Improving the Efficiency of Routing in Wireless Sensor Networks Using Network Coding and Topology Control

Yousef  Saeedi; Ehsan Kharati; Elham  Mazinan

Authors

Yousef Saeedi Department of Computer Engineering, Ar.C., Islamic Azad University, Arak, Iran Author https://orcid.org/0009-0000-6405-4102
Ehsan Kharati Department of Computer Engineering, Ar.C., Islamic Azad University, Arak, Iran Corresponding author https://orcid.org/0000-0001-7458-0683
Elham Mazinan Department of Computer Engineering, Ar.C., Islamic Azad University, Arak, Iran Author https://orcid.org/0000-0002-1040-1209

Keywords:

Routing in Wireless Sensor Networks, Network Coding, Topology Control, Whale Optimization Algorithm, Fuzzy Logic.

Abstract

Wireless sensor networks (WSNs) are invaluable in diverse fields such as environment monitoring and healthcare to agriculture and military ops. However, these networks face fundamental constraints such as node energy and decline in routing efficiency with increasing density or dynamic topologies. Such issues lead to decreased network lifetime and poor quality of service. Therefore, improved network efficiency along with reduced energy expenditure and enhanced routing remain the prime goals. This research identifies and addresses the fundamental issue that innovative approaches to decrease redundant transmissions, minimize energy dissipation, and increase network lifetime can improve the real-world operability and stability of WSNs. This study emphasizes an effective combination of topology control and network coding in WSNs to extend network lifespan. By employing a hybrid approach that integrates fuzzy logic with the Whale Optimization Algorithm (WOA), it accelerates the discovery of optimal or near-optimal routing solutions, particularly in large-scale networks. Inspired by the concept of relay nodes, the proposed method uses the operational levels of network coding and topology as key routing criteria. Compared to state-of-the-art methods, the proposed approach demonstrates clear distinctions and advantages by achieving higher energy efficiency, improved routing stability, and enhanced overall network performance. The results showed that the proposed method performs better than the AECRP routing protocol by approximately 7.48% in terms of energy consumption, 60.12% in terms of reducing the number of dead nodes (maintaining the number of live nodes), 5.19% in terms of the number of selected cluster heads, and 3.125% in terms of packet delivery rate (PDR).

References

[1] L. Nie, "DRL-MURA: A joint optimization of high-definition map updating and wireless resource allocation in vehicular edge computing networks," IEEE Internet of Things Journal, vol. 12, pp. 1946-1961, 2025, doi: 10.1109/JIOT.2024.3465553.

[2] Z. Zhu, "Design and implementation of an intelligent sports management system (ISMS) using wireless sensor networks," PeerJ Computer Science, vol. 11, p. e2637, 2025, doi: 10.7717/peerj-cs.2637.

[3] D. Kandris, C. Nakas, D. Vomvas, and G. Koulouras, "Applications of Wireless Sensor Networks: An Up-to-Date Survey," (in English), Applied System Innovation, vol. 3, no. 1, p. 14, 2020, doi: 10.3390/asi3010014.

[4] C. Nakas, D. Kandris, and G. Visvardis, "Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey," (in English), Algorithms, vol. 13, no. 3, p. 72, 2020, doi: 10.3390/a13030072.

[5] P. L. Nguyen, T. H. Nguyen, and K. Nguyen, "A Path-Length Efficient, Low-Overhead, Load-Balanced Routing Protocol for Maximum Network Lifetime in Wireless Sensor Networks with Holes," (in English), Sensors, vol. 20, no. 9, p. 2506, 2020, doi: 10.3390/s20092506.

[6] X. Zhang, X. Ding, S. Lu, and G. Chen, "Principles for Energy-Efficient Topology Control in Wireless Sensor Networks," in 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing, 2009: IEEE, pp. 1-3, doi: 10.1109/WICOM.2009.5303884.

[7] R. Mochaourab and W. Dargie, "A Fair and Energy-Efficient Topology Control Protocol for Wireless Sensor Networks," in Proceedings of the 2nd ACM International Conference on Context-Awareness for Self-Managing Systems, 2008, pp. 6-15, doi: 10.1145/1367943.1367944.

[8] J. Akbari Torkestani, "An Energy-Efficient Topology Control Mechanism for Wireless Sensor Networks Based on Transmit Power Adjustment," (in English), Wireless Personal Communications, vol. 82, pp. 2537-2556, 2015, doi: 10.1007/s11277-015-2363-9.

[9] M. Thammawichai and T. Luangwilai, "An Energy-Optimization Topology Control for Three-Dimensional Wireless Sensor Networks," (in English), Journal of Communications and Networks, vol. 25, no. 6, pp. 778-788, 2023, doi: 10.23919/JCN.2023.000050.

[10] H. Guo, Y. Qian, K. Lu, and N. Moayeri, "Backbone Routing over Multihop Wireless Networks: Increased Network Coding Opportunity," in 2010 IEEE International Conference on Communications, 2010: IEEE, pp. 1-5, doi: 10.1109/ICC.2010.5502058.

[11] R. Sharma and P. Kaushik, "Network Coding Based Virtual Backbone for Wireless Sensor Network," (in English), International Journal of Computer Science and Information Technologies, vol. 3, no. 3, pp. 4341-4344, 2012.

