Futuristic Blockchain Based Scalable and Cost-Effective 5G
Vehicular Network Architecture
COMSATS University Islamabad
Munam Ali Shah
COMSATS University Islamabad
COMSATS University Islamabad
In the present era, smart and efﬁcient vehicular network architectures
are necessary due to fast technological advancements in vehicles.
Many problems arise in these complex networks, which can be han-
dled using blockchain and the Internet of Things (IoT). We proposed
a comprehensive blockchain based 5G vehicular network architec-
ture, which is cost-effective, scalable, secure, and handles various
vehicular network issues in a smart city. The proposed architecture
consists of all essential components like reputation system, incentive
mechanism, and priority based techniques to handle different limi-
tations in the literature. Simulations results for different scenarios
depict the high execution cost of a single controller node, minor
node, and ordinary node as 106305, 85864, and 65491 gas values
and transaction cost as 130521, 109824, and 89195 gas values. The
results depict the effectiveness of the proposed architecture in terms
of scalability, time and cost-effectiveness.
Blockchain, IoT, Vehicular Network, EVs, 5G, Scalability, Net-
works, Malicious nodes, ITS, Vehicular Network Architecture.
PVLDB Reference Format:
Usama Arshad, Munam Ali Shah, and Nadeem Javaid. Futuristic Blockchain
Based Scalable and Cost-Effective 5G Vehicular Network Architecture.
PVLDB, 14(1): XXX-XXX, 2020.
In the present era, most of the population lives in urban areas to lead
an easy life away from rural life’s hardships. The technology in the
present era is making lives easier, and with each passing day, we
witness a new change in our cities and daily lives [
]. People are
moving towards cities, especially smart cities. As these cities offer
high quality of life, so in the future, all cities in the world will be
made smart. As the population is growing rapidly, we need such
smart cities that are sustainable on their own. Smart cities provide a
sense of security and ease; however, many problems also arise in a
smart city like handling a huge population and their data, privacy,
security, scalability, and adaptability. Smart cities are a collection of
different smart networks working with each other or separately in
the city. These networks provide daily services to fulﬁll the needs
of the population. From smart grids for low energy consumption to
14, No. 1 ISSN 2150-8097.
secure vehicular networks, technology is progressing rapidly, and the
world is changing faster than we can imagine. This change is only
possible thanks to the advancements in technologies like Artiﬁcial
Intelligence (AI) and blockchain. Many other ﬁelds that help revo-
lutionize the world are also changing at a great pace [
]. As these
ﬁelds grow and change, many discoveries and inventions happen
that make our lives easier and faster. To overcome the problems in
smart cities and smart networks, various models are proposed by
researchers. Traditional vehicular networks are unable to provide
security and are certainly unable to handle smart vehicles. We need
smart networks to handle smart devices. In smart vehicular networks,
technologies like the Global Positioning System (GPS) are used.
These technologies help to locate vehicles.
However, now with electric vehicles, different kinds of onboard
resources like sensors, storage devices, radars, cameras, Event Data
Recorders (EDR) etc., are used to perform different actions [
of these devices collect big data to perform their actions, and they
have to share limited data in the network to increase the throughput
and work effectively [
]. Using all these resources, devices and net-
works become aware of the environment and conditions. This helps
in increasing their overall effectiveness and performance [
]. In the
present era, the human population is highly dependent upon smart
devices. As smart devices make our lives easier, many problems
arise due to these smart devices. The main issues that arise from
these smart devices include lack of security, and privacy [
Data is considered expensive in this era, and people want to protect
it because data in the wrong hands can be dangerous. In smart net-
works, different nodes work together and share data. However, as all
network nodes are strangers to each other, it creates an environment
with a lack of trust. Different vehicles share valuable data in the
vehicular network like trafﬁc conditions in an area.
Moreover, these vehicular networks have limited devices to store
and share data. It is impossible to store this data on these small
devices. This problem is solved by using Road Side Units (RSU)
]. These RSUs save all the data collected by vehicles and pro-
vide limited amount of data when required. Sharing resources with
other nodes in a network is also a problem. Due to this, some nodes
may act selﬁshly in the network. Trust management can be done
by sharing required resources as needed. Different techniques are
proposed for this purpose [
]. Apart from simple trust management,
many incentive mechanisms are proposed based on data sharing and
storage management [
]. Other incentive mechanisms are proposed
based on data stored by nodes to promote data storage in nodes.
Incentive mechanisms are used to decrease or eliminate the selﬁsh-
ness of nodes in the networks. Nodes need to communicate faster
in a network to work effectively. In [
], the authors proposed
techniques for providing safe computing services to the lightweight
clients on the blockchain. The models provide a partially connected
and fully connected blockchain concepts. Privacy, security, and a
trustless environment are achieved using blockchain. Robustness
and ﬂexibility are also needed in networks as nodes may be far
away from each other. This leads to nodes’ failure, and to avoid
these failures, many new techniques are proposed using trust factors.
The trust factor is an amazing concept to solve many problems in
the network. Each node is rated positive or negative after every ac-
tion in the network. Apart from different blockchain based models,
multi-blockchain models are also proposed over time. In [
authors proposed a blockchain based model with two blockchains.
One blockchain is used to detect fraud users and the other is used
to check integrity. Convolutional Neural Networks (CNN) are also
used to authenticate nodes. However, this kind of model is limited
in terms of scalability and with increasing size, these models do
not remain cost-effective. This reputation system helps to eradicate
the selﬁshness of nodes and improve the overall performance of
the network. Hence, many issues can be solved by using different
techniques and methods.
Blockchain is the best technology for the present age. It is a decen-
tralized and distributed ledger. Due to this distributed approach, it
is also called a distributed ledger technology. Blockchain is an old
concept, but it became popular in 2008 when Bitcoin came in front
of people and revolutionized the concept of currency worldwide. It is
a distributed and decentralized currency, which had no single owner
like other currencies. With time, people realized that blockchain
technology could be used for many different applications. In simple
words, we can take blockchain as a google doc that is shared with
a group of people. This document is not copied by all the people
but only shared by all at the same time. The next big thing that
blockchain has is cryptographic hashing. After the blockchain came
to the surface in 2008, many new technologies like holochain also
emerged following the same concepts. Just like any other technol-
ogy, blockchain also consists of many components. Each component
is getting updated with time. Blockchain eradicated the need for
third-party in all kinds of transactions and provided a high level of
security and privacy that was not possible before.
Figure 1: Blockchain Structure
The three main components of blockchain are block, miners, and
nodes. The transaction data is stored in blockchain. The number of
blocks in blockchain is not ﬁxed, and each block consists of three
main things, nonce, data, and hash. A nonce is a whole number
that is changed every time data is changed. Hash is joined with this
nonce, which helps to secure blockchain. Whenever a new block
is added, it is done by the process of mining. Mining is the pro-
cess in which miners solve a complex mathematical problem to
ﬁnd the nonce, and this nonce provides a hash, which is accepted.
This process needs high computational power, making it difﬁcult for
hackers to hack the blockchain. The ﬁrst block in any blockchain
is called genesis block, and as the number of nodes increases in a
blockchain, it becomes more and more secure due to its distributed
nature. Nodes in blockchain join to form a network, and for every
update in blockchain, the majority of nodes approve the actions.
This process is called the consensus mechanism, and with time,
researchers have proposed many new consensus mechanisms for
different techniques and uses of blockchain technology. Some of
the most used consensus mechanisms include Proof of Work (PoW),
Proof of Authority (PoA), and Proof of Concept (PoC). Smart con-
tracts and tokens are used with blockchain technology to achieve
transactions without any kind of involvement of third parties.
