An Infrastructure-Based Trust Management Framework for Cooperative

ITS

Rihab Abidi

1,4 a

, Nabil Sahli

2 b

, Wassim Trojet

3 c

, Nadia Ben Azzouna

1 d

and Ghaleb Hoblos

4 e

University of Tunis, ISG, SMART Lab, Av. de la Liberte, Tunis, Tunisia

Computer Science Department, German University of Technology (GUtech), Muscat, Oman

Higher College of Technology UAE, Abu Dhabi, U.A.E.

Normandy University, UNIROUEN, ESIGELEC, IRSEEM, Av. Galilee, Normandie, France

Keywords:

Intelligent Transportation System, Trust Management, Infrastructure-Based Architecture.

Abstract:

Intelligent Transportation Systems (ITSs) have been exploited by developed countries to enhance the quality

of transportation services. However, these systems are still facing major bottlenecks to be addressed such

as the data density, precision and reliability of perceived data and computational feasibility of the nodes.

Trust management is a mechanism applied to secure the vehicular networks. However, most of the proposed

trust models that are applied to Vehicular Ad-hoc NETwork (VANET) do not address all the aforementioned

challenges of ITS. In this paper, we present a comprehensive framework of trust management speciﬁcally

designed for ITS applications. The proposed framework is an infrastructure-based solution that relies on

Smart Road Signs (SRSs) to assess the trustworthiness of trafﬁc data and nodes of the network. The idea of

the framework is to use autonomous SRSss that are able to collect raw data and evaluate it in order to alert

the drivers with reliable trafﬁc information in real time. We adopt a hierarchical architecture that exploits a

two-level trust evaluation to ensure accuracy, scalability, security and high reactivity of ITS applications. A

discussion of the framework and its strengths is presented.

1 INTRODUCTION

With the growth of trafﬁc volume, the modern society

faces major transportation challenges that may cause

serious and harmful consequences, such as accidents,

congestions, environmental consequences and even

economic impacts. Intelligent Transportation Sys-

tems (ITSs) are a promising solution that helps im-

prove the trafﬁc management by deploying sustain-

able and innovative platforms (Guerrero-Ibanez et al.,

2015).

Despite its development over the last decade, the

ITS ﬁeld still faces some challenges and bottlenecks,

mainly, precision, security and computational issues

(Lin et al., 2017). Traditional solutions that are based

on Public Key Infrastructure (PKI) are not able to se-

https://orcid.org/0000-0002-6108-7854

https://orcid.org/0000-0002-9805-6859

https://orcid.org/0000-0001-7792-4402

https://orcid.org/0000-0002-6953-2086

https://orcid.org/0000-0003-3268-5270

cure the network from legit nodes that could disturb

the network and send untrustworthy data. Trust man-

agement models are thus used to secure the network

and evaluate the precision and relevance of the shared

data. In fact, trust management models refer to the

evaluation of the accuracy and relevance of a trustee

node or data by a trustor node using quantiﬁed beliefs

in order to mitigate and reduce the impact of attackers

(Hbaieb et al., 2022).

In this regard, we propose a trust framework

to support the ITS applications. We propose an

infrastructure-based solution that uses Smart Road

Signs (SRSs) for cooperative ITS. Several architec-

tures of SRSs have been proposed in the literature.

For instance, the project proposed in (Czy

zewski

et al., 2019) is based on intelligent road signs to man-

age the road trafﬁc and prevent highway collisions.

The intelligent road signs are able to collect data

from sensors and exchange it, using LTE and LoRa

WAN technologies. However, while dynamic mes-

sage signs (DMSs) are employed in different coun-

tries, these DMSs remain passive and display trafﬁc

Abidi, R., Sahli, N., Trojet, W., Ben Azzouna, N. and Hoblos, G.

An Infrastructure-Based Trust Management Framework for Cooperative ITS.

DOI: 10.5220/0011971600003479

In Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2023), pages 329-336

ISBN: 978-989-758-652-1; ISSN: 2184-495X

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

329

information issued from trafﬁc management centers.

