1. bookTom 15 (2022): Zeszyt 1 (January 2022)
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Design and implementation of a safety algorithm on V2V routing protocol

Data publikacji: 16 Apr 2022
Tom & Zeszyt: Tom 15 (2022) - Zeszyt 1 (January 2022)
Zakres stron: 1 - 18
Otrzymano: 22 Dec 2021
Informacje o czasopiśmie
License
Format
Czasopismo
eISSN
1178-5608
Pierwsze wydanie
01 Jan 2008
Częstotliwość wydawania
1 raz w roku
Języki
Angielski
Introduction

Intelligent Transportation Systems (ITS) are mechanisms and technologies that are now the subject of intense debate and research within governmental and scientific institutions. Currently, most of the developed countries are focusing on the development of the intelligent transportation systems. Intelligent Transportation Systems concept and model were initially identified in the United States of America (George Dimitrakopoulos and Panagiotis Demestichas, 2010).

ITS support a various types of applications that are essential to the transportation industry. But some of them are still in the research phase while others in the market as a reliable solution. Traffic and fleet management applications are the only used applications in many of the European countries (Wang et al., 2012). Information and communication technologies (ICT) are integrated into ITS and applied to the transportation industry (Shaaban et al., 2021). The sensors and equipment installed in cars are used to collect and gather the data which are managed by the ITS. ITS aimed at making the present transportation system more environmentally friendly, safe, sustainable and efficient. The international market is using the intelligent transportation systems aggressively in the traffic management, road safety management, Real time transportation tracking (George Dimitrakopoulos and Panagiotis Demestichas, 2010).

Intelligent Transportation Systems integrate the connections of the most used communication models for example Vehicles to Internet (V2I) and V2V in order to enrich and utilize the applications and services that are used in this field. For reliable Intelligent Transportation System there is a need for a stable wireless communication technique like the Dedicated Short-Range Communications (DSRC) and on the other side, the deployed onboard units on the vehicles (Kenney, 2011).

There is an urgent need for continuous development of the intelligent transportation systems because of their importance to daily life. The evolution in this field will support and lead to the development of the various sectors that depend entirely on transportation. Intelligent transport programs at the same time face continuous challenges represented in the protection of customer data, data transmission speed, accuracy, and reliability so that it is ready for any network problems because it relates to the lives and properties of civilians.

Improving Intelligent Transportation Systems (ITS) includes many areas such as improving communications between vehicles, improving road infrastructures, and improving the vehicles themselves. Improving vehicles includes more dependent on electric vehicles to save the environment (Kapeller et al., 2021). Improving the road infrastructure includes paying attention to the efficiency of roads and supporting them with various means of communication, such as Road Side Units (RSU) that contribute directly to the communication between vehicles. Improving vehicles to vehicles communications includes improving VANET routing protocols to increase Quality of Services (QoS) and efficiency of ITS (Suganthi and Ramamoorthy, 2020).

ITS include many research directions and communication models; one of them is the Vehicle communication which is part and parcel in this area. Intelligent transportation systems require wireless communications between cars and roadside infrastructure. When cars work together to achieve the same goal, vehicle communication systems become more successful in avoiding accidents. This fact is because cooperative tactics can be useful when cars and roadside stations know the position of other parties (eg location, speed, address). Once a crash or an accident happens, the impacted vehicle communicates the data to other vehicles in the same VANET. Based on that all expected traffic problems will be handled proactively, this is an effective example of this type of safety application (Afzal and Kumar, 2019).

All the VANET communication models like V2V and V2I are depending on a wireless technology called DSRC-Dedicated Short-Range Communications. This wireless technology is considered as the backbone of the VANET. These communication models are working with a maximum range of about 1 KM and assigned band 5.9 GHz.

European Telecommunications Standards Institute (ETSI) developed architecture for the most appropriate protocol, on the other side IEEE created another protocol with a different way of working but supports the same vision and research direction. High node mobility and quickly changing network topologies: Because vehicles drive at different speeds, connections between them may not always be accessible, resulting in high mobility and/or rapid network changes in V2V (Ashley, 2013). Figure 1 describes the city’s holistic view of V2V connectivity.

Figure 1:

V2V Communication model.

