities. On the other hand,
energy companies operate their own charging stations for
their own purposes, and their charging information is not
transparent to the outside world. To solve these problems, Fu
et al. [56] proposed a charging system for electric cars, which
provided users with convenient charging services by real-
izing collaboration between energy companies, as shown in
Figure 3. e author used the consortium blockchain to
Target incentive
mechanism
Desired (honest)
strategy
Learning framework
Setup
State s
Policy
Action a
Agent
(i) Environmental
randomness
(ii) Apply agent
actions
Environment
DRL pipeline Update pipeline
Collect rewards
Adversarial
strategies
and outcomes
Tune setup parameters
State space
Action space
Rewords
Agent model
DRL algorithm
(i)
(ii)
(iii)
(iv)
(v)
Figure 2: Schematic diagram of the SquirRL learning framework.
Security and Communication Networks 7

realize the management and recording of charging infor-
mation between energy companies. In particular, smart
contracts were designed to balance the company’s charging-
user-scheduling problem to ensure the fairness of the
benefits of different energy companies.
e food supply chain is a complex system involving
many stakeholders, such as farmers, production plants,
distributors, retailers, and consumers. Information asym-
metry between different stakeholders is an important reason
for fraud, and the application of blockchain helps ensure
food safety. However, there are some studies that are more
inclined to study the traceability of food than supervision. In
this regard, Mao et al. designed a blockchain-based credit
evaluation system to strengthen the effectiveness of super-
vision and management of the food supply chain [57]. e
system collected credit evaluation texts from traders through
smart contracts, analyzed the texts collected by a network
called long- and short-term memory, and used the credit
results of traders as a reference for supervision and
management.
e voting system is a powerful means to ensure fairness.
Because of this, various complex security measures are used
to ensure the security of the voting system. In view of the
transparency and auditability issues of the current voting
system, Pawlak et al. used smart methods to improve the
electronic voting system based on blockchain [58]. e
system aimed to provide a secure electronic voting solution
that can resist voting tampering and fraud, and can be
audited and verified by public voters.
5.5. Resource Management Applications. Both blockchain
technology and artificial intelligence technology need the
support of network resources, so their integration in re-
source management applications involves many scenarios.
Considering that large computing and energy resources are
consumed in the process of blockchain mining, Loung et al.
proposed an optimal auction mechanism based on deep
learning to allocate edge computing resources [59]. In this
mechanism, the provider of edge computing services can
support the offloading of mining tasks from mobile devices
(i.e., miners) in a mobile blockchain environment. Based on
the analytical solution of the optimal auction, the authors
constructed a multilayer neural network structure. e
network first implemented the monotonic transformation of
absenteeism bids and then provided rules for the allocation
and conditional payment for absenteeism. e estimate of
absenteeism was used as training data to adjust the pa-
rameters of the neural network to maximize the loss
function. Similarly, Asheralieva et al. [60] conducted re-
search on resource management and pricing in IoT systems
and discussed resource management solutions in blockchain
as a service (BaaS) and mobile edge computing (MEC)
scenarios [61, 62].
Feng et al. [63] aimed to optimize the blockchain system
more comprehensively, improve the security and privacy of
MEC as much as possible, and solve the problem of task
offloading of MEC. e authors took the computing speed of
the edge computing system and the throughput of the
blockchain system as the joint optimization goal. To meet
the performance requirements of the system, the collabo-
rative offloading decision-making, energy allocation, block
size, and block interval were jointly optimized. Aiming at the
dynamic characteristics of wireless channels and available
resources, the optimization problem was modeled as a
Markov decision process problem, and a deep reinforcement
learning algorithm that can stably train neural networks was
proposed.
Liao et al. constructed a secure and intelligent task
offloading architecture using blockchain and smart contracts
to promote fair scheduling of tasks and alleviate various
security attacks [64]. e authors quantified the success
probability of task offloading through a trust index based on
subjective logic and then proposed a trust evaluation
mechanism. In addition, an online intelligent algorithm was
designed to learn the long-term optimal offloading strategy,
and a good balance was achieved between task offloading
delay, queuing delay, and switching overhead. Federated
learning that supports blockchain is usually limited by en-
ergy and CPU when performing collaborative training on
mobile devices, which will increase the training delay due to
the blockchain mining process. Hieu et al. [65] proposed a
resource management scheme based on deep reinforcement
learning. e author modeled the blockchain network in the
federated learning system as an M/M/1 queue, proposed a
random optimization model for the resource management
of the machine learning model owner, and used deep re-
inforcement learning to provide an optimal solution for the
model.
e architecture and data sources of the energy Internet
are becoming increasingly complex. It is a good opportunity
and trend to use blockchain technology to combine energy
equipment and data information. Applying blockchain
technology and broad learning technology based on the
Energy Internet platform, Zhai et al. [66] conducted mul-
tisource data fusion calculations. e authors introduced in
detail a novel combination model that comprehensively
processes energy equipment data, blockchain data, and user
Company CompanyGovernment
Blockchain
Charging equipment
Charging electric car
Figure 3: Tram charging strategy.
