Improving CNPV Protocol Performance in VANETs

http://www.iaeme.com/IJCIET/index.asp 304 editor@iaeme.com
International Journal of Civil Engineering and Technology (IJCIET)
Volume 9, Issue 11, November 2018, pp. 304–314, Article ID: IJCIET_09_11_030
Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=9&IType=11
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication Scopus Indexed
IMPROVE THE PERFORMANCE OF THE CNPV
PROTOCOL IN VANET NETWORKS
Abdulhakeem Amer A
College of Information Engineering (CoIE), Al-Nahrain University Baghdad, IRAQ
ABSTRACT
The purpose of the present study is to provide a model for improving the
performance of the CNPV protocol in VANET networks. To evaluate the protocol and
to identify the position of vehicles in this research, Simulator OMNET++ and VEINS
[1] along with SUMOI [2] has been used. OMNET++ is used to simulate a network
of several nodes, each node can communicate with each other through its own
gateway. In this study, using position verification mechanisms instead of CNPV tests
that have very little overhead and are not used in this protocol, we tried to avoid a lot
of these attacks and show that there is less computational overhead and less messy
than other research that does, but do not, use the same message exchange mechanism
as CNPV, identifies up to 90% of ill-cared vehicles and reduces ten times the amount
of message exchange and improves accuracy.
Keywords: CNPV, VANET, CNPV protocol, VANET networks, performance of
CNPV
Cite this Article: Abdulhakeem Amer A, Improve the Performance of the CNPV
Protocol in VANET Networks, International Journal of Civil Engineering and
Technology, 9(11), 2018, pp. 304–314.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=9&IType=11
1. INTRODUCTION
The cars use a message to communicate with each other. These messages are sent to
neighboring vehicles via short-range waves, decoding the receiver of the message and
extracting the information and, if necessary, participating in the routing process and
transmitting it to their neighbor car. When a vehicle wants to send a message, it chooses a
vehicle closer to its destination. To reach this goal, automobiles use different routing
algorithms. In addition, with in-car devices such as the Global Positioning System (GPS), it
gains its position and reports it to other vehicles using periodic reports. In this way, all cars
know and keep each other in a position to read this information in a table called spacing table
of neighbors. As with other data communication and data processing systems, car-related
networks are exposed to a variety of attacks and injuries. One of these attacks is fake
positioning or GPS manipulation. This attack will show the wrong location information to

Abdulhakeem Amer A
neighbors. Location information is easily accessible from systems such as GPS. The attacker
achieves his goal by using a signal transmitter that is stronger than the signals sent from real
satellites (Al- Kahtani 2012). By using a forge attack and manipulating position information
of a hostile vehicle as an intermediate reporter, messages can be discarded or altered. Also, an
abusive car can show itself in a fake position and will produce a fake crash message. Other
vehicles may be braked by the receipt of this fake message, and this will cause traffic
accidents or traffic accidents. Now, if the cars work together to produce fake position
messages in a specific location on the road, heavy traffic is simulated and traffic control
systems are fooled. If it is possible to detect this deception and separate the actual location of
cars from their false position, many of the attacks based on forgery can be prevented. Attacks
on legitimate and fully licensed vehicles are by no means identifiable by traditional security
mechanisms such as encryption of messages. One of the best CNPV positioning algorithms is
the delay, but since it uses a number of tests, including collective confirmation of vehicles to
identify the location, it certainly needs to exchange excess messages between the vehicles. In
this paper, using a series of location verification tests, instead of the CNPV tests, we evaluate
the advantage of the proposed model.
2. LITERATURE REVIEW
In this section, a number of related works related to the status of vehicles has been
investigated. Determining the location of an adjacent car by a wireless network using locating
and verifying the location. The locating process provides the ability to calculate the position
of an adjacent car provided that information is collected by other vehicles. Confirmation of
this position specifies whether the localization of the calculated value matches the actual
position of the node. Due to this locating, self-locating can be accomplished through global
satellite navigation systems. Then our spatial information can be reported by adjacent
vehicles (Leinmuller 2006, Schoch et al.). When a vehicle identifies the location of its
neighboring vehicles (on-site machines), it must be ensured that the declared locations are
geographically consistent with the actual coordinates, that is, the location must be confirmed.
In existing works, we can find several mechanisms for infrastructure or combination
networks. These mechanisms can provide solutions for locating securely by using fixed or
moving machines that provide secure communication with the certification officer.
(Papadimitratos & Mezzour et al, 2008).In fact, if a source sends a message, it receives
adjacent vehicles at different times, which depends on the distance from the sender. By
sharing information, we can deduce the transmitter's location. Multilateral systems are
becoming widespread today, and GPS and even airports are used to control the position of the
aircraft. Fiore et al. (2010). Trullols, Fiore et al. suggested a distributed mechanism for
confirming spatial position in wireless networks.This protocol is designed to be responsive
that is, a vehicle called the verifier must begin the process at a specified time to discover and
validate the position of the vehicles in their communication location. But this reactive
protocol requires a very large number of messages, thus imposing a lot of costs. In addition,
there can be a lot of delay between the start of the process and the approval of the location of
neighboring vehicles. If all cars sending a warning message start this process based on
receiving an alert message, the overall delay can be very high and the efficiency of the
message propagation process is reduced. Therefore, the use of responsive approaches is not
appropriate when cars require constant awareness of the location of their adjacent vehicles.
The CnPV is one of the best protocols that effectively determines which neighboring vehicles
are sending false information about their spatial location. (Fogue, Martinez et al. 2015)

