Fuzzy Logic based Model for Energy Consumption Trust Estimation in

Electric Vehicular Networks

Ilhem Souissi

, Nadia Ben Azzouna

, Tahar Berradia

and Lamjed Ben Said

Strategies for Modelling and ARtiﬁcial inTelligence research Laboratory (SMART Lab),

Institut Sup ´erieur de Gestion de Tunis, Universit´e de Tunis, Le Bardo, Tunis, Tunisia

Institut de Recherche en Syst`emes

Electroniques Embarqu ´es (IRSEEM Lab), ESIGELEC, Rouen, France

Keywords:

Trust, Energy Information, Attacks, Fuzzy Logic, Electric Vehicular Networks.

Abstract:

Electric vehicles emerged new applications that are strongly related to the energy constraints such as the

identiﬁcation of the optimal path toward the vehicle’s destination or toward the nearest recharging station,

selection of the path where vehicle recovers extra energy, estimation of the need to recharge according to the

actual battery state and the trafﬁc state, etc. However, in electric vehicular networks, vehicles may provide

wrong energy information due to sensors’ failure, selﬁsh or malicious reasons. Therefore, energy-related

information trustworthiness needs to be evaluated in order to preserve the quality of the presented applications.

In this paper, we address the energy-related information trustworthiness to discriminate between credible

and erroneous values. Therefore, we propose a new fuzzy-based trust model that deals with the information

uncertainties. This model aims at detecting the wrong energy information that mismatches with the vehicle’s

behavior and ensure that only trustworthy and plausible energy-information are handled. Results prove the

performance of the proposed model and its capabilities to deal with several kinds of threats in different trafﬁc

densities with high precision.

1 INTRODUCTION

The Internet of Vehicles (IoV) is a typical applica-

tion of the Internet of Things (IoT) in the transporta-

tion ﬁeld. The main vision of the IoV is to en-

able multiple components to broadcast safety, efﬁ-

ciency and infotainment services (Alam et al., 2015).

The IoV supports multiple kinds of communications

such as Vehicle-to-Sensor (V2S), Vehicle-to-Vehicle

(V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-

Network (V2N) and Vehicle-to-Human (V2H) (Sun

et al., 2016). Similarly, the emergence of the In-

ternet of Electric Vehicles (IoEV) involves the same

kinds of communications. However, the IoEV also

covers the Electric Vehicle-to-Electric Vehicle Supply

Equipment communications (Bayram and Papapana-

giotou, 2014). Over the last few years, Electric Vehi-

cles (EVs) have emerged to meet with ecological is-

sues mainly the environmental pollution and the lack

of natural resources. These vehicles are cost-effective

and easy to maintain (Bayram and Papapanagiotou,

2014) (Falk and Fries, 2012). However, EVs are

energy-constrained and suffer from the limited battery

capacity and the extensive charging time.

Alike the vehicular ad hoc networks (VANETs),

the open, distributed and highly dynamic nature of

the electric vehicular network makes it vulnerable to

many security threats that may affect the quality of

the provided services (Sumra et al., 2015). In such

network, misbehaving entities may broadcast bogus

and malicious information to affect the others’ deci-

sions. Hence, it is required to ensure the accuracy

of the received data to make effective decisions and

maintain the quality of services. Digital signature is

usually dedicated to ensure authentication, integrity

and non-repudiation (Al-Kahtani, 2012). However,

this technique cannot prevent authenticated vehicles

from misbehaving due to selﬁsh reasons, malfunction

of embedded sensors, etc. Consequently, it is required

to assess the data trustworthiness so as to ensure that

only reliable data are disseminated in the network.

In the past decade, several trust management so-

lutions were proposed to overcome the security risks

in VANETs (Soleymani et al., 2015) (Zhang, 2011).

However, these solutions still present some limita-

tions regarding the system’s complexity, network se-

curity, etc. Moreover, sensed data in vehicular net-

works suffers from the fuzzy, inaccurate and uncertain

nature due to the quality of embedded sensors, the

intermittent connection, etc. Besides, to the best of

Souissi, I., Azzouna, N., Berradia, T. and Said, L.

Fuzzy Logic based Model for Energy Consumption Trust Estimation in Electric Vehicular Networks.

DOI: 10.5220/0006863202210233

In Proceedings of the 15th International Joint Conference on e-Business and Telecommunications (ICETE 2018) - Volume 2: SECRYPT, pages 221-233

ISBN: 978-989-758-319-3

221

our knowledge, none of the existing works addressed

the trustworthiness of the energy-related information

for EVs. Accordingly, in this paper we are interested

in the accuracy of the energy-related information in

electric vehicular networks. We are also interested

in V2N communications that ensure a direct connec-

tion between vehicles and a central processing entity.

Hence, we introduce a new fuzzy-based trust model

to cope with the malicious threats that provide wrong

energy information. This model considers two main

dimensions; the instant energy veriﬁcation and the to-

tal energy veriﬁcation. On the one hand, the ﬁrst di-

mension intends to ﬁlter the inaccurate energy infor-

mation that mismatches with the vehicle’s accelera-

tion/ deceleration rate. On the other hand, the second

dimension deals with the unsteady and uncertain be-

havior of EVs. In summary, the main contributions of

this paper are:

• Propose a new fuzzy-based trust model in electric

vehicular networks to evaluate the trustworthiness

of the energy information based on two dimen-

sions: the instant energy veriﬁcation and the total

energy veriﬁcation.

