Manifold Learning With Localized Procrustes Analysis Based WSN Localization
Autor: | Shekhar Verma, Manish Kumar, Adarsh Prasad Behera, Abhishek Singh |
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Rok vydání: | 2020 |
Předmět: |
Computer science
020208 electrical & electronic engineering 010401 analytical chemistry Nonlinear dimensionality reduction 02 engineering and technology Riemannian manifold 01 natural sciences Manifold 0104 chemical sciences Sensor node Norm (mathematics) 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Procrustes analysis Isomap Instrumentation Wireless sensor network Algorithm |
Zdroj: | IEEE Sensors Letters. 4:1-4 |
ISSN: | 2475-1472 |
Popis: | In this letter, we propose a novel system for localization of a wireless sensor network using the localized Procrustes analysis with manifold learning. Due to different environmental factors, such as noise and fading, the internode distances calculated by the received signal strength indicator are erroneous and may not satisfy the $L2$ norm. The nodes seemingly lie on an unknown high-dimensional manifold that satisfies the Riemannian manifold assumption. Thus, the problem of localization is transformed into the problem of nonlinear dimensionality reduction. The location of the nodes can be learned through a manifold learning algorithm like Isomap. However, the coordinates so obtained needed to be transformed to obtain the correct locations of the nodes. We use the localized Procrustes analysis in order to align the relative locations to the absolute or global locations. The localized Procrustes analysis considers each sensor node's neighborhood and computes distinct parameters for each sensor based on the transformation parameters of the anchors present in its neighborhood. Experiment results show that our proposed system outperformed existing localization schemes and can localize the nodes with a better accuracy up to 31.27% in contrast to the least square optimization method. |
Databáze: | OpenAIRE |
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