Secure Opportunistic Routing Protocol in VANETs

Eqbal Darraji

, Iain Phillips

2 a

and Asma Adnane

Department of Computing, Mustansiriyah University, Iraq

Department of Computing, Loughborough University, U.K.

Eqbal.darraji@uomustansiriyah.edu.iq, {i.w.phillips, a.adnane}@lboro.ac.uk

Keywords:

Security, Trust, Opportunistic Routing, VANETs.

Abstract:

In this paper, we introduce a new trusted opportunistic routing protocol called Trusted Context-aware Op-

portunistic Routing (TCOR) which produces a secured routing for VANET and it is incorporated with a rec-

ommendation mechanism (TCOR-Rec). The proposed secure routing protocol aims to address the inherent

limitations in VANET routing, particularly the challenges associated with mobility metrics and the incorpora-

tion of trust metrics. By leveraging a comprehensive trust framework that integrates both direct and indirect

observation-based reputation systems, the protocol enhances the robustness and reliability of the routing pro-

cess. In addition, a similarity-based evaluation method is employed to assess the validity of recommendation

messages, facilitating the selection of the most trusted and reliable path. OMNET++ is used to implement

and simulate TCOR, which has proved its efﬁciency in comparison with other routing protocols (COR oppor-

tunistic protocol, AODV and trusted AODV). To simulate an adequate VANET environment and evaluate the

protocols under realistic conditions, different metrics and network parameters such as the network trafﬁc pat-

tern, mobility pattern, and the fading propagation model have been used. The results have shown that TCOR

and TCOR-Rec outperform traditional routing protocols by approximately 9% and 15%, respectively, in terms

of packet delivery when the attacker nodes are involved in the network.

1 INTRODUCTION

Secure routing in wireless communications has been

considered a challenging task, particularly in Mo-

bile Ad hoc Network (MANET) due to its character-

istics such as limited resources, open medium, and

distributed cooperation (Chandramouli et al., 2018).

Although applying cryptography and authentication

mechanisms achieves security that provides integrity

and conﬁdentiality, it is impractical to implement

them in Ad hoc networks since it does not solve

the issue of cooperation and internal attacks. In re-

cent years, many researchers have been redirected to

achieve MANET routing security based on trust ap-

proaches to detect internal attacks and motivate nodes

to cooperate (Smriti Jain, 2017). Comparison studies

of cryptographic and trust-based mechanisms used in

secure routing protocols show that the former requires

more computation overhead (Kumar and Banerjee,

2017) and (Jhanjhi et al., 2022). In general, the neces-

sity of trust appears only in an environment with un-

certainty characteristic, such as e-commerce. In wire-

less communications, this feature is dominant among

https://orcid.org/0000-0001-8503-7651

network nodes due to their inability to collect in-

formation about nodes that are outside their sensing

range. There are many reasons that motivate the use

of reputation and trust-based systems as a security so-

lution for wireless networks, for example, individual-

ity of the node in MANETs, the lowest cost of pro-

ducing nodes in WSNs and cryptography failure in

the face of internal attacks (Luo and Lu, 2008).

In MANETs, there is no centralised control, and,

as a result, nodes are responsible for performing all

network operations. Since there is no shared atten-

tions and individuality of nodes, these nodes may

prefer to not cooperate. This misbehaviour (non-

cooperation) is either for selﬁsh goals such as to save

resources, or malicious ones, for instance, denial of

services. Vehicular ad hoc networks (VANETs) are

a subset of traditional MANETs in which nodes are

mobile and cooperate with each other to route and

relay packets. However, these nodes act indepen-

dently without central network management support,

increasing their chance of being malicious, leading to

high risks to safety applications. The importance of

routing in VANET and MANET environments for the

successful delivery of data packets and efﬁcient sup-

Darraji, E., Phillips, I. and Adnane, A.

Secure Opportunistic Routing Protocol in VANETs.

DOI: 10.5220/0013371500003899

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 11th International Conference on Information Systems Security and Privacy (ICISSP 2025) - Volume 1, pages 397-404

ISBN: 978-989-758-735-1; ISSN: 2184-4356

397

port for applications make security for routing proto-

cols the ﬁrst crucial step to support VANET applica-

tions’ security.

2 RELATED WORK

In opportunistic routing (OR) protocols, there are rel-

atively few studies in the area that suggest trust mod-

els to improve the reliability of communication and

achieve security.