1.2 Blockchain based Vehicular Network
Blockchain based networks are the new future due to the distributed
approach of blockchain technology. Intelligent Trafﬁc System (ITS)
is becoming better with the use of the latest technologies. These
networks have to handle the latest auto-driving vehicles, which
are smart and consist of the latest technologies. Blockchain based
vehicular network architectures are the best to handle these smart
vehicles. Much research is done on vehicular networks and different
components of blockchain based networks.
Blockchain based vehicular network architecture consists of dif-
ferent components, and each of these components solves a particular
problem in such networks. With these technologically advanced ve-
hicles, we need smart networks to handle big data and smart tasks
like fast information sharing. Many problems arise in these complex
networks, like scalability, cost-effectiveness, adaptability, nodes’
selﬁshness, malicious nodes, security, and privacy, which can be
handled using blockchain and the Internet of Things (IoT).
Figure 2: Main Components of Vehicular Network Architecture
Many vehicular network architectures are proposed in the lit-
erature, and each component of this architecture is also proposed
separately to handle a particular problem. The main components of
vehicular network architecture are given below:
•Structure of Nodes
Each component is necessary to perform a particular task and the
number of components can increase or decrease according to the
1.3 Scalability vs Cost
With technological advancements, infrastructure is getting more
costly, thus, increasing the deployment and execution cost. More-
over, scalability is not achievable without sacriﬁcing execution cost,
according to the literature review. Scalability is achieved with the de-
ployment of costly infrastructure. Scalability and cost-effectiveness
are inversely proportional in vehicular network architectures pro-
posed in the literature. Figure 3 clearly depicts the relation between
scalability and cost-effectiveness.
Figure 3: Scalability Vs Cost
1.4 5G and deployment issues
5G is the latest technology, and like all technological advancements,
5G infrastructure is costly. Moreover, 5G technology has a high
frequency of waves that can not be penetrated through walls easily
and needs a lot more infrastructure than 4G. 5G deployment itself
faces many issues, and much work is done to handle all its issues
separately. Some main deployment issues are shown in Figure 4.
Figure 4: 5G Deployment Issues
1.5 Background of 5G issues
As electric vehicles replace normal vehicles in a smart city, we also
need advanced smart networks to handle these smart vehicles. Many
technological advancements like blockchain, the Internet of Things
(IoT) and latest communication technologies like 5G play an impor-
tant role in providing solutions to the modern problems. The authors
] described the different present and future trends for the 5G
network. The authors described in detail the costly infrastructure
of 5G and the deployment trends around the world. The conducted
survey clearly depicts that most of the developed world will deploy
5G by 2022. However, an underdeveloped world like Asia may take
up to 2025 to deploy 5G. The main goal of the 5G technology is to
Table 1: List of Abbreviations
ADVs Auto Driving Vehicles
AI Artiﬁcial Intelligence
CNN Convolutional Neural Network
EDR Event Data Recorders
EVs Electric Vehicles
GPS Global Positioning System
IoT Internet of Things
ITS Intelligent Trafﬁc System
MIMO Multi-Input Multi-Output
PoA Proof of Authority
PoE Proof of Event
PoW Proof of Work
RSU Road Side Unit
VNA Vehicular Network Authority
PoC Proof of Concept
VANET Vehicular Ad-hoc Network
MANET Mobile Ad-hoc Network
ISP Internet Service Provider
C-CHAIN Controller Blockchain
O-Chain Ordinary Blockchain
R-Chain Repairing Blockchain
Med-Chain Medical Blockchain
AES Advanced Encryption Standard
DES Data Encryption Standard
achieve high data rates of 100 Mbps to 1 Gbps for everyone around
the world anytime, anywhere. However, the deployment of such tech-
nology is not easy. To achieve these data rates, different technologies
like small cell networks [
], Massive Multi-Input Multi-Output
], and millimeter wave [
] are used. When we look
at an example of 5G deployments, like Samsung’s prototype model
], we can see that their prototype is providing a data rate up to 1
Gbps. However, this model is also facing the issue of distance. The
model is operating at a frequency of 27.925 GHz at a distance of
1.7 kilometers. However, this 1.7 kilometers connectivity is only in
the Line-of-Sight (LoS) transmission. When it comes to Non-Line-
of-Sight (NLoS) transmissions, it only operates over the range of
200m. 5G network started with the technology of millimeter wave.
However, as 5G has high frequency and short wavelengths when
transmitted over milliliter wave technology, link distance is reduced.
After the millimeter wave, the trend of small cells started [
cells provide the availability of networks in short ranges, with the ca-
pability to switch between different small cells. In different proposed
models, small cells are used with already available infrastructures to
provide cost-effective 5G infrastructure. Apart from cost-effective
infrastructure, much work is being done to improve connectivity
and coverage of the 5G network. The authors in [
] described the
thorough analysis of coverage for 5G networks based on small cell
networks. A multi-directional path loss model is proposed to handle
the coverage of 5G small cell networks.
Table 2: Proposed Solutions in Literature.
Proposed Model Experimentation Problem Addressed Contribution Limitation
Proposed model to pro-
vide safe security ser-
vices on blockchain. [
Etherium environment is
Malicious services pro-
vided by edge servers to
Privacy, security, and a
are achieved using
Latency in large net-
based user access strate-
gies for D2D networks.
A CNN is used for the
prediction of fake users.
Detection of fraud users.
User access based on
is proposed using
It is limited in terms of
A detailed survey on
5G deployment is carried
out and future trends are
Data is collected from
46 different chief tech-
The hype about the 5G
network. No graphical
Collected all data from
around the globe to show
the trends and develop-
ment of 5G networks.
The survey is limited in
terms of data collected as
it is collected from only
46 chief technology ofﬁ-
cers. New proposed tech-
niques can change the
A multi-directional path
loss model is proposed
to handle the coverage of
5G small cell networks.
Simulations show the im-
provement of path loss
and coverage issues.
Short distance coverage
and handoff issue of 5G
small cell networks.
Solved the issue of path
loss in 5G small cell net-
works. Handled issues of
coverage and handoff.
Path loss is handled
while network coverage
still has a short distance.
Proposed techniques for
cost optimization, cover-
age, and handoff analy-
Different scenarios are
represented with stats
and calculations. The hy-
pothesis is used to pro-
Coverage, handoff, cost
optimization in heteroge-
neous 5G networks.
joined together in 5G
to provide coverage and
performance to handoff.
LoS and NLoS scenarios
are also discussed.
A different hypothesis is
used to produce results.
Results may not be the
same in actual scenarios.
Proposed and reviewed
techniques to handle is-
sues related to interfer-
ence management in 5G
Visual representation of
different scenarios and
review of the research
ment in 5G networks.
point scheduling, and
coordination to handle
the issues related to in-
Solutions may be limited
in terms of scalability.
The proposed technique
of using the present in-
frastructure for 5G net-
represent the scenarios
of 5G networks.
Costly 5G infrastructure.
The proposed technique
of using light poles as 5G
infrastructure to reduce
the cost of infrastructure
Investments may not be
available for proposed
based trust mechanism
to detect malicious
nodes in the network.
Windows 10 operating
system with a speciﬁca-
tion of Core i7 and 16
GB ram is used. Python
is used for implementa-
Detection of malicious
nodes in the vehicular
ogy in intelligent
blockchain for vehicular
A decentralized ap-
proach may not be
feasible for a trust
based Healthcare system,
which handles storage of
patients’ data. 
