A diffusion approach to network localization

Yosi Keller, Yaniv Gur

    Research output: Contribution to journalArticlepeer-review

    19 Scopus citations


    The localization of nodes on a network is a challenging research topic. It arises in a variety of applications such as communications and sensor network analysis. We propose a computational approach to recovering the positions of network nodes given partial and corrupted distance measurements, and the positions of a small subset of anchor nodes. First, we show how to derive geometrically adaptive diffusion bases defined over the entire network, given only partial distance measurements. Second, we propose to utilize several diffusion bases simultaneously to derive multiscale diffusion frames. Last, we utilize the diffusion frames to formulate a L1 regression based extension of the anchor points coordinates to the entire network. We experimentally show that under a wide range of conditions our method compares favorably with state-of-the-art approaches.

    Original languageEnglish
    Article number5723763
    Pages (from-to)2642-2654
    Number of pages13
    JournalIEEE Transactions on Signal Processing
    Issue number6
    StatePublished - Jun 2011


    • Graph theory
    • machine learning
    • wireless sensor networks


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