Improve the Performance of the CNPV Protocol in VANET Networks
2.1. CNPV
In this protocol, all vehicles that are present on the network are equipped with
communication devices and can send safety messages or welfare messages to each other.
While all vehicles are coordinated with a common time reference, we assume that each
vehicle can determine its geographical location with a maximum error of 10 meters. Both of
the criteria for attention to time and attention to the geographical location can be achieved by
equipping the vehicle with the GPS receiver, which is an acceptable hypothesis that is about
the rapid development of this technology in the automotive industry. Therefore, the GPS
receiver must be integrated at standard level 802.11. Each vehicle X has a unique identifier
and also has a long-term private key and a long-term public key for encryption and
decryption of data.The node identity (automobile) can be a permanent identifier or a
temporary alias to give the user a confidential privacy. In addition, automobiles have a single-
use key series {ʹx ، ʹx } and they can produce a digital signature (SigX) with their own
private key.All of these can be easily solved with the research done at Cooper 2008. This
model is effective when each participant car in the system periodically sends its position and
information necessary for the operation of the protocol, such as other existing routing
protocols. The proposed protocol is designed to achieve the two main goals in a transport
environment:
1) Acquiring position of nearby vehicles 2) Verifying these situations
Therefore, a car assigns one of the following two modes to each of its adjacent vehicles:
Approved: Position declared with real geographical location of the vehicle.
Unapproved: The collected data so far is not sufficient to determine the accuracy of the
declared location. An effective information validation process uses a message exchange
mechanism that occurs in two rounds over the same time period. These two periods occur as
follows: Round 1: At this first round, each X node in the company protocol chooses a random
x time. Each node sends an anonymous HELLO message. In this message, in addition to
placing its own unique identifier, it puts a pair of values related to the adjacent vehicles
where the X node receives the Hello message in the first round. The value of a pair is an
adjacent car of Y, and the other is the moment it receives the X message HELLO from Y
(shown as yx). The Hello message is received by all adjacent X vehicles and, of course, the
instantaneous reception of messages may vary for each node. Round 2: After a fixed time that
is shown as Tguard, the nodes start the second round of the protocol. Each node X 'sends a
new message called' disclosure '. Messenger messages are sent in the same order as the Hello
messages are transmitted, that is, for each node X: t ^ 'x = + Tguard + Tguard The
message message sent by node X includes items such as sender identity, The declared
position is when the HELLO message is sent, the neighboring vehicle ID, and the time when
the neighboring vehicle sends the message the last time the message was sent (Fogue,
Martinez et al. 2015).

