Two Layer Model for
Performance Evolution of
Vehicular Networks
Nikolajs Bogdanovs
Riga Technical University,
Lomonosova iela 1, LV-1019, Riga, Latvia, phone:
+37129727288, e-mail: meistars86@inbox.lv
1
Vehicular Network
We research interests in utilizing wireless technologies like WiMax and
WiFi to provide Internet connectivity to users in moving vehicles. Such
systems, termed Drive-thru Internet, operate by placing inter-connected
roadside access points (APs) on city roads and trunk roads so as to
enable vehicular users to obtain network connectivity by temporarily
connecting to an AP as the vehicle passes through the AP’s coverage
range. The aim is to develop an effective communication system for the
Intelligent Transportation System (ITS).
An important feature of Drive-thru
Internet systems, shown in Fig.
2
The description of the object
We was calculated a goodput of standards IEEE802.11n
and IEEE802.16e for open network systems. The physical
sphere for the transmission is the wireless network. At the
first stage the data is transmitted from the mobile object to
the nearest Access Point along the protocol 802.11n.
However, the distance from the AP object should not
exceed 200 meters. Further from the AP the data is
transmitted to the remote base station on the protocol
802.16 (WiMax).
3
Experiment of IEEE802.11n for vehicle
On the picture are depicted the set up of three APs
locations in a distance of 100 meters to each other. Only
600 meters long test-bed was used to make the
experiment.
4
Closed Cyclic estimation model for
vehicular number
Following the obtained practical results we will calculate the base
station performance at variable client count. In our case the 200 meters
long base station operational zone of is divided to 5 zones, 40 meters each,
the third zone being the most adjacent to the base station.
200 meter segment consisting of 5 zones, 40 meters each,
5
Closed Cyclic estimation model for
vehicular number
Speed (km/h)
In fact, to determine how to grow the speed of vehicle was made an
experiment, as show Figure that velocity grows exponentially:
80
75
70
65
60
55
50
45
40
35
30
25
20
15
10
5
0
0
10 20
30 40
50 60
70 80
90 100 110 120 130 140 150 160 170 180 190 200
Distance (m)
Experiment 1
Experement 2
Experement 3
The Table shows the technical data of the car which was used in the
experiment. The speed grow can be depending on the vehicle data.
6
Closed Cyclic estimation model
for vehicular number
Closed network consisting of M independent nodes with N
incoming queries. Distribution is exponential with the
parameter  i .
If the interval length equals Si , and vehicle movement
speed equals i , then the intensity of vehicle service
by road interval equals:
i 
i
Si
7
Closed Cyclic estimation model for
vehicular number
Such a system can be described in a form of a closed cyclic mass service
system network with M service devices, N queries and exponentially
distributed service time. Query service intensity in the i-th interval equals
i
, as show in Figure.:
1
2
3
4
1
2
3


5
4
5
1
.
..
N
Closed cyclic system
8
Closed Cyclic estimation model for
vehicular number
Due to the periodic nature of this model
and the next step is calculated as follows:
x1  1
1
1
1
x2 
, x3 
,..., xM 
2
3
M
Buzen’s algorithm the most effective methods for closed network analysis,
as show in Table. Buzen’s matrix, at the row i and column j can be calculated
using the formula:
g (i, j )  g (i, j  1)  g (i  1, j ) xj
G( N  1)
G( N )
9
Vehicular number
Average number of moving vehicles, competing for
resources of base station is calculated in this manner:

G( N  k )
G( N  k  1) 
E (ni )    xik 
 xik 1 
G( N )
G( N ) 
k 1 
N
100
90
Vehicle Speed
80
70
60
50
40
30
20
10
0
0
2
4
6
8
Vehicle Density
10
12
14
16
10
Two Layer Model
The terminal count in each
vehicular wireless network is
usually high.
Bandwidth equation for a two
layer network:
X1 
0
1 P10
;
X 2  aX1
1
a
P12
2
11
Two Layer Model
There
1
for i-th region is obtained from measurement results,
illustrated in Figure.
1   i
12
Two Layer Model
The average time for the transmission will be varying: more time
is spending on the transmission of the data packets, which we
denote as E (t i ) .
The ACK transmission takes less time denotes it as
E (t 0 )
Then the average time of the data processing in the first node will be:
E (t i )  E (t 0 )
E (t1 ) 
2
If on the top of each transmitted packet we receive the ACK
confirmations. In this case the intensity of the processing in the first
node will be:
1
0 
E (t1 )
13
Two Layer Model
The intensity for the
2
:
1
2 
t
Where
Vf
t
lp
Vf
- effective data transfer rate for the IEEE802.16e protocol.
For the data transmission between the Access Point and the
base station is used IEEE802.16e protocol, this protocol will
have the peak transfer rate V = 50Mbps.
n
The packet length will be l p  1500bytes , but the actual speed
is determined in the following way:
Vn
Vf 
2
14
Two Layer Model
G ( N )    X i  i ,
3
n
n
i 1
Where N - number of vehicles. Function for the studied two layer
vehicular network looks like this:
N


1
j
j 1
G( N ) 
X
1

a
.

1
1  a j 0

Goodput
of the two layered network is defined as the count of
processed inquiries in a unit of time. The finished task is put out trough the
subsystem of input/output, and instantly trough it a new task is loaded.
Probability of a lack of inquiries in i-th node:


G ( N )  X G ( N  1)
i
pn 0 
i
G( N )
The output flow is equal to input flow and from this rule of flow
balance it is possible to write:
  P10 1 (1  pni  0)
15
Two Layer Model
Goodput with the probability P10=0.999 P12=0.001 for N=10 and
N=20 will be:
16
EXPERIMENTAL ESTIMATION OF VEHICULAR NETWORK
The estimation was made by the means of IxChariot software. The main feature
is the fact of speed measurement of the data transmission depending on the
remoteness of the mobile object from the base station and its moving speed.
Scheme “test-bed” is presented below :
One channel provides data transfer in GPRS mode. The second channel, being
characterized by a high data transfer rate, uses LTE mode – the mode of the next
generation of mobile communications
17
Conclusions
In this research experimental data is presented, about data
transfer rate in wireless networks of IEEE 802.11n standard
connecting moving objects. Based on the experimental data
mathematical patterns were developed binding characteristics of
vehicular flow with characteristics of data transfer system.
Experimental data is presented in this research, concerning data
transfer rate in IEEE 802.11n standard wireless network of moving
objects. Based on experimental data two mathematical models
were developed, binding the characteristics of vehicular flow and
characteristics of data transfer system.
In the presented research, a model for real data transfer rate
estimation depending from number N of moving objects located in
the wireless network base stations operational zone was
developed. Based on this research, the real data transfer rate
depends from the number and distances from base station of
objects interacting with base station.
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