• Provide a dynamic trust solution that adapts to the

different trafﬁc densities and road characteristics.

• Conduct experiments to validate the performance

of the proposed solution to deal with multiple

kinds of threats in different trafﬁc densities.

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

tion 2 presents the related work. Section 3 provides

an overview about the problem deﬁnition. Section 4

introduces the proposed trust model. Section 5 de-

tails the suggested fuzzy logic based model. Section

6 presents the simulation results and discussions and,

section 7 concludes the paper.

2 RELATED WORK

In VANETs, multiple models for trust assessment are

proposed to mitigate the security risks. These mod-

els are categorized into three main classes: the entity-

centric trust, data/message-centric trust and hybrid

trust (Zhang, 2011) (Soleymani et al., 2015).

In general, the entity-centric trust models stand on

reputation and behavior evaluation. Reputation-based

trust models integrate the previous experience, direct

experience and recommendations from third parties.

Usually, the reputation of an entity evolves over time

according to its behavior. The more this entity be-

haves properly, the higher its reputation. M

armol and

erez (M

armol and P

erez, 2012) assumed that the rep-

utation score is computed according to previous in-

teractions and recommendations from adjacent vehi-

cles and from a central trusted authority. According to

the computed reputation, the authors apply the fuzzy

logic theory to decide whether to (1) reject the mes-

sage, (2) accept the message but do not forward it,

or (3) accept and forward the message. Wei et al.

(Wei et al., 2014) also focued on reputation assess-

ment. They adopted probability to evaluate the en-

tity’s reputation based on direct and indirect observa-

tions. Soni et al. (Soni et al., 2015) proposed a trust

based scheme for location ﬁnding to help the driver

to validate or deny the presence of the desired loca-

tion. To reach this purpose, they were based on the

majority voting from nearby vehicles. Hu et al. (Hu

et al., 2015) introduced a trust model for relay selec-

tion to guarantee that only the most reliable nodes are

selected for data transmission. The relay score that

refers to the candidate trust is calculated according to

the: (1) rate of successful routed messages and (2)

similarity level in regard to the routed message. Dah-

mane et al. (Dahmane et al., 2017) also presented

a weighted trust-aware relay selection scheme. They

combined the vehicles’ and context related informa-

tion such as the distance between the transmitter and

the candidate, the quality and lifetime of communica-

tion link as well as the rate of successful routed mes-

sages.

Several other trust models focus on the data trust

instead of the entity trust. Raya et al. (Raya et al.,

2008) were the ﬁrst to investigate the message’s trust-

worthiness in ephemeral networks. They incorporated

(1) the correlative trustworthiness of the event and its

reporter, (2) the security status that reﬂects the entity

legitimacy and the (3) proximity in terms of time and

location. Mazilu et al. (Mazilu et al., 2011) were also

interested in the network security based on data trust

computation. This model uses similarity to ﬁnd out

the coincidence between locally stored measurements

and the others’ detections. Zaidi et al. (Zaidi et al.,

2014) also adopted the same methodology to validate

their own measurements. They further investigated

the correlation between the speed, ﬂow and density to

detect the rogue nodes that affect the quality of emer-

gency alerts. Alike the majority of the existing trust

models, all of the mentioned data-oriented trust mod-

els supposed that the trust-based decision should rely

on multiple messages to conﬁrm the reliability of the

reported alert.

Regarding the hybrid trust, most of research stud-

ies combine the entity and message trust to achieve

more reliable and accurate trust estimation. Usually,

the entity trust represents one of the major factor to

build the message’s trustworthiness. Both Oluoch

(Oluoch, 2015) and Yao et al. (Yao et al., 2017a) as-

SECRYPT 2018 - International Conference on Security and Cryptography

222

sumed that the entity trust depends on its reputation

while the message trust is estimated according to the:

(1) reporter’s trustworthiness, (2) correlative trust of

the event and its reporter and (3) both time and lo-

cation. Li and Song (Li and Song, 2016) proposed

an attack resistant trust scheme. They combined the:

(1) functional trust and evidences from third parties

to evaluate the entity trust and (2) similarity between

the collected reports to validate the message’s trust-

worthiness. Soleymani et al. (Soleymani et al., 2017)

incorporated three main modules to decide whether to

trust an event or not. The experience module (refers to

reputation) depends on past interactions between ve-

hicles. The plausibility module aims at verifying the

correctness of the location information. The accuracy

module includes fog nodes that intend to store events-

related to the trafﬁc state. Thus, whenever a vehicle

receives a warning, it asks fog nodes to prove or deny

the presence of the such event.

