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T3FL-GWO: Type-3 Fuzzy Logic-Based on Gray Wolf Optimization Algorithm Used to Extend the Lifetime of Wireless Sensor Network
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Abstract: (21 Views) |
Extending network lifetime due to the energy constraints of sensor nodes is one of the main challenges in wireless sensor networks (WSNs). Clustering is considered an efficient method to enhance network longevity. Although clustering in WSNs offers numerous advantages, it faces two major challenges. The first challenge lies in selecting the cluster head (CH), which is a complex problem and is recognized as an NP-hard issue. Additionally, finding the optimal cluster configuration is also challenging. In the upper layers of WSNs, several uncertainties exist, including the residual energy of nodes, cluster centrality, and the distance from the cluster to the base station (BS). To address these uncertainties and improve the lifetime of WSNs in the cluster head selection process, a combination of Type-3 Fuzzy Logic (T3FL) and the Grey Wolf Optimizer (GWO) algorithm has been employed. Type-3 Fuzzy Logic is capable of better managing the uncertainties and complexities in WSNs, enabling more accurate cluster head selection. The Grey Wolf Optimizer, as an efficient metaheuristic algorithm, assists in finding optimal solutions for complex optimization problems. The performance of the proposed T3FL-GWO algorithm has been compared with our previous method as well as other fuzzy-based algorithms. Simulation results demonstrate that this algorithm outperforms other existing approaches. These findings indicate that the integration of Type-3 Fuzzy Logic and the Grey Wolf Optimizer can effectively reduce energy consumption and enhance the overall network lifetime.
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| Keywords: wireless sensor network, clustering, uncertainty, type-3 fuzzy system, gray wolf optimizer |
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Full-Text [PDF 1410 kb]
(29 Downloads)
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Type of Study: Scientific research |
Subject:
Special
Received: 2026/05/10 | Accepted: 2026/03/16 | Published: 2026/03/16
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