8Security and Communication Networks

feedback data, using convolutional neural network modules
to deal with prediction accuracy and computational com-
plexity and using long- and short-term memory network
modules to enhance the system’s advantages in long-term
memory.
With the development of the Internet of ings, data
centers will generate massive amounts of data. On the one
hand, these data can give rise to different data-driven ser-
vices; on the other hand, they will bring about further energy
expenditures. Xu et al. [67] used grid and green energy to
solve the problem of how to reduce the overall energy ex-
penditure. To this end, the authors proposed a distributed
resource management architecture based on blockchain. e
architecture can record all transaction activities without any
scheduling. In addition, the authors used the reinforcement-
learning-based demand migration method with embedded
smart contracts to save costs.
5.6. Scalability Optimization Applications. With the increase
in the number of transactions, the scalability of the block-
chain has gradually become a key bottleneck, restricting the
development of the blockchain. e choice of consensus
algorithm plays an important role in the actual solution of
the scalability problem. e method, based on Byzantine
fault tolerance, is most preferred to solve the scalability
problem of the blockchain network. Bugday et al. [68]
proposed a new model to replace the proof of work to form a
consensus group. is consensus group allowed the use of
Byzantine fault tolerance methods in public blockchain
networks. e model used an online learning algorithm of
decision theory to calculate the reputation value for the
nodes that wish to participate in the consensus committee
and selected nodes with a higher reputation value for the
consensus committee to reduce the chance of harm to the
nodes in the consensus committee.
Another way to expand the blockchain is slicing tech-
nology. It can divide the network into fragments for con-
current transaction processing. Most of the existing
blockchain systems use proof-of-work consensus protocols
to create fragments. Ruparel et al. [69] proposed a network
sharing algorithm based on machine learning. is algo-
rithm can quickly and accurately create fragments, map the
IP addresses of nodes to geographic coordinates, and then
use the k-means clustering algorithm to divide these co-
ordinates into fragments. e nodes in the shard were
geographically closer, thereby reducing the propagation
delay in the network during intrashard communication.
Compared with the PoW-based slicing algorithm, Geo-
Sharding is significantly faster, bringing scalability to a new
level.
To solve the scalability and increase the throughput of
the Industrial Internet of ings, Liu et al. [70] proposed a
system performance optimization framework based on deep
reinforcement learning and blockchain, which quantitatively
evaluated the new blockchain system from four aspects,
namely, scalability, decentralization, security, and delay.
en, Liu et al. designed an algorithm based on deep re-
inforcement learning to dynamically adjust the block
producers, consensus algorithm, block size, and block in-
terval, which improve the performance of the system and
promote the wide application of the system.
e emerging federated edge learning technology can
not only ensure good machine learning performance but
also solve the “data island” problem caused by data privacy
issues. However, large-scale federated edge learning tech-
nology lacks a secure and effective communication model
training program, and there is no updateable and flexible
framework to update local models and global model sharing
(transaction) management. Kang et al. [71] proposed a
blockchain-based federated edge learning system, which has
a hierarchical blockchain framework composed of a main
chain and subchains. It can separately manage local model
updates or model sharing to achieve performance isolation.
is framework can also realize scalable and flexible dis-
tributed federated edge learning.
Table 2 summarizes the applications of blockchain and
artificial intelligence integration based on the above
classification.
6. Application Scenarios
In this section, we select application scenarios and practical
use cases in various fields.
6.1. Smart Grid. A smart grid is part of the energy Internet,
where everyone contributes to the energy supply
[12, 72, 73]. Distributed energy trading is the current
mainstream development trend of smart grids, but tradi-
tional centralized grid systems cannot be organically
combined with distributed energy trading. erefore, the
decentralized characteristics of smart blockchains can ef-
fectively help smart grids realize the transformation from
centralization to distribution [12]. e decentralization of
smart blockchain breaks information barriers and realizes
secure data sharing among multiple participants. In ad-
dition, smart blockchain technology can reduce the op-
eration and maintenance costs of smart grids and improve
the participation of market players.
