Connecting VANETs to Internet over IEEE 80211p in a Nakagami fading channel

Full text

Connecting VANETs to Internet over IEEE 80211p
in a Nakagami Fading Channel
Driss ABADA
Laboratory LABSIV
Faculty of sciences
Agadir, Morocco
Abdellah MASSAQ
Laboratory OSCARS
National School of Applied sciences
Marrakech, Morocco
Abdellah BOULOUZ
Laboratory LABSIV
Faculty of sciences
Agadir, Morocco
Abstract—The fast topology changes in vehicular ad hoc
networks (VANETs) may be caused by fading of the wireless
link as well as mobility of nodes. Link stability is often measured
using mobility parameters (e.g, location, velocity, direction), and
disregarding multipath fading of the signal. This may cause
network performances degradation. In this paper, we have
improved the relay selection scheme by integrating a new routing
metric which measures link stability taking into account to the
multipath fading statistics basing on contention-based forwarding
(CBF) approach. The objective is that to make vehicles able
to select the most stable with the minimum fading route from
them to the Internet road side units (RSUs). The simulation
results show that the proposed protocol enhances throughput
and decreases overhead with a comparable end to end delay.
Index Terms—Vehicular ad hoc Networks; routing; link sta-
bility; fading;
I. INTRODUCTION
Vehicular ad hoc networks (VANETs) enable vehicles to
communicate with each other (V2V) as well as with road-
side infrastructure units (V2I). These units provide different
services such as Internet access. There are several wirelesses
technologies such as IEEE 802.11p [1], [2], WIMAX [3] or
cellular networks (3G/UMTS [4], 4G/LTEA [5], [6] and 5G
[7]), which can be investigated to provide Internet connectivity
to vehicles on the roads. In the present work, we use IEEE
802.11p as Internet access technology. It allows vehicles to
communicate reliably to each other or with roadside units with
high data rate through limited coverage.
In VANETs, most existing routing protocols based primarily
on the impact of movement parameters of vehicles on link
reliability, and they have disregarded a set of factors such
as signal reception quality, path loss, fading and interference
that are relevant to the performance evaluation of the routing
protocols. Multipath fading is the main factor which affects
the throughput of mobile ad-hoc networks. The channel fading
leads to an increasing of network overhead in the PHY, MAC,
or network layer. To overcome these issues, we have modified
relay selection scheme proposed in [8] which base on the
contention-based forwarding scheme, to take into account to
channel fading. In this paper, we integrated into the relay
selection scheme, a new routing metric called effective link life
time which consists first, to use vehicle mobility parameters
to predict the link lifetime and then combine the link lifetime
with the average of multipath fading channel statistics in the
link.
The remainder of this paper is organized as follows:. In
Section II we present the related work. Our proposed work
is detailed in Section III. The performance of our protocol is
discussed in Section IV. Finally, we give the conclusion and
future work directions in Section V.
II. RELATE WORK
There are several contributions in the open literature for
connecting VANETs to the Internet. In general, these contri-
butions are divided into two categories, there are protocols
based on stationary gateways (RSUs) and the others are based
on mobile gateways.
In this paper [4] , the author proposed a new architecture
which integrates IEEE 802.11p based VANET technology
with wide range 3G/UMTS. To make communication between
vehicles and 3G / UMTS base stations more reliable, the
proposed architecture aims to cluster dynamically vehicles
according to different characteristics. Moreover, to connect
VANET to the UMTS network, the author proposed an adap-
tive mobile gateway management mechanism which aims to
select a minimum number of vehicles equipped with IEEE
802.11p and UTRAN interfaces, as the gateway in order to
allow all vehicles in the network to communicate with UMTS
network. By allowing more than one gateway to operate at an
instance, bottlenecks and congestion across the path towards
a single gateway can be eliminated.
In the research work [8], the authors proposed an efficient
routing protocol for connecting vehicular networks to the
Internet which uses the characteristics of vehicle movements
to predict the future behavior of vehicles, and to select a
route with the longest lifetime. The proposed protocol aims
to broadcast the advertisement messages through multi-hops
in the predefined zone and uses a distributed manner to select
relay for a re-broadcasting message, this approach will connect
VANETs to the Internet by minimizing overhead without
flooding network through most stable route.
