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A Realistic Location Service for VANETs
Tawfiq Nebbou*, Hacène Fouchal**, Mohamed Lehsaini* and Marwane
Ayaida**
*STIC Laboratory, Tlemcen University, Algeria
tawfiq.nebbou@gmail.com, m_lehsaini@mail.univ-tlemcen.dz
**CReSTIC, Université de Reims Champagne-Ardenne, France
{hacene.fouchal, marwane.ayaida}@univ-reims.fr
Abstract. Position-based routing also called geographic routing is con-
sidered a more promising routing approach for highly dynamic and mo-
bile networks like Vehicular Ad-hoc Networks (VANETs). In this kind
of networks, the high-speed mobility of vehicles causes rapid changes in
vehicles density and limited-time communication links. Hence, the need
of location service has become extremely important to be able to find
the position of a target node in a very short time.
This paper proposes a realistic location service for unicast routing over
VANETs in an urban environment. The proposed approach is able to find
the path with higher connectivity in urban environment by exploiting
information of each vehicle in the network to reach the destination. For
this reason, we used a new metric called Link Connectivity (LC) in order
to find the path with higher connectivity between the source vehicle and
the destination vehicle.
Keywords: VANETs, Location-based Services, Geographic Routing Protocols,
Mobility.
1 Introduction
Vehicular Ad-hoc Networks (VANETs) are an emerging technology of wireless
communication that allows to form self-organized networks. In VANETs, com-
munication among vehicles can be carried out using Vehicle-to-Vehicle (V2V)
communications or Vehicle-to-Infrastructure (V2I) communications. Moreover,
VANETS share some common features with Mobile Ad-hoc Networks (MANETs),
namely in terms of self-organization of the nodes. However, they also differs in
some issues: in VANETs, the level of node’s mobility is generally higher and
the mobility is constrained by the roads and the nodes are not so constrained in
terms of energy, computing and location since the devices are carried by vehicles.
Indeed, in VANETs the high mobility of vehicles and the short-range communica-
tions result frequent network topology changes. This causes serious challenges to
data dissemination since messages cannot be easily delivered to destination node.
The features of VANETs make topology-based routing protocols designed for
the MANETs unsuitable to VANETs since the nodes in this kind of networks
are highly mobile which causes frequent changes in topology and consequently
the probability of a successful reception of the delivered messages will be very
low. Moreover, in topology-based routing protocols either in reactive or proac-
tive mode the links between the nodes are discovered and maintained through
periodical Hello packets exchange. Thereby, to overtake this limit we should min-
imise the sending period of Hello messages, which generates a large overhead.
However, in position-based routing protocols such as GSR [10], GPSR [6], or
GyTAR [5] the nodes use the location of their neighbors and the location of the
destination node to determine the neighbor that forwards the packet to destina-
tion. These protocols require information about the position of the nodes, which
is possible and less expensive in VANETs since most vehicles are equipped with
GPS (Global Positioning System) device in order to find its own geographic po-
sition as a localization system. Most of the time, mobile nodes do not need to
store any route or routing table to the destination.
This paper proposes a realistic location service for unicast routing over VANETs.
The remainder of the paper is organized as follows. Section 2 is dedicated to
related works. Section 3 details our contribution. Section 4 concludes the study
and gives some hints about future works.
2 Related works
In the literature there are two ways to handle location services : A flooding-based
technique which is a reactive or a proactive service or a synchronization-based
technique which is based on a quorum method or a hierarchy method. The
flooding proactive approach is simple to be implemented but generates a high
overhead load since each node should send its position to the whole network.
The flooding reactive approach reduces the overhead but introduces a higher
latency. This is due to the fact that when a node needs to send a message, it
needs first to have the response to its location request sent over the network.
The synchronization-based technique aims to split the network on groups which
are not disjoint as in the quorum approach or which are hierarchical with dis-
jonction as in Grid Location Service (GLS) [9] or without disjonction as the
Hierarchical Location Service (HLS) [7].
We have proposed in [2] a hybrid approach, denoted mobility-Prediction-based
Hybrid Routing and Hierarchical Location Service (PHRHLS), coupling a VANET
routing protocol, the Greedy Perimeter Stateless Routing (GPSR), and the Hi-
erarchical Location Service (HLS). In [1], we have focused only on the hybrid
part. Most of these approaches are difficult to be implemented in real VANETs
deployment since they generate either higher overhead or higher latency. In this
paper, we propose a simple approach to provide a location service in urban en-
vironments which could be implemented easily.