Defining the managerial and technological form of handling security issues, human interaction issues, and data transmission issues considerations are all key obstacles in the implementation of V2Vs.

Some studies have begun to combine different technologies with V2V so as to maintain networks running efficiently and securely without using a conventional and stand-alone routing protocol. Imagine a group of automobiles approaching a junction and interacting with one another utilizing V2V technology, and a seamless routing method based on many protocols, rather than using the standard routing protocol. An algorithm includes a set of nodes that decide intelligently, which protocol to support them for the time being. It’s straightforward and respectful (Malik and Sahu, 2019). The main routing protocol determines the status of any network nodes that are absent. A new route goes out of date after 30 seconds and has to be reconstructed. Consequently, leadership is passed around in a random manner to equally manage the function of each node in the network, as the assigned source node entails putting one’s immediate self-interest aside for the greater good.

In our previous research (Yasser et al., 2017) a proposed V2V Implementation based on different VANET routing protocols to select the best protocol which was AODV protocol. But still ITS has some safety problems. To overcome those problems a safety mechanism will be designed over AODV protocol as a VANET routing protocol. The proposed technique depends on a hybrid model which integrate ADOV protocol with geographic one (GPS). Where AODV floods the network with control messages find a path to the target while GPS to search only the region where a path to the target is likely to be found. The simulation done on RIVERBED Modeler which provides scalable modeling and simulation, environment of a broad range of networks.

This research will be organized as follows: Related works are presented in the section “Related work”. Categories of VANET routing protocols are discussed in the section “Vanet routing protocols categories”. In the “Proposed V2V implementation” the proposed safety technique over AODV is introduced. The RIVERBED Modeler simulation environment is described in the section “Simulattion environment”. The simulation results which indicate how to select proposed routing protocol selection phase and Geographic’s protocol selection phase are presented in the section “Geographic routing protocol selection phase”.

Related work

Many researchers have proposed many different ways to improve the VANET routing protocol to increase its efficiency.

Yasser et al. (2017) proposed suitable V2V Implementation solution for developing countries which didn’t have RSUs infrastructure. A full simulation for different VANET routing protocols to select the best protocol. From that simulation results, AODV without RSU was better in most of the KPIs than other routing protocols. But still ITS has some safety problems.

Conglin Ran et al. proposed a method to improve routing security of AODV based on block chain technology (Ran et al., 2021). That method depends on a multipath quality of service (QoS) routing security algorithm based on block chain. But that technique is an on-demand QoS routing security protocol. Also that method did not study many aspects such as energy distribution, data transmission and encryption.

Tabar et al. (2016) used the position based routing to increase the QoS via reducing the delay and packet losses of the network. They used DSDV (Destination Sequenced Distance Vector) and AODV protocols but that work still need extra work to reduce the delay of the network.

Dong et al. (2014) have developed enhancing performance for the route selection. In this method, they used swarm intelligence algorithm to enhance the performance of conventional AODV algorithm.

Sindhwani et al. (2022) presented improved technique to AODV routing protocol. This method was ant colony optimization which was applied with the initial coordinates of each node. They built approach based on establish path from source to destination using multicasting technique.

From this survey, most of current solution still has some limitations in bandwidth wastage, network throughput, routing processing overhead, data losses and packet deliver. The proposed method will integrate topology with geographic based routing protocols to overcome these limitations and establish reliable V2V communication as will show the following proposed mechanism.

Vanet routing protocols categories

VANET networks require a complicated and dynamic routing protocol because of the tough nature of the network. These VANET protocols divided into two main categories, topology-based and geographic-based routing protocols (Singh et al., 2017). Some of these protocols are based on location in case of limited resources in the infrastructure or the classic and traditional approach that is based on the topology mechanism and the other category is geographic based routing protocols if every node has information of their location through infrastruce components like GPS as shown in Figure 2.

Figure 2:

VANET routing protocols.

Topology based routing protocols

To send data packets between nodes through the VANET, topology-based routing techniques leverage connection information. The techniques of the VANET routing protocols are based on two ways. The on demand way, this is called reactive method. The table driven way is called the proactive method (Yelure and Sonavane, 2019).

