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Structural Compressed Network Coding for Data Collection in Cluster-Based Wireless Sensor Networks
Yimin ZHAO Song XIAO Hongping GAN Lizhao LI Lina XIAO
IEICE TRANSACTIONS on Communications
Publication Date: 2019/11/01
Online ISSN: 1745-1345
Type of Manuscript: PAPER
network coding, compressed sensing, structure-sparse, wireless sensor networks, data collection,
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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.