In this framework, we rely on the SRSs to take ad-

vantage of their intelligence which lies in their auton-

omy, proactivity and their ability to process data with-

out the intervention of third parties (Hamdani et al.,

2022). The aim of the proposed framework is to use

autonomous and SRSs that collect real-time data from

different sources and collaborate to evaluate its trust-

worthiness.

Researchers proposed several trust models for Ve-

hicular Ad-hoc NETwork (VANET). However, most

trust models cover at most few challenges of the ITS

applications. In fact, studies show that achieving

the optimal performance of ITS applications lies in

combining several characteristics and requirements.

Mainly, the ITS applications are meant to provide

accurate information in real-time performance while

managing the message overheads and ensuring the se-

curity and privacy of the network (Ben Hamida et al.,

2015).

To the best of our knowledge, there is no model

that meets the most crucial requirements of the ITS,

namely, accuracy, scalability, security, reactivity, and

privacy.

In this context, we propose a comprehensive

framework of a trust management model that en-

hances the performance of ITS applications. The aim

of the proposed framework is to evaluate the trustwor-

thiness of reported trafﬁc events and the data sources.

Our aim is to ensure that the trust model responds to

the crucial requirements of ITS at once. In fact, we

exploit multi-sourcing of trafﬁc-related data to ensure

the overall visibility of the environment. These data

may be sensed from different transportation nodes

such as vehicles, travellers, infrastructure and even

social media. Moreover, we adopt a decentralized ar-

chitecture to reduce the communication and compu-

tation overhead.

The rest of the paper is organized as follows. Sec-

tion 2 presents the related works. Section 3 introduces

the network model. Section 4 describes the proposed

framework. We discuss the design goals in section 5.

Section 6 concludes the paper.

2 RELATED WORKS

Different trust models have been proposed to evaluate

the trustworthiness of trafﬁc data and/or nodes. Gen-

erally, researchers review the proposed trust model

according to the subject of trust: entity-centric, data-

centric, and hybrid models. In this paper we focus on

the addressed requirements of the trust model.

For instance, Bhargava and Verma propose in

(Bhargava and Verma, 2022) trust management model

which ensures accuracy and security requirements. In

fact, the authors consider the uncertainty of the data

to increase the precision. They use Dempster–Shafer

theory (DST) to aggregate direct and indirect trust

of the vehicles. In order to increase the precision

of the trust evaluation, the authors use contextual

information to deﬁne the type of the messages that

are attacked by the malicious vehicles. The con-

sidered messages are Lane Change warning (LCW),

Stopped Vehicle Warning (SVW), and Emergency

Brake warning (EBW). Moreover, the authors use ad-

ditional functions to enhance the precision of the trust

evaluation and boost the security of the model. The

used functions are the penalty, the forgetting, the re-

warding, and the forgiving functions.

Forgetting function is also used in (Zhang et al.,

2020b) by Zhang et al. and it is introduced with a de-

cay factor to cope with the On-Off attacks (OOA) and

the Newcomer attacks (NA). The idea of using these

factors is to prevent the quick increase of the trust

value of the nodes. This technique helps to encour-

age the nodes to present a good behaviour in order to

maintain their trust values. In addition, the authors

take advantage of the contextual information about

the vehicles, such as vehicle type, vehicle age, braking

performance, handling stability, etc. and the drivers’

characteristics to achieve the accuracy requirement of

the trust evaluation.

The security and privacy requirements are tackled by

Ahmed et al. in (Ahmed et al., 2022). The authors

use the blockchain technology to check the legitimacy

and authenticity of the nodes joining the network. The

new nodes that join the network for the ﬁrst time are

registered in the blockchain by the Trusted Authority.

Then, the Road Side Units (RSUs) evaluate the trust-

worthiness of the vehicles and add their updated trust

values to the blockchain ledger.

