 Open Access
 Total Downloads : 27
 Authors : Sahithi Kapuganti , Dr. S. P. Setty
 Paper ID : IJERTV7IS040280
 Volume & Issue : Volume 07, Issue 04 (April 2018)
 Published (First Online): 28042018
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Taguchi Design of Experiments for optimizing the Performance of AODV Routing Protocol in MANETS
K. Sahithi

ech, CS &SE Department Andhra University Visakhapatnam, A.P., India
Dr.S.P.Setty
Professor, CS &SE Department Andhra University Visakhapatnam, A.P., India
AbstractIn this paper we evaluate the performance of Ad Hoc on Demand (AODV) protocol in mobile ad hoc network. Using Taguchi design of experiment, the main effects of three factors (network size, transmission rates, and packet size) on two performance metrics (throughput and endtoend delay) is quantified. The analysis of mean (ANOM) and analysis of variance (ANOVA) is employed to determine the best conditions required and to identify the level of importance of factors in order to obtain the best performance of AODV protocol. The findings show that network size is the leading factor to optimize the throughput and transmission rate for minimization of delay. Also, good agreement is observed between the predicted and experimented SNR for both throughput and endtoend delay.
Keywords AODV routing protocol, Mobile AdHoc Networks, Performance metrics, Taguchi design of experiment,Analysis of Variance(ANOVA).

INTRODUCTION
A mobile ad hoc network (MANET) is a multihop wireless network formed by a group of mobile nodes that have wireless capabilities. As nodes are mobile determination of route between pair of nodes is challenging. So, routing is the most studied problem in MANETs. Routing protocols can be classified into two major classes: proactive protocols and reactive protocols.
The rest of the paper is organized as follows: Reactive routing protocol AODV is described in section 2. Section 3 deals with basic concepts of Taguchi technique, section 4 shows the experimental design and simulation environment, data analysis is focused in section 5, validation is shown in section 6, section 7 concludes the findings.

AD HOC ONDEMAND DISTANCE VECTOR ROUTING (AODV)
The On demand routing protocol, Ad Hoc OnDemand Distance Vector (AODV) Protocol finds the routes as and when required. The route discovery and route maintenance are the key elements during AODV routing.
In Route Discovery process whenever a mobile node needs to send data to a particular node, a ROUTE REQUEST (RREQ) message was flooded.
Once the RREP message was received by the sending node, the route has been established and data packets may be forwarded on that route.
The failure of the links or routers was handled in the route maintenance process through a ROUTE ERROR (RERR) message.

CONCEPT
In general, performances of routing protocols are evaluated through simulation. Most of the studies are based on one factoratatime approach, in which, only one factor is changed, keeping other factors constant. Such technique doesnt consider the interaction among factors. This strategy of analysis can be enhanced by quantifying the effects of various factors and their interactions on the performance. The statistical design of experiment (DOE) can also be used that considers the simultaneous study of effects of several factors rather than one at a time.
Design of experiments (DOE) refers to the process of planning an experiment by collecting data and analyzing using statistical methods. It is a systematic method to determine the relationship between factors affecting a process and the output of that process. This information is needed to manage process inputs in order to optimize the output.