In the research work [10], the author proposed a framework
based on the inter layer network cooperation to provide
vehicular users with the best available Internet access. This
framework consists of estimating in the MAC layer some pa-
rameters (expected delay and available throughput) and using
978-1-5090-6681-0/17$31.00 2017 IEEE

them in the network layer, in order to select the route with the
minimum bit rate requirement and the residual delay allowed
to be spent from the gateway to the destination. The routing
layer of each VANET node cooperates also with physical layer
to get information about the QoS performance of the route and
about its reliability. At the PHY layer, each node in VANET
measures periodically, the received signal strength indicator
(RSSI) on each active link to their neighbors, in order, to select
the most stable route. Simulations in urban scenarios showed
that the proposed approach achieves improved performance,
mainly in terms of effectiveness of procedures of path-to-
the-gateway discovery and maintenance, and end-to-end QoS
provisioning.
III. PROPOSED WORK
A. Link Stability Metric Estimation
The link stability metric is measured based on the new
routing metric called Effective Link Life Time (ellt) which
is estimated based on the mobility parameters and statistic of
fading as follows:
elltij =letij ×(1 −ε)(1)
where letij is a link expiration time defined in [8] which is
considered a maximum effective link life time in the ideal
environment that presents no fading (ε= 0.0). and εreflects
the wireless channel fading statistics in the current link. εcan
be set to small values in environments with no major obstacles
(e.g., highway) and takes high values in urban areas with tall
buildings [4].
The link expiration time between vehicles iand jwhich
located at positions (xi, yi),(xj, yj), and move in direction
θi,θjwith speed vi,vjat an instant, respectively. Let Rthe
transmission range of vehicles measured tacking into account
the fading. The expression of letij according to [8] is :
letij =p(a2+c2)R2−(ad −bc)2−(ab +cd)
a2+c2(2)
where,
a=vicos θi−vjcos θj
b=xi−xj
c=visin θi−vjsin θj
d=yi−yj.
The parameter εwhich characterizes the statistics of the
fading, is determined as follows:
ε= 1 −E[ψ](3)
where ψrepresents the probability of a link between vehicle i
and vehicle jwhich are in connection within link lifetime in
which the received signal power is above a certain predefined
threshold. E[ψ]is the expected value of probability ψ.
In the paper [9], the author used a Rayleigh distribution as
channel model of ad hoc network to describe the statistics
of the amplitude and phase of multipath fading. However,
Nakagami-m distribution is a more applicable in VANETs
communication [11], [12] and it is a generalized distribution
which can model different fading environments. It has greater
flexibility and accuracy in matching some experimental data
than the Rayleigh, lognormal or Rice distributions [13]. In
the present work, we use Nakagami-m distribution as wireless
channel model to describe multipath fading. According to [11],
in the Nakagami channel m=3 (fast fading), the probability that
a packet is successfully received in the absence of interference,
is deduced from the probability that the packet’s received
signal pis stronger than the reception threshold Rth , which
represents the minimum acceptable value of received power,
that is,
ψ= exp −3.Rth
p. 1+3.Rth
p+9
2.Rth
p2!(4)
As demonstrated in [11] the probability ψcan be expressed
in function of distance dand transmission range Ras follow:
ψ= exp −3.d
R2!. 1+3.d
R2
+9
2.d
R4!(5)
Due to the mobility of nodes, the relative distance dvaries
at the time, consequently, the probability ψvaries with node
movement. To account for this random variation, we replace d
in Equation (5) with a continuous random variable Z, which
represents the distance between the sender and the receiver.
As mentioned previously, using the probability ψthat a link
is not in a fade, we can estimate the link operation duration.
According to [9], the expected value of the probability ψcan
be determined as follow:
E[ψ] = ZR
dmin
ψ(z)fZ(z)dz (6)
where fZ(z)is the probability density function (pdf).
According to the expression of the function fZdefined in
[9], we distinguished tree cases of expected value E[ψ]:
•Case 1 : The distance dijbetween two vehicles iand j
is constant during prediction link life time, the expected
value E[ψ]for the link during the prediction period is
E[ψ] = exp −3.dij
R2!. 1+3.dij
R2
+9
2.dij
R4!