Ad hoc on-demand distance vector (AODV)

AODV is not initiating any communication based on the reactive approach unless a vehicle needs to transmit information to other vehicles in the VANET. It is recommended in the environments that are facing limited resources and high volume of dynamic nodes. This routing protocol has a unique capability which is not available in other routing protocols, which is the sequence number of each destination that can be used as multimode based or single mode based (Perkins et al., 2003).

The on demand technique, which is based on communicating the data only whenever there is a need for that. It is the used mechanism for the AODV. The control message of the Route Request is used to define the path when a node needs to initiate a connection. When such communication will start, an initial delay will appear as an output, and control traffic overhead will be dynamic. When the message (RREQ) is delivered to an intermediate vehicle or RSU with a direct connection to the destination vehicle or having enough acceptances to the destination vehicle through the network, so this means that there is an existent route. AODV remains with no action if a path is already established between the nodes, and AODV will submit a new request if any route becomes lost or invalid (Soni and Srivastava, 2016).

The servicing of the path is managed by the control messages as shown in Figure 3.

Figure 3:

AODV control messages (Elbadr and Zubi, 2013).

Geographic based routing protocols

When using location-based apps, geographic-based routing protocols rely on algorithms that deal with the positioning mechanism. The route nomination is based on the provided information by these applications. Furthermore, no databases holding routing data or information about the join state of neighboring nodes are provided by these protocols (Karp and Kung, 2000).

Greedy perimeter stateless routing (GPSR)

It includes many techniques but the most used protocol is the one that is using the seamless greedy method. This protocol is called Greedy Perimeter Stateless Routing (Bala and Krishna, 2015). It consists of a regular greedy forwarding mode and a perimeter forwarding recovery approach that is employed when a local optimum arises. Figure 4 shows a simple example of GPSR routing technique.

Figure 4:

GSPR routing technique (Rao et al., 2008).

As illustrated in Figure 5, x wishes to send or receive a packet addressed to D. x is going to communicate with y as per the routing protocol decision, as the range from the nearest nodes to x to D is higher than the range from D to y.

Figure 5:

GSPR routing technique (Seet et al., 2004).

Until the packet reaches D, the greedy forwarding procedure is repeated. When a local optimum is reached, GPSR’s perimeter forwarding employs the well-known right-hand rule for graph crossing. x is closer to D than its neighbors w and y, as seen in Figure 6.

Figure 6:

GSPR routing technique (Seet et al., 2004).

The radius of the dotted arc on D is equal to the distance between x and D. If two pathways exist at D, (x → y → z → D) and (x → w → v → D), then x will not use the greedy technique to transmit to w or y. In relation to D, x is a local optimum. Consequently, the right hand rule seeks to avoid this local optimum by browsing a virtual arc (which links the local optimum’s node to the destination node) in the opposite direction of a clock hand to search for the vehicle that is located in the nearest location to D than the local optimum’s node. In this case, node w will act as the contender and the data will be communication through the path (x → w → v → D) (Zhao and Cao, 2008).

Vehicle-assisted data delivery (VADD)

As shown in Figure 7, VADD is a vehicular routing strategy that leverages the carry-and-forward approach to decrease end-to-end delivery delay in scattered VANET networks from a static node to a moving node. The concept of VADD is based on the use of predictable vehicle mobility that is limited by traffic patterns and road layouts. The cars are expected to include pre-loaded digital maps that give a street-level map as well as traffic information such as traffic density and vehicle speed on roadways at various times of the day. Each of the three packet modes in VADD is dependent on the position of the node carrying the packet: Destination, Straightway, and Intersection (Shafiee and Leung, 2011).

Figure 7:

VADD routing technique (Shafiee and Leung, 2011).

Connectivity-aware minimum-delay geographic routing (CMGR)

Whenever there is a scattered VANET, CMGR utilizes the connection of the roads to optimize the possibility of packet acceptance and reduce the latency by picking non-congested highways with sufficient connectivity. Any node that needs to initiate a path to any GW creates a Route Discovery (RD) message then communicates it through the network. The GW picks the most appropriate RD based on the selection of the most appropriate path and creates a route reply (RR) message from all the RDs received at a GW for the same query but originating from various routes. If a local optimum is found, the RR is eagerly transmitted back to the target vehicle via the designated path, or it is carried using the carry-and-forward strategy.