Abdulhakeem Amer A
Figure 1 Message Transmission Algorithm in CNPV (2015.Fogue, Martinez et al)
The message exchange procedure is presented in algorithm (1). In algorithm (1), Nx
represents a set of neighboring vehicles of the X node. Additionally, during Round 1, a X-
node records a pair of y values for all vehicles from which it receives a HELLO message,
even after sending itself HELLO. Once the message exchange process is completed, each
node can match the messages sent in the first round and the adjacent vehicles that have
identified their identities or their nickname in Round 2.In addition, each node retrieves its
adjacent vehicles using the time when the HELLO message is introduced. Packets received in
the second round cannot be retrieved without referring to the first round; hence, they will be
ignored until a complete exchange of packets is performed. For example, when a Y node
receives a message from X, Y retrieves the X, tx, and HELLO transmission time sent by X
using YES, and Y stores the local txy, That is, when it receives the same message. Using this
information, Y can specify the distance that separates it from X. Indeed, if an incompatible
car knew the location of the nodes that participated in round 1, it could use this information to
schedule data that was in its HELLO message in Round 1. When the message exchange ends,
it's time for the participating cars to confirm the location of their neighboring cars. For this
purpose, each node performs several successive tests, and determines whether the declared
spatial situations are true.
2.2. THE PROPOSED MODEL FOR CONFIRMING THE LOCATION
OF VEHICLES
The proposed model in this paper is based on one of the routing and propagation protocols
known as the Local Confirmation Protocol for Locally Adopted Vehicle (CNPV) (2015.
Fogue, Martinez et al.). This protocol, which has proven its impact on two EDR and UV-Cast
propagation algorithms (2010, Viriyasitavat, Bai et al), although it has security mechanisms
but is still prone to being compromised. Considering the many risks that may occur in car
networks, this protocol is able to detect a few of these attacks.In addition, given the fact that
the protocol uses collective approval of vehicles to identify the situation, there is definitely a
need for the exchange of excess messages between vehicles. For example, when a car plans
to identify its neighbor's location, it must help its neighbors and, by aggregating their views,

decide on the location of the vehicle claimed by the claimant.Now, if this car has 10
neighbors, at least 20 messages must be exchanged between these cars. By expanding this to
a scenario of urban environments with a large number of vehicles, we find that a large
amount of messages is only for the purpose of examining the situation and asking neighbors
will be exchanged.So if you can use another mechanism with more efficiency and less
messaging, a more lightweight protocol can be designed. Hence, in this paper, using the
CNPV protocol monitoring mechanisms, which have a very low overhead and are not used in
this protocol, we tried to prevent a large number of attacks and to show that other
investigations that examined the situation But they do not use the same message exchange
mechanism as CNPV, there is less computational overhead and less message exchange.
Although several CNPV status checking mechanisms have already been presented, these
mechanisms, in addition to having more overhead, are not able to identify all forged position
attacks.Therefore, we have been able to design a new protocol called CNPV by integrating
several methods of checking the situation that has been proven in previous studies, in
addition to reaching the purpose of the protocol that identifies the position of vehicles that
has a lower overhead also impose on the network.
The assumptions of this system include:
All cars have transmitter and receiver devices and communicate with other vehicles as
they arrive on the network.
The vehicles are designed in such a way that all the algorithms and applications that they
have set for them automatically operate and require no human and driver interference in the
message exchange process.
People who engage in malicious activities on the network are fully legitimate and cannot
be identified through traditional authentication mechanisms.
Cars find their location using the GPS device and send them to their neighbors if
necessary. In addition, all vehicles have full road information such as traffic restrictions or
road maps.
2.3. MESSAGES STRUCTURE
The cars exchange messages during their presence on the network for sending and receiving
safety and welfare messages. The structure of the messages in this network contains
information such as identifier, location, and speed, which may be of a kind of data or periodic
beacon. Table 1 shows an example of the proposed structure for the safety of the car network
as presented in the article.
Table 1 The structure of packets exchanged in the network
Wave Short Messages
Wsm LengthpriorityChannel NumberSecurity TypeMessageID
Wsm Data
IsWarningMessage𝐾 𝑌AccidentPosCarID
SenderRoadID𝑌𝑋timestampMesssageType
Other Fields
We note that packet structure is determined by the context discussed in this article, and in
practice many other fields may be added to this structure for issues related to cryptography
and public and private keys and so on. In this format, MessageID is a unique message
identifier, depending on the time of production of the packet and the identifier of the vehicle