(7)
•Case 2: The two vehicles iand jonly move away from
each other during prediction link life time, the pdf of Z
is
fZ(z) = 


0if z < dij
1
R−dij if dij ≤z≤R
0if z > R
(8)
The expression of expected value E[ψ]is :
E[ψ] = 1
R−dij RR
dij exp −3.z
R2.
1+3.z
R2+9
2.z
R4dz (9)
•Case 3: The two vehicles iand jmove toward each other
during prediction link life time, the pdf of Zis
fZ(z) = 


2
R+dij if z < dij
1
R+dij if dij ≤z≤R
0if z > R
(10)

Fig. 1. Effective route life time based route selection scheme
The expression of expected value E[ψ]is :
E[ψ] =
2
R+dij Rdij
0exp −3.z
R2.(1 + 3.z
R2+
9
2.z
R4)dz +1
R+dij RR
dij exp −3.z
R2.
(1 + 3.z
R2+9
2.z
R4)dz
(11)
where dij =p(xi−xj)2+ (yi−yj)2.
Let Rn−1be the route of n−1links between nvehicles
1,2, ...n. We define Effective Route Life Time noted erlt as
the minimum of ellt of all links constructed the route. We
have:
erltn−1=M inn
i=1,j=i+1 elltij (12)
For example in Fig. 1, the vehicles L and M are selected
as relays to construct the route from RSU to vehicle S. The
erlt of each link is written over the link. erlt3is its effective
route life time which equals 8s in this case. Noted that the
message is disseminated in the opposite direction of vehicles
movement. Noted that the message is disseminated in the
opposite direction of vehicles movement, and all vehicles
move in the same direction.
The link stability function noted LS is calculated according
to the following formula proposed in [8]. In our approach,
we have replaced link expiration time with effective link life
time (ellt).
LS = 1 −exp −ellt
k(13)
where kis a constant that defines the rate at which the function
is rising.
B. Relay selection scheme
In this paper, the selection of next hop is performed
by means of contention, by adapting the contention-based
forwarding (CBF) [8] to our proposed approach. Several
approaches [14], [15] are proposed to improve contention-
based forwarding in VANETs especially in the routing. As
discussed in the previous works, CBF is more suitable for
VANETs than another type of routing approaches such as
topology-based routing, position-based forwarding.
The CBF-based relay selection scheme is a distributed
method. Each vehicle can select its self as the relay from
relative mobility parameters and multi-path fading statistics
in the link. This method improves dissemination efficiency
by selecting potentials vehicles as a relay for message re-
transmission. Therefore, it attempts to ensure reduced routing
overhead and efficient bandwidth utilization. For contention
in our proposed CBF-based relay selection scheme, the link
stability function (LS) is used to self-elect the best candidate
which is the vehicle that has the most stable link with the
sender. The contention timer function t(LS)is:
t(LS) = T×(1 −LS )(14)
where T is the maximum forwarding delay.
In CBF, the packet duplications may occur when two or
more concurrent vehicles have very close values of effective
link life time to each other, it means that the difference
between the waiting times is less than the time needed for
suppression. Combining another metric with LS will play an
important role in reducing the probability to have more than
one relay vehicle, in case the tie or the great convergence in
term of effective link life time [8], [16], [17]. It is quite
important to take into consideration to path length metric
when selecting a suitable route based on the stability because
relaying packets in the route with more number of hops will
increase medium access contention, interference, congestion,
and packet collisions. For this purpose, we decide to combine
LS with another metric proposed in [8], named progress
feature noted Pthat help vehicles to select the shortest path in
term of a number of hops. The authors defined it as follows:
P=cos (θi−θj)×dij
R(15)
where dij is the distance between vehicle i(sender), and
vehicle j(current receiver), and Ris the transmission range
of vehicles. θiand θjare respectively the direction angles of
vehicles. North axis is used by vehicles as the reference for
the direction angle.
Using weighted mean, the function fwhich combines both
metrics LS and Pis defined as follows:
f=α×LS + (1 −α)×P(16)
where αmay be selected in 1
2,1to give more weight for
LS than P.
We use a timer function that combines the calculated LS
and Pmetrics into a single function fthat is used to self-elect
the best candidate (e.g, the best relay should be the farthest
receiver that has the most stable link with the sender).The
contention timer function is:
t(f) = T×(1 −f)(17)
where Tis a maximum forwarding time.