Taguchi Technique
Taguchi technique helps in determining the optimal combination of factors. Two vital tools are utilized in Taguchi design: orthogonal arrays (OAs) and signaltonoise ratio (SNR).
The OA is selected based on factor quantities which allows the simultaneous study of all factors and estimate the effect of each factor independently. The degrees of freedom for the orthogonal array should be greater than or at least equal to the number of factors.
The variation (of factor) which affects the change in particular design parameter of the performance is computed through signaltonoise ratio (SNR). The SNR is used to measure the performance metrics as well as the significant parameters through analysis of variance (ANOVA).Three classes of the performance metric in the analysis of SNR are employed: largerthebetter, smallerthebetter and the nominalthebest. The larger SNR corresponds to the better performance metric. As an example, for the routing protocol, throughput is a largerthebetter performance metric while endtoend delay is a smallerthebetter performance metric.
Analyzing SNR
For largerthebetter case, the SNR is computed as:
thrpt = 10 log (1/r ri=1 1/y2)
For smallerthebetter case, the SNR is computed as:
delay = 10 log (1/r ri=1 y2)
Where, r is the number of simulation repetitions under same design point and y is the response value
Label
Factor
Level 1
Level 2
Level 3
A
Network size
10 nodes
30 nodes
50 nodes
B
Transmission Rate
1 Mbps
5.5 Mbps
11 Mbps
C
Packet size
512 bytes
1024 bytes
2048 bytes
The overall mean value of over the nine experiments becomes:
TABLE 1: EXPERIMENTAL PARAMETERS AND THEIR LEVELS
i=1
= 1/9 9 i
The effect of a factor is defined as the absolute difference between the maximum S/N ratio and minimum S/N ratio of 3 levels. For example, to calculate the average effect of factor A at level 1, all results of factor A at level 1 are averaged. Hence, the average effect of factor A at level 1 A1 and the effect of factor A can be written, respectively, as:
A1 = 1/3[1 + 2 + 3] and effect of A = max min A factor with the largest effect means that it has the most
significant influence on the response metric.

ANOVA
The analysis of variance (ANOVA) is used to discuss the relative effect of all factors on the response metric and to determine which factor has the highest effect. Parameters used in ANOVA are calculated by the following equations:
The sum of squares (SST) from SNR is:
i=1
SST = n (i – )2
TABLE 2: EXPERIMENTAL LAYOUT USING L9 ORTHOGONAL ARRAY
Where, i is the SNR for the ith experiment.
i=1
Design Point
Level of Factors
A
B
C
1
1
1
1
2
1
2
2
3
1
3
3
4
2
1
2
5
2
2
3
6
2
3
1
7
3
1
3
8
3
2
1
9
3
3
2
The sum of squared deviations due to each factor (SSj) is SSj = L (ji – )2
Routing Protocol
AODV
Simulation Time
900sec
Simulation Area
1500m X 1500m
Packet Size
512,1024,2048
bytes
Network size
10,30,50 nodes
Transmission rate
1,5.5,11 Mbps
Traffic Type
FTP
SIMULATION PARAMETERS
Where L is the number of levels and ji is the average SNR of the jth factor at ith level.
Also, the sum of squares of error (SSe) is given by
SSe = SST – qj=1 SSj
Where q is the number of factors and SSj is the sum of squared deviations for each factor.
The percentage contribution related to each factor j is
j = (SSj/ SST )*100
The j value gives the significance level for each factor. The Ftest can also be used to determine which factor has the most significant effect on the performance metric. The large Fratio indicates the strong effect of the factor. The Fvalue for jth factor can be evaluated as
F = (SSj/Dfj)/(SSe/Dfe)


EXPERIMENTAL DESIGN AND SIMULATION Taguchi technique of design of experiment has been
extensively used in estimating the performance of routing protocols for MANETs. Performance of AODV is evaluated based on three factors: network size, transmission rates, packet size. Table 1 show the parameters examined in this study. Each factor is examined at three different factor levels a low level
(1) a medium level (2) and a high level (3). According to the number of parameters and their levels, a L9 (33) orthogonal array (as the degree of freedom for the orthogonal array should be greater than or at least equal to, those of factors) is the most suitable array. The experimental layout for the parameters using the L9 OA is shown in Table 2. Each combination of parameter levels is called design point. Each design point corresponds to a simulation scenario.
Simulators like NS2, Glomosim, Opnet, Qualnet etc., were developed to evaluate the performance of routing protocols. Simulation of each design point is carried out using OPNET and results are collected. As each scenario is executed, the throughput and the endtoend delay are computed as performance metrics. Delay and Throughput of each design point obtained is shown in figures 1 to 9.
Figure 1 Delay and Throughput of design point 1
Figure 2 Delay and Throughput of design point 2
Figure 3 Delay and Throughput of design point 3
Figure 4 Delay and Throughput of design point 4
Figure 5 Delay and Throughput of design point 5
Figure 6 Delay and Throughput of design point 6
Figure 7 Delay and Throughput of design point 7
Figure 8 Delay and Throughput of design point 8
Figure 9 Delay and Throughput of design point 9

