A hybrid optimization with ensemble learning to ensure the stability of the VANET network based on performance analysis

Section 1

Section 1 provides the results and discussion of the proposed and implemented methods for enhancing machine learning with a hybrid optimization strategy to predict mobility in VANET. The implementation of the project (HFSA-VANET) is evaluated and compared with that of the current method (CRSM-VANET). Delay, energy consumption, dip, throughput and fairness index measurements are calculated and compared with proposals (HFSA-VANET) and existing (CRSM-VANET)29 methods. In addition, the implementation is done through NS2 stimulation and the proposed algorithm is compared with these two platforms along with the Windows 10 PRO computer, the total RAM capacity of 10 GB and the processor used is Intel core (7M) i3-6100CPU @ 3.70 GHz processor. The performance metrics are explored in the next section.

Performance Statistics

Delays occur as a packet travels from its source to its destination.

$$delay= \frac{length}{bandwidth}.$$

(19)

It is the number of packets lost due to a rogue node (DoS attack).

$$Drop=\frac{Send \;packet-receive \;package}{Send \; package}.$$

(20)

The throughput refers to the amount of packet data established about a destination, which corresponds to the total value of packets created by the sender node within a given time. The formula is as follows:

$$\mathrm{Throughput}\hspace{0.17em}=\hspace{0.17em}\mathrm{received \; data \; package }\times 8/\mathrm{data \; package \; transfer \; period}.$$

(21)

Results obtained through node

The performance statistics of the existing technique and the proposed method are compared in the table below.

The primary purpose of the performance statistics is to assess the ability of the proposed model to predict mobility in VANET. According to table 1compared to and researched with existing methodology, the proposed method improves on machine learning with a hybrid optimization strategy to predict mobility in VANET to be more successful.

Table 1 Comparison with existing approach.

The delay, power consumption, dip, throughput and fairness index of the HFSA-VANET and the CRSM-VANET are compared below.

Proposed technique achieves 99 J, 0.093690, 0.897708 for power consumption, delay value and drop value in node 20. Moreover, the new technique achieves a throughput of 31,341, which is higher than the previous approach. The proposed technique has an honesty score of 7,000,000, while the current method has a value of 8,000,000. For power consumption, delay value and dip value in node 60, the proposed approach reaches 47 J, 9.752925, 0.472094. In addition, the new method achieves a throughput of 31,341 which is higher than the previous method. The proposed strategy has an honesty score of 3,000,000, compared to 4,000,000 for the current method. The proposed technique achieves 36 J, 10.902826, 0.376633 for power consumption, delay value and drop value in node 60. Furthermore, the proposed technique achieves a throughput of 28,423 compared to 26,749 for the existing method. A fairness index value of 2,000,000 for the proposed method versus 4,000,000 for the existing method is achieved. For power consumption, delay value and dip value in node 80, the proposed approach reaches 11 J, 15.287826, 0.116375. Moreover, compared to the previous approach, the proposed strategy achieves a throughput of 18,197. The proposed approach has a fairness index of 1,000,000, while the current method has a fairness score of 2,000,000. The Figs. 3456 and 7 its Delay, Power Consumption, Decrease, Throughput, Fairness Index are obtained via node, respectively.

figure 3
figure 3

Delay plot for a proposed and existing method.

Figure 4
figure 4

Energy consumption plot for a proposed and existing method.

Figure 5
figure 5

Drop plot for proposed and existing method.

Figure 6
figure 6

Throughput plot for proposed and existing method.

Figure 7
figure 7

Fairness Index plot for the proposed and existing method.

Results obtained by speed

The speed of the proposed technique and the existing techniques are compared in terms of delay, energy consumption, drop, throughput and fairness index. The measured values ​​are shown in the table below. Table 2 shows the speed values ​​of both existing and proposed techniques.

Table 2 Comparison of the proposed method with existing speed method.

The speed is compared with the deceleration shown in FIG. 8speed versus energy shown in fig. 9speed versus fall shown in fig. 10speed versus throughput shown in fig. 11and speed versus fairness index shown in FIG. 12† The speed is compared to the deceleration, energy, drop, throughput and fairness index, and the graphical representation is shown below.

Figure 8
figure 8

Velocity vs deceleration plot for proposed and existing method.

Figure 9
figure 9

Speed ​​versus energy consumption plot for a proposed and existing method.

Figure 10
figure 10

Speed ​​vs drop plot for a proposed and existing method.

Figure 11
figure 11

Speed ​​vs throughput plot for a proposed and existing method.

Figure 12
figure 12

Speed ​​vs fairness index plot for a proposed and existing method.