When the RR is returned to the target vehicle, if it has moved from its original position, a tracking system is suggested, in which the target vehicle must broadcast its new velocity vector in its beacon packet before moving. According to the simulation findings, CMGR’s packet delivery ratio is around 25% better than VADD and A-STAR for high vehicle densities and up to 90% better for low vehicle densities. However, if the target vehicle is in a sparse location, the suggested tracking technique would almost surely fail since no traces of the vehicle will be identified (Boussoufa-Lahlaha et al., 2018).

Distance-vector-based recovery-strategy (PBR-DV)

The Position Based Routing with Distance-Vector combines the benefits of greedy routing with those of the Distance-Vector-based strategy that is used in the reactive method in the topology based routing protocol. A PBR-recovery DV approach is used when a local optimum exists. PBR-DV shifts to AODV mode when a packet reaches a local optimum, in which the local optimum’s node sends a route request (RREQ) message to search for the destination node’s shortest distance rather than itself. The vehicle communicates a route reply message, whenever the local optimum is not available in the node. Local optimum’s node receives the route reply message using its back path. Consequently, the data packet’s route is formed on such a path. The simulation findings reveal that when comparing PBR-DV to AODV, the quantity of packets delivered with PBR-DV is much larger. However, significant flooding is necessary for the non-greedy phase, which creates network congestion (Kirsch and Effelsberg, 2007).

Proposed V2V implementation

Although AODV protocol has many advantages as a VANET routing protocol like, the most recent path to the target node is provided by the Distance Sequence Number (DSN), route redundancy and memory needs are kept to a minimum, any failure in the VANET connections is dealt with quickly by this protocol, and in large VANET networks, this protocol may be employed. Comparing AODV protocol with other VANET routing protocols we select this protocol for V2V implementation as a suitable solution for developing countries (Boussoufa-Lahlaha et al., 2018).

But it has some disadvantages when using this protocol like: when old items are included in intermediary nodes, route inconsistency might arise, an expected delay is going to be occurred at the beginning of the first trial to initiate a path which is not the case in other methods, when several route reply packets for a single path are sent, there is a lot of control overhead, and it consumes more bandwidth because of proactive beaconing.

To overcome some of these limitation of this AODV protocol, the proposed method in this paper will depend on the integration between this protocol with the best one from geographic-based routing protocol as will be shown in the following proposed technique.

The main objective of this research paper is merging a potential geographic protocol with the current ITS architecture to overcome the infrastructure problems related to using topology based routing protocols only.

The telematics unit of V2V communications employing DSRC and GPS devices in each vehicle are used in the proposed ITS, which rely on WiFi connection. The system may also use preconfigured GSM/GPRS to interact with servers and traffic centers.

The proposed ITS architecture uses a hybrid method of topology (ADOV) and geographic routing protocols to offer a secure, reliable and real-time communication between vehicles to serve under all conditions. It overcomes all problems related to scarce resources and geographic obstacles as shown in Figure 8.

Figure 8:

Proposed ITS architecture.

There will be two steps to identify the proposed ITS network after choosing AODV topology routing protocol to overcome its limitations in ITS implementation:

First Phase: Choose the most functioning Geographic routing protocol on the V2V simulation environment.

Second Phase: Using this suggested Geographic routing protocol, compare the present and planned ITS architectures. As illustrated in Figures 10 and 11, the RIVERBED modulator and NS2 simulator will be utilized in this research. RIVERBED is a fully fledged network simulator that is mainly used for testing the performance of the networks using simulated events in order to get the optimum network design based on those events and KPIs. The networks are regulated using a hierarchical framework.

In phase 1, the most common Geographic routing methods will be compared in a V2V configuration based on a heavy traffic scenario, and the optimal Geographic routing protocol will be chosen based on the KPIs. Phase 2 mainly will compare the standard V2V architecture that is currently used with the new one that is based on combining with the proposed geography routing protocol and see how each solution is going to react with the simulation environment.

The V2V environment that will be used in the simulation will be very dynamic and includes many challenges in order to measure the KPIs in a proper way and get accurate results.