Zhang et al. in (Zhang et al., 2020a) consider

the accuracy, the security and the reactivity of their

trust model. They propose a learning approach that

uses a Feedforward Neural Network algorithm (FNN)

to estimate the local and global trust values. They

use several contextual information as an input to the

FNN, such as the type, the location of the trafﬁc inci-

dent, and location of the reporting vehicle. The pro-

posed model is designed to cope with Bad Mouthing

attack (BMA), On-Off Attack (OOA), and Simple At-

tack (SA). In fact, the authors combine the deep learn-

ing technique with the blockchain technology to learn

the correlation between the malicious nodes and to

predict their behaviour. Moreover, the blockchain is

used to enhance the security by checking the authen-

ticity of the vehicles and the reported events. The

VEHITS 2023 - 9th International Conference on Vehicle Technology and Intelligent Transport Systems

330

blockchain is managed by the RSUs instead of the ve-

hicles in order to ensure the scalability of the model.

Gazdar et al. in (Gazdar et al., 2022) take advan-

tage of the blockchain technology to enhance the se-

curity of the vehicular network. The authors consider

mainly the BMA. Indeed, they afﬁrm that the features

of the blockchain ensure the data integrity and avail-

ability. Moreover, they address the cold start prob-

lem by assigning an initial value to the new nodes that

join the network and saving their updated trust values

in the blockchain ledger. The authors consider also

the reactivity requirement to cope with time-sensitive

applications. In fact, they employ a lightweight tier-

based technique to compute the trustworthiness of

vehicles in order to reduce communication overhead

(Alboqomi et al., 2020).

A decentralized trust model is proposed by Chen

et al. in (Chen et al., 2020). The authors use an in-

centive mechanism to encourage nodes to participate

in the trust management process, thus increasing the

accuracy of the trust evaluation. Hence, the nodes are

either rewarded or punished based on the quality and

the workload of their contribution. Moreover, the in-

centive mechanism helps the model to cope with the

BMA by encouraging the nodes to be honest. More-

over, vehicles encrypt and sign exchanged data using

a unique and a unique key pair in order to cope with

the Sybil attack (SyA). The proposed model consid-

ers also the reactivity and scalability requirements by

designing a hierarchical architecture to reduce the la-

tency of the model. In fact, the authors employ a

decentralized consensus mechanism executed on two

layers in a parallel manner: the transaction validation

and the block veriﬁcation and consensus.

Guo et al. propose a trust model in (Guo et al.,

2020) that cope with malicious attacks. The pro-

posed model addresses mainly the accuracy require-

ment. The authors use the Reinforcement learning to

dynamically adjust the trust evaluation strategy based

on the scenario. Thus, they employ contextual infor-

mation such as time of event occurrence and loca-

tion of the event. The trust information based on in-

ternal information (direct sensing and self-experience

information) and external information (information

reported by other entities).

As presented in table 1, the beforementioned trust

management models consider, at most, one or few

requirements of the ITS applications. For instance,

few works consider only the accuracy and security

requirements, such as in (Li and Song, 2015), and

(Zhang et al., 2020b), while (Bhargava and Verma,

2022) consider also the reactivity. Moreover, we no-

ticed that the accuracy and security requirements are

the most considered among the addressed require-

ments. To the best of our knowledge, there is not a

general trust model that considers all the requirements

at once. However, the mentioned requirements are

crucial for ITS applications. In particular, accuracy

and security are critical requirements, especially for

congestion control applications (Alam et al., 2016),

(Ben Hamida et al., 2015). Indeed, the diffusion of

erroneous information, due to inaccuracy of raw data

or attacks by malicious nodes, may cause severe con-

sequences such as high congestion or even road acci-

dents. High reactivity is also a key requirement for

time sensitive applications. In case of congestion, the

drivers should be alerted in real-time to avoid trafﬁc

bottlenecks. Therefore, we propose a generic frame-

work of a trust model that may be used by ITS ap-

plications. The aim of this work is to provide a trust

framework that fulﬁls all the crucial requirements of

the ITS applications in order to enhance the provided

services.

3 OVERVIEW

The proposed framework relies on a hierarchical

infrastructure-based vehicular network where the

smart road signs are the core components. This ar-

chitecture enables a real-time dissemination of trafﬁc

information and monitoring of the Intelligent Trans-

portation Systems.

First, we consider that the trafﬁc roads are parti-

tioned into smaller regions, as shown in ﬁgure 1.

