sustainability
Article
Empowering Communications in Vehicular Networks
with an Intelligent Blockchain-Based Solution
Bacem Mbarek 1 , Nafaa Jabeur 2,* , Tomás Pitner 1 and Ansar-Ul-Haque Yasar 3
1 Faculty of Informatics, Masaryk University, Botanická 68A, 602 00 Brno, Czech Republic;
bacem.mbarek@mail.muni.cz (B.M.); tomp@ﬁ.muni.cz (T.P.)
2 Department of Computer Science, German University of Technology in Oman (GUtech),
P.O. Box 1816, Athaibah, Muscat PC 130, Oman
3 Transportation Research Institute Hasselt University, Martelarenlaan 42, 3500 Hasselt, Belgium;
ansar.yasar@uhasselt.be
* Correspondence: nafaa.jabeur@gutech.edu.om
Received: 15 August 2020; Accepted: 20 September 2020; Published: 24 September 2020
Abstract: Blockchains have emerged over time as a reliable and secure way to record transactions in
an immutable manner in a wide range of application domains. However, current related solutions
are not yet capable of appropriately checking the authenticity of data when their volumes are
huge. They are not also capable of updating Blockchain data blocks and synchronizing them within
reasonable timeframes. This is the case within the speciﬁc context of Blockchain vehicular networks,
where these solutions are commonly cumbersome when attempting to add new vehicles to the
network. In order to address these problems, we propose in this paper a new Blockchain-based
solution that intelligently implement selective communication and collaborative endorsement
approaches to reduce communications between vehicles. Our solution represents the vehicles
of the Blockchain as intelligent software agents with a Belief–Desire–Intention (BDI) architecture.
Furthermore, we propose an approach based on multi-endorsement levels to exchange data of varying
sensitive categories. This approach, which is based on endorsing scores, is also used to shorten the
admission of new vehicles into the Blockchain. We run simulations using the Hyperledger Fabric
Blockchain tool. Results show the efﬁciency of our solution in reducing the processing times of
transactions within two different scenarios.
Keywords: blockchain; vehicular network; belief–desire–intension architecture; multi-endorsement
levels; collaborative endorsement; selective communication
1. Introduction
Thanks to the increasing adoption of Internet of Things (IoT) devices into today’s vehicles,
the acquisition of road trafﬁc-related data has become easier. In addition to sharing these data,
vehicles have also become capable of interconnecting and requesting services, on-the-ﬂy, from third
parties (e.g., insurance companies) as well as from roadside infrastructure (e.g., road trafﬁc
signs) for safer and more efﬁcient commutes. To leverage these capabilities, intensive efforts,
particularly from car industries, are being spent to ultimately create autonomous connected vehicles,
which represent a key concept in the next generation of Intelligent Transportation Systems (ITS) [1].
Vehicles are, therefore, evolving toward becoming cyber–physical systems that create Vehicular
Ad-hoc NETworks (VANET) through which relevant information is timely-provided to a wide range
of stakeholders (including drivers, road trafﬁc authorities, manufactures, maintenance companies,
and insurance companies) for various applications [2]. Collecting and disseminating information
on life-threatening trafﬁc events with VANET have, particularly, become of utmost importance [3].
Sustainability 2020, 12, 7917; doi:10.3390/su12197917 www.mdpi.com/journal/sustainability
Sustainability 2020, 12, 7917 2 of 13
However, due to the current processing and communication restrictions of onboard IoT devices,
enabling secure communications while fulﬁlling the latency requirements between vehicles is yet
a major concern [1]. Indeed, these communications are still prone to various types of attacks,
including Sybil, Denial-of-Service (DoS) attack, jamming, and fabrication [2]. In addition to preventing
vehicles from conveying secure messages, these attacks are increasing privacy concerns in VANETs,
such as reliability, anonymity, and traceability [4].