Wu et al. (Wu et al., 2017) proposed a Trust-based

Routing Protocol (TRP) to mitigate the impact of ma-

licious nodes in opportunistic networks. The proto-

col addresses black hole attacks by monitoring pre-

vious hop behavior and updating trust values, mini-

mizing the inﬂuence of older transactions. Recom-

mendations are based on direct trust from the recom-

mending node, but periodically exchanging opinions

from multiple nodes could improve the effectiveness

of the protocol. Bo et al. (Bo et al., 2011) suggested

an opportunistic routing protocol for MANETs. Hy-

brid observation (direct and indirect trust) is used to

compute the trust on the forwarding node. The cost

of the trusted route is calculated according to the

node’s proximity to the destination and its trust value.

In addition, the formulation of a trusted minimum-

cost routing algorithm (MCOR) is formally produced.

The same idea of MCOR was applied by Zhizhong

et al. (Zhizhong et al., 2012) to secure routing in

VANETs and devised Trust opportunity forwarding

mechanism(TMCOR). hizhong et al. (Zhizhong et al.,

2013) proposed a protocol where each node evaluates

the trustworthiness of its neighbors based on collected

data. Trusted neighbors are ranked by routing cost,

trust value, and distance to the destination to prevent

black-hole and grey-hole attacks. The protocol was

compared to the EXoR protocol (Biswas and Morris,

2004), which relies on link quality and distance met-

rics. However, comparing with an untrusted proto-

col like EXoR may not accurately reﬂect real perfor-

mance, particularly in terms of throughput and packet

delivery ratio.

Chuanhe et al. (Huangchuanhe, 2010) intro-

duced the Trust Opportunistic Routing (TOR) proto-

col, combining expected transmission count (ETX)

and trust metrics. ETX estimates link quality via

probe packets, while trust is based on the ratio of

received to forwarded messages. Candidates are se-

lected for the highest trust and lowest transmission

count, with RTS/CTS packets for coordination. TOR

excludes malicious nodes and is compared to the

ExOR protocol (Biswas and Morris, 2004). Li and

Das (Li and Das, 2013) developed a comprehensive

trust model to predict node behaviour, using posi-

tive feedback messages as evidence of forwarding be-

haviour. Trust and message delivery ability inform

forwarding decisions, with comparisons made with

the Prophet protocol. Elakkiya et al. (Elakkiya et al.,

2014) aimed to assess the information obtained, re-

lying on trust metrics and speed for the selection of

opportunistic hops. Direct information is collected

based on immediate acknowledgements, while rec-

ommendations are evaluated using a proposed for-

mula. Trusted Prophet (T-prophet) and AODV pro-

tocols were used for evaluation.

Salehi and Boukerche (Salehi and Boukerche,

2014) proposed combining trust and link delivery

probability into a single metric to enhance wireless

network security. Candidate nodes are selected based

on this metric, with a watchdog mechanism for mon-

itoring direct neighbors. Source nodes update trust

values and detect misbehaving nodes by monitor-

ing candidate behavior, while time-based coordina-

tion among candidates is utilized. An extension of

this work (Salehi et al., 2015) introduced a trust model

that incorporates geographical location, link quality,

and trust value as local metrics for hop selection, aim-

ing at a balanced selection that prioritises historical

data. The reputation model proposed in (Salehi and

Boukerche, 2014) was extended to incorporate rec-

ommendations from other nodes in evaluating trust

values (Salehi and Boukerche, 2015). The observa-

tion mechanism relies on ideal coordination among

the candidate nodes, where each node receives an

acknowledgement message when the candidate for-

warder transmits the packet and when the sender re-

ceives this acknowledgement.

Thorat et al. (Thorat and Kulkarni, 2015) pro-

pose a trust mechanism for the CORMAN opportunis-

tic routing protocol, relying on ﬁrst-hand observation.

The top two candidates are selected by evaluating

all intermediate nodes toward the destination, using

global information to calculate a metric that combines

trust value and node proximity. Node reliability is as-

sessed using Bayesian derivation through promiscu-

ous packet forwarding monitoring, while ETX evalu-

ates the node’s closeness to the destination.

3 TRUSTED CONTEXT-AWARE

OPPORTUNISTIC ROUTING

Trusted Context-aware Opportunistic Routing

(TCOR) is based on the previously suggested

Context-aware Opportunistic Routing (COR) (Zhao

et al., 2014a) (Zhao et al., 2014b). To enhance the

security of routing and stop malicious attacks, a trust

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398

mechanism has been incorporated into opportunistic

routing to produce TCOR. TCOR uses the trust

metric in addition to the other context metrics such

as link quality, geographical location, and mobility

to make the routing decision. Trusted-Clear-To-

Broadcast (TCOR) follows the same steps as COR

with Data packets broadcast, trusted-CTB packet

generation, Trust-based Next-hop selection, and extra

elements added to the design.

