A Formal Model-Based Testing Framework for Validating an IoT

Solution for Blockchain-based Vehicles Communication

Rateb Jabbar

1,2

, Moez Krichen

, Mohamed Kharbeche

, Noora Fetais

and Kamel Barkaoui

KINDI Center for Computing Research, College of Engineering, Qatar University, Doha, Qatar

Cedric Laboratory, Computer Science Department, Conservatoire National des Arts et Metiers, France

ReDCAD Laboratory, University of Sfax, Tunisia

Qatar Transportation and Trafﬁc Safety Center, Qatar University, Qatar

kamel.barkaoui@cnam.fr

Keywords:

Blockchain, Automotive Communication, Internet of Vehicles, Model-Based Testing, Timed Automaton,

Security, Attack Trees.

Abstract:

The emergence of embedded and connected smart technologies, systems, and devices has enabled the concept

of smart cities by connecting every “thing” to the Internet and in particular in transportation through the

Internet of Vehicles (IoV). The main purpose of IoV is to prevent fatal crashes by resolving trafﬁc and road

safety problems. Nevertheless, it is paramount to ensure secure and accurate transmission and recording of data

in “Vehicle-to-Vehicle” (V2V) and “Vehicle-to-Infrastructure” (V2I) communication. To improve “Vehicle-to-

Everything” (V2X) communication, this work uses Blockchain technology for developing a Blockchain-based

IoT system aimed at establishing secure communication and developing a fully decentralized cloud computing

platform. Moreover, the authors propose a model-based framework to validate the proposed approach. This

framework is mainly based on the use of the Attack Trees (AT) and timed automaton (TA) formalisms in order

to test the functional, load and security aspects. An optimization phase for testers placement inspired by fog

computing is proposed as well.

1 INTRODUCTION

The modern IoT technologies have profoundly

transformed classical “vehicular ad-hoc networks”

(VANETs) into the “Internet of Vehicles” (IoV) (F.

Yang, S. Wang, J. Li, Z. Liu and Q. Sun, 2014). More

precisely, the IoV is deﬁned as the real-time data in-

teraction between vehicles and between vehicles and

infrastructures through information platforms, mobile

communication technology, smart terminal devices

and vehicle navigation systems. Vehicles are incor-

porated into the IoT by being connected to the Inter-

net, to the other vehicles nearby as well as to trafﬁc

information systems. Yet, there are many challenges

related to this concept due to high connectivity and

exchange of sensitive data, compromising of security

and privacy and leave the vehicles susceptible to ma-

licious entities.

The proposed Decentralized IoT Solution for Ve-

hicle communication (DISV) based on the concept of

Blockchain aims at overcoming security and privacy

challenges. By way of explanation, each member of

the IoV networks receives messages and broadcasts

them to the Blockchain server, whereas the server ver-

iﬁes the received block and decides if it should be

added to the smart contract or not.

In order to validate the adopted solution, we

propose, in addition, a “model-based testing”

(MBT) (Krichen, 2018; Krichen, 2012; Krichen,

2007; Krichen and Tripakis, 2006) framework which

allows to check the functional correctness, load and

security aspects. This framework is mainly based on

the model of Timed Automata (Bertrand et al., 2015;

Bertrand et al., 2011). The latter corresponds to a rich

modelling language which allows to describe the be-

haviors of a large class of distributed, dynamic and

real-time systems.

Regarding security aspects testing, we model

the behavior of the attacker using Attack Trees

(AT) (Krichen and Alroobaea, 2019; Kordy et al.,

2014). The root of the AT corresponds to the at-

tacker’s global goal. Internal nodes correspond to

sub-goals and leaves correspond to “Basic Attack

Steps” (BAS). Each AT is then transformed into a col-

lection of Timed Automata from which test scenarios

are extracted using test generation algorithms inspired

Jabbar, R., Krichen, M., Kharbeche, M., Fetais, N. and Bar kaoui, K.

A Formal Model-Based Testing Framework for Validating an IoT Solution for Blockchain-based Vehicles Communication.

DOI: 10.5220/0009594305950602

In Proceedings of the 15th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2020), pages 595-602

ISBN: 978-989-758-421-3

595

by the works of (Krichen, 2012; Tretmans, 1999).

Besides, we propose an approach for the optimiza-

tion of the testers placement procedure inspired by

fog computing approaches (Taneja and Davy, 2017;

Gu et al., 2017; Mahmud et al., 2018; Barcelo et al.,

2016) and by some of our previous contributions

(Ma

alej et al., 2018; Lahami et al., 2016; Lahami

et al., 2012b). This placement procedure consists in

allocating the set of testers on the different compu-

tational nodes of the system under test in an optimal

way under different kind of constraints.