Blockchain has recently arisen as a promising technology for sharing and protecting vulnerable
data in distributed and decentralized systems [5,6]. Blockchain essentially uses public-key
cryptography to sign transactions among various parties. Transactions are afterward stored in
a distributed ledger. The ledger comprises cryptographically-linked data blocks (i.e., transactions),
which are practically impossible to alter or remove. Thanks to its unique values of trust, immutability,
transparency, and disintermediation, Blockchain is being increasingly used by governmental, industrial,
and academic entities in a wide range of application domains, including banking, healthcare, logistics,
and transportation [2].
In the particular ﬁeld of transportation, various Blockchain-based solutions have been proposed,
for example, for interconnected smart vehicles (e.g., [7]), ITS (e.g., [8]), forensic applications
(e.g., [9]), secure key management (e.g., [10]), data collection and sharing (e.g., [7,11,12]), and vehicle
communications (e.g., [1,3,5,8,13,14]). In spite of the relevant approaches that have addressed vehicular
communications, Blockchain solutions are still computationally expensive. Indeed, accepting new
vehicles in the Blockchain and/or carrying out transactions yet require intensive exchanges with
endorsing peers. As road trafﬁc data are increasingly voluminous, these endorsing peers are becoming
practically unable to verify transactions and deliver their verdicts within reasonable timeframes.
The entire Blockchain is consequently slow in updating, synchronizing, and distributing
new copies of the data blocks. This slowness is signiﬁcantly hindering the scalability of any
Blockchain-based solution. In the speciﬁc context of a vehicular network, current Blockchain
mechanisms include a higher number of endorsing peers, unless they are thoroughly adapted to
ﬁt the available limited resources or replaced with revolutionary approaches. To address these
shortcomings, we argue that vehicles must be capable of selecting the right endorsing peers for the right
transactions they want to perform in order to reduce their communication overheads. As VANET is by
nature highly dynamic and opportunistic, we argue that vehicles must be aware of their surrounding
contexts and rely on the minimum number of peers to carry out their transactions. To reach these
goals, we propose in this paper to model the participant vehicles in the Blockchain according to
a Belief–Desire–Intension (BDI) architecture. This architecture has been intensively and successfully
used in the Multi-Agent System paradigm to intelligently model human behaviors and interactions [15].
Our contributions in this paper are summarized as follows: (1) a BDI architecture that endows vehicles
with intelligent mechanisms to optimize communications via a selective endorsement approach; (2) a
new approach that relies on collecting minimum endorsing scores to carry out transactions through
multiple endorsing levels; (3) an approach to promote vehicles to higher endorsing levels based on the
history and impact of their transactions; and (4) a new approach for collaborative endorsement.
In the remainder of this paper, Section 2 highlights the current literature on using Blockchain
in vehicular networks. A special attention is given to optimizing and securing communications
among vehicles. Section 3 outlines the system model of our solution. It also explains our concept of
multi-endorsing levels to manage and optimize communications in the vehicular network. Section 4
presents our approach for admitting new vehicles in the Blockchain. Section 5 is dedicated to our
solution for empowering vehicle communications with intelligence. This section particularly focuses
on our proposed BDI architecture to model the vehicle participating in the Blockchain. It also focuses
on our approaches for selective communication, collaborative endorsement, and promoting endorsers
to higher levels. Section 6 reports and assesses the results of our simulations. Section 7 presents a brief
discussion of the results obtained. Finally, Section 8 concludes the paper and outlines future works.
