In this section, a comprehensive experimental validation of the FC-DR model takes place under diverse ways. Figure 4.3 depicts the energy consumption analysis of the FC-DR method. The figure shows that the FRLDG model has offered maximum energy utilization, but has appeared as an ineffective model. Simultaneously, the MOBFO-EER model has attained slightly higher energy consumption over FRLDG model, but it is not lower than FEEC-IIR model and FC-DR models. On the other hand, the FEEC-IIR model has attained even lower energy consumption over the compared methods. But the presented FC-DR model has attained least energy consumption and appeared as an effective model under varying number of IoT nodes.
Figure 4.4 demonstrates the network existence analysis of the FC-DR method. The figure portrayed that the FRLDG method has provided lower network duration and is represented as worst method. At the same time, the MOBFO-EER approach has accomplished medium network existence than FRLDG method; however, it is not greater than FEEC-IIR and FC-DR methodologies. Besides, the FEEC-IIR scheme has reached slightly better network duration when compared with earlier technologies. However, the projected FC-DR approach has accomplished remarkable network duration and is considered as optimal model under diverse count of IoT nodes.
Figure 4.5 illustrates the PDR analysis of FC-DR approach. The figure represented that the FRLDG framework has provided least PDR and is depicted as worst technique. The MOBFO- EER scheme has reached minimal PDR over FRLDG model; it is not greater than FEEC-IIR and FC-DR technologies. Then, the FEEC-IIR framework has accomplished considerable
FIGURE 4.3 Energy consumption analysis of the FC-DR model.
FIGURE 4.4 Network lifetime analysis of the FC-DR model.
PDR than earlier approaches. Therefore, the proposed FC-DR scheme has achieved higher PDR and demonstrated as effectual technology even under different IoT nodes.
Figure 4.6 illustrates the throughput analysis of the FC-DR technique. The figure implied that the FRLDG method has provided higher throughput and is considered as an ineffective approach. Concurrently, the MOBFO-EER framework has achieved minimum throughput than FRLDG model, but it does not exceed FEEC-IIR and FC-DR approaches. The FEEC-IIR technique has obtained slightly better throughput over the earlier methods. Hence, the projected FC-DR scheme has reached a higher throughput and showcased optimal performance under various numbers of IoT nodes.
FIGURE 4.5 PDR analysis of the FC-DR model.
FIGURE 4.6 Throughput analysis of the FC-DR model.
Figure 4.7 represents the end-to-end delay analysis of the FC-DR scheme. The figure portrayed that the FRLDG approach has provided greater end-to-end delay and is considered as a worst technology. The MOBFO-EER scheme has reached better end-to-end delay over FRLDG framework, which does not exceed FEEC-IIR and FC-DR frameworks. The FEEC-IIR method has minimum end-to-end delay than the traditional models. Thus, the projected FC-DR approach has reached lower end-to-end delay and is shown as a best model under distinct number of IoT nodes.
FIGURE 4.7 End-to-end delay analysis of the FC-DR model.
This chapter has developed an energy-efficient FC-DR model for WSN-assisted IoT system. The proposed FC-DR method operates on three major stages: FC-based node clustering, data collection, and EBLC technique-based data aggregation. Initially, the IoT sensor nodes perform FC process to select CFls and organize clusters. In the second level, the CMs observe the environment and forward the data to the CH. Finally, in the third level, the CHs perform data aggregation using EBLC technique. The experimental results denoted that the FC-DR model has shown superior performance by attaining maximum energy efficiency and network lifetime. In future, the performance of the FC-DR can be further improved using routing techniques.
- 1. Aierken, N., Gagliardi, R., Mostarda, L., Ullah, Z. (2015). Ruheed-Rotated Unequal Clustering Algorithm for Wireless Sensor Networks. In 29th IEEE International Conference on Advanced Information Networking and Applications Workshops, AINA 2015 Workshops, March 24-27, 2015, (pp. 170-174), Gwangju, South Korea.
- 2. Alsheikh, M. A., Lin, S., Niyato, D., Tan, H. P. (2014). Machine learning in wireless sensor networks: algorithms, strategies, and applications. IEEE Commun Surv Tutor 16(4):1996—2018, Fourthquarter.
- 3. Khan, F. A., Ahmad, A., Imran, M. (2018). Energy optimization of PR-LEACH routing scheme using distance awareness in Internet of Things networks. Int J Parallel Progr 56:1-20.
- 4. Kuo, Y.-W., Li, C.-L., Jhang, J-H., Lin, S. (2018). Design of a wireless sensor network-based IoT platform for wide area and heterogeneous applications. IEEE Sens J 18(12):5187—5197.