Abdulhakeem Amer A
that creates it.Security Type Specifies the package type from the security perspective, the
Channel Number, to determine the communication channel for sending packets, specifies the
priority of packet forwarding when several packets are waiting to be sent on this channel. For
example, the priority of Beacon messages is more from data messages. Wsm Length and
Wsm Data are respectively packet length and data packed with itself. CarID specifies the
sender's address. The Sender Road field is used to specify road or street numbers and
AccidentPos to specify the coordinates of a collision that is two-dimensional x and y.
Timestamp is also the time of packet generation, which is automatically set within this field.
kY is the identifier of the neighboring vehicle that sends the message, which sends this
information to the neighboring vehicles along with the message.TYXis also the time when the
adjacent car of the message was sent the last time the message was sent. Couples kY and
TYXare used to implement the CNPV protocol in this scheme. MesssageType also specifies
the type of message that can be either Hello or Disclosure or WarningMessage alert message.
Table 2 Table structure of neighbors' information
History Of Neighbors
ID Position Time Road ID
Average
Speed
Trusted Expired
The structure of this list is that the ID to hold the vehicle ID, the position of the last
previous position that the vehicle claims to be present in that area, the road ID of the road or
street number that the vehicle has announced, the average speed of the vehicle speed
according to The previous table is a table that can be calculated by receiving any new
position using the last position and time elapsed. Time is the last time a message has been
sent about the location of the car. Expired to expire cars. If a car is not seen for more than a
certain period, it will expire and will no longer be used for operations such as routing or co-
operation in a position survey. The trusted vehicle shows a change that is changing, according
to a review of the car.
3. PROPOSED MODEL
When the message exchange in CNPV ends in the first and second rounds, it's time for the
participating cars to confirm the location of their neighboring cars. For this purpose, each
vehicle carries out several successive tests, in which case it determines whether the declared
spatial position is correct. One of these tests is the collective approval of vehicles, which
definitely needs to exchange excess messages between vehicles.In this paper, instead of
CNPV tests, we use position verification tests that are used in other researches. These tests
act independently on each node. Since all sensors use information from the routing layer, they
do not require the exchange of additional messages or the use of specific infrastructure.
Assuming that spatial location-based routing algorithms are used, vehicles will receive their
spatial information by GPS receivers and shipped with Beacon packets. Also, in order to
prevent misuse, the beacons are signed by the sender and stamped on time.

Figure 2 Scheduling the CNPV Adjacent Vehicle Approval Algorithm (Fogue, Martinez et al. 2015)
Figure 3 Overview of the methods used in the proposed model
As you can see in Figure 2, using the idea of independent sensors to check the position in
the proposed model (referred to in Alsharif, Wasef et al., 2011), as well as the addition of
several new sensor, the Greenshields, Channing et al., 1935), and a reverse motion check
sensor and a signal strength check, we will have a fairly complete accumulation of
independent sensors for position analysis. Each of these sensors will be able to detect one of
the types of spoofing attacks. The proposed algorithms in this model are very simple and low
computational and can be easily implemented, which is one of the unique advantages of the
proposed model in this paper.