Sustainability 2020, 12, 7917 3 of 13
2. Related Work
Several studies (e.g., [5,6]) have reported that Blockchain is a promising alternative to solve the
recurrent issues of data privacy, protection, and ownership, particularly thanks to its peer-to-peer
structure, the mechanisms it offers to increase anonymity, transparency, and protection as well as
to the absence of a trusted central authority [12]. Within the speciﬁc realm of vehicular networks,
various related architectures have been proposed. In this regard, [16] described an architecture of
a model Block-Vehicular Network (VN) in the context of smart cities. The architecture could be
used as a basis for the implementation of large-scale vehicular networks where resources are shared
more effectively. In the proposed architecture, nodes can decide from whom they would request
services based on a rating list. Ref. [17] have presented a trust-based architecture that uses Blockchain
technology to increase the privacy of users in the VANET. The architecture also aims to improve
security against MAC layer attacks, including DoS attacks, Sybil attacks, and fabrication. The solution
uses hashing techniques and timestamps to preserve the freshness of messages delivered. It also uses
message rating and a credibility approach (i.e., vehicles that convey fake messages will have low
credibility) to manage trust among vehicles during information exchange. Ref. [6] have presented
a Blockchain-based vehicle network architecture called Block-VN. Block-VN operates in a distributed
way to create a distributed transport management system within the context of smart city.
Furthermore, several Blockchain-based solutions for vehicular networks have been proposed
for secure key management (e.g., [10]), data collection and sharing (e.g., [4,7,9,11,12]), as well as
vehicle communications (e.g., [1,3,5,8,13,14,18]). With respect to key management, [10] have designed
a framework to manage security keys within an heterogeneous vehicular network. The framework
includes security managers that capture relevant vehicle information, encapsulate block to transport
keys, and perform rekeying to vehicles inside the same security domain. The framework is based
on a decentralized structure where Blockchain is used to simplify distributed key management.
With respect to data collection and sharing, [9] have proposed a permissioned Blockchain framework
for the management of information collected through vehicle on-board sensors. The framework
includes a fragmented ledger to store this information as well as additional relevant data (including
reports on car diagnosis and the history of car maintenance). The authors have included Vehicular
Public Key Management (VPKI) into the Blockchain to enable vehicles to join the proposed system
as members while ensuring their privacy. Ref. [4] have presented a privacy-preserving incentive
announcement network that enables several users to anonymously send messages within a non-fully
trusted environment. To this end, the network, called CreditCoin, is using Blockchain to ensure
privacy, trace malicious users’ identities, incentivize users to share trafﬁc information, and implement
tamper-resistant transactions. Ref. [7] have investigated how the joint use of Blockchain and
interconnected vehicles can solve the security issues related information management. In this regard,
the authors have proposed a Blockchain solution using an overlay network. The nodes of the network
are divided into clusters. The heads of the clusters are responsible for the management and the
operation of the Blockchain functions. [11] have proposed a Blockchain solution that includes an
incentive-based approach to identify road conditions, detect road accidents, and encourage the
use of less congested roads. The solution uses beacons mounted on roadside sections to detect
approaching vehicles, get their destinations, and estimate the conditions of roads accordingly. Ref. [12]
have recommended the joint use of IoT and Blockchain technologies to manage road hazards.
The authors have used the hashgraph technology (which is a distributed ledger technology) to create
communication networks between vehicles as well as other components of the vehicular network.
They have also suggested to use hashgraph to schedule requests based on their priorities and ensure
better Quality of Service (QoS) accordingly.
With respect to communication within the vehicular network, [1] have proposed the use of
a ring-signature based scheme to check the identity of vehicles that are requesting to join the network.
The authors have also used a Blockchain solution to verify and secure the exchange of information
among the successfully identiﬁed vehicles. To this end, they have created secure communication
Sustainability 2020, 12, 7917 4 of 13
channels using multi-party smart contracts. The proposed solution is capable of enabling the members
of the vehicular network to exchange almost instantaneous messages anonymously by means of
cryptographic primitives. Ref. [13] have presented a Blockchain solution to secure communications
among intelligent vehicles by using acoustic side channels and visible light communications. To solve
the critical issue of message dissemination in VANET, [3] have created a local Blockchain for the
exchange of real-world event messages between vehicles within the same country. They have also
presented a public Blockchain to store the trustworthiness of messages and nodes in a distributed
ledger. This ledger is then used to ensure appropriate secure message dissemination. Ref. [14] have
used Blockchain technology for a secure distributed message passing. The proposed framework does
not rely on a centralized server. It also rates the credibility of a message and then forwards it through
designated short-range communication. Ref. [5] have used Blockchain technology to implement
an Intelligent Vehicle-Trust Point (IV-TP) solution for secure and reliable communications among
intelligent vehicles. The solution assesses the trustworthiness of vehicles and identiﬁes their legal
and illegal actions. It also uses a reward-based system to motivate sharing information between
vehicles. Ref. [8] have developed a trust-extended decentralized authentication mechanism (TEAM)
for decentralized communications between vehicles. TEAM implements a mechanism for transitive
trust relations to improve the performance of authenticating vehicles within the Blockchain. Ref. [18]
have developed an encryption system based on Blockchain to enable the management of trust during
vehicle communications as well as during real-time cloud-based video monitoring.