The Fig.2 presents a flowchart of a proposed relay selection
scheme based on CBF.
IV. SIMULATION AND RESULT
To evaluate the performance of our proposed approach, we
have implemented our routing protocol in Network Simulator
NS2 [18]. We have compared our protocol with the protocol
developed recently in [8] for connecting VANETs to the

Fig. 2. Route selection based on effective route life time illustration
Internet noted LRP (lifetime based routing protocol), which
based only on movement parameters to measure link stability
and M-AODV+ [19]. We noted our modification approach
LRP-Enhanced. We have performed some simulations in order
to evaluate our proposed approach in term of throughput, end
to end packet delay and overhead by investigating the impact
of varying the mobility of nodes and the density of nodes on
the road.
M-AODV+ is an extension version of AODV+ [20] for
connecting VANET to the Internet. The main objective of M-
AODV+ is to support the reliability of V2V communication
in VANETs by enabling V2I and I2I communications as alter-
native communication links among vehicles when single hop
or multi-hop communication in the network is not possible.
A. Simulation environment
In this present work, we have based on the paper [21], [22]
to give insights on some measurements of the IEEE 80211p
MAC and physical layer using NS2 [18]. The data rate is
fixed to 6Mbit/s. Using MOVE [23] and SUMO [24] we
have created our highway scenario of 8000m with two lanes.
The simulation period in this work is 460s and we wait for
100s after the beginning of the simulation as the warm up
period. All vehicles move from the one end of the highway to
other end in the same direction and 10 vehicles are selected
randomly to send CBR data at rate 20 packets/s to a node that
is part of the wired network and is connected to all the base
stations. To simulate both protocols we have scheduled RSU to
broadcast the advertisement message every 5s in the predefined
broadcast geographic zone which has been considered to be a
circle with a radius of 1000m, and the message is broadcasted
in the opposite movement of nodes. After a lot of experiments,
TABLE I
NETWORK AND MOBILITY PARAMETERS IN THE SIMULATION
[21]
Paramtre Value
Mobility model Highway
Highway length 8km
Number of lanes 2
Maximum speed 10, 20, 30, 40 m/s
Number of vehicles 100, 150, 200, 250
Number of RSUs 8
Distance between RSUs 1000m
Simulation time 460s
Pause time 100s
Channel Channel/WirelessChannel
Propagation model Nagakami (m=3)
Network Interface Phy/WirelessPhyExt
MAC Mac802 11Ext
Interface queue QueueDropTailPriQueue
Antenna Type AntennaOmniAntenna
Interface queue 20
Transmission range 300 m
Routing protocols M-AODV+ [?], MBRP [?] and MFBRP
Addressing type Hierarchical
Traffic type CBR
Packet sending interval 0.05 s
Packet size 512 bytes
New parameters α= 0.8,k=erlt/2[25]
T0,00375s [25]
the adequate values of αand kare given in table II which
provides a summary of all simulation environment parameters.
M-AODV+ [19] is used in proactive gateway discovery, its
advertisement interval is fixed to 5s and its advertisement zone
is set to 3 hops, this means that an advertisement message will
be only broadcast 3 times in the network, and nodes located
further than 3 hops from a specific node have to send a route
request message in order to find a route to that specific node.
B. Simulation results
In this section, we present the analyses of the performance
of our approach LRP-Enhanced in contrast with routing pro-
tocol proposed in [8] LRP and M-AODV+.
1) Varying number of vehicles in the network: First, we
compare the performance of the routing protocols by changing
the number of nodes in the network. The maximum speed of
vehicles is fixed to 30m/s and the number of vehicular sources
is fixed to 10 vehicles.
The simulation results for network throughput, average end-
to-end delay and normalized routing control overhead, are
shown in Fig. 3, Fig. 4 and Fig. 5 respectively. From the
figures, it can be seen that the performances decrease by
increasing the number of vehicles for the tree protocols. This
degradation in term of performances is due to the interfer-
ences and congestion that occur when the number of vehicles
increases. Results show that our approach LRP-Enhanced has

Fig. 3. Throughput comparison under different number of vehicles
Fig. 4. Average end to end delay comparison under different number of
vehicles
Fig. 5. Normalized overhead routing comparison under different number of
vehicles.
