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In this section, a detailed experimental validation takes place to verify the superior nature of the represented SS-KH algorithm under three unique scenarios. A set of evaluation parameters applied to measure the performance are energy efficiency and network lifetime.
The simulation takes place using MATLAB and a detailed experimental analysis is carried out with respect to three scenarios based on the distance to BS. To ensure the reliable
TABLE 8.1 Simulation Parameters
performance of the SS-KH algorithm, results are measured based on diverse locations of BS which are mentioned as follows:
a. Scenario 1 (SI): BS at middle of the target area
b. Scenario 2 (S2): BS at corner of the target area
c. Scenario 3 (S3): BS located far away from the target area
A sample WSN consists of 200 nodes which undergo random deployment in the sensing field of 100 x 100 m2. In addition, first-order radio energy model is considered. For comparative analysis, a set of five techniques, namely SS , LEACH , DEEC , TEEN , and FUCHAR , were taken. The assumption of the simulation parameters presented in the study is provided in Table 8.1. Besides, the deployment of nodes in all the three scenarios and the corresponding cluster construction is demonstrated in Figures 8.4 and 8.5 correspondingly.
Comparative Study on Energy Efficiency Analysis
To ensure the energy efficiency analysis of SS-KH algorithm, an investigation of average remaining energy takes place under three scenarios and the outcome is shown in Figures 8.6-8.8. The usage of energy is computed by the average energy consumed by every individual node. As seen in figure, it is evident that the introduced SS-KH algorithm shows more efficiency on comparing with other methods. It is due to the fact that efficient CH and proper cluster sizes are computed by the nature of SS-KH algorithm. The proper selection of CHs as well as size of the clusters minimizes the energy requirement in the entire WSN. In addition, the LEACH shows ineffective performance due to the nature of following characteristics: random CH selection and dedicated data transmission from CHs to BS. Simultaneously, TEEN offered minimum energy consumption over LEACH. However, it fails to achieve efficient results over the SS-KH algorithm. As TEEN is a threshold-based model, the decreased number of data transmission results in minimum energy utilization.
FIGURE 8.4 Deployment of nodes under three scenarios.
However, the way of selecting CHs randomly results in maximum energy expenditure over the compared techniques. The DEEC exhibits somewhat manageable performance over the other methods.
However, the selection of TCH arbitrarily results in poor performance compared to SS-KH, SSUCP, and FUCHAR. Even though FUCHAR and SSUCP offer effective performance, they lead to inefficient outcome compared to the presented SS-KH algorithm. On all the applied different scenarios, the SS-KH algorithm showed extraordinary results over
FIGURE 8.5 Construction of clusters under three scenarios.
FIGURE 8.6 SI: Average energy consumption.
the methods used for comparison purposes. Once the data communication has begun, the remaining energy level starts to reduce and it stuck to null at certain time. In that case, the node will be declared as dead node and the alive node count begins to deceases. The clustering technique whose node count is high after the execution of numerous rounds is termed as an efficient model.
FIGURE 8.8 S3: Average energy consumption.
Comparative Study on Network Lifetime Analysis in Terms of Alive Nodes
From the various dimensions of validating the WSN lifetime, this study is based on the alive node count. From the view of deploying nodes, every node will stay alive. Figures 8.9-8.11 demonstrate the alive node count of diverse methods under three unique scenarios correspondingly. Similar to the average residual energy examination, minimum lifetime is
FIGURE 8.10 Network lifetime analysis of S2.
obtained by the LEACH over the compared methods. At the same time, TEEN performs well compared to LEACH, however, it failed to show better results over the other methods. In the same way, the DEEC, SSUCP, and FUCHAR are found to be effective over the compared techniques; however, the SS-KH is an effective technique. The figures clearly show that the maximum lifetime is attained by the SS-KH algorithm.
Figure 8.12 and Table 8.2 show the investigation of the results attained by different methods in terms of FND, HND, and LND. From the table, under the SI, it is clear that the
FIGURE 8.12 Network lifetime analysis in terms of FND, HND, and LND.
FND of the LEACH takes place by 53 rounds which its minimum network lifetime. Next, the TEEN provides better results than LEACH with the FND at 75 rounds. Simultaneously, the DEEC model somewhat lengthens the FND by 84 rounds. Next, the FUCHAR shows better lifetime with the FND of 110 rounds whereas the SS extends the FND to 170 rounds. However, the proposed SS-KH algorithm achieves maximum network lifetime with the FND of 198 rounds. Similarly, under S2 and S3, the proposed method achieves better performance in terms of FND. On measuring the results in terms of HND, under the SI, it is clear that the HND of the LEACH takes place by 996 rounds which its minimum network lifetime. Next, the TEEN provides better results than LEACH with the HND at 1001 rounds. Simultaneously, the DEEC model somewhat lengthens the HND by 1016 rounds. Next, the FUCHAR shows better lifetime with the HND of 1101 rounds whereas the SS extends the HND to 1046 rounds. However, the proposed SS-KH algorithm achieves maximum network lifetime with the HND of 1601 rounds. Similarly, under S2 and S3, the proposed method achieves better performance in terms of HND.
On measuring the results in terms of LND, under the SI, it is clear that the LND of the LEACH takes place by 1401 rounds which its minimum network lifetime. Next, the TEEN provides better results than LEACH with the LND at 1523 rounds. Simultaneously, the
TABLE 8.2 Comparison of Network Lifetime in Terms of FND, HND and LND
DEEC model somewhat lengthens the LND by 1602 rounds. Next, the FUCH AR shows better lifetime with the LND of 1715 rounds whereas the SS extends the LND to 1900 rounds. However, the proposed SS-KH algorithm achieves maximum network lifetime with the LND of 2539 rounds. Similarly, under S2 and S3, the proposed method achieves better performance in terms of LND. These values proved that the SS-KH algorithm achieves maximum network lifetime over other methods under all the scenarios.