The aforementioned works have proposed relevant Blockchain-based solutions for vehicular
networks. However, we argue that the use of intelligent approaches is still insufﬁcient, particularly to
address issues related to vehicle communications. In this respect, we argue that the extensive literature
in the artiﬁcial intelligence domain will deﬁnitively enable more ﬂexible, secure, context-aware,
privacy-aware, and resource-aware communications among vehicles.
3. System Model
Let us consider in this paper a Blockchain consisting of V connected vehicles (V = {V1, V2, ..., Vn})
and E endorsing peers (E = e1, e2, ..., em), E ⊆ V, and m ≤ n. Every vehicle uses the Blockchain by
submitting transactions as requests for services of individual interest (e.g., car maintenance) or by
sharing information of common interest (e.g., road trafﬁc accident). These transactions will be examined
by the endorsing peers and then written along with their validity in a distributed ledger. We assume
that data stored in the Blockchain are classiﬁed into p categories, based on several parameters, such as
sensitivity. Without loss of generality, we assume that the p categories are predeﬁned and are known
by all the vehicles which are members of the Blockchain network (Figure 1). We also assume that the
Blockchain includes a node called Ordering Service that is responsible for adding the transactions
(after their validations) to the Blockchain Block and sharing them with the members of the network.
The Block could be written as follows:
Block = {t11, t12, ..., t21, ..., tp1, tp2, ... } where tij refers to the transaction j of data category i.
In our model, we classify the endorsing peers into q levels (Figure 2). The higher the level of
the endorsing peer, the higher the categories of data the peer has access to as well as the higher the
categories of data it can share. For example, an endorsing peer with level 1 will have access to and can
share data of categories 1 and 2 only, whereas an endorsing peer with level 3 will have access to and
share data of categories 1, 2, 3, 4, and 5. Endorsing levels are earned over time. The Ordering Service
will keep track of the number, veracity, and impact of transactions carried out by each endorser and
generate a h-index accordingly (the h-index summarizes the value of the contributions of the vehicle
in the Blockchain as well as its trust and its endorsing levels). The endorsing level of the endorser
(i.e., the vehicle) will then be issued regularly (for example after a given number of transactions or after
a speciﬁc period) based on this h-index. In Figure 2, we explain how vehicles can join the different
levels of our endorsing structure. In fact, when a new vehicle wants to join the Blockchain, it must
collect a prerequisite minimum score (also called consensus) from endorsing peers (as recommendation
Sustainability 2020, 12, 7917 5 of 13
scores). We say in this case that the vehicle is joining a consensus level. Vehicles that are already
members of the Blockchain have two options: (1) join a higher endorsing level temporarily (in this
case they need a minimum consensus score); or (2) join a higher level ofﬁcially (in this case they need
a minimum endorsing score. The endorsing score is higher than the consensus score, particularly
since being part of an endorsing level is an ofﬁcial commitment from the vehicle). Vehicles can also be
promoted to the next levels, either automatically based on their h-indexes (calculated by the Ordering
Service) or by collecting the necessary remaining scores for their promotions. We assume in this paper
that the consensus scores as well as the endorsement scores are predeﬁned. We also assume that
endorsers have weights depending on their levels. These weights are also predeﬁned.