Routing protocols must choose some criteria to make routing decisions, for in-
stance the number of hops, latency, transmission power, bandwidth, etc. The
topology-based routing protocols suffer from heavy discovery and maintenance
phases, lack of scalability and high mobility effects short links. Therefore, ge-
ographic routing protocols are suitable for large scale dynamic networks. The
first routing protocol using the geographic information is the Location-Aided
Routing (LAR) [8]. This protocol used the geographic information in the route
discovery. This latter is initiated in a Request Zone. If the request does not suc-
ceed, it initiates another request with a larger Request Zone and the decision
is made on a routing table. Another geographic routing protocol is the Geo-
graphic Source Routing (GSR) [10]. It combines geographical information and
urban topology (street awareness). The sender calculates the shorter path (using
Djikstra algorithm) to the destination from a map location information. Then,
it selects a sequence of intersections (anchor-based) by which the data packet
has to travel, thus forming the shortest path routing. To send messages from
one intersection to another, it uses the greedy forwarding approach. The choice
of intersections is fixed and does not consider the spatial and temporal traffic
variations. Therefore, it increases the risk of choosing streets where the connec-
tivity is not guaranteed and losing packets. Like GSR, Anchor-based Street and
Traffic Aware Routing (A-STAR) [11] is anchor-based. However, it reflects the
streets characteristics. A connectivity rate is assigned to the roads depending on
the capacity and the number of bus using it. This metric is used in addition to
traditional metrics (distance, hops, latency) when making routing decisions. As
a consequence, the streets taken by busses are not always the main roads where
connectivity is ensured and the greedy approach does not consider the speed and
direction for the next hop selection. This is why improved Greedy Traffic Aware
Routing (GyTAR) [4] was designed as a geographical routing protocol adapted
to urban environments and managing the traffic conditions. A sender selects dy-
namically an intersection (depending on the streets connectivity) through which
a packet must be forwarded to reach the destination node. Between intersec-
tions, an improved greedy approach to forward packets is used. GyTAR takes
advantage from the urban roads characteristics, selects robust paths with high
connectivity and minimises the number of hops to reach an intersection. We
have compared in [3] three location-based services: Reactive Location Service
(RLS), Grid Location Service (GLS) and Hierarchical Location Service (HLS)
while coupled to the well known geographic routing protocol Greedy Perimeter
Stateless Routing (GPSR).
3 Contribution
In this section, we propose a Realistic Location Service represented by RSUs
(Road Side Units). This algorithm will help to establish a path between a source
node and a destination node. The proposed routing algorithm enables to find
the best path between two nodes based on the connectivity criterion in order
to ensure a successful reception by the destination node with help of a location
system (RSU). For this issue, we assume that in each intersection, a Road Side
Unit (RSU) is placed, and every vehicle has a static digital map to get the
position of all RSUs. Indeed, each vehicle has also the knowledge of its geographic
position using its GPS receiver, speed and direction of movement. This allows
the vehicle to find the closest RSU in order to send a request about the path to
the destination.
3.1 Realistic Location Service
Realistic Location System is a set of RSUs connected and distributed throughout
the network as shown in Figure 1. Each RSU plays the role of a location server
and maintains a part of all vehicles information. These vehicles’s information will
be periodically shared with the others location servers. The role of a location
server is not only to maintain the vehicle’s information but also to calculate the
best path between a source vehicle and a destination vehicle when it receives a
route request from a vehicle (the sender node).
Fig. 1. Location System communication Network
3.2 Proposed algorithm
Since each location server maintains and shares its part of vehicles information,
it has information about all vehicles in the network. According to all these ve-
hicles information, the location server is able to represent the network with a
graph G= (V, E) comprising a set V of vertices (intersections) together with a
set E of edges (roads) wherein each edge is associated with two vertices. More-
over, Link Connectivity (LC) is a function related to edge in order to calculate
the value of connectivity of an edge. Formally, LC can be calculated as follows:
LC :E→[0,1[
LCEdge =M LLEdge
Rtr
(1)
Where MLLedge is the Mean Link Liaison between all vehicles in the given
edge and Rtr represents the range of transmission, the Mean Link Liaison (MLL)
can be calculated as follows:
MLLEdg e =PN−1
i=1 (Rtr −dist(vehiclei−vehiclei+1 ))
N−1(2)
Where N represents the number of vehicles in the given edge and vehiclei
represents the vehicle number "i" in such edge.