Figure 1: Region-based vehicular network.

Each region is a set of connected roads that en-

compasses several nodes that manage the trafﬁc infor-

mation in these roads. Nodes include smart road signs

(SRS) as well as trafﬁc/environment data sources.

Smart road signs can be Manager or Subordinate as

deﬁned in (Sahli et al., 2022) as Master and Slave.

An Infrastructure-Based Trust Management Framework for Cooperative ITS

331

Table 1: The considered requirements of the surveyed trust models.

Paper Accuracy Security Reactivity Scalability Privacy

(Bhargava and Verma, 2022) ✓ ✓

(Zhang et al., 2020b) ✓ ✓

(Ahmed et al., 2022) ✓ ✓

(Zhang et al., 2020a) ✓ ✓ ✓

(Gazdar et al., 2022) ✓ ✓ ✓

(Chen et al., 2020) ✓ ✓ ✓ ✓

(Guo et al., 2020) ✓ ✓

A Manager SRS is in charge of collecting data from

Subordinates. Each region includes many subordi-

nates and one Manager. Data sources can be con-

nected vehicles, surveillance cameras, radar, etc. Let

us consider a region composed of one manager SRS,

n subordinates, and m data sources. In particular,

when a road is congested the data sources alert the

nearest SRSs about the trafﬁc state. Hence, the sub-

ordinate SRSs evaluate the trustworthiness of the re-

ported event and alert the drivers about possible trafﬁc

congestion. The evaluations of the subordinate SRSs

are then transmitted to the manager SRS to aggregate

them. The ﬁnal warning message will be sent to the

subordinate SRSs to update the displayed alert mes-

sages.

Figure 2 presents the general architecture of our

network system and the dissemination of the data

ﬂow. In what follows, we present the major compo-

nents of our framework.

Figure 2: General architecture of the trust model.

1. SRSs: The SRSs are the main component in the

network model. They are equipped with digital

screens to alert drivers about the trafﬁc state in

real-time (Sahli et al., 2022). These SRSs are

connected to the internet and endowed with pro-

cessing capabilities. Accordingly, they are able to

collect trafﬁc-related data and evaluate their cred-

ibility, in order to estimate and communicate the

trafﬁc state. Unlike the existing DMSs, which

display the trafﬁc state estimated by trafﬁc man-

agement centers, the SRSs are endowed with in-

telligence and autonomy allowing them to pro-

cess the sensed data without human intervention.

We adopt a hierarchical architecture based on two

types of SRSs:

(a) Manager SRSs: These SRSs are assumed to be

fully trusted by the neighbouring subordinate

SRSs. We suppose that the manager SRSs are

secured and equipped with higher storage and

communication capabilities. The main role of

manager SRSs is to aggregate the evaluations

of subordinate SRSs about the reported trafﬁc

events. In general, the manager SRS controls

the subordinates localized in its region. Ac-

cordingly, it evaluates and updates their trust

values and stores them in the Subordinates-BC

blockchain. In fact, the Subordinates-BC is

used to keep track of the trustworthiness of the

subordinates.

(b) Subordinate SRSs: Unlike the manager SRSs,

we suppose that the subordinate SRSs may be

compromised. Hence, their credibility is evalu-

ated by the manager SRSs. However, they are

responsible for the evaluation of the trustwor-

thiness of the trafﬁc events reported by the data

sources and updating the values of their trust

values. As shown in ﬁgure 2, the subordinate

SRSs contribute to the block validation to up-

date the trust values of the data sources in the

sources-BC blockchain.

2. Data sources: Data sources are a set of mo-

bile nodes, such as connected vehicles, and static

nodes, like surveillance cameras, radars, pneu-

matic tube sensors, and inductive loops. These

nodes are responsible for reporting trafﬁc events

to the subordinate SRSs. The data sources may

communicate erroneous data due to physical fail-

ure or intentionally when hijacked by malicious

attackers.