The model can be imple-
mented in a Remix or
Patient driven interop-
erability and institution-
ability is discussed.
Privacy leakage through
Blockchain based net-
work coded distributed
storage technology is
proposed with less com-
The model is analyzed
based on storage, consen-
sus speed, etc.
Low storage and high
complexity problems in
based method to store
more data on nodes with
The model can be easily
compromised as data is
stored in fewer nodes.
The authors in [
] discussed the 5G heterogeneous networks.
The cost optimization, coverage, and handoff analysis are discussed
in detail. Different technologies are joined together in 5G hetero-
geneous networks to provide coverage and performance to handoff.
LoS and NLoS scenarios are also discussed in detail. Many other
issues like inter-cell and intra-cell interference occur in small cell
5G networks. The authors in [
] proposed different enabling tech-
niques that include coordinated scheduling, multipoint scheduling,
and inter-cell interference coordination to handle the issues related to
interference management in 5G deployment of small cell networks.
The authors in [
] chronologically described different interference
issues in 5G networks and how they are solved using different tech-
niques. The authors in [
] also proposed a technique to use the
already available infrastructure instead of deploying a new costly
infrastructure. The authors proposed the technique of using light
poles to cover more areas in densely populated areas. However, this
technique also needs investments to cover the deployment costs.
So far, the main issues incurred in the existing research include the
high-cost infrastructures, short-range connectivity, interferences, and
Figure 5: Old Proposed Blockchain based Vehicular Network
The main contribution of this research work is to achieve a high
level of scalability in a blockchain based vehicular network archi-
tecture without sacriﬁcing cost-effectiveness. This comprehensively
proposed vehicular network architecture contributes to the literature
that will act as a generic base research work for future in-depth
research on all of its different components. The proposed model
achieves better access management of nodes with priority based
access management techniques to save the extra cost. The proposed
model contributes to saving the vehicular network from unneces-
sary complexities, which consume more power, storage and increase
the total execution cost. Different components are joined together
to handle some of the known issues in blockchain based vehicular
network architectures like selﬁshness of nodes, security, privacy
and failure of nodes in one complete structure keeping in mind the
cost-effectiveness and adaptability with future technologies.
2 RELATED WORK
The blockchain and IoT industry are developing rapidly and making
our lives easier. Computer servers are used widely to store data and
provide services as needed [
]. These services may be malicious
and can cause damage to devices or the network. Authentication
of services is also a problem faced in networks. Different kinds of
consensus mechanisms are used in blockchain based networks to
work efﬁciently. Many mechanisms are already provided to handle
smart devices in networks. However, most of such mechanisms need
high computational power and heavy resources. IoT devices are
becoming normal, and it is important to create efﬁcient mechanisms
to handle such devices. The main goal is to effectively handle devices
with less computational power and low resources without reducing
network efﬁciency. A consortium blockchain is normally used with
IoT devices, and the Proof of Authority (PoA) consensus mechanism
is used for the effective working of networks. Edge service providers
provide services, which may not be secure for the users. To handle
this issue, Blockchain based models are used .
Similarly, consensus mechanisms like PoA are used with different
types of blockchain. Apart from this, the new concept of off-chain
is proposed in which services can be authenticated and identiﬁed
easily. Huge networks also face latency. Blockchain based networks
solve many such issues related to privacy, security, and identiﬁcation.
Many reputation systems are also proposed to handle nodes in the
network. Moreover, blockchain based models also work effectively
with IoT devices because of their peer-to-peer connection. Two
devices in a network, directly connect with each other without any
involvement of a third party. Another problem in traditional networks
is access control of devices. Many proposed models handle access
control in the network using different algorithms .
When we look at traditional models, they always have a third party
between two entities. Blockchain based models remove most of these
traditional problems. In blockchain models, all devices share data
in a trust-less environment. Peer-to-peer connection in these models
ensures direct communication. Hence, models are most secured
and privacy based. The authors in [
] proposed a model on the
blockchain which provides an incentive mechanism to nodes on the
basis of data stored by them. Location and privacy leakage of users
is a big problem, and users are reluctant to share information. The
authors in [
] proposed a technique of blockchain based incentive
mechanism. The authors in [
] proposed rating system and the
concept of trust points to handle the malicious behavior of nodes.
ITS uses ad-hoc networks to communicate in a vehicular network,
Table 3: The motivation of proposed research.
Proposed Model Experimentation Problem Addressed Contribution Limitation
Blockchain based trust
management system is
Python and Go-language
Privacy preserving was
not achieved in vehicular
Trust management is
achieved without expos-
ing vehicles to other
Complex model limited
in terms of speed and
Proposed that use of al-
ready present Infrastruc-
ture can help to save in-
Different data from dif-
ferent scenarios are plot-
ted on a graph to show
the difference between
old models and the pro-
High cost of infras-
tructure for future and
present technologies like
Complete research work
to decrease the 5G in-
frastructure cost using al-
ready present infrastruc-
Limited scenarios are
discussed with con-
trolled variables. Our
work is motivated by the
vehicular network archi-
tecture and performance
MATLAB tool is used
Security, scalability, big
data storage, privacy.
based vehicular network
architecture with ﬁve
Network trafﬁc scenarios
between vehicles is not
covered, and the reliabil-
ity of channels in any cel-
lular network can also
Blockchain based vehic-
ular network architecture
and trust management
system is proposed. 
Ethereum and Remix
are used to represent
node activities and per-
No general vehicular net-
work architecture, which
can handle most of the
Complete vehicular net-
work architecture and so-
lution to its possible lim-
Execution time and cost
increases with increasing
Blockchain based trust
management system is
Simulations are done on
No proper blockchain
based decentralized trust
management was pro-
Trust management sys-
tem, which is decentral-
ized using blockchain.
No broad scope of work
and system is limited in
terms of scalability.
Table 4: Comparison with Other Models.
Ref No. Blockchain
Privacy Security Scalability Adaptability Cost-
 ✓ ✓ ✓ ✓
which are not secure for data transmission. The protocols used in ITS
are mostly not up to date, and work is being done to provide better
security mechanisms. The authors in [
] proposed a consensus
mechanism based on Proof of Event (PoE) rather than PoW or PoA
concepts to handle the sharing of trafﬁc data and its authenticity.
The authors in [
] proposed IoT e-business model. The traditional
model and the IoT e-business model are discussed and compared in
detail. Smart property [
] and paid data are used as commodities in
the proposed model. Moreover, it is explained how different nodes
are interacting in the network. Table 2, clearly describes different
proposed techniques and models for blockchain based networks,
especially blockchain based vehicular networks. It also describes
different deployment issues of 5G and work done by different authors
with possible limitations. Our proposed model handles limitations in
The authors in [
] proposed a blockchain based Healthcare sys-
tem, which handles data storage of patients’ data. Patient-driven
interoperability and institution-based interoperability is an issue for
a long time. Institution-based and patient-based interoperability is
discussed in detail with possible solutions. Privacy leakage through
unique ids can be a problem for patients .