In the first phase, High traffic scenario is used (40 random speed vehicles) with four different geographic routing protocols to get the best operating one in these conditions in terms of traffic received, load, dropped, retransmission attempts, throughput and delay. These selected four geographic routing protocols are:

Greedy Perimeter Stateless Routing (GPSR).

Vehicle-Assisted Data Delivery (VADD).

Connectivity-aware Minimum-delay Geographic Routing (CMGR).

Distance-Vector-Based Recovery-Strategy (PBR-DV).

Based on simulation results (as will be shown in simulation section), the selected Geographic routing protocol “PBR-DV” will be utilized in the second phase to evaluate the V2V implementation using AODV with and without Geographic Routing Protocol utilizing a heavy traffic scenario to get the best operating one in these conditions in terms of traffic received, load, dropped data, retransmission attempts, throughput, and delay.

The suggested ITS architecture was chosen based on its ability to address real-world implementation issues and to be compared to other existing deployed alternatives. In order to clearly express where the proposed ITS architecture stands in contrast to the other alternatives, a unified set of standard key performance indicators were used to identify the strength and weakness points for each architecture.

Proposed method criteria

The proposed method depends on the following criteria to choose the best VANET routing protocol.

Scalability

The scalability of routing protocol can be measured via measuring routing traffic (packets per second) which sent by each node in the network. To able to check the network is loaded or not.

The traffic routing (Tr ) in the network can be calculated from this expression: T r = P n ( P n r + P n l ) (1)where Tr is the traffic routing, Pn is the number of packets, Pnr is the number of packets received, and Pnl is the number of lost packets.

Security

It is a mandatory requirement; the transportation sector does not accept any tolerance as it impacts directly the lives of the citizens, therefore a routing protocol flaw is rare.

Effectiveness

It was necessary to ensure that data are transmitted between cars in various Hops with the least amount of data loss, meaning efficient resource utilization.

Reliability

The main objective of this criteria is to find the most operating protocol that manages the lowest possible number of faults; failing to send real-time data will also have a negative impact on the V2V network’s performance.

Performance

The performance of the routing protocol is a very crucial aspect. It measures how efficient the routing protocol is in terms of the dynamic and challenging network, it operates in.

Consistency

The most essential discovery in these criteria is how to check that V2V routing is processing at 100% all of the time, as well as how to estimate average response time, latency, and data accuracy to assess whether the V2V network is running well or requires repair.

Standardization

The purpose of this section’s comparison was to discover the most popular routing protocol that required the fewest modifications to its default settings while still providing the highest performance; this is beneficial in real-world implementations since fewer alterations guarantee better operating conditions.

VANET delay

Delay is the time consumed by a packet to path from a source to destination through the network. D = ( T r i T s i ) 1 [ S e c ] , (2)where D = Delay “VANET”, i = identifier “Packet”, Tri = Time of Reception, and Tsi = Time of Sending.

VANET throughput

It is calculated in packets per unit Time Interval Length. A v e r a g e T h r o u g h p u t = ( r e c v d S i z e / ( s t o p T i m e s t a r t T i m e ) ) , (3)where recvdSize = Store received packet’s size, stopTime = Simulation stop time, and startTime = Simulation start time.

VANET retransmission attempts

It is the number of retransmitted packets by source to the destination through all routes. R e t r a n s m i s s i o n A t t e m p t s = T o t a l n o . o f A t t e m p t s S u c c e s s f u l A t t e m p t .

VANET dropped data

It is the network packet’s number of that lost their way to the destination from the total packet’s number generated by the source. D r o p p e d D a t a = n S e n t P a c k e t s n R e c e i v e d P a c k e t s , (5)where nReceivedPackets = Number of received packets and nSentPackets = Number of sent packets.

VANET load

It is equivalent to the data transfer rate on the VANET network between all its components in a predefined period. V A N E T L o a d = N T / N S , (6)where N T is the number of bits transferred on the Network and N S is the Simulation Period “predefined period”

VANET traffic received

It is the ratio between the number of packets transmitted from the source and the of number of packets received at the destination. V A N E T T r a f f i c R e c e i v e d = Σ ( T p r ) / Σ ( T p s ) , (7)where T pr is the total packets received by all destination node, and T ps is the total packets send by all source node.