VEHITS 2023 - 9th International Conference on Vehicle Technology and Intelligent Transport Systems

332

4 DETAILED DESIGN

In this section, we present the detailed design of the

proposed framework. In fact, the proposed model is

composed of ﬁve main modules:

1. Events classiﬁcation

2. Lower layer trust evaluation

3. Higher layer trust evaluation

4. Reward/ punishment

5. Blockchain validation

4.1 Events Classiﬁcation

We suppose that trafﬁc event messages are sent to

the nearest subordinate SRS. Those messages will be

classiﬁed into groups of events according to spatio-

temporal information and semantic analysis. The idea

of the event classiﬁcation module is to assemble the

messages that report the same event. Moreover, the

event classiﬁcation module assigns a severity degree

to each group of event according to the frequency

of the reports and the type of the event. Therefore,

the group of events with the highest severity degree

will be processed before the group of events with

a lower severity degree. For instance, the module

will pay more attention to events of type “accidents”

than events of type “light congestion”. The aim is to

increase the reactivity of the model in the presence

of various trafﬁc events and to avoid serious conse-

quences.

4.2 Lower Layer Trust Evaluation

The proposed framework uses the data reported by

several data sources in order to increase the visibil-

ity of the surrounding environment. However, the

data sources may be compromised and intentionally

provide erroneous data, or unintentionally in case

of physical failure. Accordingly, a trust evaluation

method will be used by the subordinate SRSs to es-

timate the trustworthiness of the provided data and/or

the node that reported it. Several trust methodologies

proposed in the literature may be used. For example,

plausibility checking using fuzzy logic may be a suit-

able approach to evaluate both the data and the node

trustworthiness such as in (Souissi et al., 2022). Other

methodologies such as game theory and belief theory,

and regression analysis also may be used to estimate

the trustworthiness of the provided data (Wang et al.,

2016), (Chen et al., 2016), (Kang et al., 2018).

4.3 Higher Layer Trust Evaluation

The ﬁnal decision on whether to choose to trust or

ignore the reported events is made by the manager

SRS. Therefore, the manager SRS estimates the over-

all trust out of the evaluation of the subordinate SRSs

in the same region. Moreover, a manager SRS may

collaborate with nearby manager SRSs to estimate the

trustworthiness of events occurring on the edges of its

regions. The idea behind adding a second evaluation

of the reported events is that the manager SRS has a

general overview of the environment by combining

the estimations of the subordinate SRSs. Different

approaches may be used for the trust formation. For

example, static weighted sum and dynamic weighted

sum may be used to combine the trust evaluation of

the subordinate SRSs.

4.4 Reward/ Punishment

The reward/punishment module is proposed to be ap-

plied at two levels. At the higher layer, it is imple-

mented in the manager SRS in order to adjust the

trustworthiness of the subordinate SRSs. At the lower

layer, it is deployed in the subordinate SRS to adjust

the behaviour of the data sources and to boost their

cooperativeness.

4.5 Blockchain Validation

We propose to add two blockchain modules to save

and track the behaviour of the nodes in the network.

The ﬁrst blockchain is used to save the trustworthi-

ness of the data sources. This ledger is shared be-

tween the subordinate SRSs. The second blockchain

is used to store the trustworthiness of the subordinate

SRSs. The second ledger is controlled by the manager

SRS.

The use of the blockchain technology ensures

the integrity of the saved data, hence increases the

data reliability. Different consensus mechanisms may

be used in this regard. In particular, Proof-of-stake

(PoS), is an efﬁcient mechanism in terms of energy.

Other mechanisms such as Delegated Proof of Stake

(DPoD), Proof of Work (PoW), and Proof of Author-

ity (PoA) may be also used (Liu et al., 2019), (Chen

et al., 2020), (Gazdar et al., 2022).

The overall workﬂow of the trust framework is

shown in ﬁgure 3. Hereafter, we list the sequence of

steps performed by the framework.

1. The data sources report an event

2. The event classiﬁcation module classiﬁes the re-

ported events into groups

An Infrastructure-Based Trust Management Framework for Cooperative ITS

333

Figure 3: Component interaction diagram of the proposed

framework.

