IRTIV: Intelligent Routing Protocol Using Real Time ... - IEEE Xplore

1 downloads 5 Views 364KB Size Report
IRTIV: Intelligent Routing protocol using real time ... THE designing of routing protocols is a challenging task especially ... routing protocols in vehicular networks.

IRTIV: Intelligent Routing protocol using real time Traffic Information in urban Vehicular environment Omar Sami Oubbati∗ , Nasreddine Lagraa∗ , Abderrahmane Lakas† and Mohamed Bachir Yagoubi∗ ∗

Laboratory of Computer Science and Mathematics, University of Laghouat, Algeria {s.oubbati, n.lagraa, m.yagoubi}

College of Information Technology, United Arab Emirates University, PO Box 17551,Al Ain, UAE {alakas}

Abstract—Routing in vehicular Ad hoc networks is a challenging task due to the high mobility of nodes and the network fragmentations. The challenge is most important in urban environment where many constraints are added like multipath and the presence of obstacles. In this way, many protocols are proposed. In this paper, we introduce a new position-based routing scheme called IRTIV, conceived exclusively for inter-vehicle communication in a city environment, the proposed protocol aims to find the shortest connected path to the destination by taking into account the real time traffic variation which is estimated by a completely distributed manner based on the periodic exchange of Hello messages. Simulation results show that the proposed protocol increases the packet delivery ratio and reduces the end to end delay. Keywords—VANets, Routing, Urban environment, Real time traffic estimation, Greedy Forwarding.





designing of routing protocols is a challenging task especially in urban environments, where particular characteristics have to be taking into consideration like the distribution of the vehicles, and the pre-existing roads and obstacles that provide several challenges to overcome. To deliver data packets successfully to the destination in a reduced delay, all the proposed protocols [1]–[5] in this context, try to solve the problem of frequent disconnection of the network by finding the most connected end to end path. To this end, they assume that each vehicle is equipped with a Global Positioning System (GPS) and most of them use a digital map [1], [2], [4], [5]. The Map useless, can lead certainly to reduce the number of packets that will reach their final destination, due to the radio obstacles (buildings ), like in [3]. However, only a few protocols take into consideration the information of the traffic density [1], [2], which is an important factor that can enhance the performance and deals with the problem of disconnection. The traffic density computing can be achieved in a distributed mechanism [2] based on the traffic information exchanged, that will certainly generate a high overhead leading to the network congestion. In this work, we present a new routing protocol for urban vehicular Ad hoc networks called IRTIV, the solution is completely distributed and doesn’t need any pre-installed infrastructure, or additional packets. The protocol is working in the way of proactive protocols in the case of connected networks and it contains three components (Carry & Forward, Greedy Forwarding and Path Selection). The keystone of the HE

path selection component is to choose the most connected segments by calculating the segment density. This can be achieved in a real time manner by exploiting the periodic exchange of hello messages and adding to them only a few bytes describing the status of the road. This can reduce the end to end delay and the packet losses with no control messages, the protocol is able to find the most connected and the shortest path (By using the Dijkstra Algorithm in terms of the distance from the source to the destination) and tends to avoid a path that can be quickly broken in the presence of other choices. The rest of this paper is organized as follows. In section II we give an overview on the related work. In section III we detail our approach. The performances of the proposed protocol are evaluated in section IV, and finally, some concluding remarks are given in section V. R ELATED W ORKS

Nowadays, it’s obvious that Position-based routing protocols are the most popular in vehicular networks due to the assumption of normalization of GPS in ITS (Intelligent Transportation Systems). Many protocols were proposed in the last decade to deal with the unique characteristics of VANETs, especially in urban environments where obstacles prevent direct communications. These protocols can be classified into three categories as follows: A. Reactive Based Routing Like in classical Ad hoc networks, when a vehicle wants to communicate with another vehicle, must install a path if no communication was established before. Anchor-Geography based routing Protocol (AGP) [1], uses the reactive broadcasting for both finding the destination location and installing a data delivery path. To deal with the mobility of the destination, a forwarder can use a trajectory prediction based on additional information (velocity and motion direction of destination) included in data packets. In addition, depending on the number of route requests receiving by the destination, It calculates a weight for the different paths representing the lowest delay. The one obtained the high weight is chosen as the elected path. B. Greedy forwarding Based Routing The greedy forwarding is the most adopted approach by routing protocols in vehicular networks. It consists to send

