Optimized Localization Learning Algorithm for Indoor and Outdoor Localization System in WSNs

Yadav, Preeti; Sharma, Subhash C.; Singh, Onkar; Rishiwal, Vinay

doi:10.1007/s11277-023-10304-8

Cited by 4 publications

(1 citation statement)

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“…Experimental results consistently confirm the effectiveness of the IAOAB-NLS model, irrespective of fluctuations in network parameters [25]. Additionally, an optimization-based localization learning algorithm (OLLA) is proposed by [26], which demonstrates good performance in indoor and outdoor scenarios.…”

Section: Introductionsupporting

confidence: 56%

Performance of Differential Evolution Algorithms for Indoor Area Positioning in Wireless Sensor Networks

Lee,

Cheng,

et al. 2024

Electronics

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In positioning systems in wireless sensor networks, the accuracy of localization is often affected by signal distortion or attenuation caused by environmental factors, especially in indoor environments. Although using a combination of K-Nearest Neighbor (KNN) algorithm and fingerprinting matching can reduce positioning errors due to poor signal quality, the improvement in accuracy by increasing the number of reference points and K values is not significant. This paper proposes a Differential Evolution-based KNN (DE-KNN) method to overcome the performance limitations of the KNN algorithm and enhance indoor area positioning accuracy in WSNs. The DE-KNN method aims to improve the accuracy and stability of indoor positioning in wireless sensor networks. According to the simulation results, in a simple indoor environment with four reference points, when the sensors are deployed in both fixed and random arrangements, the positioning accuracy was improved by 29.09% and 30.20%, respectively, compared to using the KNN algorithm alone. In a complex indoor environment with four reference points, the positioning accuracy was increased by 32.24% and 33.72%, respectively. When the number of reference points increased to five, in a simple environment, the accuracy improvement for both fixed and random deployment was 20.70% and 26.01%, respectively. In a complex environment, the accuracy improvement was 23.88% and 27.99% for fixed and random deployment, respectively.

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Section: Introductionsupporting

confidence: 56%