Abdulhakeem Amer A
4. RESEARCH METHODOLOGY
In order to evaluate the protocol, we used the simulator OMNET++ and VEINS along with
SUMOI to identify the position of vehicles. OMNET++ is used to simulate a network of
several nodes, each node can communicate with each other through its own gateway. It is
also a simulator of the overall implementation framework and simulation. SUMO is a car
simulator that can be used to create a network of roads and vehicles. The simulator also has
the ability to create obstacles such as buildings, and it can be used to implement road traffic
and to observe the behavior of vehicles in the desired manner.To link the simulator
OMNET++ and SUMIO, we used a simulator called Veins. The network in this simulation
has several roads and intersections that interrupts each other and the cars accidentally enter
the network at specific nodes and travel through a specific route and exit the network. Figure
3 shows the overview of the map used in this simulation. As you can see, every intersection
has a number called it.
Figure 4 Network Map
5. FINDINGS
After simulating and applying different scenarios, various parameters have been evaluated
and analyzed. The number of sent and received messages has been measured in position
verification mechanisms with CNPV and without CNPV as well as the number and
percentage of hostile vehicles identified in different scenarios.
6. SIMULATION RESULTS
In this scenario, there are 100 ordinary cars on the network that arrive at specified intervals.
The cars leave the network after a long distance. In the meantime, there are 20 vehicles that
try to deceive other drivers by forcing their position. Hence, the parameter under
consideration in this simulation is the rate of identification of hostile vehicles. Identifying
these cars and ignoring the messages from them can prevent other hostile aims of these
vehicles. The rate of identification is between 0 and 100, the higher the value, the better the
model will perform. By analyzing the results, it can be seen that 90% of the fake vehicles
were detected with leeks.With the implementation of the simulation in 10 minutes, 8970
messages have been exchanged to identify the position of vehicles, and the percentage of
identification of the hostile vehicles of this system is shown in Figure 5. As you can see, the
mechanism has been able to detect 18 of these 20 hostile vehicles.

Figure 5 The number of hostile vehicles identified by location verification mechanisms
In this simulation, it attempts to implement various behaviors that the car can reproduce
itself. Also, ill-gotten cars change their behavior during their stay in the network, and act in a
way different from what they used to pretend to deceive their position. One of the behaviors
that these cars show up is false switching to move, forging positions to distant points, forging
positions in the ART area, quick jumping from one point to a further point in a short time.
Since the behavior of hostile vehicles is randomly generated in simulation, they may be in a
state of different scenarios of more hostile vehicles, and thus this mode slightly changes the
detection rate. Because hostile cars are identified by conventional cars, the more hostile
vehicles are distributed in the area, the better they are when the hostile vehicles are brought
together. It is obvious that they do not participate in the process of identifying hostile ill-
cared vehicles, and as a result, in an area where there are more or less conventional cars, the
rate of identification of abusive cars rises, and in a region where fewer cars are under review,
low identification rates comes. So, simulations were repeated several times with different
scenarios of 100, 200, 400, 300 and 500 cars, and the results we obtained are as follows.
Figure 6 Percentage of identification of hostile vehicles after several simulations

Abdulhakeem Amer A
One of the important parameters in determining the position of vehicles is whether the
vehicles within the network have adequate safety. Safety in this study means that ill-cared
vehicles cannot do malicious acts. Identifying this is one of the goals of this research or
proving the implementation of this algorithm. When an abusive vehicle attempts to spoil the
situation, it recognizes and identifies the algorithm. One of the reasons to provide simulation
results is the same. The percentage of vehicle identification is at a decent rate, with fewer
sabotage on the network.
7. DISCUSSION AND CONCLUSION
One of the best CNPV position validation algorithms is the protocol, although it has
advantages such as delaying but low accuracy and is able to detect a small number of attacks.
In addition, since the protocol uses a series of tests, such as collective approval of vehicles in
order to identify the situation after the exchange of messages in the first and second rounds, it
certainly needs to exchange excess messages between vehicles. When a car identifies the
location of its neighboring vehicles (on-site machines), it must be ensured that the declared
locations are geographically consistent with real coordinates;hence, in this paper, using
position verification mechanisms instead of CNPV tests that have very little overhead and are
not used in this protocol, we tried to avoid a large proportion of these types of attacks, and to
show that other studies that examined but do not use the messaging mechanism similar to that
of CNPV, it has less computing and messaging overhead, identifies more than 90% of ill-
used vehicles, and reduces ten times the amount of message exchange and improves
accuracy.In this paper, methods for verifying the position of the car network are examined
and these are intended as suggestions for future work: studying the improvement of the
proposed model through the deployment of roadside units in order to achieve the goal of fully
identifying bad cars, the study of the efficiency of the model presented with regard to the
effect of collision and loss of packets, urban scenarios and the number of vehicles, studying
the use of other methods of communication of vehicles such as the use of mobile networks
and radio waves when Communication channel is not available.
NOTES
1. Vehicles in Network Simulation
2. Simulation of Urban Mobility
REFERENCES
[1] Al-Kahtani, M. S. (2012, December). Survey on security attacks in Vehicular Ad hoc
Networks (VANETs). In Signal Processing and Communication Systems (ICSPCS), 2012
6th International Conference on (pp. 1-9). IEEE.
[2] Alsharif, N., Wasef, A., & Shen, X. (2011, June). Mitigating the effects of position-based
routing attacks in vehicular ad hoc networks. In Communications (ICC), 2011 IEEE
International Conference on (pp. 1-5). IEEE.
[3] Cooper, D., Santesson, S., Farrell, S., Boeyen, S., Housley, R., & Polk, W. (2008).
Internet X. 509 public key infrastructure certificate and certificate revocation list (CRL)
profile (No. RFC 5280)
[4] Fogue, M., Martinez, F. J., Garrido, P., Fiore, M., Chiasserini, C. F., Casetti, C., ... &
Manzoni, P. (2015). Securing warning message dissemination in VANETs using
cooperative neighbor position verification. IEEE Transactions on Vehicular Technology,
64(6), 2538-2550.