Most of the investigated trust schemes stand on

reputation assessment to validate the entity’s trust-

worthiness. Nonetheless, this methodology is not

well suited in highly dynamic environments since

it requires social connections that should last for a

long duration. We also highlight that some of these

schemes adopted predeﬁned measurements (e.g. the

correlative trust of the event and its reporter) that may

affect the accuracy of the trust calculation. Moreover,

most of the cited research studies adopt fuzzy logic

and probability theory to cope with the information

uncertainties in VANETs. Some of these models use

fuzzy logic theory to compute trust based on basic pa-

rameters such as time, position, etc. (M

armol and

erez, 2012) (Soleymani et al., 2017). To the best

of our knowledge, none of the existing research ad-

dressed the trustworthiness of the energy-related in-

formation for EVs. In fact, electric vehicular net-

works have the same characteristics as VANETs.

However, they have additional restrictions, mainly the

energy-related constraints. Accordingly, in this paper

we introduce a new fuzzy-based trust model that re-

lies on the similarity assessment between messages in

terms of energy consumption. This model aims at ﬁl-

tering the inappropriate reports in order to ensure that

only plausible measurements are considered to pro-

vide high quality of services.

3 PROBLEM DEFINITION

Nowadays, the emergence of cellular technologies

(e.g. 4G, 5G) and open WiFi access points enable

vehicles to directly communicate with the network

(V2N communication) particularly, with centralized

servers (Wang et al., 2014). In this paper, we as-

sume that the main roles of the server are to: (1) help

the driver to follow the optimal path (in terms of en-

ergy consumption, time and distance) and (2) decide

whether its battery state allows him to reach its des-

tination, depending on the trafﬁc state, or not. To

reach this purpose, the server analyses and processes

messages from multiple dispersed vehicles in order to

mitigate the inherent security risks that may affect the

quality of the provided services.

Often, the network includes (1) credible entities

that behave properly and (2) malicious vehicles that

misbehave due to selﬁsh reasons, sensors’ failure, etc.

These bad entities broadcast erroneous information

about their position, speed, energy consumption, etc.

Our model addresses the following types of security

threats as depicted in Figure 2 (Sumra et al., 2015):

• Sybil attack: sends multiple messages under dif-

ferent identities and from different locations to lie

about the real trafﬁc state.

• Fake information attack: injects erroneous infor-

mation about its speed, energy, etc. For example,

a vehicle says that it runs with a low speed and

it consumes a big amount of energy to discourage

the other vehicles to follow the same lane.

• Timing attack: creates a delay to prevent the

server from receiving real-time information.

• Selective forwarding attack: forwards messages

with low interest. For example a vehicle may only

transmit messages whenever it consumes energy

(i.e. denies that it recovers energy in downhill

roads) in order to say that the followed lane is

greedy in terms of energy consumption.

• On-off attack: behaves alternatively to maintain

the same level of trust. For example, a vehicle

transmits an accurate energy consumption value at

time t-1 and thereafter, it lies about the consumed

energy between the two instants t-1 and t.

• Bush telegraph attack: applies a slight modi-

ﬁcation, that cannot be perceived, to the right

measurement. For example, whenever a vehi-

cle sends its energy consumption between two in-

stants, it executes a slight modiﬁcation to deceive

the server in identifying the optimal path.

• Collusion attack: a set of vehicles collude to reach

the same purposes. For example, they lie about

the energy consumption on a speciﬁc lane to con-

vince the server that this lane is greedy in terms of

energy consumption.

We highlight that there is a crucial need to propose

an effective and reliable trust management scheme

that mainly deals with energy-related issues for EVs.

Fuzzy Logic based Model for Energy Consumption Trust Estimation in Electric Vehicular Networks

223

The main role of this scheme is to maintain the quality

of the provided services (e.g. ensure that only trusted

messages contribute during the identiﬁcation of the

optimal path).

4 SIMILARITY-BASED TRUST

ESTIMATION MODEL FOR

ELECTRIC VEHICLES

In electric vehicular networks, each vehicle periodi-

cally transmits its ID, position, speed, etc. (the struc-

ture of the message is depicted in Figure 1) to enable

the server to estimate the shortest path in terms of en-

ergy consumption, distance and travel time. Subse-

quently, we should underline that the server does not

have prior knowledge about the required energy for

the driver’s path due to the variation of the: (1) trafﬁc

condition, (2) and the state of the environment (e.g.

accident, work-zone, heavy rain or snow).

Figure 1: Structure of the transmitted message.

In this paper, we present a new trust model to al-

low the server to ﬁlter the received messages as shown

in Figure 2. Based on this model, the server will only

consider plausible and trusted messages. Firstly, it

starts by grouping vehicles that belong to the same

lane. Accordingly, it is required to evaluate the accu-

racy of the reported lane as well as the vehicle’s posi-

tion. Thereafter, the server veriﬁes the message valid-

ity to identify the outdated ones. Afterwards, it evalu-

ates the trustworthiness of the reported speed. As the

focus of this paper is on the accuracy of the reported

consumed energy for EVs, then we will stand on ex-

isting works for position, time and speed veriﬁcation

as referred in (Yang, 2013) (Soleymani et al., 2017)

(Yao et al., 2017b).

