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    » Formal Theory of Noisy Sensor Network Localization
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    FOCS
    2008
    IEEE
    110views Theoretical Computer Science» more  FOCS 2008»
    Lower Bounds for Noisy Wireless Networks using Sampling Algorithms
    14 years 2 months ago
    Lower Bounds for Noisy Wireless Networks using Sampling Algorithms
    Download  www.tcs.tifr.res.in
    We show a tight lower bound of Ω(N log log N) on the number of transmissions required to compute several functions (including the parity function and the majority function) in a...
    Chinmoy Dutta, Jaikumar Radhakrishnan
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    MOBIHOC
    2005
    ACM
    151views Computer Networks» more  MOBIHOC 2005»
    Localization and routing in sensor networks by local angle information
    14 years 7 months ago
    Localization and routing in sensor networks by local angle information
    Download  www.paradise.caltech.edu
    Location information is very useful in the design of sensor network infrastructures. In this paper, we study the anchor-free 2D localization problem by using local angle measureme...
    Jehoshua Bruck, Jie Gao, Anxiao Jiang
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    TON
    2010
    109views more  TON 2010»
    Rendered path: range-free localization in anisotropic sensor networks with holes
    13 years 5 months ago
    Rendered path: range-free localization in anisotropic sensor networks with holes
    Download  www.cse.ust.hk
    Sensor positioning is a crucial part of many location-dependent applications that utilize wireless sensor networks (WSNs). Current localization approaches can be divided into two ...
    Mo Li, Yunhao Liu
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    IWCMC
    2006
    ACM
    130views Computer Networks» more  IWCMC 2006»
    Distributed obstacle localization in large wireless sensor networks
    14 years 1 months ago
    Distributed obstacle localization in large wireless sensor networks
    Download  rtl.e-technik.uni-rostock.de
    Obstacles are but pleasing for many aspects of large realworld sensor networks. Among other things, the presence of obstacles distort the sensor node localization process and migh...
    Frank Reichenbach, Ralf Salomon, Dirk Timmermann
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    ICMLA
    2009
    167views Machine Learning» more  ICMLA 2009»
    Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule
    13 years 5 months ago
    Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule
    Download  ftp.cs.wisc.edu
    Languages that combine predicate logic with probabilities are needed to succinctly represent knowledge in many real-world domains. We consider a formalism based on universally qua...
    Sriraam Natarajan, Prasad Tadepalli, Gautam Kunapu...
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