[5] Greenshields, B. D. A study of traffic capacity, Highway Research Board Proceedings,
Vol. 14. Washington: Transportation Research.
[6] Guette, G. and B. Ducourthial (2007). On the Sybil attack detection in VANET. Mobile
Adhoc and Sensor Systems, 2007. MASS 2007. IEEE Internatonal Conference on, IEEE.
[7] Kargl, F., et al. (2005). Advanced detection of selfish or malicious nodes in ad hoc
networks. Security in Ad-hoc and Sensor Networks, Springer: 152-165.
[8] Leinmuller, T., et al. (2006). "Position verification approaches for vehicular ad hoc
networks." Wireless Communications, IEEE13(5): 16-21.
[9] Papadimitratos, P. P., et al. (2008). Certificate revocation list distribution in vehicular
communication systems. Proceedings of the fifth ACM international workshop on
VehiculAr InterNETworking, ACM.
[10] Trullols, O., et al. (2010). "Planning roadside infrastructure for information dissemination
in intelligent transportation systems." Computer Communications33(4): 432-442.
[11] Viriyasitavat, W., et al. (2010). UV-CAST: an urban vehicular broadcast protocol.
Vehicular Networking Conference (VNC), 2010 IEEE, IEEE.
[12] Dimplepreet Kaur, Tanisha and Amit Verma, A Fast Handoff Technique in VANET to
Decrease Delay in Network Using Mobile Nodes. International Journal of Electronics and
Communication Engineering and Technology, 7(6), 2016, pp. 101–106.
[13] Er. Jayant Vasu, Dr. Gaurav Tejpal and Dr. Sonal Sharma, Adatptive Differential
Evolution Based Attack Detection Protocols for Vanets, International Journal of
Mechanical Engineering and Technology 8(10), 2017, pp. 217–228
[14] Kumar P J, Manikandan N, P Suganya, S Sureka, An Approach for Dynamic Replica
Allocation for Sparse (Urban/Sub-Urban) and Dense Regions (City Side) in VANET.
International Journal of Civil Engineering and Technology, 8(8), 2017, pp. 707–713
[15] Dinu T S and Prof. S Viswanatha Rao, Medium Access Control In Vanet Using Relay
Nodes. International Journal of Advanced Research in Engineering and Technology, 7(4),
2016, pp 88–95.

Improving CNPV Protocol Performance in VANETs

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Improving CNPV Protocol Performance in VANETs

Similar to Improving CNPV Protocol Performance in VANETs (20)

More from IAEME Publication

More from IAEME Publication (20)

Recently uploaded

Recently uploaded (20)

Improving CNPV Protocol Performance in VANETs