Much work is done by researchers on privacy-preserving and
priority-based techniques. The use of vehicular ad-hoc networks
has brought a revolution to achieve trafﬁc safety. The authors in
] proposed an authentication scheme that is weight-based. They
used weights to control the malicious vehicles in the network and
gave priority to the vehicles on the basis of the weight assigned
to them. To secure the communication between the vehicles, they
used a conditional privacy-preserving scheme. Their proposed model
decreases the total costs using the SDN and priority approach. In
] authors discussed different challenges related to privacy and
security in 5G enabled vehicular networks. The research mainly
focused on the infrastructure and case study to ensure the privacy
and security challenges. A 5G enabled infrastructure is proposed
for vehicular networks. However, this model lacks a solution to
some of the known issues like scalability and adaptability. Finding
charging stations and security connecting with others is a challenge
with electric vehicles, and many infrastructures are proposed in the
literature to ensure security and privacy for electric vehicles. The
authors in [
] proposed a secure framework for electric vehicles,
which is privacy-preserving. Cryptography is used to ensure security
and privacy. The model performs well in terms of communication
costs and satisfaction ratio. The performance of the model is also
compared with recently published research. The authors in [
discussed in detail about the security, privacy, and trust management
in blockchain based solutions. They have surveyed different recently
published researches to review and classify different issues and
The authors in [
] proposed detailed scenarios and discussed
5G network and infrastructure scenarios in detail. They proposed
that instead of developing the new 5G infrastructure, we should use
already developed infrastructures. Due to high infrastructure costs,
different models are proposed to use the existing infrastructure for
5G instead of creating new infrastructures from scratch. The authors
] proposed techniques to handle issues like low storage and
high complexity. The high complexity problem of data storage in the
blockchain is addressed. Provided blockchain based method to store
more data on nodes with less complexity. However, the model can
be easily compromised, as data is stored in fewer nodes. The authors
] show how data can be secured on fog or cloud computing
with the proposed framework and how it can be saved from different
attacks. The authors in [
] proposed blockchain based distributed
vehicular network architecture and performance analysis. Problems
like security, scalability, big data storage, and privacy in a vehicular
network are discussed in detail. Management of big data in vehicular
networks while providing privacy, adaptability, and security is also
discussed. However, trafﬁc between vehicles is not covered, and the
non-reliability of channels in any cellular network also create issues.
In our recent work [
], we proposed a blockchain based vehic-
ular network architecture that was scalable, robust, and adaptable
by using vehicular network architecture similar to proposed in [
with a rating system similar to proposed in [
] to handle malicious
nodes. However, after achieving scalability, vehicular network archi-
tecture lost its cost-effectiveness and executive effectiveness. Figure
5 clearly describes our recent blockchain based vehicular network
architecture. The goal is to achieve a high level of scalability without
sacriﬁcing execution efﬁciency and cost-effectiveness. Moreover,
after the inclusion of 5G, many features may change in vehicular net-
]. However, our proposed system will work perfectly in a
5G based environment. Due to the high frequency of the 5G network
and shorter range, we need more infrastructure to build, which in-
creases the total cost of vehicular networks. Our model provides the
best solution in this case for scalability and performance. We solved
the limitations of our recent work with the new proposed model and
the system’s modiﬁcations. Simulations and results clearly describe
the effectiveness of modiﬁed vehicular network architecture. Pas-
senger’s health care is also explained in detail and how passengers’
safety and health can be insured in a smart city. Node failure and
malicious nodes are handled more effectively with the proposed
incentive mechanism. Table 3 describes different blockchain based
proposed models with possible limitations.
3 PROBLEM STATEMENT
Blockchain based vehicular network architectures have been pro-
posed in [
], which provide scalability, robustness and can
handle malicious nodes. However, using blockchain in this type of
network scenario imposes a high computational cost and is time-
consuming. Moreover, due to the costly infrastructure of the latest
technologies like 5G [
], and deployment issues [
], a cost-
effective and scalable vehicular network architecture is needed. The
decentralized mechanism used for achieving privacy and security
adds to the computational cost. Thus, making the systems less ef-
]. There is a need to improve the existing architectures
in terms of high computational cost and low efﬁciency. Table 4 de-
picts the availability of technology and features in different existing
proposed approaches and our proposed approach.
4 PROPOSED MODEL
We proposed a scalable blockchain based 5G vehicular network
architecture in a smart city and a solution to possible limitations in
blockchain based networks in a smart city necessary for the vehicular
network to work effectively. The main limitations handled by our
proposed model include privacy, security and scalability without
sacriﬁcing efﬁciency, cost, node failure, health emergencies, and
selﬁshness of nodes. Our proposed vehicular network architecture
consists of three kinds of nodes and one Vehicular Network Authority
(VNA). The kinds of nodes are as follows:
.Ordinary nodes are vehicles with less computa-
tional and execution power. These nodes request data and services
from controller nodes. Due to less storage these nodes cannot store
big data. Hence, they request only a limited amount of necessary
data from the controller nodes.
.Controller nodes are ﬁxed nodes and are known
as Road Side Units (RSUs). These nodes have high computational
and execution power. Controller nodes store huge amount of data on
them and provide services to other nodes.
.Minor nodes are vehicles with good computational
and execution power. They act as a bridge between controller nodes
and ordinary nodes if nodes are far away from each other. Nodes
are registered as minor nodes in the network based on their storage,
execution, and computational power.
Vehicular Network Authority (VNA)
.A vehicular network au-
thority also exists in the proposed model. The authority registers all
nodes and handles any failure that occurs in the network. On registra-
tion, vehicles are granted the status of minor node or ordinary node
depending on their computational and execution resources. VNA
can add or remove any node from the vehicular network based on
its reputation in the network. VNA acts as a controller of the whole
vehicular network. VNA also handles the incentive provisioning
mechanism based on the node’s reputation. VNA has full control
over the network and can make changes as needed in the network.
In case of emergency or node failure, VNA contacts the respective
services or en-route the vehicle to the closest services available. It is
assumed that the VNA can never be compromised. Figure 6 clearly
represents the ﬂow of our proposed model.
Figure 6: Flowchart Diagram of Proposed Model.
4.1 Proposed 5G Vehicular Network Architecture
on Blockchain (5G-BLOCKVN)
We proposed a 5G vehicular network architecture based on the
blockchain, which is scalable and adaptable. Apart from 5G, this
architecture can work perfectly with any communication technol-
ogy of the future without sacriﬁcing cost and execution effective-
ness. Three main nodes work together in the model under the su-
pervision of VNA. The vehicular network architecture consists of
two blockchains. One blockchain is for controller nodes, which is
called C-Chain. These nodes have to handle big data and without
blockchain if these nodes are destroyed or damaged somehow, the
data will be lost and the network will not work effectively.