RESULTS AND DATA ANALYSIS
The values of performance metrics, Throughput and Endto End Delay obtained during execution of each design point is shown in Table 3.
The SNR is used to represent the performance metric and the largest SNR is required. The analysis of means (ANOM) was carried out to determine the effects of parameter.
The throughput is defined as the total number of delivered data packets divided by the total duration of simulation time and the endtoend delay is defined as the time a data packet takes to travel from source to the destination.
A larger throughput is normally required in transmitting data packet process. Therefore, thelargerthebetter
methodology of SNR was employed for the optimization of throughput. The SNR for each performance metric of the eight experimental runs are listed in Table 4. The effect of each parameter on the SNR at different levels can be separated out since the experimental design is orthogonal. To obtain the effect of each parameter on each performance metric for each level, the SNR with same level of parameter are average for the eight experiments.
The response table of SNR for the throughput is summarized and listed in Table 5. Response table shows the optimal levels for each factor. The parameter level combination A3B3C3 is recommended. The results of ANOVA for the SNR of the throughput are shown in Table 6. It can be seen that the contribution of factor A to the throughput is the largest (81.96%). Therefore, the network size (factor A) has the greatest impact on the throughput.
Main effects plot for S/N ratio for throughput is shown in Figure 10. Since the slope of the line in first box (corresponding to network size) in figure 10 is the largest than those of others which shows network size has the greatest impact on optimizing the throughput.
Main effects plot for S/N ratio for delay is shown in Figure
11. Since the slope of the line in second box (corresponding to data rate) in figure 11 is the largest than those of others which shows data rate has the greatest impact on minimizing the delay.
Figure 10 Main effects plot for S/N ratio for throughput
Figure 11 Main effects plot for S/N ratio for delay
TABLE 3: ORTHOGONAL ARRAY WITH EXPERIMENTAL RESULTS
Design Point
Level of Factors
Performance Metrics
A
B
C
Throughput
Delay
1
1
1
1
2125.17
0.00168
2
1
2
2
2583.59
0.00058
3
1
3
3
5723.04
0.00048
4
2
1
2
5812.14
0.00131
5
2
2
3
7042.88
0.00045
6
2
3
1
4762.48
0.00044
7
3
1
3
13478.57
0.0014
8
3
2
1
10230.86
0.00037
9
3
3
2
18787.14
0.0004
TABLE 4: SNR VALUES FOR THROUGHPUT AND ENDTOEND DELAY
Design Point
Level of Factors
SNR
A
B
C
Throughput
Delay
1
1
1
1
66.54787
55.49381
2
1
2
2
68.24447
64.73144
3
1
3
3
75.15254
66.37518
4
2
1
2
75.28672
57.65457
5
2
2
3
76.95501
66.93575
6
2
3
1
73.55666
67.13095
7
3
1
3
82.59288
57.07744
8
3
2
1
80.19824
68.63597
9
3
3
2
85.47721
67.9588
Grand averag ( )
76.00129
63.55488
TABLE 5: RESPONSE TABLE FOR THROUGHPUT
Parameter
Mean SNR
Delta
Rank
Level 1
Level 2
Level 3
A
69.97667
75.26
82.75
12.77333
1
B
74.80333
75.12667
78.05667
3.253333
3
C
73.42667
76.33
78.23
4.803333
2
TABLE 6: ANALYSIS OF VARIANCE FOR THROUGHPUT
Parameter
SS
df
MS
FRatio
P (%)
A
247.1718
2
123.5859
12.82435
81.96526
B
19.27362
2
9.636811
0.548928
6.391375
C
35.11136
2
17.55568
11.64337
SSTOTAL
301.5567
100
The same analysis procedure is applied to optimize the endtoend delay. The response table of SNR for the endto end delay is listed in Table 7. The levels that gave the largest average response were selected from the response table. The parameter level combination A3B3C1 is recommended. The result of ANOVA in Table 8 shows that the transmission rates (factor B), 95.81% is the most important factor that effect performance endtoend delay.
TABLE 7: RESPONSE TABLE FOR ENDTOEND DELAY
Parameter
Mean SNR
Delta
Rank
Level 1
Level 2
Level 3
A
62.1967
63.9267
64.55
2.353333
2
B
56.76
66.7633
67.15
10.39
1
C
63.75
63.4667
63.4567
0.293333
3
TABLE 8: ANALYSIS OF VARIANCE FOR ENDTOEND DELAY
Parameter
SS
df
MS
FRatio
P(%)
A
8.919622
2
4.459811
0.042848
4.105613
B
208.1683
2
104.0841
1250.844
95.81778
C
0.166422
2
0.083211
0.076602
SSTOTAL
217.2543
100