Subsequently, the server assesses the reliability of

the reported energy consumption at each instant based

on the: (1) vehicle’s behavior (i.e. accelerates or de-

celerates) and (2) correlation between the speed varia-

tion and the sign of energy. The aim behind the instant

energy veriﬁcation is to ﬁlter messages coming from

malicious vehicles that broadcast false energy infor-

mation. At the last step, the server evaluates the sim-

ilarity between the overall reported energy, by each

vehicle, on each lane according to: (1) the vehicle’s

position regarding the lane, and (3) the correlation be-

tween the average speed and the consumed energy. In

this stage, the server can detect the bush telegraph,

on-off and selective forwarding attacks.

Figure 2: Overview of the proposed model.

This model can effectively deal with several kinds

of threats (as depicted in Figure 2). The similarity

assessment between messages in terms of all of the

aforementioned parameters (position, time, speed and

energy) allows the server to identify and reject mes-

sages coming from malicious EVs. We underline that

this model is time-effective since it can speed up the

selection of the most trusted messages based on a se-

quential ﬁltering. That is to say that whenever the

server detects weird and incredible information, it di-

rectly rejects them without the need to pass through

the other steps. We also underline that the fuzzy logic

theory can effectively evaluate the message’s trust-

worthiness in terms of energy consumption. The rea-

son behind the use of such theory is because of its

ability to: (1) deal with fuzzy, uncertain and impre-

cise measurements and (2) transform uncertain and

imprecise information into precise and accurate re-

sults. In the next section, we present and detail the

role of fuzzy logic to differentiate between trustwor-

thy and untrustworthy messages.

5 FUZZY-BASED TRUST

ASSESSMENT APPROACH

In this section, we detail the proposed fuzzy-based

model for energy trust assessment. Indeed, fuzzy

logic is able to transform the information-based per-

ception into information-based accurate measure-

ments (Klir and Yuan, 1995) (Zadeh, 2004). This the-

ory consists of three main phases:

• Fuzziﬁcation: converts the real domain into fuzzy

domain. At this stage, it is required to specify the

system’s inputs/ outputs, the size of the universe

SECRYPT 2018 - International Conference on Security and Cryptography

224

of discourse, the fuzzy classes and membership

functions (Klir and Yuan, 1995).

• Inference: there are three main inference meth-

ods: min-max method, max-prod method and

sum-prod method (Klir and Yuan, 1995). In this

paper, we use the min-max method since it is the

most commonly used one due to its simple struc-

ture. The inference process aims to represent the

correlation between the inputs and outputs using

the fuzzy rules.

• Defuzziﬁcation: transforms the fuzzy domain into

accurate and precise domain. Several defuzzi-

ﬁcation methods exist in the literature: bisector

method, mean of maxima method and centroid

method (Saade and Diab, 2004). In this paper,

we use the centroid method since it provides more

effective results than the other models (Saade and

Diab, 2004). The centroid is computed as follows:

Centroid =

µ(x

)

µ(x

)

and µ(x

) denote the fuzzy value and aggregated

membership function, respectively.

5.1 Assumptions

Our fuzzy-based trust model considers a set of as-

sumptions as follows:

(a) Each vehicle periodically sends its energy con-

sumption.

(b) The vehicle’s ID remains static until the vehicle

moves from one lane to another.

to affect the instant/ total energy consumption

should not exceed the number of legitimate en-

tities.

(d) The number of vehicles that belong to the same

lane is greater than two.

(e) The energy trust is estimated for the same type of

electric vehicles.

(f) Only trusted messages in terms of position, trans-

mission time and speed are handled during the en-

ergy veriﬁcation.

(g) The speed is the most inﬂuential factor that has a

great impact on the energy consumption for EVs

(Badin et al., 2013). The more the EV accelerates

or decelerates, the higher the energy consumed or

recovered, respectively.

5.2 Description of the Proposed Model

In this subsection, we detail the presented model for

the: (1) Instant Energy Veriﬁcation (IEVer) and, (2)

Total Energy Veriﬁcation (TEVer) in order to en-

sure that only trusted energy information is consid-

ered by the server. Both IEVer and TEVer processes

are instantly triggered (i.e. at each time t) to cope

with false energy information. IEVer cannot address,

alone, to the whole energy veriﬁcation problem since

the vehicle’s behavior may evolve over time (e.g.

send/no − send/send behavior). Accordingly, the

TEVer is required to verify that the vehicle, always,

behaves properly throughout the lane.

To accomplish the IEVer and TEVer, we use the

fuzzy logic theory so as to evaluate the similarity

level between each input and the corresponding me-

dian value. We state that the use of the median strat-

egy can better reﬂect the energy trust than the mean

strategy. Actually, this latter may distinctly deviate

if malicious vehicles provide wrong information that

extensively differs from real information. In fact, the

median is a commonly used strategy in statistics and

probability theory (Cadenas et al., 2012). It depends

on the sample size as well as the reported values (e.g.

energy consumption). Accordingly, the establishment

of fuzzy classes, for each input, is strongly related to

the estimation of the median value. We should also

underline that fuzzy classes are dynamically estab-

lished to deal with the speciﬁcations of each road type

(highway, urban zone, etc.) as well as the trafﬁc den-

sity.

IEVer: Instant Energy Veriﬁcation. Initially, the

server checks the coincidence between the reported

energy consumption and the speed variation between

two instants t-1 and t. Therefore, if an electric ve-

hicle EV

accelerates or decelerates and the reported

energy is negative or positive respectively then, EV

classiﬁed as malicious. However, only based on the

coincidence between the speed variation and the sign

of energy, we cannot ensure that the reported energy

is absolutely accurate since a malicious EV may lie

about the amount of the consumed/ recovered energy.