3. The lower layer trust evaluation module receives

the group of events to evaluate their trustworthi-

ness

4. The lower layer trust evaluation module requests

the trust values of the data sources from the

blockchain

5. The lower layer trust evaluation module receives

the necessary data from the blockchain

6. The lower layer trust evaluation module estimates

the trustworthiness of the reported event

7. The alert message is then sent to be displayed on

the SRSs

8. The assessments of the subordinate SRSs are sent

to the higher layer trust evaluation module to ag-

gregate their evaluations

9. The higher layer trust evaluation module re-

quests the trust values of the salve SRSs from the

blockchain ledger

10. The higher layer trust evaluation module receives

the necessary data from the blockchain

11. The higher layer trust evaluation module executes

the ﬁnal trust evaluation of the reported event us-

ing the assessments of the subordinate SRS

12. The displayed alert message is updated accord-

ing to the ﬁnal evaluation of the higher layer trust

evaluation module

13. The ﬁnal trust assessment is then sent to the re-

ward/punishment module to evaluate the trustwor-

thiness of the data sources and the subordinate

SRS

14.(a) The reward/punishment module updates the

trust values of the subordinate SRSs and sent

it to be saved in the blockchain ledgers

(b) The reward/punishment module updates the

trust values of the data sources and sent it to

be saved in the blockchain ledgers

5 CASE STUDY

In this section, we present a case study to explain the

dissemination and the process of event alert messages

within the proposed framework. Figure 4 describes

the discussed case study.

Figure 4: Representation of the case study scenario.

We suppose that the considered region is com-

posed of a manager SRS, two subordinate SRSs, a

surveillance camera, radar and six vehicles. In this

context, we use the surveillance camera, the radar and

the vehicles as our data sources. In this scenario, we

suppose that an accident has occurred on a road tran-

scend between two vehicles, V1 and V2. We suppose

that vehicle V3 and V4 reported to the second SRS

the occurrence of a crash event to SRS2. We sup-

pose that V5 is a malicious node and reported to SRS2

that trafﬁc ﬂow is normal. We suppose also that the

surveillance camera detected the presence of the acci-

dent and reported it to SRS2. Moreover, we suppose

that vehicle V6 and the radar reported that there is a

light congestion to SRS1. Hereafter, we describe the

process of the evaluation of the alert messages.

1. The reported events will be classiﬁed into event

groups using the spatio-temporal information and

semantic analysis. Let us consider that V3, V4,

V6, the radar and the surveillance camera sent

the detected events approximately at the same

time and neighbouring localization. Moreover,

the module will identify that the light congestion

may be related to the crash event. Accordingly,

we will have two groups of events:

VEHITS 2023 - 9th International Conference on Vehicle Technology and Intelligent Transport Systems

334

• E1 that combines the accident report by V3,

V4, the surveillance camera and the light con-

gestion by V6 and the radar.

• E2 that reports a normal trafﬁc ﬂow by V5.

The group of events E1 will be assigned the high-

est severity degree because it reports an event of

type accident and has the highest number of re-

ports. Accordingly, it will be processed in priority.

Similarly, E2 will have the lowest severity degree.

2. SRS1 and SRS2 evaluate the trustworthiness of

the reported events in E1 while considering the

trustworthiness of the data sources stored in the

blockchain.

3. A warning message will be then displayed on

SRS1 and SRS2. Let us assume that the trust as-

sessment of SRS1 of the event E1 is 0.7. Hence,

the displayed message will be “70% chance that

there is an accident ahead”. The idea is to in-

crease the reactivity of the framework. Therefore,

the drivers will be aware of the possibility of the

presence of an accident, even before the ﬁnal eval-

uation of the manager SRS.

4. The evaluations of SRS1 and SRS2 will be trans-

mitted to the manager SRS to aggregate them, and

provide the ﬁnal trust value of the event while

considering the trustworthiness of the subordinate

SRSs that are registered in the blockchain.

5. The displayed messages will be updated after the

ﬁnal evaluation of the manager SRS.

6. The ﬁnal evaluation of the manager SRS will be

used to update the trustworthiness of the data

sources and the subordinate SRSs.