Figure 1. Copies of the Blockchain Block with varying data categories.
Figure 2. Cases of vehicle car join consensus or endorser levels.
Sustainability 2020, 12, 7917 6 of 13
4. Admission Control in the Blockchain
During the registration phase, a new vehicle willing to join the Blockchain has the choice to join
a consensus level (see Figure 2) temporarily (i.e., without being an endorser) or ofﬁcially (i.e., the vehicle
will be an endorser). To this end, the vehicle must collect the minimum score for the selected level.
This score will be obtained based on recommendations from neighboring vehicles at the moment
of submitting the request to join the Blockchain. Since the vehicular network is by nature highly
dynamic, the endorsing peers in the vicinity of the vehicle will vary over time. The vehicle may
be capable of collecting the required score easily. The vehicle may also ﬁnd difﬁculties to reach its
aim. In this case, it may consider waiting until better options for obtaining the required score are
available. Waiting could be the best option for the vehicle. Indeed, if the vehicle decides to join the
Blockchain ofﬁcially, it will be registered in the level for which it obtained the necessary score. To go for
higher levels, the vehicle will need to increase its contributions to the Blockchain and carry out trusted
transactions with high impact. To do so, the vehicle must be endowed with intelligent mechanisms to
make the appropriate decisions at the right time, based on the available contextual data as well as the
data of the vehicle itself. In this paper, we calculate the score S of a given vehicle Sv as follows:
Sv =
q
∑
i=1
(
k
∑
j=1
Sv,j)Wi, (1)
where Wi denotes the weight of the endorser of level i, SV,j is the score given to V by the neighbor
vehicle j which is an endorsing peer in the Blockchain of level i. We assume here that scores are
received only from the neighbors who accepted the request to score V. For example, in the scenario of
Figure 3, the vehicle V (represented with a black circle) has 7 neighbors of level 2. However, only 4 of
them accepted giving scores (represented in Figure 3 as circles with bold boundaries). According to
the conﬁguration of Figure 3, the score of the vehicle V is calculated as follows:
SV = (S(V,v1) + S(V,v2) + s(V,v4) + S(V,v8)) ∗ w3 + (S(V,v3) + S(V,v5) + s(V,v6)) ∗ w2 + S(V,v7) ∗ w1
Figure 3. Scenario for the calculation of the score of a vehicle.
5. Empowering Vehicle Communications with Intelligence
During the admission control phase, any vehicle willing to join the Blockchain temporarily or
ofﬁcially will need the bare minimum score for the consensus level it is aiming for. This approach
Sustainability 2020, 12, 7917 7 of 13
will enable the vehicle to reduce the number of endorsers and therefore reduce the number
of communications. If the vehicle is lucky enough, it may obtain the required score via the
recommendations of currently available endorser vehicles in the neighborhood. Once the vehicle
is admitted ofﬁcially in the Blockchain, it should start building trust relationships with peers,
especially the endorsing ones. These relationships will be particularly helpful in decreasing
communication overheads and obtaining recommendations with higher weights (based on the weight
of the endorsers’ levels). To build and sustain trust relationships as well as identify the appropriate
endorsing collaborators, the vehicle must be capable of intelligently identifying its current options,
plan its actions, and reﬂect on their outcomes. We explain in what follows our solution to reach
this goal.
5.1. Belief–Desire–Intension Architecture for Blockchain Vehicles
In order to allow the vehicles to reason adequately about occurring events as well as contribute to
the Blockchain (for example by sharing information or endorsing new vehicles to join the network),
we propose to represent every vehicle that has successfully joined the Blockchain by an intelligent
software agent. We also propose to model this agent according to a Belief–Desire–Intension (BDI)
architecture (Figure 4). BDI is a widely used architecture in multi-agent systems. It has particularly
proven its performance in intelligently modelling human behaviours, their reasoning, and their
interactions [15]. Beliefs include the local information that the agent (i.e., vehicle) has about itself
(e.g., its current speed, position, engine status, etc.) and its surrounding (e.g., road infrastructure,
neighboring vehicles, and events of interest in the vicinity like announced obstacles/crashes).
