Target Positioning and Tracking in WSNs Based on AFSA

Lee, Shu-Hung; Cheng, Chia-Hsin; Lin, Chien-Chih; Huang, Yung-Fa

doi:10.3390/info14040246

Cited by 4 publications

(1 citation statement)

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“…They introduced a novel Weight-Based Optimization (WBO) filter to optimize RSSI data, along with the measurement data from accelerometer and compass sensors, and utilizes sensor fusion techniques to achieve a positioning accuracy of up to 68 cm. In addition, a localization scheme combining artificial fish swarm algorithm (AFSA) with a region segmentation method (RSM), hybrid adaptive visual pursuit (HAVP) method, and dynamic AF selection (DAFS) method is proposed in [21], in which the total average positioning error was reduced by 96.1%, and the positioning time was shortened by 26.4% using the HAVP for the target positioning. Reference [22] introduces an algorithm that ensures robustness against environmental irregularities for localizing sensor nodes within regions delineated by anchor node networks, with the objective of achieving higher precision at the lower boundary, while also offering an analytical framework for sensor localization.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%