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
Para coletar eficientemente leituras de sensores em redes de sensores sem fio baseadas em cluster, propomos um esquema de codificação de rede comprimida estrutural (SCNC) que considera conjuntamente o sensoriamento estrutural comprimido (SCS) e a codificação de rede (NC). O esquema proposto explora a compressibilidade estrutural das leituras dos sensores para compressão e reconstrução de dados. A codificação de rede linear aleatória (RLNC) é usada para reprojetar as medições e, assim, aumentar a confiabilidade da rede. Além disso, calculamos o consumo de energia da transmissão intra e inter-cluster e analisamos o efeito do tamanho do cluster no consumo total de energia de transmissão. Para esse fim, introduzimos um algoritmo iterativo de recuperação de dispersão reavaliada para abordar o efeito tudo ou nada do RLNC e diminuir o erro de recuperação. Experimentos mostram que o esquema SCNC pode diminuir o número de medições necessárias para a decodificação e melhorar a robustez da rede, principalmente quando a taxa de perda é alta. Além disso, o algoritmo de recuperação proposto tem melhor desempenho de reconstrução do que vários outros algoritmos de recuperação de última geração.
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},}
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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 -