Zhang Guangzhi, Cai Shaobin, Xiong Naixue
Computer Science Department, Harbin Engineering University, Harbin 150001, China.
Department of Information Engineering, Suihua University, Suihua 152000, China.
Sensors (Basel). 2018 Feb 3;18(2):450. doi: 10.3390/s18020450.
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 /2 corrupted errors where 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 /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.
无线传感器网络（WSN）面临的一个显著挑战是，由于传感器节点的能量限制，如何高效地传输收集到的数据。由于WSN的广播特性，网络编码将显著提高WSN的网络吞吐量。然而，网络编码通常会在整个网络中传播单个原始错误。由于网络编码中错误传播的特殊性质，大多数纠错方法无法纠正超过网络最大流最小割/2的损坏错误。为了最大化网络编码在WSN中的有效性，迫切需要一种新的纠错机制来应对传播错误。基于WSN固有的社交网络特性和L1优化，我们提出了一种新颖的方案，该方案成功纠正了超过/2的损坏错误。此外，即使错误发生在网络的所有链路上，我们的方案也能成功纠错。通过引入一个秘密通道和一个专门设计的能够捕获一些错误的矩阵，我们改进了John和Yi的模型，使其能够纠正网络编码中通常会完全污染100%接收消息的传播错误。利用WSN固有的社交特性，我们提出了一种新的分布式方法，在传感器节点之间建立基于声誉的信任，以识别信息丰富的上游传感器节点。借助社交网络的相关理论，选择信息丰富的中继节点并赋予其高信任值。L1优化和利用社交特性这两种方法相互配合，能够纠正WSN中执行网络编码时传播错误比例甚至恰好为100%的情况。通过仿真实验验证了纠错方案的有效性。
