Optimizing DV-Hop localization through topology-based straight-line distance estimation

Computer Networks, Journal Year: 2025, Volume and Issue: 258, P. 111025 - 111025

Published: Jan. 5, 2025

Language: Английский

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V-shaped and S-shaped binary artificial protozoa optimizer (APO) algorithm for wrapper feature selection on biological data

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(3)

Published: Jan. 21, 2025

Language: Английский

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IPML-ANP: An integrated polynomial manifold learning model and anchor node placement for wireless sensor node localization

Peer-to-Peer Networking and Applications, Journal Year: 2025, Volume and Issue: 18(2)

Published: Feb. 3, 2025

Language: Английский

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HGGRKO: An Optimized Hybrid Approach for Precision Node Localization in Wireless Sensor Networks

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: March 24, 2025

Localization, or position in Wireless Sensor Networks (WSNs), is one of the most challenging and crucial tasks a range tracking monitoring applications. This problem brought on by need to disperse network over large areas provides recently acquired location data unidentified devices. With conventional localization methods, scalability computation time constraints are frequent problems. In this paper, novel hybrid optimization strategy proposed enhance precision robustness node within WSNs. The HybridisedGreylag Goose Red Kite Optimization (HGGRKO) represents that combines two efficient metaheuristic techniques from (RKO) Greylag (GGO) algorithms accomplish objective framework. main HGGRKO-based architecture minimize error between detected actual locations each WSN. HGGRKO technique uses exploration capabilities GGO algorithm exploitation capacities RKO improve accuracy. method selects anchor nodes carefully further reduce errors. can be used number nodes, boost coverage rates, maintain connections. To evaluate effectiveness approach, MATLAB software utilized. findings demonstrate approach outperforms terms speed, localized count, minimization across variety counts, execution time.

Language: Английский

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Enhanced Target Localization in the Internet of Underwater Things through Quantum-Behaved Metaheuristic Optimization with Multi-Strategy Integration

Journal of Marine Science and Engineering, Journal Year: 2024, Volume and Issue: 12(6), P. 1024 - 1024

Published: June 19, 2024

Underwater localization is considered a critical technique in the Internet of Things (IoUTs). However, acquiring accurate location information challenging due to heterogeneous underwater environment and hostile propagation acoustic signals, especially when using received signal strength (RSS)-based techniques. Additionally, most current solutions rely on strict mathematical expressions, which limits their effectiveness certain scenarios. To address these challenges, this study develops quantum-behaved meta-heuristic algorithm, called quantum enhanced Harris hawks optimization (QEHHO), solve problem without requiring assumptions. The algorithm builds original (HHO) by integrating four strategies into various phases avoid local minima. initiation phase incorporates good point set theory computing enhance population quality, while random nonlinear introduced transition expand exploration region early stages. A correction mechanism enhancement combining slime mold (SMA) quasi-oppositional learning (QOL) are further developed find an optimal solution. Furthermore, RSS-based Cramér–Raolower bound (CRLB) derived evaluate QEHHO. Simulation results demonstrate superior performance QEHHO under conditions compared other state-of-the-art closed-form-expression- meta-heuristic-based solutions.

Language: Английский

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H2HCO-BT+: Improving Wireless Sensor Network Node Localization Accuracy by Hybrid Hunting Cat Optimization and Battle Tactics Optimization

Published: Aug. 28, 2024

Language: Английский

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A Robust Approach for Energy‐Aware Node Localization in Wireless Sensor Network Using Fitness‐Based Hybrid Heuristic Algorithms

International Journal of Communication Systems, Journal Year: 2024, Volume and Issue: 38(2)

Published: Dec. 25, 2024

ABSTRACT In wireless sensor network (WSN) applications, the Received Signal Strength Indicator (RSSI) value from original signal is determined for computing distance between unidentified and beacon nodes in WSN. However, several factors including noise, diffraction, scattering, some obstructions affect precision of localization techniques. This paper aims to implement a smart node scheme WSNs by estimating shortest unknown using RSSI factor. Initially, positioned at known position, exact location computed hybrid optimization concept. The objective proposed method reduce average error, it derived assigning each nodes. Optimization plays vital role providing clear communication among without any hindrance. hybridized algorithm named as Fitness‐aware Hybrid One‐to‐One with Archery Optimizer (FHOOAO) used positioning node. After nodes, their best positions are identified considering maximum number hops. Finally, experimentation done three different forms WSN such S‐shape, H‐shape, and. C‐shape. simulation experiments demonstrate superior outcomes model compared alternative methods, also enhances efficiency

Language: Английский

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