6.2. Internet of Vehicles. With the development of com-
munication technologies, Internet of Vehicles is playing an
increasingly important role in smart transportation [74–76].
e Internet of Vehicles can help solve existing traffic and
road safety problems through vehicular communication, but
there may be a crisis of trust and safety hazards in the process
of information exchange [77, 78]. Smart blockchain can
provide trust guarantees, reliable data security, and effective
incentive mechanisms, and can also escort the development
of Internet of Vehicles technology. Blockchain introduces
elements, such as cars, people, and service providers, into the
chain. rough its transparency, anonymity, and immuta-
bility characteristics, it can ensure mutual trust between
different elements, strengthen data information security,
and promote data information sharing.
Security and Communication Networks 9

Table 2: Summary of integrated applications of blockchain and artificial intelligence.
Subject Literature Time Contribution Goal
Sharing applications
[41] 2018 Proposed a data collection scheme based on deep
reinforcement learning
Smart mobile terminal data collection and
sharing
[42] 2019 Used deep reinforcement learning to optimize the
system’s cache resource utilization
Realize secure resource sharing in wireless
network
[43] 2020
Selected nodes through deep reinforcement
learning to improve the efficiency of federated
learning
Solve the problem of collaborative training in
the Internet of Vehicles
[44] 2020 Provides blockchain-based privacy preserving
multimedia intelligent video surveillance
Ensure the integrity and security of cloud-
based intelligent monitoring systems
[45] 2019
Integrated machine learning and natural language
processing, which can detect different types of
cardiovascular clinical data
Predict the type of illness and simplify the
diagnosis process
[46] 2018 Proposed a data inspection module based on
machine learning Securely sharing personal information
Security applications
[47] 2019 Combined machine learning and fuzzing to detect
contract vulnerabilities Detect smart contract vulnerabilities
[48] 2020 Proposed a vulnerability detection model based on
GNN Detect smart contract vulnerabilities
[49] 2019 Proposed a detection framework based on deep
reinforcement learning
Detect loopholes in the blockchain incentive
mechanism
[50] 2018 Proposed a classification model combining data
mining and machine learning Detect Ponzi schemes in Ethereum
[51] 2019 Proposed a DOORChain model that integrates
deep learning, ontology, and operations research
Detect malicious transactions in the
blockchain
Transaction
application
[52] 2018
Proposed two prediction models based on cyclic
convolutional network and long short-term
memory algorithm, respectively
Predict bitcoin price
[53] 2019 Proposed an association scheme based on binary
classification Bitcoin address correlation analysis
[54] 2018 Proposed a recognition scheme based on DNN Bitcoin address-user identification
Deposit application
[55] 2020 Proposed a vaccine blockchain system integrated
with machine learning Vaccine supervision and recommendation
[56] 2020 Proposed a smart tram charging system based on
consortium blockchain
Solve the problem of independent operation
of energy companies and opaque charging
information
[57] 2018 Designed a blockchain-based credit evaluation
system
Strengthen the effectiveness of supervision
and management of the food supply chain
[58] 2019 Designed an electronic voting system based on
blockchain using intelligent agents Guarantee the security of voting
Resource
applications
management
[59] 2018 Proposed an optimal auction mechanism based on
deep learning Edge computing resource allocation
[60] 2019 Proposed a new type of hierarchical reinforcement
learning algorithm
Dynamic resource management of the IoT
system
[63] 2019 Proposed an actor-critic algorithm with
asynchronous advantages of stable training
Solve the computing offload problem of
mobile edge computing
[64] 2020
Proposed a secure and intelligent vehicle task
offloading strategy based on blockchain and
learning algorithms
Reduce task delay and switching overhead
under the premise of ensuring security,
privacy, and fairness
[65] 2020 Proposed a resource management scheme based
on deep reinforcement learning System resource management
[66] 2020 Proposed a fusion model of blockchain and width
learning Forecast user energy demand
[67] 2017 Researched the smart resource management
strategy of cloud data center based on blockchain Save the energy cost
Scalability
optimization
applications
[68] 2019 Utilized machine learning to build a consensus
committee Improve blockchain scalability
[69] 2020 Addressed clustering and fragmentation based on
k-means algorithm Efficient fragmentation
[70] 2019 Designed a deep reinforcement learning algorithm
to improve the scalability of the blockchain
Solve the scalability problem of the Industrial
Internet of ings and improve throughput
[71] 2020 Combined federated learning to improve
blockchain scalability Design a secure federal edge learning system
10 Security and Communication Networks

6.3. Supply Chain. Blockchain technology has become an
important technical means to break through the develop-
ment constraints of traditional supply chains because of its
decentralization, high reliability, and immutability. Using
the blockchain network to publish the information data
stored in the database can leverage the accurate and rapid
sharing and collaboration of logistics data as well as effec-
tively solve the problem of information asymmetry between
upstream and downstream enterprises in the supply chain
system. e application of artificial intelligence technology
in the blockchain system can redefine the supply chain by
automating the entire workflow. When integrated with the
blockchain, the artificial intelligence platform can discover
useful information from point-of-sale sales data, historical
purchase data, etc., so that data characteristics can be
identified, and predictive analysis can be implemented,
including future demand forecasts, sales model forecasts,
path planning, and network management.