The remainder of this article is structured as fol-

lows. In section 2, an overview about Blockchain,

Model Based Testing (MBT) and Timed Automata is

presented. Section 3 is dedicated for reviewing sev-

eral Blockchain techniques to IoT and practically for

IoV. The design of the proposed solution is presented

in Section 4. In Section 5, we present several details

about the adopted testing framework. Section 6 is

dedicated for the testers placement optimization prob-

lem. Finally, Section 7 summarizes the main ﬁnding

and gives new directions for future research.

2 PRELIMINARIES

2.1 Blockchain

Blockchain has emerged through Bitcoin, introduced

in 2008 by Satoshi Nakamoto. Bitcoin can be de-

ﬁned as a decentralized global currency cryptosystem.

Blockchain employed in Bitcoin allows the use of se-

cure and decentralized digital money in a payment

system. This peer-to-peer network does not possess

a central authority, and as such is powered entirely by

the users. Its computing architecture is distributed and

all the transactions are publicly announced. Thus, the

users have a consensus about a single history of the

transactions, referred to as a ledger. The transactions

are separated into blocks; subsequently, each user re-

ceives a timestamp and then it will be published. It is

challenging to modify published blocks as the hash of

the previous blocks is inserted in the next successors

in each block of the chain.

2.2 Model-Based Testing

Model-Based Testing (MBT) (Krichen, 2018;

Krichen, 2010; Krichen, 2007; Krichen and Tripakis,

2006) is a methodology where the system of interest

is described by a mathematical model which encodes

the behavior of the considered system. This method-

ology consists in using this mathematical model to

compute abstract test scenarios. These sequences

of model are then transformed into concrete test

sequences which are executed on the considered

“System Under Test” (SUT). The verdict of this test-

ing activity is provided by comparing the observed

outputs from the system with the outputs generated

by the model.

2.3 Timed Automata

“Timed Automata” (TA) (Bertrand et al., 2015;

Bertrand et al., 2011) are an expressive and simple

tool for describing the behavior of computer systems

which combine continuous and discrete mechanisms.

TA may be represented as ﬁnite graphs enriched with

a ﬁnite set of clocks deﬁned as real entities whose

value progresses continuously over time.

3 RELATED WORKS

(X. Huang and Liu, 2018) developed an ecosystem

model on the basis of Blockchain electric vehicle and

charging pile management. This model employs El-

liptic Curve Cryptography (ECC) for the computation

of hash functions of charging piles of electric vehi-

cles. Furthermore, (J. Kang and Hossain, 2017) de-

veloped PETCON, a P2P electricity-trading system,

for illustrating localized and comprehensive opera-

tions of P2P electricity trading. The PETCON system

employs a consortium Blockchain method to analyze,

verify, and share transaction records publicly, while it

is not necessary to have a reliable authority.

Besides, CreditCoin, a privacy-preserving

scheme, was created by (L. Li and Zhang, 2018)

in order to ensure that adequate announcements

are forwarded without revealing users’ identities.

This scheme employs the Blockchain for sending

anonymous announcements through an aggregation

protocol between vehicles. Moreover, (Z. Yang and

Leung, 2017) proposed a Blockchain-based reputa-

tion system to assess data credibility in the IoV. (Y.

Yuan and FY. Wang., 2016) developed a Blockchain

solution aimed at solving security problems and

performance limitations in Intelligent Transportation

Systems (ITS). (Leiding and Hogrefe., 2016) merged

the Blockchain technology with vehicular ad-hoc

network VANET.

(A. Lei and Sun, 2017) introduced dynamic key

management using Blockchain for establishing com-

munication systems to be used in vehicles that do

not need the administration from the central man-

ager. By relying on a decentralized Blockchain

structure, it eliminates any other authorities. (A.

Dorri, M. Steger, S. S. Kanhere, and R. Jurdak,

ENASE 2020 - 15th International Conference on Evaluation of Novel Approaches to Software Engineering

596

The application layer

Blockchain

The network layer

The perception layer

Cloud

Administration

Decentralized

cloud

Decentralized

cloud

Figure 1: The architecture of the proposed Internet of Things solution.

2017) created a Blockchain technology mechanism

that does not reveal any private data of vehicle users.

(Sean Rowan and Goldrick, 2017) employed the vis-

ible light and acoustic side channels and applied the

Blockchain technology for securing intelligent vehi-

cle communication. (Madhusudan Singh, 2017) de-

veloped a framework for a secure trust-based environ-

ment with peer-to-peer communication between in-

telligent vehicles without disturbing/interfering other

intelligent vehicles through the Blockchain mecha-

nism for the intelligent vehicle communication envi-

ronment. However, the use of this framework is lim-

ited only to smart cars.

