A Performance Analysis of an Enhanced Graded Precision Localization Algorithm for Wireless Sensor Networks

Yuvarasu, Mani; Balaram, Allam; Chandramohan, S.; Sharma, Dilip Kumar

doi:10.1080/01969722.2023.2166709

Cited by 15 publications

(2 citation statements)

References 25 publications

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“…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. Shilpi and Kumar, A. proposed that the method improves localization accuracy in a variety of isotropic, O-shaped anisotropic, and S-shaped anisotropic wireless sensor networks, thereby reducing the influence of various anisotropy factors by utilizing the nonparametric Jaya algorithm (JA) and range-aware reliable anchor pairs (RAP) selection method, which provides better localization accuracy compared to four existing node localization methods, including Distance Vector (DV)-maxHop [23].…”

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%

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

Lee,

Cheng,

et al. 2024

Electronics

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“…This diagram is then further exploited for position estimation. Localization in WSN is an active area of research and many other location estimation algorithms have also been proposed, such as [13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

A Distance Vector Hop-Based Secure and Robust Localization Algorithm for Wireless Sensor Networks

et al. 2023

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Location information of sensor nodes in a wireless sensor network is important. The sensor nodes are usually required to ascertain their positions so that the data collected by these nodes can be labeled with this information. On the other hand, certain attacks on wireless sensor networks lead to the incorrect estimation of sensor node positions. In such situations, when the location information is not correct, the data may be labeled with wrong location information that may subvert the desired operation of the wireless sensor network. In this work, we formulate and propose a distance vector hop-based algorithm to provide secure and robust localization in the presence of malicious sensor nodes that result in incorrect position estimation and jeopardize the wireless sensor network operation. The algorithm uses cryptography to ensure secure and robust operation in the presence of adversaries in the sensor network. As a result of the countermeasures, the attacks are neutralized and the sensor nodes are able to estimate their positions as desired. Our secure localization algorithm provides a defense against various types of security attacks, such as selective forwarding, wormhole, Sybil, tampering, and traffic replay, compared with other algorithms which provide security against only one or two types. Simulation experiments are performed to evaluate the performance of the proposed method, and the results indicate that our secure localization algorithm achieves the design objectives successfully. Performance of the proposed method is also compared with the performance of basic distance vector hop algorithm and two secure algorithms based on distance vector hop localization. The results reveal that our proposed secure localization algorithm outperforms the compared algorithms in the presence of multiple attacks by malicious nodes.

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Plant Disease Detection Using Convolution Neural Network

Rani,

Lavanya,

Madhuri

et al. 2024

2024 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)

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A Performance Analysis of an Enhanced Graded Precision Localization Algorithm for Wireless Sensor Networks

Cited by 15 publications

References 25 publications

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

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

A Distance Vector Hop-Based Secure and Robust Localization Algorithm for Wireless Sensor Networks

Plant Disease Detection Using Convolution Neural Network

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