better results in terms of throughput, delay, and overhead
compared to LRP and M-AODV +. This is due to the fact that
the proposed approach reduces frequent changes and breaks
of routes because the routes selected are more stable and
have a high packet reception probability. As result, the loss
of packets is reduced, so the throughput increases and the
overhead decreases. Although the LRP protocol selects the
most stable routes, some of them may have more fading,
which makes the probability of reception in these routes very
low, which causes rapids and frequents changes topology and
requiring a large number of control messages to research for
new routes. M-AODV + is based exclusively on the number of
hops to choose the appropriate routes, however, major of these
routes are less stable, this will increase frequent route breaks
and produce an extra overhead to find alternative routes. Note
that the both approaches LRP-Enhanced and LRP also take
into consideration the shortest path as a parameter of selection
but with less weight than the stability.
Fig. 6. Throughput comparison under different maximum speed
Fig. 7. Average end to end delay comparison under different maximum speed
2) Varying maximum speed: Secondly, we fixed the number
of nodes at 200 vehicles and the number of vehicular sources
at 10 sources, to evaluate the performance of the routing
protocols with increasing maximum speed.
Fig. 6 illustrates the network throughput, while Fig. 7 shows
the average end-to-end delay for the routing protocols and
Fig. 8 depicts the normalized overhead routing with varying
maximum speed. For all of the routing protocols, performances
decrease with increasing vehicle mobility. From the figures,
we find that more the speed of the vehicles increases more
the performance degrades for all protocols. This is due to
the fact that when the vehicles move with high speed, the
routes break quickly. This will increase dropping packets in
the network. However, the M-AODV + protocol is the least
efficient protocol compared to the both approaches LRP and
LRP-Enhanced. The reason is that this protocol does not
integrate any metric that characterizes the stability of routes.
The M-AODV + selects less stable routes, which make them
invalid in very short time because of the high mobility. In
this case, the packets wait until a valid new route is found.
LRP has better performance than M-AODV +. However, it
is less efficient than LRP-Enhanced, this is due to the fact
that LRP uses a stability metric based on the mobility of the
nodes in order to select the paths having longest lifetimes.
The problem is that these paths might include more fading.
The frequent changes topology introduced by fading as well
as by high speed of nodes, increase packets loss and overhead.

Fig. 8. Normalized overhead routing comparison under different maximum
speed
V. CONCLUSION
VANETs will play an important role in the future. Con-
necting VANETs to the Internet, many new applications can
be offered to improve intelligent transportation systems. These
applications may require a better routing performance. Provid-
ing Internet connectivity to vehicles in the fading environments
with good network performance is a challenge, knowing that
the multipath fading is the main factor which affects a network
performance. In this paper, we have enhanced routing protocol
by considering a new routing metric which combines the
impact of movement parameters and multipath fading channel
statistics on the link stability. Simulation results show that
our approach LRP-Enhanced achieves better performance than
routing protocol LRP and M-AODV+ over a range of network
performance measures.
REFERENCES
[1] V. Jayaraj, C.Hemanth, R.G.Sangeetha, ”A survey on hybrid MAC
protocols for vehicular ad-hoc networks,” Elsevier, 2016.
[2] M. Amadeo, C. Campolo, A.Molinaro, ”Enhancing IEEE 802.11p/WAVE
to provide infotainment applications in VANETs,” Elsevier, 2010.
[3] P.D. Dorge, D. .S. Dorle, M. B. Chakole, ”Implementation of MIMO
and AMC Techniques in WiMAX Network based VANET System,” I.J.
Information Technology and Computer Science, 2016, pp. 60-68.
[4] A. Benslimane, T. Taleb, R. Sivaraj, Dynamic Clustering-Based Adaptive
Mobile Gateway Management in Integrated VANET 3G Heterogeneous
Wireless Networks, IEEE Journal on Selected Areas in Communications,
Vol. 29, 2011, pp. 559-570.
[5] G. Araniti, C. Campolo, M. Condoluci, A. Iera, A. Molinaro, ”LTE for
Vehicular Networking: A Survey,” IEEE Communications Magazine, vol.
51, 2013, pp. 148-157.
[6] Hameed Mir, Fethi Filali, ”LTE and IEEE 802.11p for vehicular net-
working: a performance evaluation,” EURASIP Journal on Wireless
Communications and Networking, 2014.
