Compressive data aggregation in wireless sensor networks using sub-Gaussian random matrices

Xiaohan Yu, Seung Jun Baek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper, we study a data aggregation problem in wireless sensor networks. We propose a Compressive Sensing (CS) based strategy which is able to reduce energy consumption and data collection latency. We adopt a random sensing matrix with entries drawn i.i.d. according to strictly sub-Gaussian distributions. Such a matrix have property such that a fraction of its entries are equal to zero with high probability. This enables us to collect data from only a fraction of the network without affecting data recovery, which helps reduce communication overheads. Linear networks and planar networks are considered. We compare the energy consumption and latency performance of our strategy with those of Compressive Data Gathering (CDG) scheme. Analytical and simulation results show that our scheme can reduce up to 44% and 67% of the energy consumption for linear and planar networks respectively, when the number of nodes is large. A significant improvement on the latency performance is observed as well.

Original languageEnglish
Title of host publicationIEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
Pages2103-2108
Number of pages6
DOIs
Publication statusPublished - 2013 Dec 1
Event2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, PIMRC 2013 - London, United Kingdom
Duration: 2013 Sep 82013 Sep 11

Other

Other2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, PIMRC 2013
CountryUnited Kingdom
CityLondon
Period13/9/813/9/11

Fingerprint

Wireless sensor networks
Energy utilization
Agglomeration
Linear networks
Gaussian distribution
Recovery
Communication

Keywords

  • Compressive Sensing (CS)
  • Data Aggregation
  • Wireless Sensor Networks (WSNs)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Yu, X., & Baek, S. J. (2013). Compressive data aggregation in wireless sensor networks using sub-Gaussian random matrices. In IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC (pp. 2103-2108). [6666491] https://doi.org/10.1109/PIMRC.2013.6666491

Compressive data aggregation in wireless sensor networks using sub-Gaussian random matrices. / Yu, Xiaohan; Baek, Seung Jun.

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC. 2013. p. 2103-2108 6666491.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Yu, X & Baek, SJ 2013, Compressive data aggregation in wireless sensor networks using sub-Gaussian random matrices. in IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC., 6666491, pp. 2103-2108, 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, PIMRC 2013, London, United Kingdom, 13/9/8. https://doi.org/10.1109/PIMRC.2013.6666491
Yu X, Baek SJ. Compressive data aggregation in wireless sensor networks using sub-Gaussian random matrices. In IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC. 2013. p. 2103-2108. 6666491 https://doi.org/10.1109/PIMRC.2013.6666491
Yu, Xiaohan ; Baek, Seung Jun. / Compressive data aggregation in wireless sensor networks using sub-Gaussian random matrices. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC. 2013. pp. 2103-2108
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