Based on these previous KPIs, we will do a comparison between different four geographic-based VANET routing protocols to select the best one. Then use this best one to integrate it with AODV to build the proposed mechanism as will be shown in the following simulation.

Simulattion environment

The main purpose of this simulation is to find a practical solution for the current challenges in the V2V environments in order to comply with the strict operating conditions of the ITS. A High Traffic scenario will be used in this simulation in order to fit with the real implementation including its challenges. First; a geographic routing protocol will be chosen after getting the results of the predefined KPIs of the simulation from more than one location based routing protocols. The second step is to use the same simulation environment on two different routing protocols, a standard VANET routing protocol without the chosen Geographic Routing “AODV” and the other one with the chosen Geographic Routing “AODV + PBR-DV”. Based on the output of the second step, the research will be able to prove if applying a security algorithm using an additional geographic routing protocol over the standard topology routing protocol will improve the performance and reliability of the V2V network or not (Figure 9).

Figure 9:

High traffic scenario—NS2.

The simulation is using the most popular network modulators that are designed to test the VANET routing protocols and how they act with the continuous and dynamic events that occur in the network. These network modulators are aiming to utilize the performance of the VANET network from different aspects like delay and throughput. After applying the enhancements on the simulator, and get the desired outcome. The final layout can be extracted from the simulators in order to be applied on the actual implementations. The used network simulators in this paper are NS2 and RIVERBED as shown in Figures 10 and 11. RIVERBED will be used to support the large scale of the simulation environment which includes very dynamic moving nodes. NS2 will be used for emulation functionalities and parallel simulation (Chambers, 2016).

Figure 10:

High traffic scenario—RIVERBED.

Figure 11:

VANET delay.

Nodes are randomly installed in a 9,000 × 1,000 m region to determine the VANET routing protocol phase. RIVERBED, a network simulator, is used for simulations. A transceiver is installed in each node. Radio waves with a broadcast range of 250 m are used to communicate between nodes. Vehicles will be considered as the moving nodes with varied speeds. 2 Mbps is the used bandwidth of the channel. Table 1 shows the simulation parameters utilized in the simulation.

Simulation parameters.

Parameter Value
Total simulation time 12 min
Simulation area 9000 m × 1000 m
Total no. of vehicles 50 vehicles
Vehicles mobility Random starting from 0 to 40 km/hr
Mobility model Random waypoint model
Number of lanes 2
IEEE 802.11p data rate 1 Mbps
Channel bandwidth 2 Mbps
Packet size 512 bytes
Transmission range per hop 250 m
Node processing delay sec
Geographic routing protocol selection phase

Selecting the most geographic-based routing protocol to integrate with AODV using previous simulation tool depend on the KPI discussed in the previous proposed method. These KPI include Delay, throughput, retransmission attempts, dropped data, load, and traffic Received. The results of these simulation will be shown in detail in the following section.

VANET delay

The PBR-DV routing started the simulation as the highest delay in comparison with other protocols due to the reactive mode which is the main drive for this protocol. After initiating the proper paths in the network, PBR-DV became the lowest delay which is a good sign for a sustainable performance. On the other side, VADD routing protocol was impacted severely by the delay due to the unpredictability of the nodes, as shown in Figure 11.

VANET throughput

The PBR-DV and GSPR routing protocols have the highest throughput, as shown in Figure 12, meaning that they are the best in terms of unit processing for a given duration. The VADD protocol had a medium throughput, whereas the CMGR protocol had the lowest.

Figure 12:

VANET throughput.

VANET retransmission attempts

The VADD routing protocol started the simulation with the lowest retransmission attempts because of the low count of the moving nodes, which gave it a competitive edge. Once the count starts to be high with the unpredicted behavior, it did many retransmission trials to catch up with the high mobility in the network. For PBR-DV, it took some time to be in a better shape in terms of retransmission attempts due to the on demand technique, as shown in Figure 13.

Figure 13:

VANET retransmission attempts.

VANET dropped data

The PBR-DV routing protocol is the best in terms of the dropped data as it builds the routes based on a clear understanding for the location of the moving nodes in the network. GSPR and CMGR routing protocols are facing a high rate of dropped data due to using the recovery mode consistently, as shown in Figure 14.