For example, a vehicle may accelerate a little bit but,

it indicates that it consumes a high amount of energy

that mismatch with its acceleration rate. Therefore,

we use the fuzzy logic to deal with such situation and

decide whether the reported energy is appropriate or

not. The process for the IEVer is given in Algorithm

In this study, we consider the coincidence be-

tween: (1) the Acceleration (A) and the Consumed

Energy (CE) where A and CE are positive, and (2)

Fuzzy Logic based Model for Energy Consumption Trust Estimation in Electric Vehicular Networks

225

Input variable 'Instant Energy (IE)'

Input variable 'Acceleration/

Deceleration (A/D)'

Output variable 'Instant Energy Trust'

Figure 3: Fuzzy classes and membership functions for IEVer.

Algorithm 1: Detect untrustworthy messages in regard

to Instant Energy.

for each received message m at time t do % m is trusted in regard

to the speed

1. Verify the coincidence between the speed variation and the sign

of energy

Extract the speed (S

t−1

) and (S

) at time t-1 and t

Extract the instant energy consumption (IE) between t-1 and t

if ((S

)-(S

t−1

)≥0) then % Vehicle accelerates

if (IE ≥0) then m is probably trustworthy % Vehicle consumes energy

else m is untrustworthy

end if

else % Vehicle decelerates

if (IE ≥0) then m is untrustworthy

else m is probably trustworthy % Vehicle recovers energy

end if

2. Apply the fuzzy logic to identify the untrustworthy messages

Determine the median Consumed/ Recovered Energy and the corre-

sponding Acceleration/ Deceleration rate

Build the Fuzzy Classes (FCs) and membership functions (refer to Fig-

ure 3)

Build the rule base (refer to Table 1)

Apply the defuzziﬁcation method

if (Trust(IE)≥Threshold) then m is trustworthy

else m is untrustworthy

end if

end for

the Deceleration (D) and the Recovered Energy (RE)

where D and RE are negative. Indeed, in electric

vehicular networks, EVs are designed to recover en-

ergy whenever the vehicle decelerates or goes down-

hill. Therefore, in our model, we create two Fuzzy

Inference Systems (FIS). The ﬁrst FIS is dedicated to

check the reliability of CE while the second one is in-

terested in the trust evaluation of RE. However, we

suppose that the Instant Energy (IE) for CE and RE

can be both modeled similarly (IE{CE} =

IE{RE}

)

as shown in Figure 3. As well, both A and D can be

represented in the same ﬁgure (A =

). Accordingly,

our FIS considers the acceleration/ deceleration and

the instant consumed/ recovered energy as the sys-

tem’s inputs and the instant energy trust as the output.

We suppose that Fuzzy Classes (FCs) are dynami-

cally established according to the trafﬁc state. In fact,

FCs for IE depend on the median energy value as de-

scribed below:











FC1 : [0, 0, IE

]

FC2 : [IE

, IE

] IE

= IE

+ IE

FC3 : [IE

, IE

] IE

= 2 ∗ IE

FC4 : [IE

, IE

, + ∞, + ∞[ IE

= IE

+ IE

refers to the median consumed/ recovered

energy at time t. Regarding the fuzzy classes for

A/D, they are determined in correspondence with

the identiﬁed classes for IE. Particularly, for each

{CE/RE} value, our solution identify the conve-

nient A

rate as depicted in Figure 3.

Based on the used inputs, we propose the rule ta-

ble (Table 1) that includes sixteen rules. This value

depends on the number of the inputs (two inputs) and

the corresponding fuzzy sets (four fuzzy sets for each

input: low, medium, high, very high). Table 1 repre-

sents the correlation between the inputs and the out-

put. It illustrates that A/D should be proportional to

IE{CE/RE}. For example, if the consumed/ recov-

ered energy is below the computed median value IE

then the vehicle’s acceleration/ deceleration should

not exceed A

Algorithm 1 shows that if the computed trust value

is below a deﬁned threshold, then the message will be

discarded. Therefore, only trustworthy messages in

regard to instant energy will be handled in the next

step. We highlight that, although the IEVer allows

the detection of wrong energy information, EVs may

launch other kinds of threats that cannot be supported

by the IEVer alone. Next, we show the need of the

TEVer to detect the bush telegraph, on-off and selec-

tive forwarding attacks in order to enhance the quality

of the provided services.

TEVer: Total Energy Veriﬁcation. TEVer aims at

ensuring that EVs have not lied at all. Therefore, only

the most appropriate total energy consumption is con-

sidered by the server. Algorithm 2 is adopted to mea-

sure the trust in the total energy consumption by each

SECRYPT 2018 - International Conference on Security and Cryptography

226

Table 1: Fuzzy rules for IEVer.