VALIDATION
Figure 12 and figure 13 shows the linear model analysis for throughput and delay respectively. Factor with the highest coefficient shows the greatest impact on the response metric. It is observed that for throughput it is network size and for delay it is data rate. The other factors have low value of coefficient. The value of R2 from the figures12 and 13 indicate that the model perfectly fits the data. In the figure 12, the corresponding Pvalue of network size is 0.027 < 0.05 i.e. this factor is statistically significant for throughput and in the figure 13,the Pvalue for data rate is 0.011 < 0.05 showing that it has the most significant affect on delay at 5% level of significance ( = 0.05) or 95 % confidence level. From the tables in figure 12 and figure 13 the Pvalue for other factors which is greater than 0.05 show that these factors are insignificant.
Figure 1210 Linear regression model for SNR values of throughput
Figure 1311 Linear regression model for SNR values of delay
The interpretation about the factor effects using the orthogonal array design can be considered correct and satisfied if experimented values of SNR fall within the range of confidence interval.
The optimal predicted SNR for throughput and endtoend delay can be computed using
where is the grand average, jk is the means SNR for jth factor at the optimal kth level, and q is the number of factors that significantly affect.
The predicted SNR for throughput and endtoend delay obtained is 87.03409 and 68.3402 respectively.
Confidence interval (C.I.) around the predicted SNR of drop rates above is computed as
where F(,v1,v2) is the value from F distribution, is confidence level, v1 is the df of the mean performance, and v2 is the df for error, Ve is variance of error, and Ne is number of effective replications. The predicted SNR using the optimal levels is
expected to fall in the range of . With 95% confidence level ( = 0.05), the confidence interval around the estimated SNR is 5.14. Thus, the confidence interval for the expected throughput and endtoend delay is [92.174 81.894] and [73.48
63.2] respectively.
TABLE 9: RESULT OF CONFIRMATION EXPERIMENT FOR THROUGHPUT
SNR
Optimal factorlevel combinations A3B3C3
Predicted
Confidence interval
Experimented
87.034
[92.174 81.894] 89.7752
TABLE 10: RESULT OF CONFIRMATION EXPERIMENT FOR DELAY
SNR
Best factorlevel combinations A3B3C1
Predicted
Confidence interval
Experimented
68.340
[73.48 63.2] 68.565
Table 9 and Table 10 shows the comparison of the expected SNR throughput and delay at 95% confidence level with the measured SNR value of confirmation experiment. It is seen that the experimented values of throughput and delay fall in the range of confidence interval.
Improvement in the results with performance of simulation using optimal combination of factors levels for both throughput and delay is observed as shown in the figures 13 and figure 14 respectively. Values of throughput and delay obtained using optimal combination of factors levels are 30,815.09 and 0.000373 respectively. These values of both throughput and delay are transformed into SNR.
Figure 1412 Increase in Throughput with optimal combination of factors levels
Figure 14 Variation of Delay with optimal combination of factors levels

CONCLUSION

In this paper, the effects of three factors (network size, transmission rate, packet size) is evaluated simultaneously using Taguchi experimental design with regards to two performance metrics (throughput and endtoend delay). The simulation data is analyzed and the results are summarized as follows:
Based on ANOM and ANOVA, the best factor combination obtained for the throughput is A3B3C3 and endtoend delay is A3B3C1. The network size (81.96%) is the most important parameter that contributes to the performance of throughput, while the transmission rate (95.81%) is the most important parameter that contributes to the performance of endtoend delay. Also, good agreement is observed between the predicted and actual SNR for throughput and endtoend delay.
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