The following steps are the key modules of the

TCOR protocol.

• Data Packet Broadcast:

In this step, the source node starts broadcasting

a data packet when it is needed, location and the

destination are included. When a neighbour re-

ceives this packet, it will calculate the DFD, in-

clude it in the CTB packet and reply immediately

(which will be called Trusted CTB ”TCTB” in

TCOR). The source node will set a timer, which is

called a CTB timer, in order to get as many CTB

packets as possible from all the neighbours.

• Trusted CTB Packet Generation:

A Trusted Clear-To-Broadcast (TCTB) control

packet is generated by the neighbour that receives

the broadcast data packet. Each neighbour receiv-

ing a data packet from a source node will imme-

diately reply with a TCTB packet without setting

a DFD timer, the TCTB includes the DFD value.

At the source node, all the TCTB packets received

within the CTB timer period will be saved in a

list (TCTB senders list) in order to select the best

next hop among TCTB senders after CTB timer

expires. This process will be repeated by each in-

termediate node that is selected as the best can-

didate (best next hop) until getting the generated

TCTB packet by the destination. The DFD is cal-

culated very similarly to COR protocol but the en-

ergy parameter was excluded due to no limitations

on energy consumption in VANET. The following

equation explains how the DFD is computed:

DFD = (α × LinkQuality+

β × Progress + δ×LIV E) × DFD

Max

(1)

• Trust-Based Next Hop Selection:

When the CTB timer expires, the source node will

select the best candidate from its TCTB senders

list, in terms of trust and quality of services, as

the next hop forwarder. The TCTB sender that

has the highest trust value will be selected as the

best next hop. When multiple TCTB senders have

the same trust value, the one with the lowest DFD

value will be selected as the best next hop.

• Tear Down Timer:

A tear-down timer is used to maintain the stabil-

ity of connections for the selected route and se-

lect the more trusted route after a certain period.

The value of this period/timer can be determined

based on the network status (e.g. network con-

gestion and nodes’ mobility). This timer plays a

very important role in establishing a reliable and

trusted route as it helps a node rediscover a route

after a certain time based on trust and other con-

text parameters, such as link quality, progress, and

mobility. This timer will be started for each des-

tination, and when it expires, the (intermediate)

node will tear down all the information that has

about this destination: route, next-hop trust value

and other related buffers. Figure 1 describes the

process steps of Tear down timer.

Figure 1: Process steps of Tear down timer.

To accelerate the setup process of the trust system,

the ﬁrst-hand trust information shared with other net-

work nodes periodically and according to a condi-

tion to avoid overhead communication and time de-

lay. The period of information sharing is selected

based on the CTB timer and updating the trust value.

However, this approach exposes the system to false

report attacks, making effective mitigation measures

essential. A common method to evaluate the trust-

worthiness of a recommending node is similarity (Bo

et al., 2011; Zhizhong et al., 2013), which measures

the alignment of opinions and recommendations from

multiple nodes regarding the observed node.

4 SIMULATION SETTINGS

In this section, we will introduce the simulation de-

tails, as well as Trusted AODV (TAODV) which we

used as a comparison protocol with our proposed trust

routing TCOR.

Secure Opportunistic Routing Protocol in VANETs

399

4.1 Trusted AODV Routing

TAODV Routing Protocol was proposed by Kamel et

al.(Kamel et al., 2017) to detect black hole attack.

This trusted routing protocol uses the destination se-

quence number to detect the malicious node as the

attacker node tries to attract the source node by send-

ing a fake RREP message. The proposed mechanism

examined the safety status for each RREP message

received. Figure 2 illustrates the steps of the RREP

process.

Each participating node in the network has a trust

level (TL) and malicious node tables (MN). TL de-

termines the level of trust for each node that partic-

ipates in the network routing activities. All partici-

pating nodes are at a trusted level in the initial stage

exactly 60. This level is updated during the simula-

tion on the basis of the safety status for each incoming

RREP message. To verify the validity of the incom-

ing RREP and its safety status, a threshold needs to

be deﬁned. The threshold is calculated using formula

T h =

∑

i=1

(rpSeqNo

− rtSeqNo

) (2)

Where N is the number of nodes in the routing

table of node (p). rpSeqNo is the dst sq no of the

destination node (i), rtSeqNo is the current sequence

number for destination node (i) in the routing table of

node (p).

