A Neighborhood Grid Clustering Algorithm for Solving Localization Problem in WSN Using Genetic Algorithm

Chen, Junfeng; Sackey, Samson Hansen; Ansere, James Adu; Zhang, Xuewu; Ayush, Altangerel

doi:10.1155/2022/8552142

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…Some of these studies are summarized as shown in Table VI. The [115], [116] proposed a clustering method called Neighborhood Grid Cluster (NGC) to address the localization of WSNs nodes challenges and reduce energy consumption. The NGC approach utilizes the GA optimization process to improve the efficiency and reliability of localization algorithms (to estimate the target node's position).…”

Section: A Evolutionary Algorithmsmentioning

confidence: 99%

Optimization Algorithms for Wireless Sensor Networks Node Localization: An Overview

Ahmad,

Alhasan,

Wazirali

et al. 2024

IEEE Access

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Wireless Sensor Networks (WSNs) play a critical role in numerous applications, and the accurate localization of sensor nodes is vital for their effective operation. In recent years, optimization algorithms have garnered significant attention as a means to enhance WSN node localization. This paper presents an in-depth exploration of the necessity of localization in WSN nodes and offers a comprehensive review of optimization algorithms used for this purpose. The review encompasses a diverse range of optimization techniques, including evolutionary algorithms, swarm intelligence, and metaheuristic approaches. Key factors, such as localization accuracy, scalability, computational complexity, and robustness, are systematically evaluated and compared across various optimization algorithms. Additionally, the paper sheds light on the strengths and limitations of each optimization approach and discusses their applicability in different WSN deployment scenarios. The insights provided in this review serve as a valuable resource for researchers and practitioners seeking to optimize WSN node localization, thus promoting the efficient and reliable operation of WSNs in diverse real-world applications.

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Section: A Evolutionary Algorithmsmentioning

confidence: 99%

Optimization Algorithms for Wireless Sensor Networks Node Localization: An Overview

Ahmad,

Alhasan,

Wazirali

et al. 2024

IEEE Access

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“…In [33], data aggregation and clustering-based methods are presented to improve the lifetime of the network based on the node localization and routing mechanism. In [34], a grid clustering method is presented to identify sensor node locations based on the genetic algorithm. In [35], a detailed review is performed to analyze localization problems and their performance.…”

Section: Literature Surveymentioning

confidence: 99%

High uncertainty aware localization and error optimization of mobile nodes for wireless sensor networks

Nandyala¹,

Gandhi²

2023

IJ-AI

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<p>The localization of mobile sensor nodes in a wireless sensor network (WSN) is a key research area for the speedy development of wireless communication and microelectronics. The localization of mobile sensor nodes massively depends upon the received signal strength (RSS). Recently, the least squared relative error (LSRE) measurements are optimized using traditional semidefinite programming (SDP) and the location of the mobile sensor nodes was determined using the previous localization methods like least squared relative error and semidefinite programming (LSRE-SDP), and approximate nonlinear least squares and semidefinite programming (ANLS-SDP). Therefore, in this work, a novel high uncertainty aware-localization error correction and optimization (HUA-LECO) model is employed to minimize the aforementioned problems regarding the localization of mobile sensor nodes and enhance the performance efficiency of root mean square error (RMSE) results. Here, the position of target mobile sensor nodes is evaluated based on the gathered measurements while discarding faulty data. Here, an iterative weight updation approach is utilized to perform localization based on Monte Carlo simulations. Simulation results show significant improvement in terms of RMSE results in comparison with traditional LSRE-SDP and ANLS-SDP methods under high uncertainty.</p>

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“…To the best of our knowledge, existing grid clustering algorithms work by dealing with nodes and cells. The former mainly includes FDGB [ 21 ] and GCBD [ 22 ], while the latter mainly includes GBCN [ 23 ], GCDPP [ 24 ], NGCGAL [ 25 ], and CMSPGD [ 26 ]. However, different grid-based clustering methods have their own considerations in grid space, node or cell processing, and cluster generation strategies, resulting in differences in clustering performance.…”

Section: Related Workmentioning

confidence: 99%

A Novel Clustering Method Based on Adjacent Grids Searching

Li,

Zhong,

Liao

et al. 2023

Entropy

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Clustering is used to analyze the intrinsic structure of a dataset based on the similarity of datapoints. Its widespread use, from image segmentation to object recognition and information retrieval, requires great robustness in the clustering process. In this paper, a novel clustering method based on adjacent grid searching (CAGS) is proposed. The CAGS consists of two steps: a strategy based on adaptive grid-space construction and a clustering strategy based on adjacent grid searching. In the first step, a multidimensional grid space is constructed to provide a quantization structure of the input dataset. The noise and cluster halo are automatically distinguished according to grid density. Moreover, the adaptive grid generating process solves the common problem of grid clustering, in which the number of cells increases sharply with the dimension. In the second step, a two-stage traversal process is conducted to accomplish the cluster recognition. The cluster cores with arbitrary shapes can be found by concealing the halo points. As a result, the number of clusters will be easily identified by CAGS. Therefore, CAGS has the potential to be widely used for clustering datasets with different characteristics. We test the clustering performance of CAGS through six different types of datasets: dataset with noise, large-scale dataset, high-dimensional dataset, dataset with arbitrary shapes, dataset with large differences in density between classes, and dataset with high overlap between classes. Experimental results show that CAGS, which performed best on 10 out of 11 tests, outperforms the state-of-the-art clustering methods in all the above datasets.

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A Neighborhood Grid Clustering Algorithm for Solving Localization Problem in WSN Using Genetic Algorithm

Cited by 6 publications

References 29 publications

Optimization Algorithms for Wireless Sensor Networks Node Localization: An Overview

Optimization Algorithms for Wireless Sensor Networks Node Localization: An Overview

High uncertainty aware localization and error optimization of mobile nodes for wireless sensor networks

A Novel Clustering Method Based on Adjacent Grids Searching

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