In speed 20, the proposed approach reaches 1980 J, 1.873793, 19.954160 in terms of power consumption, retardation value and drop value. In addition, the new method achieves a Throughput of 150, which is greater than the previous method. The proposed approach has an honesty score of 6,000,000, while the current method also has a 6,000,000 number. The proposed technique achieves 1880 J, 390.117000, 18.883762 for power consumption, retardation value and drop value in speed 40. Moreover, the new approach achieves a throughput of 35, which is higher than the existing method. The recommended technique has a fairness value of 3,000,000, but the current method has a score of 4,000,000. In speed 60, the proposed approach reaches 2220 J, 654.169557, 22.597974 in terms of power consumption, delay and droplet value. In addition, the proposed strategy yields a throughput of 22 versus 16 for the current method. The proposed technique has a fairness index of 2,000,000 while the current method has a fairness index of 3,000,000. The recommended method achieves 880 J, 1223.026093, 9.309993 for power consumption, retardation value and drop value in speed 80. Furthermore, the new technique achieves a throughput of 8 and the existing technique achieves a throughput of 6. The proposed technique has a fairness score of 0.000000, while the current method has one of 2,000,000. The Figs. 891011 and 12 its Delay, Energy Consumption, Drop, Throughput, Fairness Index are obtained by speed respectively. Section 2 deals with the results obtained through the MATLAB software.

Section 2

This section covers the experimental results obtained through MATLAB (VERSION 2020a) for evaluating performance with the NS2 tool. In addition, we also include an additional parameter to ensure the network lifetime of the proposed model. Therefore, the performance can be proven as very effective as the existing technique. Here, the performance of the proposed model is evaluated using different machine learning approaches, such as ANN-HFSA-VANET, SVM-HFSA-VANET, NB-HFSA-VANET, and DT-HFSA-VANET. Thus, the proposed model results can be compared and proven to be more effective than all other existing techniques.

Initially, the proposed model is evaluated separately with ANN-HFSA-VANET, SVM-HFSA-VANET, NB-HFSA-VANET and DT-HFSA-VANET. The next fig. 131415 and 16 show the graphical results of ANN, SVM, NB and DT, respectively. On the other hand, to show a comparison based on the aggregation of different machine learning techniques comparing the proposed method with the single graphical results.

Figure 13
figure 13

a) Dropout ratio, (b) F1 score, (c) parcel delivery rate, (d) Throughput ratio, (e) End to end delay.

Figure 14
figure 14

a) Dropout ratio, (b) F1 score, (c) parcel delivery rate, (d) Throughput ratio, (e) End to end delay.

Figure 15
figure 15

a) Dropout ratio, (b) F1 score, (c) parcel delivery rate, (d) Throughput ratio, (e) End to end delay.

Figure 16
figure 16

a) Dropout ratio, (b) F1 score, (c) parcel delivery rate, (d) Throughput ratio, (e) End to end delay.

Parameter analysis of ANN-HFSA-VANET

This section deals with the different kinds of parameters of ANN-HFSA-VANET and is analyzed in the graph shown in Fig. 13

The above figure 13 illustrates the various performance analyzes based on ANN-HFSA-VANET, where (a) shows that the proposed technique has obtained minimum failure rate, (b) shows that maximum F1 score has been achieved by using of the proposed technique, (c) illustrates that the maximum packet delivery ratio has been obtained for the ANN-HFSA-VANET, (d) and (e) show that the proposed ANN-HFSA-VANET generated high throughput and minimal delay, respectively.

Parameter Analysis of Decision Tree (DT)-HFSA-VANET

This section deals with the different kinds of parameters of the decision tree and analyzed in the graphs shown in Fig. 14

figure 14, shows the analysis of parameters of DT-HFSA-VANET. (a) shows the minimum dropout rate of the DT-HFSA-VANET, (b) covers the maximum score for F1 score of DT-HFSA-VANET and its analysis, (c) shows the parcel delivery rate of DT-HFSA-VANET, and are values ​​plotted, (d) is about the throughput ratio of DT-HFSA-VANET, (e) is about end-to-end delay of DT-HFSA-VANET. Default parameters are analyzed and graphed, and the values ​​are incremented at the end of each parameter graph.

Parameter analysis of Navie Baves (NB)-HFSA-VANET

This section deals with the different types of parameters of Navie Baves and is analyzed in the graph in Fig. 15

In fig. 15a shows that minimum outages, (b) shows that maximum F1 score, (c) shows maximum packet delivery ratio, (e) shows that minimum delay, respectively for the proposed NB-HFSA-VANET.

Parameter analysis of SVM

This section deals with the different kinds of parameters of SVM and is analyzed in the graph shown in Fig. 16

In fig. 16a drop-out ratio was achieved with minimum ratio, (b) F1 score was achieved with maximum score, (c) parcel delivery ratio was achieved with maximum, and (e) shows minimum delay.

Parameters for analyzing different data types

This section deals with the parameters of various data and their analysis. The values ​​are plotted in a graph.

from fig. 17the parameter analysis value of data types is examined and plotted in a graph that (a) indicates the network lifetime for each second, (b) the power consumption of data packets used per second, (c) the throughput ratio of data types and their performance, (d) deals with the packet delivery ratio of different types of data performance.

Figure 17
figure 17

a) Plot for network lifetime, (b) Energy consumption, (c) Throughput ratio, (d) Parcel delivery rate.

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