6.4. Health Care. With the development of the social
economy, health care has entered a stage of rapid devel-
opment. However, there are certain problems that need to be
resolved. On the one hand, users have extremely high re-
quirements for the security of personal information and
health data; on the other hand, data sharing between medical
institutions can achieve an accurate and effective diagnosis
and medical treatment. Blockchain can solve the above
problems. rough its immutability, the blockchain is
conducive to data tracking and anti-counterfeiting while
using a reliable trust mechanism. e blockchain can realize
secure data sharing. e use of artificial intelligence tech-
nology can then mine the hidden value behind the data,
thereby allowing more comprehensive data analysis.
7. Problems and Challenges
In this section, we point out the problems and challenges in
the integration of blockchain and artificial intelligence.
7.1. Scalability. e scalability issue is the key to the smooth
implementation of smart blockchain applications. Block-
chain decentralized application (DApp) must run on the
underlying platform of the existing blockchain. If the per-
formance and scalability of the system are insufficient, it
cannot be implemented as a large-scale application. On the
premise of ensuring data security and decentralization, the
scalability challenges of blockchain mainly include three
aspects, namely, consistency issues, network delays, and
performance limitations. To ensure the security of the
blockchain, most nodes need to reach a consensus on the
transaction data. One-sided pursuit of scalability reduces the
consistency requirement of the distributed network, which
will cause the blockchain to bifurcate. Since the blockchain is
a peer-to-peer distributed network, the network delay be-
tween nodes will limit the scalability of the entire system,
especially those with longer delays. e third point is the
limitation of transaction performance on the scalability of
the blockchain, which is also the core reason that restricts the
implementation of blockchain applications. To ensure se-
curity and eventual consistency, blockchain transactions
cannot be performed in parallel, which makes it difficult to
increase transaction throughput.
7.2. Security and Privacy. Among the challenges of block-
chain application, landing, security, and privacy protection
are important issues. As the infrastructure of the Internet of
Value, the information between the nodes of the blockchain
system is open and transparent, and it may contain private
information that users do not want to disclose. erefore,
how to protect user privacy is the key to whether blockchain
applications can be implemented on a large scale. Common
blockchain privacy protection methods include information
hiding and identity confusion. Identity obfuscation tech-
nology partially anonymizes the user’s identity on the
blockchain and uses privacy protection signature technol-
ogies, such as group signatures and ring signatures, to
confuse the identity information of both parties to the
transaction, making it impossible to correspond to the real
user. When necessary, the supervisor can use the supervi-
sor’s private key to view user information to ensure identity
security.
Information hiding uses technologies, such as zero-
knowledge proof and secure multiparty computing, to
conduct transactions without revealing any private infor-
mation and to ensure the credibility of the results, which
effectively protects the user’s transaction privacy. However,
the increase in the calculation process leads to a system with
reduced efficiency, and further improvement is needed in
actual applications. How to make rational use of artificial
intelligence algorithm to improve the low efficiency is a
difficult problem. In addition, the application of artificial
intelligence algorithm to distributed environment obviously
needs to redesign the existing algorithm.