4 PROPOSED SOLUTION

The adopted architecture is composed of three layers.

Figure 1 depicts the architecture of the proposed so-

lution.

4.1 The Perception Layer

In order to test possible scenarios involving various

components, an Android application has been devel-

oped in the Internet of Vehicle system (AV) and for

infrastructure (AP). On the ﬁrst hand, AV is an An-

droid application consisting of two sub-systems. The

ﬁrst subsystem is the Vehicle Data Collection System

(VDCS) which collects data about the trip and the car.

The second one is the Driver Drowsiness Detection

system that collects data about the driver’s behavior

to identify if he or she is drowsy or not. More in-

formation about this system can be found in (Jabbar

et al., 2018a; Jabbar et al., 2018b; Jabbar et al., 2019;

Jabbar et al., 2020).

Mainly, the Android application has four pages as

shown in Figure 2: The ﬁrst page serves for logging

in by using a username and password. Following the

authentication, the user can start a new trip, or ac-

cess the information about the last ﬁve trips on the

second page. If the user chooses a new trip, the ap-

plication will start recording and displaying all infor-

mation as described in the previous section. Then, it

will send the collected data via the web service to the

cloud server. In the fourth page, the front camera will

capture and display the driver’s face.

4.2 The Network Layer

The network layer establishes the connection between

the servers and transmits, and processes the sensor

data. The application can use either Wi-Fi or mobile

internet (3G/3G+/4G) to send the data to the server.

This collection process uses the hybrid system to

gather and save data locally before transmitting it to

the server. This technique has been proven to be

highly effective for data collection when the internet

connection is poor or unstable.

4.3 The Application Layer

Regarding the application layer, it contains two prin-

cipal compounds: Central cloud server and the com-

munication system using a Blockchain Network.

First, the central cloud server delivers application-

speciﬁc services to the end-user. It sends the collected

data to the web services for processing and analy-

A Formal Model-Based Testing Framework for Validating an IoT Solution for Blockchain-based Vehicles Communication

597

Figure 2: Screenshot of the four main pages of the Android application for Vehicles (AV).

Figure 3: Test generation principle.

sis before showing them to the end-user. Second,

the Blockchain network is responsible for managing

communication between cars and trafﬁc and trans-

portation systems. Every time slot, the car sends the

collected data to the central server via a web service,

including the current location and the status of the

connection to one of the existing Blockchain servers.

5 TESTING FRAMEWORK

5.1 Test Generation Principle

Our generation procedure is inspired by the work

of (Tretmans, 1999). A test case may be considered

as a tree. The nodes of the test tree may be seen as

collections of states S of the model of the SUT. The

adopted test generation procedure is in charge of ex-

tending the test tree by deﬁning successors to an ev-

ery leaf node, as shown in Figure 3. For every non-

acceptable output a

the test tree moves to f ail and

for every acceptable output b

, the test tree moves to

a new node which corresponds to the set of states that

the system can reach after producing b

. The tester

may also decide to emit a valid input c from the cur-

rent node (dashed arrow).

5.2 Combining Functional and Load

Aspects

At this level, our goal is to combine load and func-

tional aspects in our modelling since our system is

made of a number of interacting and concurrent com-

ponents. For this purpose, we adopt an extended vari-

ant of Timed Automata equipped with integer shared

variables.

As illustrated in Figure 4, the used integer vari-

able of the proposed timed automaton corresponds to

the number running instances of the considered sys-

tem. In this example, we demonstrate how the answer

time to generate an action b may vary according to the

number of running instances.

5.3 Testing Security Aspects using

Attack Trees

In the literature, “Attack Trees” (Krichen and Al-

roobaea, 2019; Kordy et al., 2014) are used to assess

the security of critical systems. They allow to repre-

sent graphically the strategy of a given attacker. An

example of an AT is proposed in Figure 5 (Krichen

and Alroobaea, 2019). In this example, the consid-

ered attacker aims at cracking the password of some

protected ﬁles.

In general, the root of an attack tree corresponds

to the global goal of the attacker and the leaves of

the tree correspond to basic attack steps the attacker

needs to combine in order to achieve its global goal.

Internal nodes correspond to intermediary sub-goals.

The attack tree has two types of gates namely AND-

Gates and OR-Gates. On the ﬁrst hand, an AND-gate

means that in order to fulﬁll the goal a parent-node

all sub-goals of children-nodes of the considered node

have to be achieved. On the other hand, an OR-Gate

means that the goal of a parent-node can be achieved

by fulﬁlling the sub-goal of only one of its children-

ENASE 2020 - 15th International Conference on Evaluation of Novel Approaches to Software Engineering

598

Figure 4: An example showing how the response time of the

system under test varies regarding the current load level.

nodes.