6 PERFORMANCE ANALYSIS

In this section, we discuss the presumptions we con-

sidered in the design of the framework of the trust

management framework to present its strengths.

1. Presumption 1: The proposed framework en-

sures the scalability of the trust model to follow

the continuous growth of the ITS environment.

Discussion: The decentralized architecture of the

framework will help to scale down the trafﬁc over-

head of the network. The partitioning of the road

network into smaller regions limits the number

of messages exchanged between the nodes of the

network. Moreover, we propose an infrastructure-

based architecture without internal communica-

tion between the data sources.

2. Presumption 2: The proposed framework in-

creases the accuracy of the displayed trafﬁc state.

Discussion: The multi-sourcing data fusion re-

duces the uncertainty of the environment obser-

vation. Moreover, the two-level trust evaluation

scheme helps to have a global vision of the reli-

ability of the reported trafﬁc events. In the low

evaluation layer we exploit the data provided by

the data sources. In the upper evaluation layer we

use the assessments of the subordinate SRSs.

3. Presumption 3: The proposed framework en-

sures the integrity, the availability, and the privacy

of the data.

Discussion: The use of the blockchain technology

ensures that the stored data is immutable. Blocks

validation requires the validation of the majority

of nodes to ensure the integrity of the informa-

tion. The ledgers of the blockchain are replicated

and synchronized, which avoids single-point fail-

ure. Therefore, it ensures the availability of the

data. Moreover, blockchain ledgers can only be

accessed by SRSs which protect sensitive data

from being leaked.

4. Presumption 4: The proposed framework in-

creases the reactivity of the ITS.

Discussion: The classiﬁcation of the reported

events and the evaluation of the severity of the

events using the frequency of the reports and their

types help to process the events of highest priority.

Moreover, the decentralized and hierarchical ar-

chitecture combined with the blockchain technol-

ogy reduces the communication overhead (Gazdar

et al., 2022). Hence, it reduces the response time

of the ITS.

5. Presumption 5: The proposed framework of the

trust model meets the requirements of the ITS ap-

plications.

Discussion: Due to the increasing number of the

ITS components and to the time sensitivity of

their applications, especially the trafﬁc congestion

management applications, the architecture of the

ITS applications must be scalable, accurate and

reactive to the changes of the environment. As

discussed in the proposition above, the proposed

framework would be scalable and able to provide

accurate information in a short response time.

7 CONCLUSION

We propose in this paper an open framework for a

trust management model to be enforced in ITS. The

aim is to enhance the performance of ITS by ensur-

ing its scalability, accuracy, security and the reactiv-

ity. We presented the general modules that should be

An Infrastructure-Based Trust Management Framework for Cooperative ITS

335

enforced and we discussed their efﬁciency and bene-

ﬁts. However, the proposed framework might be un-

suitable for rural environments. We intend to further

investigate and identify the appropriate techniques to

be applied in each module. Besides, we intend to run

validation experimentation to test the validity of the

hypotheses.

REFERENCES

Ahmed, W., Di, W., and Mukathe, D. (2022). Privacy-

preserving blockchain-based authentication and trust

management in vanets. IET Networks.

Alam, M., Ferreira, J., and Fonseca, J. (2016). Introduc-

tion to intelligent transportation systems. In Intelligent

transportation systems, pages 1–17. Springer.

Alboqomi, O., Gazdar, T., and Munshi, A. (2020). A new

blockchain-based trust management protocol for ve-

hicular ad hoc networks. In The 4th International

Conference on Future Networks and Distributed Sys-

tems (ICFNDS), pages 1–5.

Ben Hamida, E., Noura, H., and Znaidi, W. (2015). Secu-

rity of cooperative intelligent transport systems: Stan-

dards, threats analysis and cryptographic countermea-

sures. Electronics, 4(3):380–423.

Bhargava, A. and Verma, S. (2022). Duel: Demp-

ster uncertainty-based enhanced-trust level scheme for

vanet. IEEE Transactions on Intelligent Transporta-

tion Systems.

