Compressive data aggregation from Poisson point process observations

Giancarlo Pastor, Ilkka Norros, Riku Jäntti, Antonio Caamaño

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    3 Citations (Scopus)


    This paper introduces Stochastic Compressive Data Aggrega The Poisson point process (PPP) models the random deployment, and at the same time, allows the efficient implementation of an adequate sparsifying matrix, the random discrete Fourier transform (RDFT). The signal recovery is based on the RDFT which reveals the frequency content of smooth signals, such as temperature or humidity maps, which consist of few frequency components. The recovery methods are based on the accelerated iterative hard thresholding (AIHT) which sets all but the largest (in magnitude) frequency components to zero. The adoption of the PPP allows to analyze the communication and compression aspects of S-CDA using previous results from stochastic geometry and compressed sensing, respectively.
    Original languageEnglish
    Title of host publicationWireless Communication Systems (ISWCS), 2015 International Symposium on
    PublisherIEEE Institute of Electrical and Electronic Engineers
    ISBN (Electronic)978-1-4673-6540-6, 978-1-4673-6539-0
    Publication statusPublished - 2015
    MoE publication typeA4 Article in a conference publication
    Event12th International Symposium on Wireless Communication Systems, ISWCS 2015 - Brussels, Belgium
    Duration: 25 Aug 201528 Aug 2015
    Conference number: 12


    Conference12th International Symposium on Wireless Communication Systems, ISWCS 2015
    Abbreviated titleISWCS


    • stochastic geometry
    • point process
    • compressed sensing
    • compressive sampling
    • data aggregation
    • sensor networks


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