Accurate and Scalable DV-Hop-Based WSN Localization with Parameter-Free Fire Hawk Optimizer

Doğan Yıldız ()
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Doğan Yıldız: Department of Electrical and Electronics Engineering, Faculty of Engineering, Ondokuz Mayıs University, 55139 Atakum, Samsun, Türkiye

Mathematics, 2025, vol. 13, issue 20, 1-45

Abstract: Wireless Sensor Networks (WSNs) have emerged as a foundational technology for monitoring and data collection in diverse domains such as environmental sensing, smart agriculture, and industrial automation. Precise node localization plays a vital role in WSNs, enabling effective data interpretation, reliable routing, and spatial context awareness. The challenge intensifies in range-free settings, where a lack of direct distance data demands efficient indirect estimation methods, particularly in large-scale, energy-constrained deployments. This work proposes a hybrid localization framework that integrates the distance vector-hop (DV-Hop) range-free localization algorithm with the Fire Hawk Optimizer (FHO), a nature-inspired metaheuristic method inspired by the predatory behavior of fire hawks. The proposed FHODV-Hop method enhances location estimation accuracy while maintaining low computational overhead by inserting the FHO into the third stage of the DV-Hop algorithm. Extensive simulations are conducted on multiple topologies, including random, circular, square-grid, and S-shaped, under various network parameters such as node densities, anchor rates, population sizes, and communication ranges. The results show that the proposed FHODV-Hop model achieves competitive performance in Average Localization Error (ALE), localization ratio, convergence behavior, computational, and runtime efficiency. Specifically, FHODV-Hop reduces the ALE by up to 35% in random deployments, 25% in circular networks, and nearly 45% in structured square-grid layouts compared to the classical DV-Hop. Even under highly irregular S-shaped conditions, the algorithm achieves around 20% improvement. Furthermore, convergence speed is accelerated by approximately 25%, and computational time is reduced by nearly 18%, demonstrating its scalability and practical applicability. Therefore, these results demonstrate that the proposed model offers a promising balance between accuracy and practicality for real-world WSN deployments.

Keywords: wireless sensor networks (WSNs); node localization; DV-Hop algorithm; metaheuristics; fire hawk optimizer (FHO) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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