Beliefs also include a copy of the Blockchain containing the data belonging to the categories that
the vehicle has access to. Beliefs could be true or false and are subject to change during the operation
of the agent. Desires reﬂect the objectives or the situations that the agent would like to accomplish.
Desires could be the sharing of given information or the acceptance/refusal to endorse a given vehicle.
Intentions refer to the actions that the agent has chosen to execute.
Figure 4. Belief–Desire–Intention (BDI) architecture for Blockchain vehicles.
Sustainability 2020, 12, 7917 8 of 13
The basic operation of the agent is as follows: every vehicle will be always listening to beacons and
communications from neighboring vehicles. Once new information is received, a Revision Function
(RF) is executed in order to update its current beliefs. For example, if the temperature of the engine
is reported to be high then the RF will update the status of the vehicle to “maintenance needed”.
Based on the new beliefs, an Option Generation Function (OGF) is run to update the desires of the
vehicle. For example, if the temperature of the engine has reached a worrying level then the agent will
explore the possible options, like stopping the car and call for support, attending the ﬁrst car-repair
workshop, or calling the insurance company. The option of stopping the car may be inappropriate
if the vehicle is currently located in a remote area at a night time. Here, for each possible action,
the agent will calculate its related reward (if any), its communication cost, its priority, etc. Once the
set of possible actions are identiﬁed, an Analysis Function (AF) is executed to elaborate and execute
an action plan. The outputs of the actions are assessed and the intentions of the agent are updated
accordingly. The new updates will be used by the RF for further actions. We explain this concept
within the speciﬁc context of carrying out a transaction in the Blockchain (Figure 5).
Figure 5. UML state-diagram for carrying out a transaction in the Blockchain.
5.2. Collaborative Endorsement
Agent A may request agent B with a higher endorsing level to help in sharing sensitive information
as explained in the previous section. A will select B based on its recorded trust. If B accepts the request
of A then both agents will conclude an agreement according to which B will carry out the transaction
on its name and share the reward with A. If the level of B is not enough to carry out the transaction
then A and B may consider to create a Transaction Team (TT) and share the information on both
names. A may also consider inviting additional trusted neighboring peers to join the TT. In this case,
Sustainability 2020, 12, 7917 9 of 13
the members of the team will share the reward based on their levels. Sharing the reward could be done
in several ways. In this paper, we use the following formula:
Ri =
WiciR
∑n
j=1 Wjcj
, (2)
where Wi denote the weight of the level of the agent i, ci denote the contribution of the agent i in the
consensus score (i.e., required score to carry out the transaction), and R is the total reward that will be
obtained from sharing the information.
5.3. Promotion of Endorsers’ Levels
As mentioned in Section 3, the participants in the Blockchain are classiﬁed into several levels.
Higher endorsing levels will allow the corresponding vehicle to get higher priorities in carrying out
transactions, obtaining higher trust levels, accessing more data with higher categories, etc. Joining
higher endorsing levels is a right that a vehicle can claim or could obtain automatically. To upgrade
a given endorsing level, the Blockchain will examine the history of transactions and contributions
performed by the vehicle. This history is weighed based on its credibility as well as on its impact.
For example, an information about an oil spill on a road section would save lives and avoid serious
damages to vehicles. If this information is found to be useful to vehicles crossing this road section
then its impact is high and the vehicle will be rewarded. Based on a predeﬁned schedule (e.g., every
month or after a speciﬁc number of transactions), the Blockchain will calculate the total reward
obtained by the vehicle and convert them into a score. This score is added to the current score of
the vehicle and a related h-index is generated. If the vehicle reaches an h-index sufﬁcient enough for
a given higher endorsing level then the Blockchain will automatically announce its promotion to that
level. The vehicle can accept or refuse the promotion based on its own interest. The vehicle can also
explicitly request to be promoted from the Blockchain based on its current contributions as well as
based on recommendations obtained from endorsers with higher levels. To do the right calculations,
the Blockchain will announce to the vehicles the scores that they must obtain to get promoted to the
next levels. Announcements will be done when the Blockchain calculates the rewards of the vehicles.