In the AODV routing mechanism, routes are se-

lected based on the sequence number and hop count

to the destination. Upon receiving an RREP message,

the MN table is checked. If the originator node is

listed, the message is discarded; otherwise, its safety

status is evaluated using the formula 4 (Kamel et al.,

2017).

∆Seq = rpSeqNo

− rtSeqNo

(3)

S = ∆Seq − α × T h (4)

Here α is the distance from the threshold that se-

cures the information. Based on the value S calculated

using formula 4, the incoming information will be de-

ﬁned as safe or unsafe according to formula 5(Kamel

et al., 2017):

SecurityStatus =

(

I f S ≤ 0 Sa f e RREP

I f S > 0 unsa f e RREP

(5)

When an unsafe RREP message is detected, the

TL of the originating node will decrease by one. The

originating node will be inserted in the MT table when

its TL becomes negative.

Figure 2: RREP process steps of Trusted AODV (Kamel

et al., 2017).

4.2 Attacks Simulation

To evaluate the proposed trusted routing protocol,

misbehaving / attacker nodes must be added to the

network. There are many attackers that perpetrate

the network layer; however, only the most popular

attacks were implemented and taken part in the net-

work: Black hole and grey hole attacks. The attacks

were implemented in a sophisticated way, where the

attacker nodes behave normally in terms of route dis-

covery (exchanging control packets), for instance in

COR and AODV, they will reply with CTB and RREP,

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400

respectively, in order to take part in the established

path. However, when they are involved in a selected

routing path, they will drop all data packets that pass

throw them. The grey hole attack was carried out to

drop 50 % of the data packets in a ﬂuctuating manner.

Thus, the attacker nodes have the opportunity to be

selected as a router in order to be able to execute the

attack.

4.3 Simulation Environment

VEINS 4.5 platform has been utilised to conﬁgure

simulation experiments. This simulator emulates

the VANET environment by coupling OMNeT++ 5.0

(Varga and Hornig, 2008) and SUMO(Ben Mussa

et al., 2016)(DLR - Institute of Transportation Sys-

tems, 2015) to conﬁgure network simulation and road

trafﬁc. The mobility model for the used map (Man-

hattan map) is generated using SUMO with a maxi-

mum speed limit of 14m/s. The packet delivery ra-

tio (PDR) and the end-to-end delay (e2edelay) (Zhao

et al., 2014a) are the metrics that were used to evalu-

ate the proposed protocol. PDR presents the relation-

ship between the data packets delivered to the desti-

nation application layer and the data packets transmit-

ted by sources. However, e2edelay presents the time

taken by the transmitted data packet from the source

to the destination.

5 PERFORMANCE EVALUATION

The results of the simulation experiments that were

carried out with the routing protocols in an urban en-

vironment (Manhattan map 750×750m

) were evalu-

ated based on PDR and e2edelay. These experiments

were run ten times with 100 nodes randomly deployed

in the network, and each experiment took 120 seconds

to run. Multiple loads (10, 20 and 50 connections)

were tested, and these connections (source and des-

tination) were chosen randomly using the same seed

number on each run. An interval of each connection

was selected in a random manner that differs between

5 and 25 seconds for 10 pps of load 100 bytes to simu-

late the critical safety application. Figure 3 illustrates

the PDR of routing protocols with different load net-

works.

In Figure 3, the COR and TCOR protocols

achieve the highest probability of data packet de-

livery, especially when the number of connections

increases and reaches approximately 13% higher

than the traditional routing protocols (AODV and

TAODV). This is due to the opportunistic feature of

these routing protocols (which conﬁrms the motiva-

Figure 3: PDR of COR, AODV, TCOR and TAODV in

Manhattan map (100 nodes).

tion of this research study (Biswas and Morris, 2004;

Wang et al., 2012; Boukerche, 2009) to establish a

path (as the path is established when data packets

move) and to use multiple contexts of information to

make a route decision. In AODV, the path is estab-

lished before the start of sending data packets (send-

ing RREQ and waiting for RREP), which increases

the number of dropped packets in the network layer

due to the queue fullness and unavailability of routes.

TAODV performs the same way as AODV does, since

the modiﬁcations cannot affect its performance with-

out attacks.

Figure 4: Average of e2edelay with number of connections

without attacks.