7.3. Data Collaboration between On-Chain and Off-Chain
Storage. Traditional information systems and blockchain
systems are two ways of storing data, and each has limita-
tions. On the one hand, blockchain needs to improve
performance through off-chain storage and computing
systems; on the other hand, traditional information systems
need blockchain technology to ensure the safe sharing and
credibility of data. is requires an effective combination of
blockchain technology and traditional information systems,
and the most critical point is to ensure the relevance and
consistency of the data on the chain and the data off the
chain. Moreover, the development of artificial intelligence
cannot be separated from data. Artificial intelligence tech-
nology is still facing many problems, such as poor data
quality, data monopoly, data abuse, and so on. e inter-
vention of blockchain gives these problems new develop-
ment opportunities. Only by correctly combining the data
on the chain with the data off the chain, can the combination
Security and Communication Networks 11

of blockchain and artificial intelligence be truly applied to
the real economy.
8. Future Work
In this section, we look forward to the possible research work
on the integration of blockchain and artificial intelligence in
the future.
8.1. Hybrid Architecture Combining On-Chain and Off-Chain
Storage. In view of different distributed scenarios, the
transaction and data storage modes of smart blockchain in
the future may become a hybrid architecture, combining on-
chain and off-chain storage. Off-chain storage has the ad-
vantages of faster efficiency, lower cost, and higher privacy,
but it is difficult for off-chain data to take advantage of the
blockchain trust. A key research direction in the future is to
closely integrate the on-chain and off-chain data so that the
trust on the chain can be mapped to the off-chain data.
8.2. Balance between Performance Improvement and Security
Guarantees. Although blockchain has many advantages in
various aspects, its own performance bottleneck still limits
its practical application. Most of the technical challenges of
the blockchain itself focus on performance issues, especially
transaction throughput, transaction confirmation delay, and
block capacity. Different solutions such as directed acyclic
graphs, transaction sharing, off-chain transactions, and
block expansion have been proposed to solve the perfor-
mance problems of the blockchain, but they will inevitably
reduce the credibility and security of the blockchain.
However, in scenarios with stronger privacy protection
requirements, some cryptographic schemes with higher
security are applied to the blockchain system, which im-
proves the degree of privacy protection and reduces the
transaction efficiency of the blockchain system. An im-
portant concern for further breakthroughs in blockchain
technology is determining how to directly balance perfor-
mance improvement and privacy protection.
8.3. Distributed Trust Construction. In the application sce-
narios enabled by blockchain, there is more cooperation and
intercommunication between devices. e basis of coop-
eration is the existence of trust between the partners; that is,
they all believe that the identity of the other party and the
provided information are true and reliable. Blockchain
technology naturally guarantees the authenticity and reli-
ability of data due to its consensus mechanism and im-
mutability modification, which can better build trust in an
open network. In a scenario in which a node has a specific
identity and role, the identity of the node where the device is
located needs to be authenticated. is requires the con-
struction of distributed trust in the blockchain scenario. A
key research direction in the future is to authenticate the
identities of other nodes without a central authority.
8.4. Improve User Awareness and Enhance Legal Regulations.
e development of blockchain is fast, and the introduction
of relevant industry regulations is relatively lagging, so
chaos and bubbles inevitably exist. Deleveraging, strong
supervision, and the ups and downs of the capital market
have made people always wait and see the blockchain, and
some illegal behaviors under the guise of blockchain have
been repeatedly prohibited. erefore, people’s under-
standing of blockchain technology is not uniform, and the
dividing line between coin and chain is also very blurred.
erefore, it is necessary to strengthen the popularization
of blockchain knowledge for the public.
9. Conclusion
As two most cutting-edge technologies, blockchain and
artificial intelligence have the corresponding integration
opportunities in addition to their own advantages, which can
completely revolutionize the information technology in the
future. In this paper, we introduce the background
knowledge of artificial intelligence and blockchain in detail,
conduct an in-depth analysis of the feasibility of the inte-
gration of blockchain and artificial intelligence, and com-
prehensively summarize the research work on the
integration of blockchain and artificial intelligence in the
domestic market and overseas. Finally, we point out the
promising application scenarios and future work.
Conflicts of Interest
e authors declare that they have no conflicts of interest.
Acknowledgments
is work was supported by the National Key Research and
Development Program of China under Grant No.
2020YFB1807500, the National Natural Science Foundation
of China under Grant Nos. 62001357 and 61802080, the
Guangdong Basic and Applied Basic Research Foundation
under Grant Nos. 2020A1515110496 and 2020A1515110079,
the Education Bureau of Guangzhou Municipality Higher
Education Research Project under Grant No. 201831827, the
Key Research and Development Programs of Shaanxi under
Grant Nos. 2019ZDLGY13-07 and 2019ZDLGY13-04, and
the Guangzhou University Research Project under Grant
Nos. RQ2020085 and RD2020076.
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