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Distributed Recovery

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Compressed Sensing for Distributed Systems

Part of the book series: SpringerBriefs in Electrical and Computer Engineering ((BRIEFSSIGNAL))

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Abstract

This chapter surveys a few basic algorithms for distributed reconstruction from compressive measurements in a network of nodes, which may be sensors or nodes that collect measurements from different sensors. This estimation problem can be recast into an optimization problem where a convex and separable loss function should be minimized subject to sparsity constraints. The goal of the network is to handle distributed sparse estimation. Clearly, to achieve such a goal, the nodes must share, at least partially, their estimation. A single node typically has limited memory and processing capability; therefore, cooperation is the key to compensate for this lack and achieve satisfactory performance. Cooperation, however, raises the problem of communication among nodes, which is known to be the largest consumer of the limited energy of a node, compared to other functions such as sensing and computation. Particular attention is devoted to energy efficiency, in terms of transmissions and memory requirements.

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Authors and Affiliations

  1. Electronics and Telecommunications, Polytechnic University of Turin, Turin, Italy

    Giulio Coluccia, Chiara Ravazzi & Enrico Magli

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  1. Giulio Coluccia
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  2. Chiara Ravazzi
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  3. Enrico Magli
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Correspondence to Giulio Coluccia .

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Coluccia, G., Ravazzi, C., Magli, E. (2015). Distributed Recovery. In: Compressed Sensing for Distributed Systems. SpringerBriefs in Electrical and Computer Engineering(). Springer, Singapore. https://doi.org/10.1007/978-981-287-390-3_5

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  • DOI: https://doi.org/10.1007/978-981-287-390-3_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-287-389-7

  • Online ISBN: 978-981-287-390-3

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