Moreover, ordinary nodes and minor nodes cannot handle big
data. Hence we cannot use the same blockchain with them. Minor
nodes and ordinary nodes can request the services or data from
controller nodes as needed. They have a separate blockchain termed
as O-Chain to store temporary data needed at the time. O-Chain
saves the data necessary for the services at a particular time and
takes its data from C-Chain as needed. The minor nodes are the most
important nodes in the model. As 5G requires a huge infrastructure
to work effectively, which is costly. Hence, we introduced minor
nodes for scalability. Ordinary nodes request controller nodes for
the services, however, if they are out of range they will request from
minor nodes and minor nodes will act as a bridge between controller
nodes and ordinary nodes. Minor nodes can also request services
from controller nodes as needed or from other minor nodes when no
controller node is available in the range. This solves the problem of
scalability, however, more number of minor nodes requesting each
other can increase the overall cost and efﬁciency will be decreased
as time will be increased to handle and response all requests of the
network. To maintain efﬁciency, all nodes must perform their tasks
efﬁciently in the network, which can be achieved by using incentive
mechanism and reputation system. Figure 7 clearly describes our
Figure 7: Proposed 5G Vehicular Network Architecture on
4.2 Reputation System for 5G-BLOCKVN
In a vehicular network, all nodes communicate with each other and
it is important for all them to act properly in the network. To avoid
malicious behaviour of nodes, we proposed a reputation system for
nodes. Unlike other complex reputation systems that lack speed and
accuracy, we proposed the simplest system with either a positive
or negative rating. Minor nodes act as the bridge between all the
nodes in the network hence they can show malicious behaviour in
the network. To increase the scalability they must work best in the
network. To make sure these nodes work perfectly reputation system
Figure 8: Reputation System for 5G-BLOCKVN
All nodes have their own proﬁles which can be seen before com-
munication. Hence vehicles can request services from nodes which
have good proﬁles. Whenever two vehicles communicate with each
other, both rate each other either positively or negatively after com-
munication. If the rating is positive, 1 is added to the proﬁle and
if the rating is negative, 1 is subtracted from the proﬁle. Whenever
a proﬁle hits 0, the vehicle is removed from the network by VNA
after checking the history of the proﬁle. The history of the proﬁle
is checked to make sure that the proﬁle is hitting 0 due to rapid
negative ratings from other vehicles. In this way, the efﬁciency of
the whole network is increased. Figure 8 represents our proposed
4.3 Incentive Mechanism for 5G-BLOCKVN
Minor nodes have better storage, execution, and computational
power than ordinary nodes. Hence, they may act selﬁshly in the
network to save their resources. To control this selﬁshness of nodes,
an incentive mechanism is proposed. As we already have a reputation
system, we will use the data from the reputation system to reward or
penalize nodes instead of a complex incentive mechanism. VNA will
reward the controller nodes with good behavior on monthly or yearly
and penalize on selﬁsh behavior, respectively. Figure 9 describes the
proposed incentive mechanism in detail.
Figure 9: Incentive Mechanism for 5G-BLOCKVN
4.4 Priority Based Technique for 5G-BLOCKVN
Scalability can be achieved achieved on the expense of cost-effectiveness.
Nodes look for the closest node in range to communicate. This means
too many minor nodes are involved in the network and each will
have an executional cost and time delay. To handle this issue, we
proposed priority based connections. Figure 10 describes the old and
new proposed approach. This priority of nodes can be shown by two
(1) CASE 1: Priority Based Ordinary Node Communication
(2) CASE 2: Priority Based Minor Node Communication
Figure 11 clearly shows both the cases in detail.
4.4.1 Priority Based Ordinary Node Communication. In the
case of an ordinary node, it should always look for the closest con-
troller node in the network. However, if controller node is not avail-
able in the range then it must look for the most away minor node in
4.4.2 Priority Based Minor Node Communication. In the case
of minor node, it should always look for the closest controller node
in the network. However, if controller node is not available in the
range then it must look for the most away minor node in range.
Figure 10: Priority based Technique for 5G-BLOCKVN
Figure 11: Priority based Technique for 5G-BLOCKVN (Two
4.5 Privacy-Preserving and Security
Vehicles should only be identiﬁed by VNA and in case of reputation,
the vehicle’s privacy should be preserved. To tackle the problem of
privacy leakage, instead of using some complex system, we simply
used unique ids for ordinary nodes and minor nodes. This unique id
consists of two parts: the ﬁrst part consists of ﬁxed unique numbers
only known by VNA while the other part consists of randomly gener-
ated numbers. This randomly generated part changes automatically
rapidly after an interval of time. This technique changes the id of
every vehicle in the network continuously, which preserves privacy
while keeping nodes known to VNA. When it comes to security, a
high level of security can be achieved for communication using the
latest encryption techniques.
4.6 Node Failure and Passenger Healthcare
Node failure is the biggest issue in vehicular networks and this
could be dangerous for the entire vehicular network. Due to the
decentralized blockchain, no data is lost even if any node fails in
any way. The basic failure of a node may include sensor failure or
device failure. In such a case, VNA will automatically contact the
closest repairing ﬁrm and share the location and repairing history of
the vehicle with the ﬁrm. Repairing history is stored on a repairing
blockchain called R-Chain. In case of any medical emergency in the
vehicle, vehicles are automatic en-route to closest health services.
Medical blockchain, called a Med-Chain, holds the data of patients,
which can be accessed by the doctors only if patients allow them
the access. Figure 12 clearly represents node failure and passengers’
Figure 12: Node Failure and Passenger Healthcare Manage-
4.7 5G Cost-Effective Infrastructure for
Controller nodes are the main source points of the 5G network as part
of the infrastructure. Using NLoS, we can achieve the range up to
1.7km, and LoS can reach a range from 200m to 1000m. However, as
we have signiﬁcantly fewer stationary controller nodes we used more
minor nodes for connections. Minor nodes in our model have high
computational and execution power. VNA makes any node minor
node based on its onboard resources. These minor nodes can act as
5G network MIFI-HotSpots until we can use technology like 5G
small cell networks and MIMO on vehicles. This approach enables
the nodes to become dynamic and mobile 5G source points, which
will increase the overall range of the 5G network. The incentive
mechanism motivates the nodes to act as a minor node. The cost
of 5G source point integration is handled 50% by VNA while the
other 50% by the minor node owner which can be adjusted with
given incentives. This approach helps to reduce the overall cost
of the infrastructure and to increase the overall range of the 5G
network. Using already present infrastructure for 5G deployment is
a promising technique to achieve cost-effectiveness in any scenario.
4.8 Proposed Algorithm
5G network has a higher frequency than 4G and traditional networks.
Hence, due to high frequency and shorter wavelength, it has a lower
range. In different scenarios, 5G may have a different range values
from 200m to 1000m. However, the reliable range value will always
be 70% of the total range.
Unlike 4G, 5G needs a lot more costly infrastructure because of its
high-frequency waves that can only travel to low ranges. As higher
frequency waves have shorter wavelengths and more bandwidth, this
means that 5G has a shorter range but it can carry much more data.
Algorithm 1: CASE 1: Priority Based Ordinary Node
Input: Service requested
Output: Service received
if 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑙𝑒𝑟𝑁𝑜𝑑𝑒 =𝑃𝑟𝑒𝑠 𝑒𝑛𝑡 then
OrdinaryNode connects ControllerNode ;
else if 𝐶𝑜𝑛𝑡𝑟 𝑜𝑙𝑙 𝑒𝑟 𝑁 𝑜𝑑𝑒 =𝑁𝑜 𝑡𝑃 𝑟𝑒𝑠𝑒𝑛𝑡 && 𝑀 𝑖𝑛𝑜𝑟 𝑁 𝑜𝑑𝑒 =
𝑃𝑟 𝑒𝑠𝑒𝑛𝑡 then
if 𝑅𝑎𝑛𝑔𝑒 >700 then
OrdinaryNode connects MinorNode;
else if 𝑅𝑎𝑛𝑔𝑒 <700 then
Algorithm 2: CASE 2: Priority Based Minor Node Com-
Input: Service requested
Output: Service received
if 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑙𝑒𝑟𝑁𝑜𝑑𝑒 =𝑃𝑟𝑒𝑠 𝑒𝑛𝑡 then
MinorNode connects ControllerNode;
else if 𝐶𝑜𝑛𝑡𝑟 𝑜𝑙𝑙 𝑒𝑟 𝑁 𝑜𝑑𝑒 =𝑁𝑜 𝑡𝑃 𝑟𝑒𝑠𝑒𝑛𝑡 && 𝑀 𝑖𝑛𝑜𝑟 𝑁 𝑜𝑑𝑒 =
𝑃𝑟 𝑒𝑠𝑒𝑛𝑡 then
if 𝑅𝑎𝑛𝑔𝑒 >700 then
MinorNode connects MinorNode;
else if 𝑅𝑎𝑛𝑔𝑒 <700 then
Algorithms 1 and 2 represent case 1 and case 2 for the proposed
priority based communication approach for nodes.