6. Performance Evaluation
To assess the performance of our solution, we used the Hyperledger Caliper software tool [19].
This tool can produce reports containing various performance indicators, including resource utilization,
transactions per second, and transaction latency. We mainly focused on analyzing the transactions’
execution times. To this end, we ran our simulations within two scenarios: (1) by varying the number
of vehicles in the Blockchain network; and (2) by varying the number of transactions while maintaining
a ﬁxed number of vehicles (Table 1). We ran our simulations for 80 s on a 2 km-long four-lane road
section (two lanes in each direction).
Table 1. Simulation parameters.
Parameter Value
Range of vehicle speeds 40 to 120 km/h
Number of road lanes 4 (two in each direction)
Road length 2 km
Time of simulation tend = 80 s
MAC IEEE 802.11b
In the ﬁrst scenario, the number of vehicles ranged from 50 to 250. We considered the scenario
of sharing data about road trafﬁc. The execution time was mainly spent on processing transactions,
peer-to-peer communications, and updating the Blockchain ledger accordingly. According to our
obtained results (reported in Figure 6), an average time of about 50 ms was needed to read one
Sustainability 2020, 12, 7917 10 of 13
transaction in the Blockchain. This time was shorter than what was reported in several studies
(e.g., [20]). We can particularly remark that the number of vehicles had limited effects on the processing
time of transactions. Indeed, in contrast with the existing approaches where the vehicle should
be endorsed by a number of peers that increases with the number of vehicles in the simulations,
our solution keeps the number of endorsing entities limited as long as a minimum endorsing score
is obtained. This is mainly enabled by our BDI agents that intelligently identify and communicate
with the right peers to process the transactions. In the second scenario of our simulations, we report
in Figure 7 the total execution times to process a varying number of transactions while ﬁxing the
number of vehicles to 250. As expected, processing more transactions increased the execution time.
However, if we consider individual transactions, we can remark that the average execution time with
our solution decreased. Indeed, when the number of transactions is equal to 50, the total time to
process all the transactions is 4 s. The average time to process one transaction is, therefore, 0.08 s.
However, for 250 transactions, the total execution time is 14 s and, hence, each transaction needs an
average of 0.056 s to be executed. We explain this performance by the fact that the BDI agents did
not need to repeat the same process of identifying and contacting peers to endorse their transactions.
They, indeed, examined the transactions and followed the same procedures to carry out transactions
involving similar data categories and requiring similar endorsing scores.
Figure 6. Average execution time of each transaction by varying the number of vehicles.
Figure 7. Execution time of processing all the transactions by varying the number of transactions.
7. Discussions
We have implemented a new Blockchain-based solution using the Blockchain open-source
platform: Hyperledger Fabric [21]. This platform has been reported as one of the most suitable
platforms for the vehicular network [22]. It particularly allowed us to manage huge amounts data
collected thanks on-board vehicle devices.
Sustainability 2020, 12, 7917 11 of 13
Thanks to their on-board devices, vehicles are now capable of acquiring huge amounts of data.
To make the best use of these data, vehicles must be capable of exchanging them securely and
anonymously. However, sharing large volumes of data over the Blockchain will make it slower and
more cumbersome. In order to address this issue, our solution includes intelligent mechanisms for
selective communications. In fact, if an important change happens in the vehicle (e.g., engine crash),
the agent will need to identify the required score to submit an urgent transaction to the Blockchain.