[12] O. T. Valle, C. Montez, G. Medeiros de Araujo, F. Vasques, and R. Moraes, "NetCoDer: A Retransmission Mechanism for WSNs Based on Cooperative Relays and Network Coding," (in English), Sensors, vol. 16, no. 6, p. 799, 2016, doi: 10.3390/s16060799.

[13] X. Liu, X. Gong, and Y. Zheng, "Reliable Cooperative Communications Based on Random Network Coding in Multi-Hop Relay WSNs," (in English), IEEE Sensors Journal, vol. 14, no. 8, pp. 2514-2523, 2014, doi: 10.1109/JSEN.2014.2310899.

[14] X. Shao, C. Wang, and J. Gao, "Research on Network Coding Aware Energy Efficient Routing for Wireless Sensor Networks," (in English), EURASIP Journal on Wireless Communications and Networking, vol. 2018, no. 1, p. 231, 2018, doi: 10.1186/s13638-018-1245-8.

[15] S. Laurindo, R. Moraes, C. Montez, and F. Vasques, "Combining Network Coding and Retransmission Techniques to Improve the Communication Reliability of Wireless Sensor Network," (in English), Information, vol. 12, no. 5, p. 184, 2021, doi: 10.3390/info12050184.

[16] M. Khalily-Dermany, M. J. Nadjafi-Arani, and S. Doostali, "Combining Topology Control and Network Coding to Optimize Lifetime in Wireless-Sensor Networks," (in English), Computer Networks, vol. 162, p. 106859, 2019, doi: 10.1016/j.comnet.2019.106859.

[17] T. C. Dao, N. T. Tam, and H. T. T. Binh, "Node Depth Representation-Based Evolutionary Multitasking Optimization for Maximizing the Network Lifetime of Wireless Sensor Networks," (in English), Engineering Applications of Artificial Intelligence, vol. 128, p. 107463, 2024, doi: 10.1016/j.engappai.2023.107463.

[18] J. Wang, Y. Zhang, X. Wang, P. Mao, and B. Liu, "A Multi-Objective Parameter Optimization Approach to Maximize Lifetime of Wireless Sensor Networks Inspired by Spider Web," (in English), The Journal of Supercomputing, vol. 79, no. 2, pp. 1263-1288, 2023, doi: 10.1007/s11227-022-04676-0.

[19] R. Marappan et al., "Efficient Evolutionary Modeling in Solving Maximization of Lifetime of Wireless Sensor Healthcare Networks," (in English), Soft Computing, vol. 27, no. 16, pp. 11853-11867, 2023, doi: 10.1007/s00500-023-08623-w.

[20] A. Seyyedabbasi, F. Kiani, and T. Allahviranloo, "Optimal Data Transmission and Pathfinding for WSN and Decentralized IoT Systems Using I-GWO and Ex-GWO Algorithms," (in English), Alexandria Engineering Journal, vol. 63, pp. 339-357, 2023, doi: 10.1016/j.aej.2022.08.009.

[21] G. Naveen and J. Prathap, "Network Energy Optimization and Intelligent Routing in WSN Applicable for IoT Using Self-Adaptive Coyote Optimization Algorithm," (in English), International Journal of Communication Systems, vol. 36, no. 9, p. e5464, 2023, doi: 10.1002/dac.5464.

[22] H. Sadia et al., "Intrusion Detection System for Wireless Sensor Networks: A Machine Learning Based Approach," Ieee Access, vol. 12, pp. 52565-52582, 2024, doi: 10.1109/access.2024.3380014.

[23] S. S. Elashry, A. S. Abohamama, H. M. Abdul-Kader, M. Z. Rashad, and A. F. Ali, "A Chaotic Reptile Search Algorithm for Energy Consumption Optimization in Wireless Sensor Networks," (in English), IEEE Access, 2024, doi: 10.1109/ACCESS.2024.3374781.

[24] A. Ullah et al., "A Hybrid Approach for Energy Consumption and Improvement in Sensor Network Lifespan in Wireless Sensor Networks," (in English), Sensors, vol. 24, no. 5, p. 1353, 2024, doi: 10.3390/s24051353.

[25] S. Ullah et al., "A Comprehensive Survey of Wireless Area Networks: On PHY, MAC, and Network Layers Solutions," (in English), Journal of Medical Systems, vol. 36, no. 3, pp. 1-30, 2013.

[26] B. M. H. Zade, N. Mansouri, and M. M. Javidi, "An improved beluga whale optimization using ring topology for solving multi-objective task scheduling in cloud," Computers & Industrial Engineering, vol. 200, p. 110836, 2025, doi: 10.1016/j.cie.2024.110836.

[27] M. F. Çorapsız, "Lifetime Maximization of Wireless Sensor Networks while Ensuring Intruder Detection," (in English), Soft Computing, pp. 1-19, 2024, doi: 10.1007/s00500-024-09692-1.

[28] B. M. Sahoo, H. M. Pandey, and T. Amgoth, "GAPSO-H: A Hybrid Approach towards Optimizing the Cluster Based Routing in Wireless Sensor Network," (in English), Swarm and Evolutionary Computation, vol. 60, p. 100772, 2021, doi: 10.1016/j.swevo.2020.100772.