In the current proposed algorithms, the range value is 1000m. Hence,
we used 700m. 5G provides a signal range of average 1000m in
which the minimum range in literature is around 200m and 1.7km is
the maximum range. Los provides more coverage than NLos as 5G
waves are non-penetrable waves.
5 SIMULATIONS AND EXPERIMENTATION
Smart contracts of the proposed system are deployed on the operat-
ing system, Windows 10 Pro. The device used for simulation and
experimentation has a core i5 processor of 7th generation, 8GB Ram,
and 4GB shared graphics. The implementation is done using solid-
ity language on tools including Remix and Ganache. Remix also
provides fake accounts for experimentation and with each account
provides 100 fake ether as balance. Ethereum platform is used which
provides public blockchain with Proof of Work (PoW) consensus
mechanism. Smart contracts are deployed and tested on the Remix to
represent different tasks of vehicular networks. Metamask is used as
a wallet. Sumo and Omnet++ are used with veins and inet modules to
simulate a demo network to check the effectiveness of the proposed
priority based approach. Python and Google Colaboratory are used
for graphical representations.
6 RESULTS AND DISCUSSION
Scalability is achieved without sacriﬁcing cost-effectiveness. Cost-
effectiveness is achieved at different phases in the proposed model,
which include reducing the cost at the deployment of nodes and
reduction of the total cost while increasing scalability. Moreover,
scalability is shown with respect to increasing cost. Cost is shown in
the form of transaction cost and execution cost. With the increasing
development of blockchain platforms and new versions of solidity
language, execution cost decreases. Transaction cost depends on
the number of variables and features used, which may vary in dif-
ferent implementation scenarios. Total time is also decreased for a
single transaction as the total number of nodes needed for a single
transaction are decreased.
Figure 13: Transaction Cost for Deployment of Nodes with re-
spect to Scalability.
6.1 Scalability and Cost-effectiveness
As scalability is increased we need more infrastructure for the net-
works. As this infrastructure is costly we are using three kinds of
nodes in our proposed architecture. Graphical representation 13 and
14 clearly describe the deployment cost in terms of execution and
transaction cost for different nodes in the blockchain network with
respect to scalability. With an increasing number of nodes, we can
see the increasing cost for deployment. For deployment, the execu-
tion cost for a single controller node is 106305, for a single minor
node, 85864, and for a single ordinary node is 65491. Transaction
cost for a single controller node is 130521, for a single minor node,
109824, and for a single ordinary node is 89195. Transaction costs
may vary with different features according to the implementation
scenario. As graphical representation 14 and 13 clearly shows that
the cost of controller nodes is much higher than minor nodes, while
Figure 14: Execution Cost for Deployment of Nodes with re-
spect to Scalability.
Figure 15: Execution Cost of Minor Nodes with respect to Scal-
the cost of minor nodes is higher than ordinary nodes. Therefore
in our proposed model we reduced the deployment of controller
nodes to a minimum and used low-cost minor nodes and ordinary
nodes. This reduction of controller nodes drastically reduces the
6.2 Minor Nodes or Forwarding Nodes
Minor nodes or forwarding nodes are the main nodes after the con-
troller nodes and the total cost of each transaction depends on the
number of minor nodes involved in the transaction. As each minor
node has its own transaction and execution cost. As the scalability
increases more and more minor nodes are involved in the transaction
thus increasing the total cost. In huge networks, this will cause costly
transactions. Graphical representation 15 and 16 show the increase
of transaction and execution cost with respect to scalability. As the
Figure 16: Transaction Cost of Minor Nodes with respect to
Figure 17: Decrease in Transaction Cost with respect to Nodes.
number of nodes is increasing for a single transaction, the total cost
The number of nodes plays an important role in any transaction and
when scalability is achieved, more nodes are involved in transactions.
Hence the total time of any transaction is directly proportional to
the number of nodes used. Graphical representation 19 shows how
time is increasing for a single transaction with an increasing number
of nodes. If a single transaction takes, 1 sec for a single transaction
between two nodes, 10 nodes will take 9 sec for a single transaction.
Graphical representation 20 shows how time is decreased by
decreasing the number of nodes in each transaction.
Figure 18: Decrease in Execution Cost with respect to Nodes.
Figure 19: Time Required for Transaction with respect to
6.4 Time and Cost-effectiveness due to Priority
As the number of nodes in a single transaction increase, the total time
and cost also increases. Proposed priority based approach promises
less number of nodes involved in each transaction, thus decreasing
the, Time, total execution cost and transaction cost for each transac-
tion. The graphical representation shows the difference between cost
and time, before and after the use of the proposed approach. The
decrease in the number of nodes after the proposed priority based
approach is directly responsible for the decrease in overall costs and
time which is shown by graphical representations 17 and 18.
6.5 Comparison between old and new nodes
For different scenarios, number of nodes may vary however in each
case total number of nodes involved in the transaction decrease. To
Figure 20: New Time for Transaction with respect to New Num-
ber of Nodes.
get an accurate number of nodes in the simulation we repeated the
simulation and took the average number of nodes required for a
complete transaction with respect to scalability. Due to this decrease
in the total number of nodes in each transaction, the whole proposed
model becomes scalable and cost-effective at the same time.
Figure 21: Comparison of Minor Nodes after Priority based Ap-
proach with respect to Scalability.
Graphical representation 21 shows the comparison of the number
of minor nodes involved in each complete transaction before and
after the proposed priority based approach. This decrease in total
time affects the total cost and makes each transaction less costly,
unlike the old model where cost was increasing with scalability.
This provides us with a cost-effective model that can be scalable
by increasing the number of nodes. The proposed priority based
approach promises less number of nodes involved in each transaction,
thus decreasing the time, total execution cost, and transaction cost
for each transaction.
A comprehensive blockchain based vehicular network architecture is
proposed in this research work. Our proposed architecture consists
of different components, which are essential for a blockchain based
vehicular network architecture. Each component in the proposed
model is there to solve a particular problem keeping in mind the
adaptability, cost-effectiveness, and scalability. The proposed model
handles issues like scalability, high execution and transaction cost,
adaptability, robustness, security, and privacy. We included an incen-
tive mechanism in our proposed model to handle the selﬁshness of
nodes and a reputation system to handle the maliciousness of nodes.
Instead of using the same costly controller nodes, we used three dif-
ferent types of nodes, thus decreasing the total deployment cost, and
using priority access techniques we made sure that fewer nodes are
used for each transaction thus decreasing the total transaction time,
execution, and transaction cost. Two different blockchains, O-Chain
and C-Chain, are used to ensure privacy and security. VNA is used
to control the whole vehicular network and it is assumed that VNA
cannot be compromised in any way. Node failure and passengers’
healthcare scenarios are also discussed in detail. 5G implementation
scenario is also discussed for blockchain based vehicular network
architecture with a robust and cost-effective approach.