Figure 14:

VANET dropped data.

VANET load

In terms of load, The VADD and PBR-DV routing protocols are taking advantage of their designed algorithm to keep the load as minimal as possible. While GSPR and CMGR routing protocols are facing clear problems with the load because of the frequency tracing mechanism that leads to intensive use of the network’s resources as shown in Figure 15.

Figure 15:

VANET load.

VANET traffic received

The CMGR routing protocol has the lowest traffic received due to the continuous tracing, which is a key feature in the design of this protocol. The other three routing protocols have a high traffic received due to many reasons like the greedy method, partitioning issue and on demand procedure that act as a burden on those routing protocols, as shown in Figure 16.

Figure 16:

VANET traffic received.

Based on the simulation output, the PBR-DV is the best VANET Routing Protocols in this scenario that is based on a high traffic with unpredicted behavior for the moving nodes with different speeds. The PBR-DV supports the concept of using a functional routing protocol that can be used under many and different conditions. The PBR-DV is the geographic routing protocol in terms of delay, throughput, and retransmission attempts (Table 2).

Summarize comparison between different protocols.

KPI PBR-DV VADD GSPR CMGR
VANET delay Very low High Low Low
VANET Throughput Very high High Very high Low
VANET retransmission attempts Very low Very high High Low
VANET dropped data Very low Very High Very high Very low
VANET load Very low Very high Very low Very high
VANET traffic received Very low Very low Very low Very low
Proposed routing protocol selection phase

After selecting PBR-DV as the best geographic-based routing protocol to integrate with AODV protocol, we need compare this integrated proposed mechanism with standalone AODV. We will use the same KPIs which used in first phase of the proposed method. The results of these simulation will be shown in detail in the following sections.

VANET delay

The AODV + PBR-DV routing protocol started with a high delay in order to initiate the paths in the network. Once the paths are working properly as shown in Figure 17, the delay became lower than the delay of the standalone AODV routing protocol. The AODV + PBR-DV routing protocol uses two methodologies at the same time to apply a mapping table for the network based on the node locations.

Figure 17:

VANET delay.

VANET throughput

The regular AODV protocol is shown to be higher than the AODV + PBR-DV routing protocol, since failure messages appear in a greater trend owing to the simultaneous usage of AODV and PBR-DV employing two separate methodologies, as illustrated in Figure 18.

Figure 18:

VANET throughput.

VANET retransmission attempts

The AODV + PBR-DV routing protocol was determined to have the lowest Retransmission because it can check connection with all nodes, as opposed to the regular AODV protocol, which cannot verify connection with all nodes owing to geographic limits, as shown in Figure 19.

Figure 19:

VANET retransmission attempts.

VANET dropped data

The AODV + PBR-DV routing protocol was discovered to have the lowest Dropped Data. As demonstrated in Figure 20, the data in this protocol would be able to reach the nodes that are experiencing geographic issues in the traditional AODV protocol.

Figure 20:

VANET dropped data.

VANET load

Because the control messages transmission rate in the AODV + PBR-DV routing protocol is greater than the rate in the AODV + PBR-DV routing protocol, the AODV standard routing protocol has the greatest Load, as shown in Figure 21.

Figure 21:

VANET load.

VANET traffic received

The AODV + PBR-DV routing protocol was discovered to have the greatest Traffic Received, since it is a mixed routing protocol strategy that manages the routes frequently rather than waiting for a broadcast to be transmitted, as seen in Figure 22.

Figure 22:

VANET traffic received.

The main objective of the paper was proved by the results of the simulation. The PBR-DV geographic routing protocol was able to support and overtook the weakness points in the stand-alone AODV routing protocol in terms of delay, retransmission attempts and load. The AODV + PBR-DV routing protocol is a reasonable choice for the real tough transportation environments that include many challenges like geographic obstacles and infrastructure limitations (Table 3).

Summarize comparison between proposed and AODV protocol.

