# PERFORMANCE VALIDATION

The analysis of the experimental results of the QOKHC technique took place on diverse aspects. The results are examined in terms of energy, lifetime, and number of packets sent to BS.

Figure 11.4 depicts the network lifetime analysis of the QOKHC algorithm in terms of number of alive nodes under varying rounds. The figure stated that the alive nodes are high by the QOKHC algorithm, whereas alive nodes are minimum for the gray wolf optimization (GWO) algorithm. At the same time, the KH algorithm has tried to exhibit better network lifetime by attaining higher number of alive nodes over GWO algorithm, but failed to surpass QOKHC algorithm. In the execution round of 500, the number of alive nodes by QOKHC algorithm is 143 nodes, whereas minimal number of 88 and 112 alive nodes are exhibited by the GWO and KH algorithms, respectively.

**FIGURE 11.4 Alive node analysis of proposed QOKHC algorithm.**

FIGURE 11.5 Network lifetime analysis of proposed QOKHC algorithm.

Similarly, under the execution round of 1,000, the number of alive nodes attained by the QOKHC algorithm is 54 nodes, whereas the GWO and KH algorithms have achieved minimal alive node count of 8 and 22 nodes, respectively. Likewise, on the round number of 1,400, all the nodes become dead by the GWO algorithm and only one node stays alive by the KH algorithm. But a maximum of 15 nodes are alive by the QOKHC algorithm. These values ensured the betterment in the network lifetime of the QOKHC algorithm.

Figure 11.5 shows the network lifetime analysis of the QOKHC algorithm in terms of FND, HND, and LND. The figure portrayed that the QOKHC algorithm has delayed the FND to 198 rounds, whereas it occurred earlier in the round numbers of 87 and 112 rounds by the GWO and KH algorithms, respectively. At the same time, the HND occurs at the round number of 795 by the QOKHC algorithm, whereas the GWO and KH algorithms exhibit minimal lifetime with 447 and 588 rounds, respectively. Finally, all the nodes become dead at the rounds of 1,124 and 1,411 rounds by the GWO and KH algorithms, respectively. However, the QOKHC algorithm demonstrated maximum network lifetime by attaining LND at 1,675 rounds.

Figure 11.6 examines the analysis of the QOKHC algorithm in terms of the number of packets reaching the BS under varying rounds. The figure stated that the number of packets reaching the BS is high by the QOKHC algorithm, whereas packet count reaching the BS is minimum by the GWO algorithm. At the same time, the KH algorithm has tried to exhibit a higher number of packet count reaching the BS over GWO algorithm, but failed to surpass QOKHC algorithm. In the execution round of 500, the packet count reaching the BS by QOKHC algorithm is 2,880 nodes, whereas the minimal number of 1,598 and 2,350 packets reached the BS by the GWO and KH algorithms, respectively. Similarly, under the execution round of 1,000, the number of packets received by the QOKHC algorithm is 4,980 nodes, whereas by the GWO and KH algorithms the minimal packet counts reaching the BS are 2,760 and 4,230 nodes, respectively. Likewise,

FIGURE 11.6 Analysis of number of packets reaching the BS by QOKHC algorithm.

in the round number of 1,400, maximum packet count reached the BS by the QOKHC algorithm. Therefore, the proposed method exhibits active network operation and more number of packets reaching the BS.

Figure 11.7 shows the average energy consumption analysis of the QOKHC algorithm and existing models. The figure portrayed that the QOKHC algorithm has demonstrated maximum residual energy compared to existing models. Besides, the residual energy is lower for the GWO and KH algorithms compared to the QOKHC algorithm. In the execution round of 500, the average energy consumed by QOKHC algorithm is 0.34 J, whereas the higher energy consumption are 0.4 and 0.38 J, respectively. At the same time, in the execution round of 1,000, average energy consumed by the QOKHC algorithm is

FIGURE 11.7 Energy efficiency analysis of the QOKHC method.

0.68 J, whereas the GWO and KH algorithms have achieved higher energy consumption of 0.75 and 0.62 J, respectively. These values portrayed that the QOKHC algorithm has consumed least amount of energy and offered maximum network lifetime over the compared methods.

