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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Untuk mengumpul bacaan penderia dengan cekap dalam rangkaian penderia wayarles berasaskan kluster, kami mencadangkan skim pengekodan rangkaian termampat struktur (SCNC) yang mempertimbangkan secara bersama pengesanan mampat struktur (SCS) dan pengekodan rangkaian (NC). Skim yang dicadangkan mengeksploitasi kebolehmampatan struktur bacaan sensor untuk pemampatan dan pembinaan semula data. Pengekodan rangkaian linear rawak (RLNC) digunakan untuk memproyeksikan semula ukuran dan dengan itu meningkatkan kebolehpercayaan rangkaian. Tambahan pula, kami mengira penggunaan tenaga penghantaran intra dan antara kluster dan menganalisis kesan saiz kluster ke atas jumlah penggunaan tenaga penghantaran. Untuk itu, kami memperkenalkan algoritma pemulihan sparsity yang ditimbang semula berulang untuk menangani kesan semua atau tiada RLNC dan mengurangkan ralat pemulihan. Eksperimen menunjukkan bahawa skim SCNC boleh mengurangkan bilangan ukuran yang diperlukan untuk penyahkodan dan meningkatkan keteguhan rangkaian, terutamanya apabila kadar kehilangan adalah tinggi. Selain itu, algoritma pemulihan yang dicadangkan mempunyai prestasi pembinaan semula yang lebih baik daripada beberapa algoritma pemulihan terkini yang lain.
Yimin ZHAO
Xidian University
Song XIAO
Xidian University
Hongping GAN
Xidian University
Lizhao LI
Xidian University
Lina XIAO
Xidian University
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Salinan
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
Salinan
@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},}
Salinan
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 -