IEICE TRANSACTIONS on Communications
Structural Compressed Network Coding for Data Collection in Cluster-Based Wireless Sensor Networks
Summary :
To efficiently collect sensor readings in cluster-based wireless sensor networks, we propose a structural compressed network coding (SCNC) scheme that jointly considers structural compressed sensing (SCS) and network coding (NC). The proposed scheme exploits the structural compressibility of sensor readings for data compression and reconstruction. Random linear network coding (RLNC) is used to re-project the measurements and thus enhance network reliability. Furthermore, we calculate the energy consumption of intra- and inter-cluster transmission and analyze the effect of the cluster size on the total transmission energy consumption. To that end, we introduce an iterative reweighed sparsity recovery algorithm to address the all-or-nothing effect of RLNC and decrease the recovery error. Experiments show that the SCNC scheme can decrease the number of measurements required for decoding and improve the network's robustness, particularly when the loss rate is high. Moreover, the proposed recovery algorithm has better reconstruction performance than several other state-of-the-art recovery algorithms.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E102-B No.11 pp.2126-2138
- Publication Date
- 2019/11/01
- Publicized
- 2019/05/21
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2018EBP3363
- Type of Manuscript
- PAPER
- Category
- Network
Authors
Yimin ZHAO
Xidian University
Song XIAO
Xidian University
Hongping GAN
Xidian University
Lizhao LI
Xidian University
Lina XIAO
Xidian University
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Yimin ZHAO, Song XIAO, Hongping GAN, Lizhao LI, Lina XIAO, "Structural Compressed Network Coding for Data Collection in Cluster-Based Wireless Sensor Networks" in IEICE TRANSACTIONS on Communications,
vol. E102-B, no. 11, pp. 2126-2138, November 2019, doi: 10.1587/transcom.2018EBP3363.
Abstract: To efficiently collect sensor readings in cluster-based wireless sensor networks, we propose a structural compressed network coding (SCNC) scheme that jointly considers structural compressed sensing (SCS) and network coding (NC). The proposed scheme exploits the structural compressibility of sensor readings for data compression and reconstruction. Random linear network coding (RLNC) is used to re-project the measurements and thus enhance network reliability. Furthermore, we calculate the energy consumption of intra- and inter-cluster transmission and analyze the effect of the cluster size on the total transmission energy consumption. To that end, we introduce an iterative reweighed sparsity recovery algorithm to address the all-or-nothing effect of RLNC and decrease the recovery error. Experiments show that the SCNC scheme can decrease the number of measurements required for decoding and improve the network's robustness, particularly when the loss rate is high. Moreover, the proposed recovery algorithm has better reconstruction performance than several other state-of-the-art recovery algorithms.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2018EBP3363/_p
Copy
@ARTICLE{e102-b_11_2126,
author={Yimin ZHAO, Song XIAO, Hongping GAN, Lizhao LI, Lina XIAO, },
journal={IEICE TRANSACTIONS on Communications},
title={Structural Compressed Network Coding for Data Collection in Cluster-Based Wireless Sensor Networks},
year={2019},
volume={E102-B},
number={11},
pages={2126-2138},
abstract={To efficiently collect sensor readings in cluster-based wireless sensor networks, we propose a structural compressed network coding (SCNC) scheme that jointly considers structural compressed sensing (SCS) and network coding (NC). The proposed scheme exploits the structural compressibility of sensor readings for data compression and reconstruction. Random linear network coding (RLNC) is used to re-project the measurements and thus enhance network reliability. Furthermore, we calculate the energy consumption of intra- and inter-cluster transmission and analyze the effect of the cluster size on the total transmission energy consumption. To that end, we introduce an iterative reweighed sparsity recovery algorithm to address the all-or-nothing effect of RLNC and decrease the recovery error. Experiments show that the SCNC scheme can decrease the number of measurements required for decoding and improve the network's robustness, particularly when the loss rate is high. Moreover, the proposed recovery algorithm has better reconstruction performance than several other state-of-the-art recovery algorithms.},
keywords={},
doi={10.1587/transcom.2018EBP3363},
ISSN={1745-1345},
month={November},}
Copy
TY - JOUR
TI - Structural Compressed Network Coding for Data Collection in Cluster-Based Wireless Sensor Networks
T2 - IEICE TRANSACTIONS on Communications
SP - 2126
EP - 2138
AU - Yimin ZHAO
AU - Song XIAO
AU - Hongping GAN
AU - Lizhao LI
AU - Lina XIAO
PY - 2019
DO - 10.1587/transcom.2018EBP3363
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E102-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2019
AB - To efficiently collect sensor readings in cluster-based wireless sensor networks, we propose a structural compressed network coding (SCNC) scheme that jointly considers structural compressed sensing (SCS) and network coding (NC). The proposed scheme exploits the structural compressibility of sensor readings for data compression and reconstruction. Random linear network coding (RLNC) is used to re-project the measurements and thus enhance network reliability. Furthermore, we calculate the energy consumption of intra- and inter-cluster transmission and analyze the effect of the cluster size on the total transmission energy consumption. To that end, we introduce an iterative reweighed sparsity recovery algorithm to address the all-or-nothing effect of RLNC and decrease the recovery error. Experiments show that the SCNC scheme can decrease the number of measurements required for decoding and improve the network's robustness, particularly when the loss rate is high. Moreover, the proposed recovery algorithm has better reconstruction performance than several other state-of-the-art recovery algorithms.
ER -
English