# CONCLUSION

This chapter has developed an efficient QOKHC-based clustering algorithm for IoT sensor networks. Once the nodes are deployed, the nodes are initialized and information exchange takes place. Then, the node executes the QOBL algorithm and selects the CHs in an appropriate way. After the CHs are properly chosen, the nearby nodes join the CHs and construct clusters. The analysis of the experimental results of the QOKHC technique takes place on diverse aspects and the results are examined under several aspects. The obtained simulation outcome depicted the proposed model in terms of network lifetime, energy, and number of packets sent to BS. As a part of future work, the performance of the QOKHC model can be improvised by the use of routing techniques.

# REFERENCES

- 1. S. Li, L.D. Xu, and S. Zhao, “The Internet of Things: a survey,”
*Information Systems Frontiers,*vol. 17, pp. 243-259, 2015. - 2. S. Arjunan and S. Pothula, “A survey on unequal clustering protocols in wireless sensor networks,”
*Journal of King Saud University: Computer and Information Sciences,*vol. 31, no. 3, pp. 304-317, 2019. - 3. S. Arjunan and P. Sujatha, “Lifetime maximization of wireless sensor network using fuzzy based unequal clustering and ACO based routing hybrid protocol,”
*Applied Intelligence,*vol. 48, no. 8, pp. 2229-2246, 2018. - 4. S. Arjunan, S. Pothula, and D. Ponnurangam, “F5N-based unequal clustering protocol (F5NUCP) for wireless sensor networks,”
*International Journal of Communication Systems,*vol. 31, no. 17, p. e3811, 2018. - 5. J. Uthayakumar, T. Vengattaraman, and P. Dhavachelvan, “A new lossless neighborhood indexing sequence (NIS) algorithm for data compression in wireless sensor networks,”
*Ad Hoc Networks,*vol. 83, pp. 149-157, 2019. - 6. N. A. Pantazis, S. A. Nikolidakis, and D. D. Vergados, “Energy-efficient routing protocols in wireless sensor networks: a survey,”
*IEEE Communications Surveys & Tutorials,*vol. 15, no. 2, pp. 551-591,2012. - 7. R. Singh and A. K. Verma, “Energy efficient cross layer based adaptive threshold routing protocol for WSN,”
*AEU - International Journal of Electronics and Communications,*vol. 72, pp. 166-173, 2017. - 8. E. Alnawafa and I. Marghescu, “New energy efficient multihop routing techniques for wireless sensor networks: static and dynamic techniques,”
*Sensors,*vol. 18, no. 6, p. 1863, 2018. - 9. A. Al-Baz and A. El-Sayed, “A new algorithm for cluster head selection in LEACH protocol for wireless sensor networks,”
*International Journal of Communication Systems,*vol. 31, no. 1, e3407, 2018. - 10. D. T. Hai, L. H. Son, and T. L. Vinh, “Novel fuzzy clustering scheme for 3D wireless sensor networks,”
*Applied Soft Computing,*vol. 54, pp. 141-149, 2017. - 11. Y. Zhang, P. Li, and L. Mao, “Research on improved low energy adaptive clustering hierarchy protocol in wireless sensor networks,”
*Journal of Shanghai Jiaotong University: Science,*vol. 23, no. 5, pp. 613-619, 2018.

- 12. M. Azharuddin and P. K. Jana, “PSO-based approach for energy-efficient and energy- balanced routing and clustering in wireless sensor networks,”
*Soft Computing,*vol. 21, no. 22, pp. 6825-6839, 2017. - 13. P. Kuila and P. K. Jana, “Energy efficient clustering and routing algorithms for wireless sensor networks: particle swarm optimization approach,”
*Engineering Applications of Artificial Intelligence,*vol. 33, pp. 127-140, 2014. - 14. J. Wang, Y. Gao, W. Liu, A. Sangaiah, and H.-J. Kim, “An improved routing schema with special clustering using PSO algorithm for heterogeneous wireless sensor network,”
*Sensors,*vol. 19, no. 3, p. 671, 2019.