Low Cost Error Correction for Multi-Hop Data Aggregation Using Compressed Sensing

Guangming CAO; Peter JUNG; Slawomir STANCZAK; Fengqi YU

doi:10.1587/transinf.E97.D.331

Low Cost Error Correction for Multi-Hop Data Aggregation Using Compressed Sensing

Guangming CAO, Peter JUNG, Slawomir STANCZAK, Fengqi YU

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Packet loss and energy dissipation are two major challenges of designing large-scale wireless sensor networks. Since sensing data is spatially correlated, compressed sensing (CS) is a promising reconstruction scheme to provide low-cost packet error correction and load balancing. In this letter, assuming a multi-hop network topology, we present a CS-oriented data aggregation scheme with a new measurement matrix which balances energy consumption of the nodes and allows for recovery of lost packets at fusion center without additional transmissions. Comparisons with existing methods show that the proposed scheme offers higher recovery precision and less energy consumption on TinyOS.

Publication: IEICE TRANSACTIONS on Information Vol.E97-D No.2 pp.331-334

Publication Date: 2014/02/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1587/transinf.E97.D.331

Type of Manuscript: LETTER

Category: Information Network

Authors

Guangming CAO
  Shenzhen Institutes of Advanced Technology,Hong Kong,Beijing
Peter JUNG
  Technische Universit{"a}t Berlin
Slawomir STANCZAK
  Technische Universit{"a}t Berlin
Fengqi YU
  Shenzhen Institutes of Advanced Technology

Keyword

large-scale wireless sensor networks, compressed sensing, packet loss, energy balancing

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Guangming CAO, Peter JUNG, Slawomir STANCZAK, Fengqi YU, "Low Cost Error Correction for Multi-Hop Data Aggregation Using Compressed Sensing" in IEICE TRANSACTIONS on Information, vol. E97-D, no. 2, pp. 331-334, February 2014, doi: 10.1587/transinf.E97.D.331.
Abstract: Packet loss and energy dissipation are two major challenges of designing large-scale wireless sensor networks. Since sensing data is spatially correlated, compressed sensing (CS) is a promising reconstruction scheme to provide low-cost packet error correction and load balancing. In this letter, assuming a multi-hop network topology, we present a CS-oriented data aggregation scheme with a new measurement matrix which balances energy consumption of the nodes and allows for recovery of lost packets at fusion center without additional transmissions. Comparisons with existing methods show that the proposed scheme offers higher recovery precision and less energy consumption on TinyOS.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.E97.D.331/_p

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@ARTICLE{e97-d_2_331,
author={Guangming CAO, Peter JUNG, Slawomir STANCZAK, Fengqi YU, },
journal={IEICE TRANSACTIONS on Information},
title={Low Cost Error Correction for Multi-Hop Data Aggregation Using Compressed Sensing},
year={2014},
volume={E97-D},
number={2},
pages={331-334},
abstract={Packet loss and energy dissipation are two major challenges of designing large-scale wireless sensor networks. Since sensing data is spatially correlated, compressed sensing (CS) is a promising reconstruction scheme to provide low-cost packet error correction and load balancing. In this letter, assuming a multi-hop network topology, we present a CS-oriented data aggregation scheme with a new measurement matrix which balances energy consumption of the nodes and allows for recovery of lost packets at fusion center without additional transmissions. Comparisons with existing methods show that the proposed scheme offers higher recovery precision and less energy consumption on TinyOS.},
keywords={},
doi={10.1587/transinf.E97.D.331},
ISSN={1745-1361},
month={February},}

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TY - JOUR
TI - Low Cost Error Correction for Multi-Hop Data Aggregation Using Compressed Sensing
T2 - IEICE TRANSACTIONS on Information
SP - 331
EP - 334
AU - Guangming CAO
AU - Peter JUNG
AU - Slawomir STANCZAK
AU - Fengqi YU
PY - 2014
DO - 10.1587/transinf.E97.D.331
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2014
AB - Packet loss and energy dissipation are two major challenges of designing large-scale wireless sensor networks. Since sensing data is spatially correlated, compressed sensing (CS) is a promising reconstruction scheme to provide low-cost packet error correction and load balancing. In this letter, assuming a multi-hop network topology, we present a CS-oriented data aggregation scheme with a new measurement matrix which balances energy consumption of the nodes and allows for recovery of lost packets at fusion center without additional transmissions. Comparisons with existing methods show that the proposed scheme offers higher recovery precision and less energy consumption on TinyOS.
ER -