TDOA-based WSN localization with hybrid covariance matrix adaptive evolutionary strategy and gradient descent distance techniques

Wireless Sensor Networks (WSNs) play a vital role in modern intelligent systems, with localization techniques critical for determining the precise positions of sensor nodes. Time Difference of Arrival (TDOA) is a widely utilized method for node localization. However, its accuracy suffers in noisy en...

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Main Authors: V.G. Saranya, S. Karthik
Format: Article
Language:English
Published: Elsevier 2025-01-01
Series:Alexandria Engineering Journal
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Online Access:http://www.sciencedirect.com/science/article/pii/S1110016824016892
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author V.G. Saranya
S. Karthik
author_facet V.G. Saranya
S. Karthik
author_sort V.G. Saranya
collection DOAJ
description Wireless Sensor Networks (WSNs) play a vital role in modern intelligent systems, with localization techniques critical for determining the precise positions of sensor nodes. Time Difference of Arrival (TDOA) is a widely utilized method for node localization. However, its accuracy suffers in noisy environments. This study introduces an innovative hybrid methodology that integrates Covariance Matrix Adaptive Evolutionary Strategy (CMA-ES) with Gradient Descent Distance (GDD) optimization to enhance the accuracy of TDOA-based localization. The hybrid approach utilizes the robust search capabilities of CMA-ES for initial location estimation, thereafter employing GDD to iteratively decrease the mean squared error (MSE) between estimated and real node locations. Simulations performed on several network settings showed substantial improvements. The TDOA-CMAES with GDD strategy demonstrated a maximum enhancement of 24.8 % in localization accuracy, a 36 % decrease in mean localization error (MLE), and an 18 % reduction in computation time relative to traditional approaches such as JAYA and Particle Swarm Optimization (PSO) and other current techniques. The hybrid method produced an Average Localization Error (ALE) much lower than existing techniques, illustrating its resilience in noisy conditions. Our hybrid TDOA-CMAES-GDD localization system is good for real-time WSN applications that need to quickly and accurately locate nodes because it reduces the time it takes to calculate and improves its accuracy. The method thus offers a strong, effective approach for improving TDOA-based localization in noisy settings.
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spelling doaj-art-64993630815646fa85fbb1e90b63fd542025-01-29T05:00:19ZengElsevierAlexandria Engineering Journal1110-01682025-01-01112723738TDOA-based WSN localization with hybrid covariance matrix adaptive evolutionary strategy and gradient descent distance techniquesV.G. Saranya0S. Karthik1Corresponding author.; Department of Electronics and Communication Engineering, Faculty of Engineering & Technology, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, IndiaDepartment of Electronics and Communication Engineering, Faculty of Engineering & Technology, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, IndiaWireless Sensor Networks (WSNs) play a vital role in modern intelligent systems, with localization techniques critical for determining the precise positions of sensor nodes. Time Difference of Arrival (TDOA) is a widely utilized method for node localization. However, its accuracy suffers in noisy environments. This study introduces an innovative hybrid methodology that integrates Covariance Matrix Adaptive Evolutionary Strategy (CMA-ES) with Gradient Descent Distance (GDD) optimization to enhance the accuracy of TDOA-based localization. The hybrid approach utilizes the robust search capabilities of CMA-ES for initial location estimation, thereafter employing GDD to iteratively decrease the mean squared error (MSE) between estimated and real node locations. Simulations performed on several network settings showed substantial improvements. The TDOA-CMAES with GDD strategy demonstrated a maximum enhancement of 24.8 % in localization accuracy, a 36 % decrease in mean localization error (MLE), and an 18 % reduction in computation time relative to traditional approaches such as JAYA and Particle Swarm Optimization (PSO) and other current techniques. The hybrid method produced an Average Localization Error (ALE) much lower than existing techniques, illustrating its resilience in noisy conditions. Our hybrid TDOA-CMAES-GDD localization system is good for real-time WSN applications that need to quickly and accurately locate nodes because it reduces the time it takes to calculate and improves its accuracy. The method thus offers a strong, effective approach for improving TDOA-based localization in noisy settings.http://www.sciencedirect.com/science/article/pii/S1110016824016892Wireless sensor networksTime Difference of ArrivalCovariance Matrix Adaptive Evolutionary StrategyAnd Gradient descent distance
spellingShingle V.G. Saranya
S. Karthik
TDOA-based WSN localization with hybrid covariance matrix adaptive evolutionary strategy and gradient descent distance techniques
Alexandria Engineering Journal
Wireless sensor networks
Time Difference of Arrival
Covariance Matrix Adaptive Evolutionary Strategy
And Gradient descent distance
title TDOA-based WSN localization with hybrid covariance matrix adaptive evolutionary strategy and gradient descent distance techniques
title_full TDOA-based WSN localization with hybrid covariance matrix adaptive evolutionary strategy and gradient descent distance techniques
title_fullStr TDOA-based WSN localization with hybrid covariance matrix adaptive evolutionary strategy and gradient descent distance techniques
title_full_unstemmed TDOA-based WSN localization with hybrid covariance matrix adaptive evolutionary strategy and gradient descent distance techniques
title_short TDOA-based WSN localization with hybrid covariance matrix adaptive evolutionary strategy and gradient descent distance techniques
title_sort tdoa based wsn localization with hybrid covariance matrix adaptive evolutionary strategy and gradient descent distance techniques
topic Wireless sensor networks
Time Difference of Arrival
Covariance Matrix Adaptive Evolutionary Strategy
And Gradient descent distance
url http://www.sciencedirect.com/science/article/pii/S1110016824016892
work_keys_str_mv AT vgsaranya tdoabasedwsnlocalizationwithhybridcovariancematrixadaptiveevolutionarystrategyandgradientdescentdistancetechniques
AT skarthik tdoabasedwsnlocalizationwithhybridcovariancematrixadaptiveevolutionarystrategyandgradientdescentdistancetechniques