Distributed regression: an efficient framework for modeling sensor network data

By Carlos Guestrin, Peter Bodík, Romain Thibaux, Mark A. Paskin, Samuel Madden. Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004, 2004

495 citations recorded by the source corpus.

Subfields: Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/8f4728b434e0c7d6f01dae506702aac86476cd01

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In Computer Networks and Communications

Citations in the corpus

Distributed Multi-task Learning for Sensor Network (2017, result): “[10] is the existing work which is most similar to our work.”
Discrete Signal Processing on Graphs (2012, unknown): “Examples in clude regression algorithms [31], wavelet decompositions [32], [33], [34], [30], [35], filter banks on graphs [36], [37], denoising [38], and compression [39].”
Online outlier detection in sensor data using non-parametric models (2006, unknown): “Copyright 2006 VLDB Endowment, ACM 1-59593-385-9/06/09 Recently proposed techniques [16, 20], that operate on ap-”
Incremental Adaptive Strategies Over Distributed Networks (2007, methodology): “This work can also be extended to operate over the collaboration protocols proposed in [ 32 ] and [33] and to other adaptive algorithms at the node level.”
A Survey of Fault Management in Wireless Sensor Networks (2007, unknown): “Typically, a model of sensor data is needed and missing data can be interpolated using spatial and temporal correlations among sensor readings [32–36].”
Near-optimal sensor placements: maximizing information while minimizing communication cost (2006, methodology): “This is an extension of the well-known regression problem [8], where we use the measured sensor data to predict values at locations where no sensors are placed.”
Approximate Data Collection in Sensor Networks using Probabilistic Models (2006, background): “One way to significantly reduce communication cost in sensor networks is to perform in-network aggregation (e.g., AVG and MIN) [19] [11] or data reduction via wavelets or distributed regression [22] [12].”
Distributed learning in wireless sensor networks (2005, methodology): “Along these lines, [33] developed a distributed algorithm based on a distributed Gaussian elimination algorithm executed on a cleverly engineered junction tree.”
Materials that couple sensing, actuation, computation, and communication (2015, methodology): “A distributed algorithm for performing regression is presented in [45], where regression model consisting of a linear combination of local measurements over a window of time.”
Graph Signal Processing: Overview, Challenges, and Applications (2017, background): “Coming from another angle, motivated by processing data collected by sensor networks where sensors are irregularly placed, different authors develop regression algorithms [62], wavelet decompositions [63], [64], [65], [61], [66], filter banks on graphs [67], [68], de-noising [69], and compression”
Model-based approximate querying in sensor networks (2005, methodology): “More recently, a suite of efficient algorithms has been developed for robustly solving inference tasks in a distributed fashion [29, 41].”
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications (2014, methodology): “1) Distributed regression framework: In [69], Guestrinet al.”