Graphical representations clearly describe the increasing deploy-
ment cost, execution cost, transaction cost, and time with an increas-
ing number of nodes and the deployment cost differences between
different types of nodes. Simulations results clearly depict the effec-
tiveness of the proposed architecture in terms of scalability, time,
and cost-effectiveness. We concluded that the proposed architecture
effectively handles different issues of blockchain based vehicular net-
works. With the execution cost of a single controller node as 106305,
minor node as 85864, ordinary node as 65491, and transaction cost of
a single controller node as 130521, minor node as 109824, ordinary
node 89195 gas values respectively the total execution and transac-
tion cost is decreased making the model cost-effective. Due to the
distributed nature of blockchain technology, VNA handles the incen-
tive mechanism and various tasks, thus, ensuring cost-effectiveness.
Use of existing infrastructure, a priority based approach and decrease
in all kinds of costs at each level of the proposed model a high level
of cost-effectiveness is ensured.
8 FUTURE WORK
In the future, we will handle the 5G deployment issues in a vehicular
network architecture related to handoff, interference, and coverage
in detail. It is assumed that VNA can not be compromised hence
we will discuss different scenarios related to VNA in detail. The
use of already present infrastructures in vehicular network architec-
tures promises a cost-effective solution both for 5G technology and
vehicular network architectures in a smart city.
The authors wish to thank the anonymous reviewers for their valuable
H. Kumar, M. K. Singh, M. P. Gupta, and J. Madaan, “Moving towards smart cities:
Solutions that lead to the Smart City Transformation Framework,” Technol. Forecast.
Soc. Change, vol. 153, p. 119281, Apr. 2020, doi: 10.1016/j.techfore.2018.04.024.
K. Salah, M. H. U. Rehman, N. Nizamuddin, and A. Al-Fuqaha, “Blockchain for
AI: Review and open research challenges,” IEEE Access, vol. 7, pp. 10127–10149,
2019, doi: 10.1109/ACCESS.2018.2890507.
B. Fleming, “Smarter and safer vehicles [Automotive Electronics],” IEEE Vehicular
Technology Magazine, vol. 7, no. 2. pp. 4–9, 2012, doi: 10.1109/MVT.2012.2190223.
H. Mousannif, I. Khalil, and S. Olariu, “Cooperation as a Service in VANET:
Implementation and Simulation Results,” Mob. Inf. Syst., vol. 8, no. 2, pp. 153–172,
2012, doi: 10.1155/2012/853853.
K. Zhang, J. Ni, K. Yang, X. Liang, J. Ren, and X. S. Shen, “Security and Privacy
in Smart City Applications: Challenges and Solutions,” IEEE Commun. Mag., vol. 55,
no. 1, pp. 122–129, Jan. 2017, doi: 10.1109/MCOM.2017.1600267CM.
H. Kheliﬁ, S. Luo, B. Nour, H. Moungla, and S. Hassan Ahmed, “Reputation-
Based Blockchain for Secure NDN Caching in Vehicular Networks,” Dec. 2018, doi:
H. Kheliﬁ, S. Luo, B. Nour, H. Moungla, S. H. Ahmed, and M. Guizani, “A
blockchain-based architecture for secure vehicular Named Data Networks,” Comput.
Electr. Eng., vol. 86, p. 106715, Sep. 2020, doi: 10.1016/j.compeleceng.2020.106715.
T. Roosta, M. Meingast, and S. Sastry, “Distributed reputation system for tracking
applications in sensor networks,” 2006, doi: 10.1109/MOBIQW.2006.361781.
X. Huang, R. Yu, J. Kang, and Y. Zhang, “Distributed reputation management for
secure and efﬁcient vehicular edge computing and networks,” IEEE Access, vol. 5, pp.
25408–25420, Nov. 2017, doi: 10.1109/ACCESS.2017.2769878.
M. Singh and S. Kim, “Intelligent Vehicle-Trust Point: Reward based Intelli-
gent Vehicle Communication using Blockchain,” Jul. 2017, Accessed: Oct. 18, 2020.
[Online]. Available: http://arxiv.org/abs/1707.07442.
Y. Xu, G. Wang, J. Yang, J. Ren, Y. Zhang, and C. Zhang, “Towards Secure Net-
work Computing Services for Lightweight Clients Using Blockchain,” Wirel. Commun.
Mob. Comput., vol. 2018, 2018, doi: 10.1155/2018/2051693.
K. Kaur, S. Garg, G. Kaddoum, F. Gagnon, and S. H. Ahmed, “Blockchain-based
lightweight authentication mechanism for vehicular fog infrastructure,” May 2019,
D. Lin and Y. Tang, “Blockchain consensus based user access strategies in D2D
networks for data-intensive applications,” IEEE Access, vol. 6, pp. 72683–72690,
2018, doi: 10.1109/ACCESS.2018.2881953.
F. Grijpink, T. Härlin, H. Lung, and A. Ménard, “Cutting through the 5G hype: Sur-
vey shows telcos’ nuanced views,” 2019. Accessed: Oct. 18, 2020. [Online]. Available:
Insights/Cutting through the 5G hype Survey shows telcos nuanced views/Cutting
through the 5G hype-web-ﬁnal.ashx.
B. Mafakheri, T. Subramanya, L. Goratti, and R. Riggio, “Blockchain-based
Infrastructure Sharing in 5G Small Cell Networks,” in 14th International Conference
on Network and Service Management, CNSM 2018 and Workshops, 1st International
Workshop on High-Precision Networks Operations and Control, HiPNet 2018 and 1st
Workshop on Segment Routing and Service Function Chaining, SR+SFC 2018, 2018,
I. Khan, M. Singh, and D. Singh, “Compressive sensing-based sparsity adaptive
channel estimation for 5G massive MIMO systems,” Appl. Sci., vol. 8, no. 5, p. 754,
May 2018, doi: 10.3390/app8050754.
J. Zhang, X. Ge, Q. Li, M. Guizani, and Y. Zhang, “5G Millimeter-Wave Antenna
Array: Design and Challenges,” IEEE Wirel. Commun., vol. 24, no. 2, pp. 106–112,
Apr. 2017, doi: 10.1109/MWC.2016.1400374RP.
W. Roh et al., “Millimeter-wave beamforming as an enabling technology for 5G
cellular communications: Theoretical feasibility and prototype results,” IEEE Commun.
Mag., vol. 52, no. 2, pp. 106–113, 2014, doi: 10.1109/MCOM.2014.6736750.
Y. Zhong, X. Ge, H. H. Yang, T. Han, and Q. Li, “Trafﬁc matching in 5g ultra-
dense networks,” IEEE Commun. Mag., vol. 56, no. 8, pp. 100–105, Aug. 2018, doi:
J. Chen, X. Ge, and Q. Ni, “Coverage and handoff analysis of 5g fractal small cell
networks,” IEEE Trans. Wirel. Commun., vol. 18, no. 2, pp. 1263–1276, Feb. 2019,
M. A. Ouamri, M. E. Ote¸steanu, A. Isar, and M. Azni, “Coverage, Handoff and
cost optimization for 5G Heterogeneous Network,” Phys. Commun., vol. 39, p. 101037,
Apr. 2020, doi: 10.1016/j.phycom.2020.101037.
F. Qamar, M. H. D. N. Hindia, K. Dimyati, K. A. Noordin, and I. S. Amiri,
“Interference management issues for the future 5G network: a review,” Telecommun.
Syst. , vol. 71, no. 4, pp. 627–643, 2019, doi: 10.1007/s11235-019-00578-4.
