A Kernel-Based Node Localization in Anisotropic Wireless Sensor Network | Zendy

Wenxiu He | Zendy; Fangfang Lu | Zendy; Jingjing Chen | Zendy; Yi Ruan | Zendy; Tingjuan Lu | Zendy; Yi Zhang | Zendy

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A Kernel-Based Node Localization in Anisotropic Wireless Sensor Network

Author(s) -

Wenxiu He,

Fangfang Lu,

Jingjing Chen,

Yi Ruan,

Tingjuan Lu,

Yi Zhang

Publication year - 2021

Publication title -

scientific programming

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.269

H-Index - 36

eISSN - 1875-919X

pISSN - 1058-9244

DOI - 10.1155/2021/9944358

Subject(s) - wireless sensor network , beacon , kernel (algebra) , computer science , node (physics) , isotropy , polynomial kernel , key distribution in wireless sensor networks , kernel method , algorithm , wireless , computer network , wireless network , artificial intelligence , mathematics , telecommunications , physics , discrete mathematics , support vector machine , acoustics , quantum mechanics

Wireless sensors localization is still the main problem concerning wireless sensor networks (WSN). Unfortunately, range-free node localization of WSN results in a fatal weakness–, low accuracy. In this paper, we introduce kernel regression to node localization of anisotropic WSN, which transfers the problem of localization to the problem of kernel regression. Radial basis kernel-based G-LSVR and polynomial-kernel-based P-LSVR proposed are compared with classical DV-Hop in both isotropic WSN and anisotropic WSN under different proportion beacons, network scales, and disturbances of communication range. G-LSVR presents the best localization accuracy and stability from the simulation results.

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