Publication

Near-optimal Sensor Placement for Signals lying in a Union of Subspaces

Martin Vetterli, Juri Ranieri, Dalia Salem Hassan Fahmy El Badawy
2014
Conference Papers

Abstract

Sensor networks are commonly deployed to measure data from the environment and accurately estimate certain parameters. However, the number of deployed sensors is often limited by several constraints, such as their cost. Therefore, their locations must be opportunely optimized to enhance the estimation of the parameters. In a previous work, we considered a low-dimensional linear model for the measured data and proposed a near-optimal algorithm to optimize the sensor placement. In this paper, we propose a more complex model to further reduce the amount of sensors without degrading the quality of the estimation. Moreover, we introduce a greedy algorithm for the sensor placement for such model and show the near-optimality of its solution. Finally, we verify with numerical experiments the advantage of the proposed model in reducing the number of sensors while maintaining intact the estimation performance

Official source

https://infoscience.epfl.ch/entities/publication/4d04caa0-f7b0-4da9-a78e-a9ccc1ff128a

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Martin Vetterli, Juri Ranieri, Dalia Salem Hassan Fahmy El Badawy
2014
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/4d04caa0-f7b0-4da9-a78e-a9ccc1ff128a

About this result

Related concepts (19)

Wireless sensor network

Wireless sensor networks (WSNs) refer to networks of spatially dispersed and dedicated sensors that monitor and record the physical conditions of the environment and forward the collected data to a central location. WSNs can measure environmental conditions such as temperature, sound, pollution levels, humidity and wind. These are similar to wireless ad hoc networks in the sense that they rely on wireless connectivity and spontaneous formation of networks so that sensor data can be transported wirelessly.

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Nonlinear dimensionality reduction, also known as manifold learning, refers to various related techniques that aim to project high-dimensional data onto lower-dimensional latent manifolds, with the goal of either visualizing the data in the low-dimensional space, or learning the mapping (either from the high-dimensional space to the low-dimensional embedding or vice versa) itself. The techniques described below can be understood as generalizations of linear decomposition methods used for dimensionality reduction, such as singular value decomposition and principal component analysis.

Dimensionality reduction

Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension. Working in high-dimensional spaces can be undesirable for many reasons; raw data are often sparse as a consequence of the curse of dimensionality, and analyzing the data is usually computationally intractable (hard to control or deal with).

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