S. S. Sarma and R. Hazra, “Interference Mitigation Methods for D2D Communi-
cation in 5G Network,” in Advances in Intelligent Systems and Computing, 2020, vol.
1040, pp. 521–530, doi: 10.1007/978-981-15-1451-7_54.
J. Benseny, J. Walia, H. Hämmäinen, and J. Salmelin, “City strategies for a
5G small cell network on light poles,” In 2019 CTTE-FITCE: Smart Cities &
Information and Communication Technology (CTTE-FITCE) doi: 10.1109/CTTE-
Y. Kryvenchuk, O. Vovk, A. Chushak-Holoborodko, V. Khavalko, and R. Danel,
“Research of servers and protocols as means of accumulation, processing and opera-
tional transmission of measured information,” in Advances in Intelligent Systems and
Computing, Sep. 2020, vol. 1080 AISC, pp. 920–934, doi: 10.1007/978-3-030-33695-
S. Tuli, R. Mahmud, S. Tuli, and R. Buyya, “FogBus: A Blockchain-based Light-
weight Framework for Edge and Fog Computing,” Journal of Systems and Software,
vol. 154. Elsevier Inc., pp. 22–36, Aug. 01, 2019, doi: 10.1016/j.jss.2019.04.050.
O. Novo, “Scalable access management in IoT using blockchain: A performance
evaluation,” IEEE Internet Things J., vol. 6, no. 3, pp. 4694–4701, Jun. 2019, doi:
Y. Ren, Y. Liu, S. Ji, A. K. Sangaiah, and J. Wang, “Incentive Mechanism of Data
Storage Based on Blockchain for Wireless Sensor Networks,” Mob. Inf. Syst., vol.
2018, 2018, doi: 10.1155/2018/6874158.
B. Jia, T. Zhou, W. Li, Z. Liu, and J. Zhang, “A blockchain-based location privacy
protection incentive mechanism in crowd sensing networks,” Sensors (Switzerland),
vol. 18, no. 11, p. 3894, Nov. 2018, doi: 10.3390/s18113894.
M. Singh and S. Kim, “Branch based blockchain technology in intel-
ligent vehicle,” Comput. Networks, vol. 145, pp. 219–231, Nov. 2018, doi:
Y. T. Yang, L. Der Chou, C. W. Tseng, F. H. Tseng, and C. C. Liu, “Blockchain-
Based Trafﬁc Event Validation and Trust Veriﬁcation for VANETs,” IEEE Access, vol.
7, pp. 30868–30877, 2019, doi: 10.1109/ACCESS.2019.2903202.
Y. Zhang and J. Wen, “The IoT electric business model: Using blockchain technol-
ogy for the internet of things,” Peer-to-Peer Netw. Appl., vol. 10, no. 4, pp. 983–994,
Jul. 2017, doi: 10.1007/s12083-016-0456-1.
Z. Zheng et al., “An overview on smart contracts: Challenges, advances and
platforms,” Futur. Gener. Comput. Syst., vol. 105, pp. 475–491, Apr. 2020, doi:
W. J. Gordon and C. Catalini, “Blockchain Technology for Healthcare: Facili-
tating the Transition to Patient-Driven Interoperability,” Computational and Struc-
tural Biotechnology Journal, vol. 16. Elsevier B.V., pp. 224–230, Jan. 01, 2018, doi:
H. Kurdi, S. Alsalamah, A. Alatawi, S. Alfaraj, L. Altoaimy, and S. H. Ahmed,
“Healthybroker: A trustworthy blockchain-based multi-cloud broker for patient-
centered ehealth services,” Electron., vol. 8, no. 6, p. 602, May 2019, doi: 10.3390/elec-
H. Zhong, Y. Geng, J. Cui, Y. Xu, and L. Liu, “A weight-based conditional privacy-
preserving authentication scheme in software-deﬁned vehicular network,” vol. 9, p.
54, 2020, doi: 10.1186/s13677-020-00198-3.
C. Lai, R. Lu, D. Zheng, and X. S. Shen, “Security and privacy challenges in
5g-enabled vehicular networks,” IEEE Netw., vol. 34, no. 2, pp. 37–45, Mar. 2020,
G. Kumar et al., “A Privacy-Preserving Secure Framework for Electric Vehicles in
IoT Using Matching Market and Signcryption,” IEEE Trans. Veh. Technol., vol. 69,
no. 7, pp. 7707–7722, Jul. 2020, doi: 10.1109/TVT.2020.2989817.
B. Mikavica and A. Kosti´
c, “Blockchain-based solutions for secu-
rity, privacy, and trust management in vehicular networks: a survey,” J. Supercomput.,
X. Liu, H. Huang, F. Xiao, and Z. Ma, “A Blockchain-Based Trust Manage-
ment with Conditional Privacy-Preserving Announcement Scheme for VANETs,”
IEEE Internet Things J., vol. 7, no. 5, pp. 4101–4112, May 2020, doi:
M. N. Patwary, S. Junaid Nawaz, M. A. Rahman, S. K. Sharma, M. M. Rashid, and
S. J. Barnes, “The Potential Short- And Long-Term Disruptions and Transformative
Impacts of 5G and beyond Wireless Networks: Lessons Learnt from the Development
of a 5G Testbed Environment,” IEEE Access, vol. 8, pp. 11352–11379, 2020, doi:
M. Dai, S. Zhang, H. Wang, and S. Jin, “A Low Storage Room Requirement Frame-
work for Distributed Ledger in Blockchain,” IEEE Access, vol. 6, pp. 22970–22975,
Mar. 2018, doi: 10.1109/ACCESS.2018.2814624.
K. Gu, N. Wu, B. Yin, and W. Jia, “Secure Data Query Framework for Cloud and
Fog Computing,” IEEE Trans. Netw. Serv. Manag., vol. 17, no. 1, pp. 332–345, Mar.
2020, doi: 10.1109/TNSM.2019.2941869.
T. Jiang, H. Fang, and H. Wang, “Blockchain-based internet of vehicles: Dis-
tributed network architecture and performance analysis,” IEEE Internet Things J., vol.
6, no. 3, pp. 4640–4649, Jun. 2019, doi: 10.1109/JIOT.2018.2874398.
U. Arshad, S. Javaid, S. Ahmed, B. Seemab, and N. Javaid, “A Futuristic
Blockchain based Vehicular Network Architecture and Trust Management System,” In
2019 International Conference on Advances in the Emerging Computing Technologies
(AECT) pp. 1–6, doi: 10.1109/aect47998.2020.9194160.
P. K. Sharma, S. Y. Moon, and J. H. Park, “Block-VN: A distributed blockchain
based vehicular network architecture in smart city,” J. Inf. Process. Syst., vol. 13, no.
1, pp. 184–195, 2017, doi: 10.3745/JIPS.03.0065.
Z. Yang, K. Yang, L. Lei, K. Zheng, and V. C. M. Leung, “Blockchain-based
decentralized trust management in vehicular networks,” IEEE Internet Things J., vol.
6, no. 2, pp. 1495–1505, Apr. 2019, doi: 10.1109/JIOT.2018.2836144.
M. A. Rahman, M. S. Hossain, M. M. Rashid, S. Barnes, and E. Hassanain,
“IoEV-Chain: A 5G-Based Secure Inter-Connected Mobility Framework for the In-
ternet of Electric Vehicles,” IEEE Netw., vol. 34, no. 5, pp. 190–197, Sep. 2020, doi: