The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Pour collecter efficacement les lectures des capteurs dans les réseaux de capteurs sans fil basés sur des clusters, nous proposons un schéma de codage de réseau compressé structurel (SCNC) qui prend en compte conjointement la détection compressée structurelle (SCS) et le codage de réseau (NC). Le schéma proposé exploite la compressibilité structurelle des lectures des capteurs pour la compression et la reconstruction des données. Le codage de réseau linéaire aléatoire (RLNC) est utilisé pour reprojeter les mesures et ainsi améliorer la fiabilité du réseau. De plus, nous calculons la consommation d'énergie de la transmission intra et inter-cluster et analysons l'effet de la taille du cluster sur la consommation totale d'énergie de transmission. À cette fin, nous introduisons un algorithme itératif de récupération de parcimonie repondéré pour traiter l’effet tout ou rien du RLNC et réduire l’erreur de récupération. Les expériences montrent que le schéma SCNC peut réduire le nombre de mesures nécessaires au décodage et améliorer la robustesse du réseau, en particulier lorsque le taux de perte est élevé. De plus, l’algorithme de récupération proposé présente de meilleures performances de reconstruction que plusieurs autres algorithmes de récupération de pointe.
Yimin ZHAO
Xidian University
Song XIAO
Xidian University
Hongping GAN
Xidian University
Lizhao LI
Xidian University
Lina XIAO
Xidian University
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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
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@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},}
Copier
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 -