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Sensors (Basel). 2018 Feb 3;18(2). pii: E450. doi: 10.3390/s18020450.

The Application of Social Characteristic and L1 Optimization in the Error Correction for Network Coding in Wireless Sensor Networks.

Author information

1
Computer Science Department, Harbin Engineering University, Harbin 150001, China. zhangguangzhi@hrbeu.edu.cn.
2
Department of Information Engineering, Suihua University, Suihua 152000, China. zhangguangzhi@hrbeu.edu.cn.
3
Computer Science Department, Harbin Engineering University, Harbin 150001, China. caishaobin@hrbeu.edu.cn.
4
Computer Science Department, Huaqiao University, Xiamen 361021, China. caishaobin@hrbeu.edu.cn.
5
Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA. dnxiong@ieee.org.

Abstract

One of the remarkable challenges about Wireless Sensor Networks (WSN) is how to transfer the collected data efficiently due to energy limitation of sensor nodes. Network coding will increase network throughput of WSN dramatically due to the broadcast nature of WSN. However, the network coding usually propagates a single original error over the whole network. Due to the special property of error propagation in network coding, most of error correction methods cannot correct more than C/2 corrupted errors where C is the max flow min cut of the network. To maximize the effectiveness of network coding applied in WSN, a new error-correcting mechanism to confront the propagated error is urgently needed. Based on the social network characteristic inherent in WSN and L1 optimization, we propose a novel scheme which successfully corrects more than C/2 corrupted errors. What is more, even if the error occurs on all the links of the network, our scheme also can correct errors successfully. With introducing a secret channel and a specially designed matrix which can trap some errors, we improve John and Yi's model so that it can correct the propagated errors in network coding which usually pollute exactly 100% of the received messages. Taking advantage of the social characteristic inherent in WSN, we propose a new distributed approach that establishes reputation-based trust among sensor nodes in order to identify the informative upstream sensor nodes. With referred theory of social networks, the informative relay nodes are selected and marked with high trust value. The two methods of L1 optimization and utilizing social characteristic coordinate with each other, and can correct the propagated error whose fraction is even exactly 100% in WSN where network coding is performed. The effectiveness of the error correction scheme is validated through simulation experiments.

KEYWORDS:

L1 optimization; error correction; error propagation; network coding; social network

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