978-1-4799-3223-8/14/$31.00 ©2014 IEEE

Lj =1 , is reachable R j =1 , is reachable


R j =1 , is reachable Lj =1 , is reachable










Lj =1 , is reachable R j =0 , is not reachable

R j =1 , is reachable Lj =1 , is reachable



Z I nt ersect ion

R j =1 , is reachable Lj =0 , is not reachable

Select -Vehicle

Broadcast er Vehicle Fart hest Vehicle Ordinary Vehicle

Fig. 1: Calculating of the density

a data packet to the farthest vehicle allowing reaching the destination or the next closest intersection to the destination like in the Greedy Perimeter Coordinator Routing (GPCR) [4], but it doesn’t avoid the problem of local optimum. To overcome such problem, A-STAR [5] (Anchor-based Street and Traffic Aware Routing) uses the street map to compute the sequence of intersections (anchor) through which a packet must pass to reach its destination. It takes into account the city buses routes information to identify anchor paths with higher connectivity. However, this technique can suffer from problems of connectivity in a certain time period. C. Real-time traffic Based Routing In this category, vehicles located in the dense connected segment of roads, between a source and a destination, are preferred as forwarders. To this end, two assumptions are taken into consideration: (i) the use of a location service, such as the Grid Location Service (GLS) [6], to discover the destination position and, (ii) the availability of the vehicles density of neighboring segments of roads using different techniques. GyTAR [2] (an improved Greedy Traffic aware Routing Protocol), uses a dedicated multihop packet called CDP (Cell Data Packet) generated by each vehicle leaving a road segment and reaches an intersection, in order to calculate the number of vehicles between two successive junctions. A vehicle wants forwarding a packet, calculates for each segment that allows reaching the destination a score based on the distance and the density. Then, it sends the packet with the road segment obtained the highest score. The main drawback of this protocol is the problems caused by the transmission of CDP like the added overhead, channel collision and channel utilization. III.


We present in this section our proposed routing protocol for vehicular networks in city environments. The main objective of this protocol is to find the shortest connected path in a high dynamic network. In the case of presence of vehicle in an intersection, the decision of routing is taken instantaneously by this vehicle, in the way of proactive routing protocols, based on a weight calculated permanently and in real time for each segment. A technique of carry & forward is used in other case. A. Hypothesis For the right functionnality of the protocol, we assume that:

All the vehicles are equipped with a Global Positioning System (GPS).

The position of the destination is known by the source vehicle thanks to the GLS[6] .

A digital map is used by any vehicle to locate the neighboring path junctions.

A table of neighbors is created and updated priodically by all vehicles.

The hello message is modified by adding new fields in order to allow nodes to calculate the number of vehicles and the connectivity between two successive junctions.

B. Functionality of the protocol The proposed Intelligent Routing protocol using real time Traffic Information in urban Vehicular environment (IRTIV) can be running in three steps: (i) first, the traffic is estimated continuously by all nodes in junctions, then following to this, (ii) a path is selected, and finally (iii) the data is sending with the shortest and the most connected path. 1) Real time traffic estimation: The traffic density on the road segment is calculated in a distributed manner using modified Hello messages (cf. Figure 2) that allow to calculate the total number of vehicles between two junctions and to set the connectivity flags. The new Hello message contains the number of direct neighboring vehicles located in the right (DRN) and the left (DLN), the total number of the vehicles until the next intersection located in the left (VL) and in the right (VR) and two flags (Lj & Rj ) to indicate the connectivity between two successive intersections (cf. Figure 1). To calculate the density of a road segment, each vehicle broadcasts periodically the new modified hello message format. Upon receiving a hello message from the farthest neighbor, a vehicle calculate its VL and VR (the total number of the vehicles until the next intersection located in the left and resp in the right) using the following equations 1 : V L = DLN + V Lf arthest V R = DRN + V Rf arthest

vehicle vehicle


So, vehicles located in the intersections (X, Z, Y and H in figure 1), where the decisions of routing are taken, they construct a base of knowledge about the connectivity. Suppose that vehicle H (cf. Figure 1) has a data packet to send, it has a global vision of all segments around and where is

