ALIAN: Accurate Localization by Improved Bat Search Algorithm with DV-hop in Wireless Sensor Networks

Accurate node localization is a fundamental requirement in Wireless Sensor Networks, as it underpins reliable data communication, efficient coverage, and prolonged network lifetime. Range-free localization methods such as DV-Hop are widely adopted because of their low cost and hardware simplicity; however, their accuracy degrades in irregular and large-scale deployments, mainly due to inaccurate hop-size estimation. To address this limitation, this paper proposes ALIAN (Accurate Localization by Improved Bat search Algorithm with DV-hop in wireless sensor Network), a hybrid range-free localization approach that integrates the DV-Hop algorithm with an Improved Bat Search Algorithm (IBSA). The proposed IBSA combines the global exploration capability of the Bat Search Algorithm with the clan-based exploitation and diversity-restoration mechanisms of Elephant Herding Optimization, enabling effective refinement of the node-position estimates obtained from DV-Hop. An anchor-wise hop-size correction mechanism is further introduced to reduce distance-estimation error, while the hybrid optimization framework minimizes localization error by adaptively balancing exploration and exploitation and avoiding premature convergence to local optima. Extensive MATLAB simulations conducted under varying node densities, anchor ratios, and communication radii demonstrate that ALIAN consistently outperforms conventional DV-Hop, DECHDV-Hop, and MAOA-DV-Hop. Specifically, ALIAN achieves the lowest average localization error (ALE) of 0.1233 and the highest localization ratio of 98.75%, reducing the ALE by approximately 60% relative to conventional DV-Hop and by about 25% and 33% relative to DECHDV-Hop and MAOA-DV-Hop, respectively. In addition, ALIAN lowers the localization-related communication overhead by approximately 81% compared with conventional DV-Hop and completes localization about 45% and 32% faster than DECHDV-Hop and MAOA-DV-Hop, respectively. These results confirm that ALIAN provides an accurate, scalable, and communication-efficient solution for node localization in irregular and resource-constrained wireless sensor networks.

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Publication Details

Journal
International Journal of Computational Intelligence Systems
Published
2026-09-19
DOI
https://doi.org/10.1007/s44196-026-01435-4
Primary Topic
Indoor and Outdoor Localization Technologies
Type
article
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article

ALIAN: Accurate Localization by Improved Bat Search Algorithm with DV-hop in Wireless Sensor Networks

Panimalar Kathiroli
International Journal of Computational Intelligence Systems
Indoor and Outdoor Localization Technologies
article

ALIAN: Accurate Localization by Improved Bat Search Algorithm with DV-hop in Wireless Sensor Networks

Panimalar Kathiroli
article en

Abstract

Accurate node localization is a fundamental requirement in Wireless Sensor Networks, as it underpins reliable data communication, efficient coverage, and prolonged network lifetime. Range-free localization methods such as DV-Hop are widely adopted because of their low cost and hardware simplicity; however, their accuracy degrades in irregular and large-scale deployments, mainly due to inaccurate hop-size estimation. To address this limitation, this paper proposes ALIAN (Accurate Localization by Improved Bat search Algorithm with DV-hop in wireless sensor Network), a hybrid range-free localization approach that integrates the DV-Hop algorithm with an Improved Bat Search Algorithm (IBSA). The proposed IBSA combines the global exploration capability of the Bat Search Algorithm with the clan-based exploitation and diversity-restoration mechanisms of Elephant Herding Optimization, enabling effective refinement of the node-position estimates obtained from DV-Hop. An anchor-wise hop-size correction mechanism is further introduced to reduce distance-estimation error, while the hybrid optimization framework minimizes localization error by adaptively balancing exploration and exploitation and avoiding premature convergence to local optima. Extensive MATLAB simulations conducted under varying node densities, anchor ratios, and communication radii demonstrate that ALIAN consistently outperforms conventional DV-Hop, DECHDV-Hop, and MAOA-DV-Hop. Specifically, ALIAN achieves the lowest average localization error (ALE) of 0.1233 and the highest localization ratio of 98.75%, reducing the ALE by approximately 60% relative to conventional DV-Hop and by about 25% and 33% relative to DECHDV-Hop and MAOA-DV-Hop, respectively. In addition, ALIAN lowers the localization-related communication overhead by approximately 81% compared with conventional DV-Hop and completes localization about 45% and 32% faster than DECHDV-Hop and MAOA-DV-Hop, respectively. These results confirm that ALIAN provides an accurate, scalable, and communication-efficient solution for node localization in irregular and resource-constrained wireless sensor networks.

International Journal of Computational Intelligence Systems
SRM Institute of Science and Technology (IN)
Openalex Percentile: Top 20%
Indoor and Outdoor Localization Technologies
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