After deﬁning the attack tree modelling the behav-

ior of the attacker, the second step consists in trans-

forming the obtained tree into a network of Timed

Automata which will serve as an input for our test

generation procedure. The proposed transformation

is inspired by the transformation proposed in (Kumar

et al., 2015).

6 OPTIMIZATION OF TESTERS

PLACEMENT

This problem is inspired by fog computing ap-

proaches (Taneja and Davy, 2017; Gu et al., 2017)

and by some of our previous contributions (Ma

alej

et al., 2018; Lahami et al., 2016; Lahami et al.,

2012b). It consists in allocating the set of testers on

the different computational nodes of the SUT in an

optimal manner under several types of constraints as

mentioned below.

6.1 Different Types of Constraints

Node Constraints. For example in (Arkian et al.,

2017), both CPU and storage were taken into account.

In (Gu et al., 2017), the authors considered CPU,

RAM and storage constraints.

Network Constraints. In (Mahmud et al., 2018)

only latency constraint was taken into account. In ad-

dition in (Gupta et al., 2017; Ottenw

alder et al., 2013),

both bandwidth and latency were considered by the

authors.

Energy Constraints. For instance in (Barcelo et al.,

2016), the fog nodes were characterised with their

energy capacities. Moreover, The writers of (Souza

et al., 2016) deﬁned the notion of “energy cells” to

estimate the energy needed by the fog nodes.

6.2 Objective Functions

Energy. Energy optimization was taken into ac-

count from distinct levels. For example, the authors

of (Barcelo et al., 2016) considered a linear objec-

tive measure of the energy consumption likewise in

(Huang et al., 2014), the adopted goal consisted in di-

minishing the communication energy cost.

Execution Time and Network Delay. For exam-

ple this objective function was adopted by the authors

of (Skarlat et al., 2016). In addition in (Xia et al.,

2018), the response time was optimised in order to

augment the requests number to be served before a

chosen deadline.

Migrations. In (Ottenw

alder et al., 2013), the mi-

grations number was optimised by reducing the net-

work use without impacting its latency. Likewise in

(Yang et al., 2016), the migrations number was opti-

mised along with latency and resource consumption.

6.3 Algorithms

Search-based Algorithms. In (Gupta et al., 2017)

an algorithm was proposed to ﬁnd a placement sce-

nario for internet of things applications. In addition

in (Guerrero et al., 2019), a distributed search method

was proposed for similar goals.

Dynamic Programming. In (Souza et al., 2016) the

placement problem was modelled as a multidimen-

sional knapsack problem (MKP). Likewise in (Rah-

bari and Nickray, 2017), the placement problem was

modelled as a knapsack instance.

Mathematical Programming. This technique (Gu

et al., 2017) is always adopted for solving optimiza-

tion problems by investigating the space of the con-

sidered objective functions.

Game Theory. In (Zhang et al., 2017), the place-

ment problem was encoded as a pair of games. The

ﬁrst one was introduced to calculate the cardinality of

the set of necessary execution blocks and the second

one was proposed to set prices in order to maximise

the corresponding ﬁnancial proﬁts.

A Formal Model-Based Testing Framework for Validating an IoT Solution for Blockchain-based Vehicles Communication

599

Figure 5: Example of an AT.

7 CONCLUSION

This study proposed an innovative Decentralized IoT

solution for Vehicle communication (DISV) estab-

lished with three primary layers based on Blockchain.

Moreover, the article proposed an MBT approach in

order to validate the proposed solution. The proposed

testing approach is mainly based on the use of Attack

Trees and Timed Automata in order to check func-

tional, load and security aspects. An optimization

phase for testers placement inspired by fog comput-

ing was also proposed.

Finally, DISV is an essential component of the

Advanced Driver Assistance Systems (ADAS) that

can potentially improve the transportation safety and

mobility. In the future, we aim to establish a net-

work for vehicles based on Blockchain to enable users

to pay for tolls, parking spaces, and electrical charg-

ing by machine-to-machine transactions. Regarding

test cases execution, standard-based platforms may be

adopted like Testing and Test Control Notation ver-

sion 3 (TTCN3) (Lahami et al., 2012a; Lahami et al.,

2016). Moreover, it is necessary to use convenient

test isolation techniques for avoiding interference be-

tween system functionalities and test scenarios as pro-

posed and explained in (Lahami and Krichen, 2013).

ACKNOWLEDGEMENTS

This publication was made possible by QUCP-

CENG-2019-1 grant from the Qatar University. The

statements made herein are solely the responsibility

of the authors.

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