- 5. Farman, H., Jan, B., Javed, H., Ahmad, N., Iqbal, J., Arshad, M., Ali, S. (2018). Multi-criteria based zone head selection in Internet of Things based wireless sensor networks. Future Gener Comput Syst 87:364-371.
- 6. Mahajan, S., Dhiman, P. K. (2016). Clustering in WSN: a review. Int ] Adv Res Comput Sci 7(3):198-201.
- 7. Ramachandran, N., Perumal, V. (2018). Delay-aware heterogeneous cluster-based data acquisition in Internet of Things. Comput Electr Eng 65:44-58.
- 8. Aadri, A., Idrissi, N. (2017). An Energy Efficient Hierarchical Routing Scheme for Wireless Sensor Networks. In Computer Science & Information Technology (pp. 137-148).
- 9. Singh, M., Kumar, N. (2018). Energy efficient routing protocol in IoT. J Netw Commun Emerg Technol 8:5.
- 10. Rajpoot, P., Dwivedi, P. (2019). Multiple parameter based energy balanced and optimized clustering for WSN to enhance the lifetime using MADM approaches. Wirel Pers Commun 106(2):829-877.
- 11. Arjunan, S., Pothula, S. (2019). A survey on unequal clustering protocols in wireless sensor networks. J King Saud Univ Comput Inform Sci 31(3), 304-317.
- 12. Arjunan, S., Sujatha, P. (2018). Lifetime maximization of wireless sensor network using fuzzy based unequal clustering and ACO based routing hybrid protocol. Appl Intell 48(8):2229-2246.
- 13. Arjunan, S., Pothula, S., Ponnurangam, D. (2018). F5N-based unequal clustering protocol (F5NUCP) for wireless sensor networks. Int J Commun Syst 31(17):e3811.
- 14. Uthayakumar, J., Vengattaraman, T., Dhavachelvan, P. (2019). A new lossless neighborhood indexing sequence (NIS) algorithm for data compression in wireless sensor networks. Ad Hoc Netw 83:149-157.
- 15. Uthayakumar, J., Vengattaraman, T., Amudhavel, J. (2017). A simple data compression algorithm for anomaly detection in wireless sensor networks. IntJ Pure Appl Math 117(19):403-410.
- 16. Uthayakumar, J., Vengattaraman, T., Amudhavel, J. (2017). A simple lossless compression algorithm in wireless sensor networks: an application of wind plant data. ПОAB/8(2):281-288.
- 17. Shanmukhi, M., Patil, A., Asif, S., Amudhavel, J. (2018). A novel hybrid cluster based protocols for wireless sensor networks. Int J Pure Appl Math 119(14):479-488.
- 18. Shanthi, G., Sundarambal, M. (2019). FSO-PSO based multihop clustering in WSN for efficient medical building management system. Cluster Comput 22(5):12157—12168.
- 19. Parwekar, P. (2020). SGO A New Approach for Energy Efficient Clustering in WSN. In Sensor Technology: Concepts, Methodologies, Tools, and Applications (pp. 716-734), IGI Global.
- 20. Saihood, A.A., Hasan, Z.S. (2019). Enhanced WOA for mobile energy efficient and delay aware clustering in WSN. Int J Adv Res Comput Sci 10(5):8.
- 21. Mehra, P.S., Doja, M.N., Alam, B. (2020). Fuzzy based enhanced cluster head selection (FBECS) for WSN. / King Saud Univ Sci 32(1):390—401.
- 22. Kumaratharan, N., Padmapriya, N., Dharani, A. (March 2019). A Survey on Improved PSO Routing and Clustering in WSN. In 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN) (pp. 1-5), IEEE.
- 23. Sharma, R., Vashisht, V., Singh, U. (2019). EEFCM-DE: energy-efficient clustering based on fuzzy C means and differential evolution algorithm in WSNs. IET Communications 13(8):996-1007.
- 24. Liu T., Peng J., Yang J., Chen G., Xu W. (2017). Avoidance of energy hole problem based on feedback mechanism for heterogeneous sensor networks. Int J Distrib Sensor Netw 13(6), doi: https://doi.org/10.1177/1550147717713625.
- 25. Ever, E., Luchmun, R., Mostarda, L., Navarra, A., Shah, P. (2012). UHEED: An Unequal Clustering Algorithm for Wireless Sensor Networks. In Proceedings of the 1st International Conference on Sensor Networks - Volume 1: SENSORNETS (pp. 185-193).
- 26. Bahbahani, M. S., Alsusa, E. (2018). A cooperative clustering protocol with duty cycling for energy harvesting enabled wireless sensor networks. IEEE Trans Wireless Commun 17(1): 101—111.
- 27. Moraes, C., Har, D. (2017). Charging distributed sensor nodes exploiting clustering and energy trading. IEEE Sensors J 17(2):546-555.