Hello Message Format Frame Control

2 Bytes

Duration Adress1 (DA) Source adress


2 Bytes

6 Bytes

Pos 4 Bytes

6 Bytes

6 Bytes

Seq Control HT control

2 Bytes

4 Bytes

Additional Fields DRN VL VR 1 Byte 2 Bytes 2 Bytes

DLN 1 Byte

Frame Body


0 – 2312 Bytes 4 Bytes

Lj 1 bit

Source/ Forwarder

Rj 1 bit D w = 300

Lj 0 0 1 1

Rj Meaning 0 There are no connectivity on the path 1 There are connectivity on the right only 0 There are connectivity on the left only 1 This path is connected

Forwarder D w = 100

Dest inat ion

Fig. 2: Hello message format D w = 200

the appropriate segment for forwarding based on the weight given for each segment. Vehicle H intercepts a hello message broadcasted by vehicle E, in which (cf. Figure 3), it will learn, the number of the vehicles and the connectivity information of the segment. Based on the intercepted Hello message, the vehicle H will deduct that this path is disconnected and cannot reach the other side of the segment (left junction, Lj = 0) and consequently this segment will be not selected to transit the data packet. Pos














Fig. 3: An intercepted hello packet

2) Path Selection: To ensure delivering a data packet to a destination, IRTIV uses Greedy Forwarding and Carry & Forward mechanisms according to the situation of the network, the path selection is used uniquely on the intersections (junctions) because they are the only places where a routing decision is taken based on the traffic density estimation and the shortest path to the final destination. When a vehicle enters an intersection area, it calculates a weight for each road segment based on traffic density and Dijkstra algorithm. The one with the highest weight that means there are enough vehicles providing connectivity and it is the nearest road segment to the destination (cf. Figure 4). The weight is calculated as follows: W eighti =

N B − V ehicles · (Lj × Rj ) Dw


Where N B − V ehicles = (V L or V R) + 1.


and Dw is the Dijkstra weight to the destination in term of distance. Under a low vehicle density, the forwarders on intersections don’t learn the connectivity information and the number of vehicles in certain segments, in this case the vehicle will base only on the Dijkstra weight for such segments that didn’t provide their connectivity information and their total number of nodes., Figure 4 shows one such scenario, when the source vehicle calculates the Dw for each segment. Based on the velocity vector the source vehicle decides to carry the packet until the next intersection because its velocity vector is towards the shortest path to the destination.

: Ordinary Vehicle

: Dest inat ion Vehicle : Carry & Forward

: Source/ Forwarder Vehicle

: Int ersect ion (junct ion)

: Greedy Forwarding

Fig. 4: Path Selection

3) Data delivering mechanism: As mentioned before, our protocol uses two mechanisms to deliver data to destination, greedy forwarding in the case of connected segment between two successive junctions, and improved carry and forward in the case of disconnected segment (cf. Figure 4). Greedy forwarding consists to choose the nearest vehicle in the direction to the destination as a forwarder, that will repeat the same process until the packet reaches the destination or a junction. However, the Improved Carry & Forward is used when there is not a nearest neighbor to the destination and the last one is out of range. To ensure fast delivery, we choose the vehicle with highest velocity as forwarder, it’s for that reason that we call it improved carry and forward. Pseudo code of our approach is illustrated in Algorithm 1. Algorithm 1: IRTIV pseudo code 1 C ← The current vehicle; 2 D ← The destination vehicle; 3 J ← The next intersection (Junction); 4 Nc ← The set of one hop neighbors of C; 5 if C = D then 6 Received packet (Success); 7 else 8 if D ∈ Nc then 9 Forward (packet,D); 10 else 11 if Position(C) ∈ Intersection areas then 12 foreach Segmenti do i 13 W eighti = N B−VDehicles · (Lji × Rji ) wi 14 15 16 17 18 19

J ← M ax of all (Segmenti , W eighti ); if ∃ vehicle ∈ Nc then Greedy Forwarding (packet, vehicle); else The improved carry & forward (packet,C); Wait For Neighbours();



0,4 0,35

A. Simulation environment The performance of our protocol is evaluated using ns2.34 [7] simulator, and we compared it with AODV [8] and GyTAR [2]. The simulation scenario represents an area 3×3 km2 of a city map. The important mobility tool [9] is used to generate the traffic and mobility Manhattan model. The road segments are bidirectional, the vehicles speeds are up to 60 km/h, and the number of vehicles is varying between 80 and 180 depending on the adopted scenario. The following table I summarizes the most important parameters:


Simulation area Number of intersection Number of roads Communication range Number of packets senders Data packet size Number of vehicles Vehicle speed

3000m × 3000m 9 24 300m 35 1 KB 80-180 0-60 Km/h

The protocol performances are evaluated based on two parameters (i) Packet Delivery Ratio (PDR) representing the ratio of successfully delivered packets to the total number of packets generated by the source. (ii) End-to-End Delay (EED) representing the average between the instant of sending the packet and the instant of receiving it. Figure 5 shows that our protocol IRTIV gives a best performances in comparison with the other protocols in term of packet delivery ratio. It’s clear that the PDR of IRTIV and GyTAR increases as the number of vehicles increase, due to the enhancement of connectivity as they choose in different ways the most connected segment, but, IRTIV achieves a better performance due the use of the improved carry & forward in sparse network . However, for AODV, the broadcast of route request packet can increase the collision in the medium which leads to a low PDR. For the average end-to-end delay figure 6 illustrates that IRTIV outperforms largely AODV and GyTAR whatever the vehicles densities. IRTIV achieves the lowest end-to-end delay in reason of the combination between the greedy forwarding, the Dijkstra algorithm, the availability of connected paths instantaneously. 1 0,9

Packets delivery ratio

0,8 0,7 0,6 0,5






0,2 0,1 0 70






0,3 0,25






0,1 0,05

0 70




Number of vehicles

Fig. 5: Packets delivery ratio vs. vehicle density





Number of vehicles

Fig. 6: End-to-End delay vs. vehicle density


TABLE I: Simulation parameters Parameter

End-to-End delay [s]



In this work, we proposed a new intersection-based protocol specific for VANets called IRTIV, which in the presence of at least a vehicle in an intersection, it chooses in the way of proactive protocols, the next farthest node belonging to the shortest and the most connected road segment as forwarder. To this end, IRTIV calculates for each road segment a weight by combining the real time traffic density and the Dijkstra algorithm. Simulation results show that our protocol gives best performances compared to GyTAR and AODV and enhances the packet delivery ration and the end to end delay. As future work, we intend to bring new features to our approach in order to both adapt it to other environments like rural and highway areas and improve it by taking into account the traffic light impact in urban city. R EFERENCES [1] Y. SHI, X.-y. JIN, and S.-z. CHEN, “Agp: an anchor-geography based routing protocol with mobility prediction for vanet in city scenarios,” The Journal of China Universities of Posts and Telecommunications, vol. 18, pp. 112–117, 2011. [2] M. Jerbi, S.-M. Senouci, T. Rasheed, and Y. Ghamri-Doudane, “Towards efficient geographic routing in urban vehicular networks,” Vehicular Technology, IEEE Transactions on, vol. 58, no. 9, pp. 5048–5059, 2009. [3] V. Naumov and T. R. Gross, “Connectivity-aware routing (car) in vehicular ad-hoc networks,” in INFOCOM 2007. 26th IEEE International Conference on Computer Communications. IEEE. IEEE, 2007, pp. 1919–1927. [4] C. Lochert, M. Mauve, H. F¨ußler, and H. Hartenstein, “Geographic routing in city scenarios,” ACM SIGMOBILE Mobile Computing and Communications Review, vol. 9, no. 1, pp. 69–72, 2005. [5] B.-C. Seet, G. Liu, B.-S. Lee, C.-H. Foh, K.-J. Wong, and K.-K. Lee, “A-star: A mobile ad hoc routing strategy for metropolis vehicular communications,” in NETWORKING 2004. Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile and Wireless Communications. Springer, 2004, pp. 989–999. [6] J. L. J. J. D. SJ, R. K. De Couto David, and R. Morris, “A scalable location service for geographic ad hoc routing,” 2000. [7] N. The Network Simulator. (2013) @ONLINE. [Online]. Available: [8] S. R. Das, E. M. Belding-Royer, and C. E. Perkins, “Ad hoc on-demand distance vector (aodv) routing,” 2003. [9] F. Bai, N. Sadagopan, and A. Helmy, “User manual for important mobility tool generators in ns-2 simulator,” University of Southern California, 2004.

Suggest Documents