Rule Input A/D Input IE Output Trust(IE) Rule Input A/D Input IE Output Trust(IE)

1 low low very high 9 high low very Low

2 low medium high 10 high medium medium

3 low high low 11 high high very high

4 low very high very low 12 high very high medium

5 medium low high 13 very high low very low

6 medium medium very high 14 very high medium low

7 medium high medium 15 very high high medium

8 medium very high very low 16 very high very high high

Algorithm 2: Detect untrustworthy messages in regard

to Total Energy.

for each received message m at time t do % m is trusted in

regard to IE

Extract the speed (S

) at time t

Extract the energy consumption (E

) between t-1 and t

if (laneID

t−1

==laneID

) then % Vehicle still belongs to the

same lane

=(nAS

t−1

)/(n + 1) % Update the average speed

T E

=T E

t−1

% Update the total energy

consumption

else % Vehicle moves to a new lane

% Initialize the average speed

T E

% Initialize the total energy

consumption

end if

Extract the coordinates of the involved lane (X

min

, X

max

)

% X

min

refers to the start abscissa and X

max

refers to the end abscissa

Determine the median of the Total Energy Consumption (TE

Med) and

the corresponding Average speed (AS Med)

Build the Fuzzy Classes (FCs) and membership functions (refer to Fig-

ure 4)

Build the rule base (refer to Table 2)

Apply the defuzziﬁcation method

if (Trust(TE)≥Threshold) then m is trustworthy

else m is untrustworthy

end if

end for

EV throughout a speciﬁc lane.

In this study, the proposed FIS for TEVer takes

three inputs into consideration: (1) the vehicle’s posi-

tion in regard to the lane (Pos

lane

), the average speed

(AS), and the total energy consumption (T E). The

output of this FIS is the total energy trust (Trust(T E)).

Regarding the Pos

lane

, the range is between X

min

and

max

that, respectively, refer to the starting and ending

abscissa. FCs for Pos

lane

are described as follows:











FC1 : [X

min

, X

min

, X

] X

= (3/4)X

FC2 : [X

, X

] X

= (1/4)X

= X

+ X

FC3 : [X

, X

] X

= X

+ X

FC4 : [X

, X

max

]

= (X

max

-X

min

)/2 is the basis of the FCs estab-

lishment for the parameter Pos

lane

. It refers to the

midpoint of the lane and it depends on its length. In

regard to T E, the range is between 0 and +∞. FCs

are deﬁned as follows:











FC1 : [0, 0, T E

] T E

= T E

FC2 : [0, T E

, T E

]

FC3 : [T E

, T E

] T E

= T E

+ T E

FC4 : [T E

, T E

] T E

= 2 ∗ T E

FC5 : [T E

, T E

] T E

= T E

+ T E

T E

is the median value for the total energy con-

sumption. It represents the basis of the speciﬁcation

of FCs. If T E exceeds the value T E

, then T E will

be directly rejected. Regarding the AS range, it varies

from the minimum to the maximum reported speed.

For the sake of simplicity, we suppose that the maxi-

mum range tends to inﬁnity (+∞). We should remind

that only trusted messages in terms of speed infor-

mation are considered in the TEVer process. Fuzzy

classes for AS are dependent upon the median value

for TE

. That is to say that, after the estimation of the

median value for TE, we can identify its equivalent in

terms of AS. Therefore, the proposed FCs for AS can

be described as below:











FC1 : [AS

min

, AS

min

, AS

]

FC2 : [AS

, AS

] AS

= AS

min

[(AS

− AS

min

)/2]

= AS

+ AS

FC3 : [AS

, AS

, + ∞, + ∞[ AS

= 2 ∗ AS

min

refers to the minimum followed speed at the

involved lane while AS

is the equivalent speed to the

median T E (i.e. T E

). For example, we suppose that

a set of vehicles are dispersed throughout a speciﬁc

lane. Therefore, if a vehicle runs with AS

then, it

usually consumes around T E

whenever it reaches a

particular position in the lane (near to X

). Table 2 il-

lustrates the dependencies between the suggested pa-

rameters (Pos

lane

, AS, T E and Trust(TE)). This ta-

ble shows that T E should be proportional to AS and

Pos

lane

. Actually, our study considers the vehicle’s

position into consideration since it can provide addi-

tional information about the reliability of the energy

consumption. For example, two vehicles EV

and EV

Fuzzy Logic based Model for Energy Consumption Trust Estimation in Electric Vehicular Networks

227

Input variable "Pos_lane"

Input variable "Total Energy (TE)"

Output variable "Total Energy Trust"

Input variable "Average Speed (AS)"

Figure 4: Fuzzy classes and membership functions for TEVer.

follow the same lane. EV

is still in the beginning

of the lane while EV

, almost, reaches the end of the

same lane. Therefore, EV

and EV

should not con-

sume the same amount of energy although they run

with the same average speed.

In brief, the proposed fuzzy-based trust model

consists of two main phases (IEVer and TEVer) to

enable the server to ﬁlter the inappropriate energy-

related information. In each phase, we use fuzzy logic

to detect wrong energy information that mismatches

with the vehicle’s behavior in terms of acceleration/

deceleration rate and average speed. In the next sec-

tion, we evaluate the performance of the proposed

model versus several kinds of threats.