Figure 4 illustrates the mean of e2edelay for each

tested routing protocol with multiple loads. The mean

of e2edelay was calculated for each run of the ex-

periment, and then the average was computed for all

the runs. As shown in that ﬁgure with AODV, the

packets take longer to be delivered in various sce-

narios (10, 20, and 50 connections). This is due to

the route initialisation process that starts route dis-

covery (start broadcasting a RREQ message and wait

until the RREP message is received) to establish a

path to the destination. As TAODV performs the

same way as AODV without attacks, it consumes

the same time delivering the packets. COR shows

Secure Opportunistic Routing Protocol in VANETs

401

the shortest e2edelay due to its dependence on mul-

tiple forwarders (multiple next hop) and uses multi-

ple items of context of information to select the next

hop. TCOR and COR are different because TCOR

sets a CTB timer to receive multiple replies from the

sender’s neighbours. In general, COR and TCOR out-

perform AODV and TAODV in terms of the packet

delivery ratio and the time it takes to deliver the pack-

ets to the destination. The routing protocol perfor-

mance results are analysed and discussed when the

network is under black hole or grey hole attacks.

• The performance of AODV signiﬁcantly declines

in terms of packet delivery ratio (PDR) as the

number of black hole attacks and network connec-

tions increases. The drop in AODV performance

is due to its discovery process and its mechanism

to establish a path and not incorporating any trust

algorithm.

• TCOR outperforms the other routing protocols

tested in PDR and e2edelay as seen in ﬁgures

5, 6, 7. 11, 12 and 13. It performs bet-

ter than the other tested routing protocols by ap-

proximately 10%, particularly when the number

of connections is equivalent to 50 pairs. This is

due to its mechanism relying on the trust metric

along with contextual parameters, such as mobil-

ity, geographic location, and link quality, to make

the routing decision. It also achieves the shortest

e2edelay as it selects the more trusted neighbour

and the closest one to the destination as a next hop

(forwarder).

• TCOR incorporating recommendations (TCOR-

Rec) improves TCOR performance in terms of

PDR, as it helps the network nodes share their ex-

periences (direct trust) with their neighbours dur-

ing the path establishment without any further ex-

change messages (as the recommendations were

included in the control packets that are used to

discover a route). That improvement increases

by about 5% in the scenario where 50 pairs of

connections are exchanging data. In TCOR-Rec,

there is a slight increase in e2edelay compared to

TCOR because TCOR-Rec increases the number

of candidates’ neighbours, and thus it will be more

likely to select the more trusted neighbour even

though it is not the closest one.

• TCOR defences against the black hole attacks by

detecting and excluding them from taking part in

the path establishment, and this is clear through

the increased amount in the PDR in comparison

with COR in all different urban scenarios (10, 20

and 50 connections) as illustrated in ﬁgures: 5, 6

and 7.

Figure 5: PDR of 10 connections with different number of

black hole attacks in Manhattan map.

Figure 6: PDR of 20 connections with different number of

black hole attacks in Manhattan map.

Figure 7: PDR of 50 connections with different number of

black hole attacks in Manhattan map.

6 CONCLUSIONS

The results show that the proposed trusted opportunis-

tic routing protocol (TCOR), which incorporates trust

and recommendations, outperforms COR, AODV, and

TAODV. By integrating trust and recommendation

metrics (TCOR and TCOR-Rec) in route selection,

TCOR improves packet delivery by up to 9% and

14%, respectively. Additionally, TCOR demonstrates

greater resilience to network topology changes and

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402

Figure 8: PDR of 10 connections with different number of

grey hole attacks in Manhattan map.

Figure 9: PDR of 20 connections with different number of

grey hole attacks in Manhattan map.

Figure 10: PDR of 50 connections with different number of

grey hole attacks in Manhattan map.

routing issues, such as packet drops in black-hole or

grey-hole scenarios.

Experiments indicate that opportunistic routing

protocols outperform traditional AODV and TAODV

by approximately 15% in Packet Delivery Ratio and

end-to-end delay, even in the absence of network at-

tacks. This highlights their efﬁciency in reliably de-

livering data packets in highly dynamic wireless net-

works.

As future work, VANET routing performance will

be further evaluated using additional metrics, such

Figure 11: E2e delay of 10 connections with different num-

ber of black hole attacks in Manhattan map.

Figure 12: E2e delay of 20 connections with different num-

ber of black hole attacks in Manhattan map.

Figure 13: E2e delay of 50 connections with different num-

ber of black hole attacks in Manhattan map.

as jitter, rejected packets, and dropped packets, to

achieve optimal QoS for time-critical applications.

In conclusion, the proposed TCOR protocol aligns

well with these characteristics and delivers promising

results, even in the presence of network issues, includ-

ing attacks.

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