3.3 Routing Algorithm
Route Request When the source vehicle has to send some data to destination
vehicle, then, initially it sends a Route-Request message to the closest RSU and
this message to the other RSUs using a routing scheme based on the Greedy
Forwarding approach. We assume that each vehicle knows the geographical po-
sition of all RSUs in the network, this information is obtained using the map
already embedded.
Route Reply When the RSU receives a Route-Request message from the sender
vehicle, it will calculate the best path between the source vehicle and the destina-
tion vehicle. This path is composed of a sequence of intersections through which
the packets will transit to reach the destination vehicle. Finally, the path found
will be encapsulated in a message Route-Reply and forwarded to the sending
vehicle using a routing process based on the Greedy Forwarding approach.
Update Position Table Each RSU maintains an update of a table, which
contains a list of positions, speeds and time stamps. When a RSU receives a
vehicle beacon or a local table from an other RSUs, it updates the positions,
speeds and the receiving times of theses vehicles. The position table will be
shared with all others RSUs periodically. The objective is that each RSU knows
the positions of all vehicles in the network.
Forwarding between two intersections Once the source vehicle receives the
Route-Replay message, it adds it in its header packet and deduces the destination
intersection. Then, it forwards the packet to the closest neighbor to the destina-
tion intersection using a routing scheme based on improved greedy forwarding
approach.
4 Conclusion and Future Works
We have proposed a simple location service algorithm which could be imple-
mented in urban environments.
This proposed location system exploits better all the information of the net-
work to locate a vehicle and determinate the best path. However, this solution
needs an aware-infrastructure which increases the cost of the network deploy-
ment.
As future works, we intend to implement our protocol on a simulator and
run it on a large network in order to handle the scalability of our algorithm
approach and decreasing the number of RSUs by using a distributed location
system algorithm.
References
1. M. Ayaida, M. Barhoumi, H. Fouchal, Y. Ghamri-Doudane, and L. Afilal. HHLS:
A hybrid routing technique for vanets. In 2012 IEEE Global Communications
Conference, GLOBECOM 2012, Anaheim, CA, USA, December 3-7, 2012, pages
44–48. IEEE, 2012.
2. M. Ayaida, M. Barhoumi, H. Fouchal, Y. Ghamri-Doudane, and L. Afilal.
PHRHLS: A movement-prediction-based joint routing and hierarchical location
service for vanets. In Proceedings of IEEE International Conference on Communi-
cations, ICC 2013, Budapest, Hungary, June 9-13, 2013, pages 1424–1428. IEEE,
2013.
3. M. Ayaida, H. Fouchal, L. Afilal, and Y. Ghamri-Doudane. A comparison of reac-
tive, grid and hierarchical location-based services for vanets. In Proceedings of the
76th IEEE Vehicular Technology Conference, VTC Fall 2012, Quebec City, QC,
Canada, September 3-6, 2012, pages 1–5. IEEE, 2012.
4. M. Jerbi, S.-M. Senouci, R. Meraihi, and Y. Ghamri-Doudane. An improved ve-
hicular ad hoc routing protocol for city environments. In Communications, 2007.
ICC ’07. IEEE International Conference on, pages 3972 –3979, june 2007.
5. M. Jerbi, S. M. Senouci, T. Rasheed, and Y. Ghamri-Doudane. Towards efficient
geographic routing in urban vehicular networks. IEEE Transactions on Vehicular
Technology, 58(9):5048–5059, Nov 2009.
6. B. Karp and H. T. Kung. Gpsr: Greedy perimeter stateless routing for wireless
networks. In Proceedings of the 6th Annual International Conference on Mobile
Computing and Networking, MobiCom ’00, pages 243–254, New York, NY, USA,
2000. ACM.
7. W. Kiess, H. Fussler, J. Widmer, and M. Mauve. Hierarchical location service
for mobile ad-hoc networks. SIGMOBILE Mob. Comput. Commun. Rev., 8:47–58,
October 2004.
8. Y.-B. Ko and N. H. Vaidya. Location-aided routing (lar) in mobile ad hoc networks.
Wirel. Netw., 6(4):307–321, July 2000.
9. J. Li, J. Jannotti, D. S. J. De Couto, D. R. Karger, and R. Morris. A scalable
location service for geographic ad hoc routing. In Proceedings of the 6th annual in-
ternational conference on Mobile computing and networking (MobiCom’00), pages
120–130, New York, NY, USA, 2000.
10. C. Lochert, H. Hartenstein, J. Tian, H. Fussler, D. Hermann, and M. Mauve. A
routing strategy for vehicular ad hoc networks in city environments. In IEEE
IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683), pages
156–161, June 2003.
11. 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 ommunications. 2004.