Chen, X., Ding, J., and Lu, Z. (2020). A decentralized trust

management system for intelligent transportation en-

vironments. IEEE Transactions on Intelligent Trans-

portation Systems, 23(1):558–571.

Chen, Y., Weng, S., Guo, W., and Xiong, N. (2016). A game

theory algorithm for intra-cluster data aggregation in

a vehicular ad hoc network. Sensors, 16(2):245.

Czy

zewski, A., Sroczy

nski, A.,

Smiałkowski, T., and Hoff-

mann, P. (2019). Development of intelligent road

signs with v2x interface for adaptive trafﬁc control-

ling. In 2019 6th International Conference on Models

and Technologies for Intelligent Transportation Sys-

tems (MT-ITS), pages 1–7. IEEE.

Gazdar, T., Alboqomi, O., and Munshi, A. (2022). A decen-

tralized blockchain-based trust management frame-

work for vehicular ad hoc networks. Smart Cities,

5(1):348–363.

Guerrero-Ibanez, J. A., Zeadally, S., and Contreras-Castillo,

J. (2015). Integration challenges of intelligent trans-

portation systems with connected vehicle, cloud com-

puting, and internet of things technologies. IEEE

Wireless Communications, 22(6):122–128.

Guo, J., Li, X., Liu, Z., Ma, J., Yang, C., Zhang, J., and Wu,

D. (2020). Trove: A context-awareness trust model for

vanets using reinforcement learning. IEEE Internet of

Things Journal, 7(7):6647–6662.

Hamdani, M., Sahli, N., Jabeur, N., and Khezami, N.

(2022). Agent-based approach for connected vehicles

and smart road signs collaboration. Computing and

Informatics, 41(1):376–396.

Hbaieb, A., Ayed, S., and Chaari, L. (2022). A survey of

trust management in the internet of vehicles. Com-

puter Networks, 203:108558.

Kang, J., Yu, R., Huang, X., Wu, M., Maharjan, S., Xie,

S., and Zhang, Y. (2018). Blockchain for secure

and efﬁcient data sharing in vehicular edge comput-

ing and networks. IEEE Internet of Things Journal,

6(3):4660–4670.

Li, W. and Song, H. (2015). Art: An attack-resistant

trust management scheme for securing vehicular ad

hoc networks. IEEE transactions on intelligent trans-

portation systems, 17(4):960–969.

Lin, Y., Wang, P., and Ma, M. (2017). Intelligent transporta-

tion system (its): Concept, challenge and opportunity.

In 2017 ieee 3rd international conference on big data

security on cloud (bigdatasecurity), ieee international

conference on high performance and smart comput-

ing (hpsc), and ieee international conference on intel-

ligent data and security (ids), pages 167–172. IEEE.

Liu, X., Huang, H., Xiao, F., and Ma, Z. (2019). A

blockchain-based trust management with conditional

privacy-preserving announcement scheme for vanets.

IEEE Internet of Things Journal, 7(5):4101–4112.

Sahli, N., Trojet, W., Zhang, Z., and Abdallah, N. O.

(2022). Towards a network of dynamic message signs

for congestion alerting. Computing and Informatics,

41(2):609–626.

Souissi, I., Ben Azzouna, N., Abidi, R., Berradia, T., and

Ben Said, L. (2022). Sp-trust: a trust management

model for speed trust in vehicular networks. In-

ternational Journal of Computers and Applications,

44(11):1065–1073.

Wang, Y., Cai, Z., Yin, G., Gao, Y., Tong, X., and Han,

Q. (2016). A game theory-based trust measurement

model for social networks. Computational social net-

works, 3(1):1–16.

Zhang, C., Li, W., Luo, Y., and Hu, Y. (2020a). Ait: An

ai-enabled trust management system for vehicular net-

works using blockchain technology. IEEE Internet of

Things Journal, 8(5):3157–3169.

Zhang, J., Zheng, K., Zhang, D., and Yan, B. (2020b).

Aatms: An anti-attack trust management scheme in

vanet. IEEE Access, 8:21077–21090.

VEHITS 2023 - 9th International Conference on Vehicle Technology and Intelligent Transport Systems

336