6 PERFORMANCE EVALUATION

In this section, the performance of the proposed

fuzzy-based trust model is evaluated in regard to

the consistency of the energy consumption. Accord-

ingly, we are interested in the energy-related threats as

shown in Figure 2. Therefore, we mainly focus on the

fake information, bush telegraph, selective forward-

ing, on-off and collusion attacks. We use MAT LAB to

create a fuzzy-based inference engine and SU MO to

generate trafﬁc data (Krajzewicz et al., 2006). SUMO

provides a set of ﬁles that include information related

to each vehicle in every simulation time step such as

the vehicle’s ID, the consumed energy between two

time steps, the actual battery capacity, the speed, etc.

In our simulation, we suppose that the length of the

lane segment is equal to 2000m. We also conduct

simulations for 100s where each step takes around 1s.

Regarding the number of vehicles, it varies from n=10

to n=100. Finally, we set the threshold to the neutral

value 0.5 (i.e. if the trust value is below 0.5 then the

message is suspicious otherwise, it is trustworthy).

Figure 5 illustrates the correlation between the in-

puts (A and IE) and output (Trust(IE)). We suppose

that A and IE vary from 0m/s

−2

to 1m/s

−2

and from

0W to 10W , respectively. We also suppose that the

median value for IE is equal to 4W and the corre-

sponding acceleration is equal to 0.3m/s

−2

. Accord-

ingly, Figure 5 shows that trust increases whenever A

and IE are proportional.

Figure 5: Correlation between inputs and output for IEVer.

We notice that, if A and IE vary from 0m/s

−2

to 0.5m/s

−2

and from 0W to 6W respectively, then

trust is increasingly high. However, if A is between

0.5m/s

−2

and 1m/s

−2

, and IE is between 0W and

4W , then trust is between 0.08 and 0.25. There-

fore, the more the EV accelerates, the higher the IE

should be and, subsequently, the more the Trust(IE)

increases.

Figure 6 depicts the correlation between the inputs

(Pos

lane

AS and T E) and output (Trust(T E)). This

SECRYPT 2018 - International Conference on Security and Cryptography

228

Table 2: Fuzzy rules for TEVer.

Rule Input

Pos

lane

Input AS Input T E Output

Trust(TE)

Rule Input

Pos

lane

Input AS Input T E Output

Trust(TE)

1 start low very low high 31 middleE low very low low

2 start low low medium 32 middleE low low low

3 start low medium low 33 middleE low medium high

4 start low high low 34 middleE low high medium

5 start low very high low 35 middleE low very high low

6 start medium very low medium 36 middleE medium very low low

7 start medium low high 37 middleE medium low low

8 start medium medium low 38 middleE medium medium medium

9 start medium high low 39 middleE medium high high

10 start medium very high low 40 middleE medium very high low

11 start high very low low 41 middleE high very low low

12 start high low high 42 middleE high low low

13 start high medium medium 43 middleE high medium medium

14 start high high low 44 middleE high high high

15 start high very high low 45 middleE high very high low

16 middleS low very low medium 46 end low very low low

17 middleS low low high 47 end low low low

18 middleS low medium low 48 end low medium medium

19 middleS low high low 49 end low high high

20 middleS low very high low 50 end low very high low

21 middleS medium very low low 51 end medium very low low

22 middleS medium low high 52 end medium low low

23 middleS medium medium medium 53 end medium medium low

24 middleS medium high low 54 end medium high high

25 middleS medium very high low 55 end medium very high medium

26 middleS high very low low 56 end high very low low

27 middleS high low medium 57 end high low low

28 middleS high medium high 58 end high medium low

29 middleS high high low 59 end high high medium

30 middleS high very high low 60 end high very high high

Figure 6: Correlation between inputs and output for TEVer.

ﬁgure shows that AS and T E vary from 30km/h to

100km/h and from 0W to 50W , respectively. We sup-

pose that the median value for T E is equal to 20W and

the corresponding AS is equal to 60km/h. We further

suppose that the vehicle almost reaches the end of the

lane (Poslane=1900m).

Accordingly, whenever the vehicle reaches the

end of the lane, then trust seems to be high (around

0.82) only if AS and T E are proportional. For exam-

ple, we notice that if AS is between 30km/h (ASmin)

and 100km/h (ASmax) and T E between 0W and 30W

then trust does not exceed 0.4 since the EV is in the

end of the lane (Pos

lane

=1900m). However, in regard

to the same position, if AS and T E vary from 75km/h

to 100km/h and from 40W to 50W respectively, then

trust can reach 0.83. Nevertheless, if T E exceeds

50W then, trust decreases regardless the AS.

We perform a series of experiments for different

percentages of false messages (P=10%, 25%, 40%)

and with/without collusion attack to evaluate the pre-

cision of the proposed IEVer solution as illustrated in

Figure 7. We should also highlight that false readings

are randomly generated. Simulation results show that

IEVer performs well to detect the misbehaving enti-

ties that provide wrong energy information that mis-

Fuzzy Logic based Model for Energy Consumption Trust Estimation in Electric Vehicular Networks

229

matches with their acceleration or deceleration rate.

Viewed from Figure 7, it is obvious that the IEVer

resists to the false data injection attack with/without

collusion and achieves a high precision regardless the

number of vehicles that belong to the same lane.

