Lifetime Maximization Using Grey Wolf Optimization Routing Protocol with statistical Technique in WSNs

Baida'a Abdul Qader Khudor, Dheyaa Mezaal Hussein, Yousif Abdulwahab Kheerallah, Jawad Alkenani, Imad S. Alshawi


Generally, the greatest issue in Wireless Sensor Networks (WSNs) is to make sensor nodes powered by low-cost batteries with limited power last as long as feasible. Thus, energy conservation is an essential topic in WSNs. Since wireless data transmission consumes a substantial amount of energy, the routing mechanism plays a crucial role in preserving the available energy. Consequently, energy-efficient routing methods may save battery power and extend the network's lifespan. Using complicated protocols to properly design data routing may save energy use, but may cause processing delays. This paper offers the Grey Wolf Optimization Routing Protocol (GWORP), extended by using a select optimal path method with detects the statistical best value novel routing mechanism. It is used to discover the ideal route from the source node to the destination (sink) and reuse that path in a manner that ensures energy consumption is evenly distributed across the nodes of a WSN while lowering the time required to find the routing path from scratch each time. Interestingly, GWORP was shown to be more effective than the PSORP (Particle Swarm Optimization Routing Protocol) algorithm in terms of lowering energy usage and minimizing end-to-end latency. The findings also indicate that the network lifespan attained by GWORP might be about 73% longer than that achieved by PSORP.

Full Text:



J. Alkenani and K. A. Nassar, “Network Performance Analysis Using Packets Probe For Passive Monitoring,” Informatica, vol. 46, no. 7, 2022.

I. S. Alshawi, A.-K. Y. Abdulla, and A. A. Alhijaj, “Fuzzy dstar-lite routing method for energy-efficient heterogeneous wireless sensor networks,” Indones. J. Electr. Eng. Comput. Sci., vol. 19, no. 2, pp. 1000–1010, 2020.

G. Saranraj, K. Selvamani, and P. Malathi, “A novel data aggregation using multi objective based male lion optimization algorithm (DA-MOMLOA) in wireless sensor network,” J. Ambient Intell. Humaniz. Comput., no. 0123456789, 2021, doi: 10.1007/s12652-021-03230-9.

J. Alkenani, K. A. Nassar, I. Technology, and C. Information, “Network Monitoring Measurements for Quality of Service : A Review,” no. May, 2022, doi: 10.37917/ijeee.18.2.5.

F. Zhu and J. Wei, “An energy-efficient unequal clustering routing protocol for wireless sensor networks,” Int. J. Distrib. Sens. Networks, vol. 15, no. 9, p. 1550147719879384, 2019.

J. Alkenani and K. A. Nassari, “Enhance work for java based network analyzer tool used to analyze network simulator files,” vol. 29, no. 2, pp. 954–962, 2023, doi: 10.11591/ijeecs.v29.i2.pp954-962.

D. Kandris, C. Nakas, D. Vomvas, and G. Koulouras, “Applications of wireless sensor networks: an up-to-date survey,” Appl. Syst. Innov., vol. 3, no. 1, p. 14, 2020.

J. Alkenani and K. A. Nassar, “Enhanced system for ns2 trace file analysis with network performance evaluation,” Iraqi J. Intell. Comput. Informatics, vol. 1, no. 2, pp. 119–130, 2022.

M. Tanushree, H. Roopa, and V. Vani, “Hierarchical Cluster based Data Aggregation with Fault Tolerance to extend Network Lifetime,” pp. 785–789, 2020.

D. K. Altmemi, A. A. Abdulzahra, and I. S. Alshawi, “A New Approach Based on Intelligent Method to Classify Quality of Service,” Informatica, vol. 46, no. 9, 2022.

M. Al Mazaideh and J. Levendovszky, “A multi-hop routing algorithm for WSNs based on compressive sensing and multiple objective genetic algorithm,” J. Commun. Networks, no. 99, pp. 1–10, 2021.

I. S. Alshawi, Z. A. Abbood, and A. A. Alhijaj, “Extending lifetime of heterogeneous wireless sensor networks using spider monkey optimization routing protocol,” vol. 20, no. 1, pp. 212–220, 2022, doi: 10.12928/TELKOMNIKA.v20i1.20984.

O. Younis and S. Fahmy, “HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks,” IEEE Trans. Mob. Comput., vol. 3, no. 4, pp. 366–379, 2004.

A. D. Amis, R. Prakash, T. H. P. Vuong, and D. T. Huynh, “Max-min d-cluster formation in wireless ad hoc networks,” in Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No. 00CH37064), 2000, vol. 1, pp. 32–41.

I. S. AlShawi, L. Yan, W. Pan, and B. Luo, “Fuzzy chessboard clustering and artificial bee colony routing method for energy‐efficient heterogeneous wireless sensor networks,” Int. J. Commun. Syst., vol. 27, no. 12, pp. 3581–3599, 2014.

Z. Zhang, M. Ma, and Y. Yang, “Energy-efficient multihop polling in clusters of two-layered heterogeneous sensor networks,” IEEE Trans. Comput., vol. 57, no. 2, pp. 231–245, 2008.

Y. Lin, J. Zhang, H. S.-H. Chung, W. H. Ip, Y. Li, and Y.-H. Shi, “An ant colony optimization approach for maximizing the lifetime of heterogeneous wireless sensor networks,” IEEE Trans. Syst. Man, Cybern. Part C (Applications Rev., vol. 42, no. 3, pp. 408–420, 2011.

D. R. Das Adhikary and D. K. Mallick, “A fuzzy-logic based relay selection scheme for multi-hop wireless sensor networks,” in 2015 1st International Conference on Next Generation Computing Technologies (NGCT), 2015, pp. 285–290.

A. Rodríguez, C. Del-Valle-Soto, and R. Velázquez, “Energy-efficient clustering routing protocol for wireless sensor networks based on yellow saddle goatfish algorithm,” Mathematics, vol. 8, no. 9, p. 1515, 2020.

A. Hussain and G. El-Howayek, “A Sleep-Scheduling Oil Detection Routing Protocol for Smart Oceans Using Internet of Things,” in 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), 2020, pp. 1–6.

P. Tembhre and K. Cecil, “Low power consumption heterogeneous routing protocol in WSN,” in 2020 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), 2020, pp. 310–314.

C.-M. Yu and M.-L. Ku, “A Novel Balanced Routing Protocol for Lifetime Improvement in WSNs,” in 2022 IEEE International Conference on Consumer Electronics (ICCE), 2022, pp. 1–3.

X. Zhao, H. Zhu, S. Aleksic, and Q. Gao, “Energy-efficient routing protocol for wireless sensor networks based on improved grey wolf optimizer,” KSII Trans. Internet Inf. Syst., vol. 12, no. 6, pp. 2644–2657, 2018.

D. Ruan and J. Huang, “A PSO-based uneven dynamic clustering multi-hop routing protocol for wireless sensor networks,” Sensors, vol. 19, no. 8, p. 1835, 2019.

S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer,” Adv. Eng. Softw., vol. 69, pp. 46–61, 2014.

G. Halidoddi and R. Pandu, “A GOA Based Secure Routing Algorithm for Improving Packet Delivery and Energy Efficiency in Wireless Sensor Networks,” Int. J. Intell. Eng. Syst., vol. 14, no. 6, pp. 311–320, 2021.

Z. Sun, Z. Zhang, C. Xiao, and G. Qu, “DS evidence theory based trust ant colony routing in WSN,” China Commun., vol. 15, no. 3, pp. 27–41, 2018.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.