Article

Distributed sparse random projections for refinable approximation

Authors:

Minos Garofalakis,

Kannan RamchandranAuthors Info & Claims

IPSN '07: Proceedings of the 6th international conference on Information processing in sensor networks

Pages 331 - 339

https://doi.org/10.1145/1236360.1236403

Published: 25 April 2007 Publication History

Abstract

Consider a large-scale wireless sensor network measuring compressible data, where n distributed data values can be well-approximated using only k « n coefficients of some known transform. We address the problem of recovering an approximation of the n data values by querying any L sensors, so that the reconstruction error is comparable to the optimal k-term approximation. To solve this problem, we present a novel distributed algorithm based on sparse random projections, which requires no global coordination or knowledge. The key idea is that the sparsity of the random projections greatly reduces the communication cost of pre-processing the data. Our algorithm allows the collector to choose the number of sensors to query according to the desired approximation error. The reconstruction quality depends only on the number of sensors queried, enabling robust refinable approximation.

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Cited By

Azarnia GTinati MYousefi Rezaii T(2021)Distributed and Cooperative Compressive Sensing Recovery Algorithm for Wireless Sensor Networks with Bi-directional Incremental TopologySignal and Data Processing10.52547/jsdp.18.3.6518:3(65-76)Online publication date: 1-Dec-2021
https://doi.org/10.52547/jsdp.18.3.65
Gu XZhu MZhuang L(2021)Highly Efficient Spatial–Temporal Correlation Basis for 5G IoT NetworksSensors10.3390/s2120689921:20(6899)Online publication date: 18-Oct-2021
https://doi.org/10.3390/s21206899
Mehrjoo SKhunjush FGhaedi A(2020)Fully distributed sleeping compressive data gathering in wireless sensor networksIET Communications10.1049/iet-com.2019.007714:5(830-837)Online publication date: Mar-2020
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Distributed sparse random projections for refinable approximation

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Published In

cover image ACM Conferences

IPSN '07: Proceedings of the 6th international conference on Information processing in sensor networks

April 2007

592 pages

ISBN:9781595936387

DOI:10.1145/1236360

General Chair:
Tarek Abdelzaher
University of Illinois at Urbana-Champaign
,
Program Chairs:
Leo Guibas
Stanford University
,
Matt Welsh
Harvard University

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 25 April 2007

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IPSN07

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IPSN07: The 6th International Conference on Information Processing in Sensor Networks

April 25 - 27, 2007

Massachusetts, Cambridge, USA

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Overall Acceptance Rate 143 of 593 submissions, 24%

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Cited By

Azarnia GTinati MYousefi Rezaii T(2021)Distributed and Cooperative Compressive Sensing Recovery Algorithm for Wireless Sensor Networks with Bi-directional Incremental TopologySignal and Data Processing10.52547/jsdp.18.3.6518:3(65-76)Online publication date: 1-Dec-2021
https://doi.org/10.52547/jsdp.18.3.65
Gu XZhu MZhuang L(2021)Highly Efficient Spatial–Temporal Correlation Basis for 5G IoT NetworksSensors10.3390/s2120689921:20(6899)Online publication date: 18-Oct-2021
https://doi.org/10.3390/s21206899
Mehrjoo SKhunjush FGhaedi A(2020)Fully distributed sleeping compressive data gathering in wireless sensor networksIET Communications10.1049/iet-com.2019.007714:5(830-837)Online publication date: Mar-2020
https://doi.org/10.1049/iet-com.2019.0077
Hao JWang RZhang BZhuang YChen B(2018)Compressed Sensing Based Joint Rate Allocation and Routing Design in Wireless Sensor NetworksWireless Communications & Mobile Computing10.1155/2018/62614532018(22)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1155/2018/6261453
Yu XBaek S(2017)Energy-Efficient Collection of Sparse Data in Wireless Sensor Networks Using Sparse Random MatricesACM Transactions on Sensor Networks10.1145/308557613:3(1-36)Online publication date: 16-Aug-2017
https://dl.acm.org/doi/10.1145/3085576
Li YLi JJi YGu YChen L(2017)Coflow-aware dynamic routing for SDN-based data center networks2017 9th International Conference on Wireless Communications and Signal Processing (WCSP)10.1109/WCSP.2017.8171151(1-6)Online publication date: Oct-2017
https://doi.org/10.1109/WCSP.2017.8171151
Li JZheng HFeng XChen Z(2017)Traffic estimation in road networks via compressive sensing2017 9th International Conference on Wireless Communications and Signal Processing (WCSP)10.1109/WCSP.2017.8171137(1-6)Online publication date: Oct-2017
https://doi.org/10.1109/WCSP.2017.8171137
Li GZhang LLin FChen MWang Z(2017)SAILoc: A novel acoustic single array system for indoor localization2017 9th International Conference on Wireless Communications and Signal Processing (WCSP)10.1109/WCSP.2017.8171099(1-6)Online publication date: Oct-2017
https://doi.org/10.1109/WCSP.2017.8171099
Kortas MMeghdadi VBouallegue AEzzeddine THabachi OCances J(2017)Routing aware space-time compressive sensing for Wireless Sensor Networks2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)10.1109/PIMRC.2017.8292355(1-6)Online publication date: Oct-2017
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Han ZZhang XZhang DZhang CDing S(2017)A Data gathering algorithm based on compressive sensing in lossy wireless sensor networks2017 2nd International Conference on Frontiers of Sensors Technologies (ICFST)10.1109/ICFST.2017.8210492(146-153)Online publication date: Apr-2017
https://doi.org/10.1109/ICFST.2017.8210492
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