Moreover, we notice that the detection of the col-

lusion attack is achieved with higher precision com-

pared with the absence of such attack since all of bad

entities collude to provide similar wrong energy in-

formation (i.e. very low/ very high energy consump-

tion). That is to say that some vehicles, that consume

a small/big amount of energy, collude to convince the

server that they consume a big/small amount of en-

ergy, respectively. In such situation, these entities can

be detected with a high precision that usually reaches

98%. In the second instance (i.e. without collusion),

each vehicle independently lies about its energy. In

this case, the detection of bad entities is reduced a

little bit due to the dynamicity of fuzzy classes. We

also notice that the detection of false data injection at-

tack is moderately accomplished with around 87% (if

n=10 and P=40%) and 90% (if n=100 and P=40%)

since the rate of wrong messages has lower impact on

the selection of the median energy in high trafﬁc den-

sity compared with in low trafﬁc density.

However, the IEVer cannot resist to the bad enti-

ties that: (1) consume high amount of energy but they

pretend that they consume much more, (2) attribute a

slight modiﬁcation of their real energy consumption

(i.e. Bush telegraph) in each time step or, (3) selec-

tively forward their energy consumption (i.e. selec-

tive forwarding or on-off). To overcome these lim-

itations, we propose to evaluate the accuracy of the

overall energy consumption (TEVer).

Figure 8 shows that TEVer is effective to over-

come these limitations. First, we should underline

that the more the vehicle is approaching to the end of

the lane, the better the detection of malicious nodes.

In Figure 8 we conduct different experiments to prove

that TEVer deals with the On-off attack in different

scenarios: (1) send/send/no − send, (2) send/no −

send/send and (3) no − send/no − send/send. Ac-

cording to Figure 8, it is obvious that TEVer allows

the detection of the on-off attack with high precision,

especially for the case no − send/no − send/send

(higher than 92%). In this case, the detection of such

attack is very effective since the total consumed en-

ergy is very low compared with the expected one.

Hence, the more the vehicle behaves improperly, the

higher the precision. Moreover, we notice that when-

ever the trafﬁc density increases (n=100 and P=40%),

the precision increases simultaneously compared with

in low trafﬁc density (n=10 and P=40%). This can

be explained by the fact that, for the same percentage

of malicious vehicles, the identiﬁcation of the median

total energy consumption is more accurate in high

trafﬁc density than low trafﬁc density. Similarly, we

believe that TEVer is effective to deal with the selec-

tive forwarding attack since it almost behaves like the

on-off attack.

Regarding the bush telegraph attack, we perform

multiple experiments with different α values as de-

picted in Figure 9. α refers to the variation rate of

the real consumed energy that can increase (+α) or

decrease (-α). Indeed, TEVer is more effective with

the bush telegraph attack that provides high energy

information (+α) than the bush telegraph attack that

reduces the real energy consumption (-α) especially

when α=20% and α=30%. Figure 9 shows also that

the more the total energy deviates (i.e. α increases),

the higher the precision. Moreover, we notice that the

detection of this attack is improved whenever the traf-

ﬁc density increases since the number of false mes-

sages has lower effect on the establishment of fuzzy

classes in the second instance (i.e n=100) compared

with the ﬁrst one (i.e. n=10). Therefore, in regard to

the same percentage of false messages (e.g. P=25%),

we notice that for n=10, the precision is around 50%

and for n=100, the precision reaches 70%.

In summary, we can deduce that the proposed

model for IEVer and TEVer is resilient to multiple

threats in different trafﬁc densities. This model pro-

vides a high protection against the malicious entities

that misbehave due to selﬁsh, malicious and uninten-

tional reasons.

7 CONCLUSION AND FUTURE

WORK

In this paper, a new fuzzy-based trust model was pro-

posed to evaluate the accuracy of the energy infor-

mation reported by electric vehicles. The proposed

solution consists of two main dimensions: the instant

energy veriﬁcation and total energy veriﬁcation. The

aims of this model are mainly to (1) ﬁlter the wrong

energy information that mismatches with the vehicle’s

behavior (e.g. acceleration and deceleration) and (2)

ensure that only credible and plausible information is

considered to enhance the quality of the provided ser-

vices in electric vehicular networks. Results and anal-

ysis prove the effectiveness of the proposed solution

to detect several kinds of threats, that broadcast wrong

energy information, in different trafﬁc densities. As

future work, we intend to propose a self-adaptive trust

model for electric vehicular networks. The purpose of

this model is to adopt the convenient methodologies

for trust assessment, according to the context and the

SECRYPT 2018 - International Conference on Security and Cryptography

230

Figure 7: Performance of IEVer versus the false data injection attack (with/without collusion attack). Precision for (a) n=10

and (b) n=100.

Figure 8: Performance of TEVer versus the On-off attack. Precision for (a) n=10 and (b) n=100.

Figure 9: Performance of TEVer versus the Bush Telegraph attack. Precision for (a) n=10 and (b) n=100.

characteristics of the provided applications. ACKNOWLEDGEMENTS

This work was supported by the PHC Utique pro-

gram of the French Ministry of Foreign Affairs and

Ministry of higher education and research and the

Tunisian Ministry of higher education and scientiﬁc

research in the CMCU project number 16G1404.

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231

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