An Enhanced Clustering Strategy for Wireless Sensor Networks with Robust Failure Recovery
Corresponding author: Maruthi Hanumanthappa Chandrappa
Abstract
Wireless Sensor Networks (WSNs) face challenges in balancing energy efficiency and operational robustness due to limited battery resources and frequent node failures. This paper introduces the Enhanced Balanced Clustering with Secondary Head (EBCSH) protocol, a novel strategy that significantly improves network longevity and fault tolerance. EBCSH employs k-means clustering for balanced cluster formation, fuzzy logic for intelligent Cluster Head (CH) selection based on residual energy and spatial centrality, and introduces a Secondary Cluster Head (SCH) mechanism to provide seamless failure recovery. A Poisson-based failure model governs the SCH transition logic, enabling the timely replacement of failed CHs with minimal communication overhead. Simulation results, averaged over 10 trials and conducted in MATLAB R2023b, demonstrate that EBCSH sustains network functionality for up to 8100 rounds, markedly outperforming benchmark protocols such as LEACH, BCF, and LEACH-USC. Furthermore, the proposed method achieves balanced energy distribution, low energy variance, and a near-perfect failure recovery rate (98%). These enhancements make EBCSH a promising protocol for mission-critical WSN deployments in dynamic and failure-prone environments. The paper includes detailed methodological formulation, performance analysis, and discussion of potential extensions to further optimize resilience and energy efficiency.
Keywords:
wireless sensor networks, fault tolerance, cluster head selection, energy-efficient clustering, secondary cluster head mechanismDownloads
References
A. Shukla and S. Tripathi, "A multi-tier based clustering framework for scalable and energy efficient WSN-assisted IoT network," Wireless Networks, vol. 26, no. 5, pp. 3471–3493, Jul. 2020. DOI: https://doi.org/10.1007/s11276-020-02277-4
H. E. Alami and A. Najid, "Optimization of energy efficiency in wireless sensor networks and internet of things," in Nature-Inspired Computing Applications in Advanced Communication Networks, 2019, pp. 89–127. DOI: https://doi.org/10.4018/978-1-7998-1626-3.ch005
C. Nakas, D. Kandris, and G. Visvardis, "Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey," Algorithms, vol. 13, no. 3, Mar. 2020, Art. no. 72. DOI: https://doi.org/10.3390/a13030072
H. Mohapatra and A. K. Rath, "Fault tolerance through Energy Balanced Cluster Formation (EBCF) in WSN," in Advances in Intelligent Systems and Computing, 2018, vol.851, pp. 313–321. DOI: https://doi.org/10.1007/978-981-13-2414-7_29
J. Singh, S. S. Yadav, V. Kanungo, N. Yogita, and V. Pal, "A node overhaul scheme for energy efficient clustering in wireless sensor networks," IEEE Sensors Letters, vol. 5, no. 4, pp. 1–4, Mar. 2021. DOI: https://doi.org/10.1109/LSENS.2021.3068184
M. Adnan, L. Yang, T. Ahmad, and Y. Tao, "An unequally clustered multi-hop routing protocol based on fuzzy logic for wireless sensor networks," IEEE Access, vol. 9, pp. 38531–38545, Jan. 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3063097
S. Khriji, D. E. Houssaini, I. Kammoun, and O. Kanoun, "A Fuzzy Based Energy Aware Unequal Clustering for Wireless Sensor Networks," in Ad-hoc, Mobile, and Wireless Networks, 2018, pp. 126–131. DOI: https://doi.org/10.1007/978-3-030-00247-3_12
S. P. Singh and S. C. Sharma, "A novel energy efficient clustering algorithm for wireless sensor networks," Engineering Technology & Applied Science Research, vol. 7, no. 4, pp. 1775–1780, Aug. 2017. DOI: https://doi.org/10.48084/etasr.1277
P. Kathiroli and K. Selvadurai, "Energy efficient cluster head selection using improved Sparrow Search Algorithm in Wireless Sensor Networks," Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 10, Part A, pp. 8564–8575, Nov. 2022. DOI: https://doi.org/10.1016/j.jksuci.2021.08.031
M. Tay and A. Senturk, "A New Energy-Aware Cluster Head Selection Algorithm for Wireless Sensor Networks," Wireless Personal Communications, vol. 122, no. 3, pp. 2235–2251, Feb. 2022. DOI: https://doi.org/10.1007/s11277-021-08990-3
B. M. Sahoo, H. M. Pandey, and T. Amgoth, "A genetic algorithm inspired optimized cluster head selection method in wireless sensor networks," Swarm and Evolutionary Computation, vol. 75, Dec. 2022, Art. no. 101151. DOI: https://doi.org/10.1016/j.swevo.2022.101151
N. Sikarwar and R. S. Tomar, "A New Approach for Wireless Sensor Networks based on Tree-based Routing using Hybrid Fuzzy C-Means with Genetic Algorithm," Engineering Technology & Applied Science Research, vol. 14, no. 3, pp. 14141–14147, Jun. 2024. DOI: https://doi.org/10.48084/etasr.7078
M. Rami Reddy, M. L. Ravi Chandra, P. Venkatramana, and R. Dilli, "Energy-Efficient Cluster Head Selection in Wireless Sensor Networks Using an Improved Grey Wolf Optimization Algorithm," Computers, vol. 12, no. 2, Feb. 2023, Art. no. 35. DOI: https://doi.org/10.3390/computers12020035
A. Jalili, M. Gheisari, J. A. Alzubi, C. Fernández-Campusano, F. Kamalov, and S. Moussa, "A novel model for efficient cluster head selection in mobile WSNs using residual energy and neural networks," Measurement Sensors, vol. 33, Apr. 2024, Art. no. 101144. DOI: https://doi.org/10.1016/j.measen.2024.101144
N. Kumar, P. Rani, V. Kumar, S. V. Athawale, and D. Koundal, "THWSN: Enhanced Energy-Efficient Clustering Approach for Three-Tier Heterogeneous Wireless Sensor Networks," IEEE Sensors Journal, vol. 22, no. 20, pp. 20053–20062, Jul. 2022. DOI: https://doi.org/10.1109/JSEN.2022.3200597
M. A. Aydin, B. Karabekir, and A. H. Zaim, "Energy Efficient Clustering-Based Mobile Routing Algorithm on WSNs," IEEE Access, vol. 9, pp. 89593–89601, Jan. 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3090979
S. Oubadi, L. Derdouri, Z. Laboudi, and M. Demri, "A Distributed Energy-Efficient Clustering Routing Protocol with Dynamic Round-Length for Wireless Sensor Networks," Engineering Technology & Applied Science Research, vol. 15, no. 3, pp. 22818–22829, Jun. 2025. DOI: https://doi.org/10.48084/etasr.10507
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