[7] B. Akbil, D. Aboutajdine, ”Improved IDMA for Multiple Access of
5G,” International Journal of Communication Networks and Information
Security (IJCNIS), vol. 7, 2015, pp. 138-146.
[8] A. Benslimane, S. Barghi, C. Assi, ”An Efficient Routing Protocol for
Connecting Vehicular Networks to the Internet,” Pervasive and Mobile
Computing Journal, Elsevier publisher, 2010.
[9] S. Chen, H. Jones, D. Jayalath, ”Effective link operation duration: a new
routing metric for mobile ad hoc networks,” International Conference on
Signal Processing and Communication Systems,ICSPCS, 2007.
[10] A.Iera, A.Molinaro, S.Polito, and G. Ruggeri, ”A MULTILAYER CO-
OPERATION FRAMEWORK FOR QOS AWARE INTERNET ACCESS
IN VANETS,” Ubiquitous Computing and Communication Journal, 2008.
[11] M. Killat, H. Hartenstein, ”An Empirical Model for Probability of Packet
Reception in Vehicular Ad Hoc Networks,” EURASIP Journal on Wireless
Communications and Networking, vol. 2009, 2009, pp. 1-12.
[12] A. Khan, S. Sadhu, and M. Yeleswarapu, ”A comparative analysis of
DSRC and 802.11 over Vehicular Ad hoc Networks,” Project Report,
Department of Computer Science, University of California, 2009, pp. 1-
8.
[13] M. K. Mishra, N. S. and A. K. Sharma , ”Efficient BER Analysis of
OFDM System over Nakagami-m Fading Channel,” International Journal
of Advanced Science and Technology, 2011.
[14] F. Hrizi, C. Bonnet, J. Hrri, F. Filali, Adapting Contention-Based For-
warding to Urban Vehicular Topologies for Active Safety Applications,
Annals of telecommunications, Springer, vol. 68, 2013, pp. 267-285.
[15] M. Asgari, M. Ismail, Raed Alsaqour, ”Reliable Recovery Strategy
for Contention-based Forwarding in Vehicular Ad hoc Network Streets,”
ARPN Journal of Engineering and Applied Sciences, vol. 10, 2015 , pp.
9197-9207.
[16] H. Fler, M. Ksemann, M. Mauve, H. Hartenstein. and J. Widmer,
”Contention based forwarding for mobile ad-hoc networks,” Elsevier’s
Ad Hoc Networks, vol. 1, 2003, pp. 351 - 369.
[17] H. Alshaer, E. Horlait, ”An optimized adaptive broadcast scheme for
inter-vehicle communication,” IEEE 61st Vehicular Technology Confer-
ence 5, 2840-2844, 2005.
[18] ”The Network Simulator NS2,” http://www.isi.edu/nsnam/ns/.
[19] J. Wantoro, I. W. Mustika, ”M-AODV+: An Extension of AODV+
Routing Protocol for Supporting Vehicle-to-Vehicle Communication in
Vehicular Ad hoc Networks,” Communication, Networks and Satellite
(COMNETSAT), IEEE International Conference, pp. 39-44, November
2014.
[20] A. Hamidian, ”A Study of Internet Connectivity for Mobile Ad hoc
Networks in NS2,” Masters Thesis, Faculty of Engineering, LTH at Lund
University, 2003.
[21] D. Abada, A. Massaq, A. Boulouz, ”Enhacing relay selection scheme
for connecting VANETs to Internet over IEEE 802.11p in Congested and
Fading Environment Scenarios,” International Review on Computers and
Software (IRECOS), 2016, pp.410 - 419.
[22] Y. Wang, A. Ahmed, B. Krishnamachari, K.Psounis, ”IEEE 802.11p
Performance Evaluation and Protocol Enhancement,” IEEE International
Conference on Vehicular Electronics and Safety, 2008, pp. 317-332.
[23] F.K. Karnadi, Z.H. Mo, K.C Lan, ”Rapid generation of realistic simu-
lation for vanet ,” IEEE WCNC, 2007.
[24] ”Simulation of Urban Mobility,” http://sumo.sourceforge.net.
[25] S. Barghi, A. Benslimane, C. Assi, ”A lifetime-based routing protocol
for connecting vanets to the internet,” in: WOWMOM, 2009, pp. 19.