If the vehicle is able to obtain this score thanks to its endorsement level then the transaction is submitted
right away to the Blockchain. In the opposite case, the agent must identify and contact the appropriate
group of endorsers that will help collect the expected score. The vehicle may have varying neighbors in
time and space with various levels in the Blockchain. The agent must reuse its previous experience to
make an optimum selection of the endorsers. It is sufﬁcient for the agent to secure the bare minimum
number of endorsers to collect the required score. Nevertheless, the agent would seek higher scores
in the hope to obtain a higher priority for the processing of its transaction and/or to upgrade its
level in the Blockchain (see Section 5.2). Upgrading this level is necessary and would motivate agents
to share data through the Blockchain. Indeed, it may happen that the agent is attempting to carry
out a transaction that includes sensitive data for which it does not have the required sharing rights
(for example, a private vehicle may cross a criminal accident. It comes only to the police, but not to
private cars, to announce that the driver is armed and dangerous). Three options are possible for the
agent. In the ﬁrst option, the agent will seek to collect the necessary consensus score to temporarily
join the endorsing level that will allow for performing the transaction. In this case, the agent will
run the same admission control process described in Section 3. In the second option, the agent will
carry out a collaborative endorsement process (see Section 5.2). To execute the transaction with the
ﬁrst or the second options, we propose to allow the agent to record its mutual trust with the agents
with whom it is dealing. This trust will particularly allow the agent to focus on exchanging with
the right endorsing peers for the right transaction (e.g., the endorsing peers that will accept scoring
requests). Communications with the Blockchain could, therefore, be reduced and response time for
data sharing could be shortened. In the third option, the agent will submit the transaction to the
Blockchain. The transaction will then be scheduled and processed based on the level of the agent as
well as the priorities of any other transactions submitted to the Blockchain. In this case, the transaction
may be delayed and the vehicle would not obtain the requested service at a convenient time.
8. Conclusions
Several solutions have used Blockchain to enable increasingly voluminous data to be securely
and anonymously exchanged within vehicular networks. However, due to the growing number
of related transactions, the Blockchain endorsing processes have become slow and cumbersome.
The existing approaches that have attempted to solve this issue are not ﬂexible enough to speed up this
process and allow the admission of new vehicles into the Blockchain within reasonable timeframes.
In order to solve these challenges, we proposed in this paper a new Blockchain-based solution
that intelligently implements selective communication and collaborative endorsement to reduce
communications among vehicles. Our solution models the vehicles of the Blockchain as intelligent
software agents with Belief–Desire–Intention (BDI) architecture. It also uses an approach based on
multi-endorsement levels to exchange data of varying sensitive categories. According to our approach,
it is sufﬁcient for a vehicle to collect the bare minimum score to carry out a transaction through
the appropriate endorsing level for its data. Furthermore, our approach uses scoring mechanisms
to enable new vehicles to join the Blockchain. The same approach is enabling the vehicles of the
Blockchain to get promoted to higher endorsing levels and thus increase their priorities in sharing
data and reducing the duration of their endorsement processes. We simulated our solution within
two basic scenarios and obtained encouraging results. Indeed, our initial performance measures
reﬂect shorter transaction processing times as well as shorter times to admit new vehicles in the
vehicular network Blockchain. As future works, we plan to improve the operation of our BDI agents
Sustainability 2020, 12, 7917 12 of 13
as well as to enhance the implementation of our scoring mechanisms toward a better collaborative
endorsement between vehicles. Another future work is to use IEEE 802.11p based on real-world
measurements and radio propagation models of Vehicle-to-Vehicle (V2V) networks in different
environments, including highway, rural, and urban areas.
Author Contributions: The authors conﬁrm contribution to the paper as follows: Study conception and design:
B.M., and N.J. Data collection and validation: B.M. and N.J. Software, analysis and interpretation of results: B.M.
Manuscript preparation, writing, and editing: B.M., N.J, T.P., and A.-U.-H.Y. All authors reviewed the results and
approved the ﬁnal version of the manuscript.
Funding: This research received no external funding
Acknowledgments: This research was supported by ERDF “CyberSecurity, CyberCrime and Critical Information
Infrastructures Center of Excellence” (No. CZ.02.1.01/0.0/0.0/16 019/0000822).
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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