KPI AODV AODV + PBR-DV
VANET delay Low Very low
VANET throughput Very high Very high
VANET retransmission attempts Low Very low
VANET dropped data Low Low
VANET load Very high Very high
VANET traffic received Very high Very high
Conclusions

Vehicle to Vehicle Communication now faces many challenges related to the undeveloped state and limitations in the network resources which show and reflect dereliction in the credibility aspect. Therefore, using a supporting communication infrastructure like the geographic routing protocol beside the normal topology routing protocol, which will be an additional safety factor for these networks. Based on the paper’s results, it was clear that the V2V communication model became better when using two routing protocols with different techniques at the same time to close the gaps in terms of network throughput, delay and retransmission load.

The proposed VANET routing protocol depends on integrating one of topology routing protocol (AODV) with one of geographic routing protocol to improve AODV. Improving AODV routing protocol using was based on finding the most acceptable geographic routing protocol in all KPIs from the paper’s perspective, then integrate it with AODV topology based protocol.

Based on the simulation output, the PBR-DV is the best VANET Routing Protocols in this scenario that is based on a high traffic with unpredicted behavior for the moving nodes with different speeds. The PBR-DV supports the concept of using a functional routing protocol that can be used under many and different conditions. The PBR-DV is the geographic routing protocol in terms of delay, throughput, and retransmission attempts.

The PBR-DV geographic routing protocol was able to support and overtook the weakness points in the stand-alone AODV routing protocol in terms of delay, retransmission attempts and load. The AODV + PBR-DV routing protocol is a reasonable choice for the real tough transportation environments that include many challenges like geographic obstacles and infrastructure limitations.

Figure 1:

V2V Communication model.
V2V Communication model.

Figure 2:

VANET routing protocols.
VANET routing protocols.

Figure 3:

AODV control messages (Elbadr and Zubi, 2013).
AODV control messages (Elbadr and Zubi, 2013).

Figure 4:

GSPR routing technique (Rao et al., 2008).
GSPR routing technique (Rao et al., 2008).

Figure 5:

GSPR routing technique (Seet et al., 2004).
GSPR routing technique (Seet et al., 2004).

Figure 6:

GSPR routing technique (Seet et al., 2004).
GSPR routing technique (Seet et al., 2004).

Figure 7:

VADD routing technique (Shafiee and Leung, 2011).
VADD routing technique (Shafiee and Leung, 2011).

Figure 8:

Proposed ITS architecture.
Proposed ITS architecture.

Figure 9:

High traffic scenario—NS2.
High traffic scenario—NS2.

Figure 10:

High traffic scenario—RIVERBED.
High traffic scenario—RIVERBED.

Figure 11:

VANET delay.
VANET delay.

Figure 12:

VANET throughput.
VANET throughput.

Figure 13:

VANET retransmission attempts.
VANET retransmission attempts.

Figure 14:

VANET dropped data.
VANET dropped data.

Figure 15:

VANET load.
VANET load.

Figure 16:

VANET traffic received.
VANET traffic received.

Figure 17:

VANET delay.
VANET delay.

Figure 18:

VANET throughput.
VANET throughput.

Figure 19:

VANET retransmission attempts.
VANET retransmission attempts.

Figure 20:

VANET dropped data.
VANET dropped data.

Figure 21:

VANET load.
VANET load.

Figure 22:

VANET traffic received.
VANET traffic received.

Simulation parameters.

Parameter Value
Total simulation time 12 min
Simulation area 9000 m × 1000 m
Total no. of vehicles 50 vehicles
Vehicles mobility Random starting from 0 to 40 km/hr
Mobility model Random waypoint model
Number of lanes 2
IEEE 802.11p data rate 1 Mbps
Channel bandwidth 2 Mbps
Packet size 512 bytes
Transmission range per hop 250 m
Node processing delay sec

Summarize comparison between different protocols.

KPI PBR-DV VADD GSPR CMGR
VANET delay Very low High Low Low
VANET Throughput Very high High Very high Low
VANET retransmission attempts Very low Very high High Low
VANET dropped data Very low Very High Very high Very low
VANET load Very low Very high Very low Very high
VANET traffic received Very low Very low Very low Very low

Summarize comparison between proposed and AODV protocol.

KPI AODV AODV + PBR-DV
VANET delay Low Very low
VANET throughput Very high Very high
VANET retransmission attempts Low Very low
VANET dropped data Low Low
VANET load Very high Very high
VANET traffic received Very high Very high

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