Abstract: Data gathering plays a significant role in Wireless Sensor Networks（WSNs）. This paper proposes a Compressed Network Coding（CNC） scheme to improve the communication efficiency. Network Coding（NC） is always used to address the dynamic topology problem of WSN. Utilizing the correlation of the readings from nodes of sensor networks, and the similarity between the operation in Random Linear Network Coding（RLNC） scheme and the random projection operation in Compressed Sensing（CS）, it introduces a method combined compressive sensing and network coding to solve the all-or-nothing problem of network decoding by designing the packet form and local coding vectors. CNC scheme guarantees that the sensing data can be accurately recovered with a high probability even if the number of received packets is less than the total number of source nodes in the network. Simulation shows that it just needs half the number of packets which conventional network coding scheme needs to reconstruct measurements with reasonable quality. Compared with the traditional NC scheme, CNC increases the efficiency of data gathering over 20%.

Key words: wireless sensor networks, compressed sensing, random linear network coding

摘要： 介绍了应用于无线传感器网络（Wireless Sensor Networks，WSN）中的一种数据传输方案——压缩网络编码（Compressed Network Coding，CNC）。在WSN中，通常应用网络编码（Network Coding，NC）来适应拓扑结构的动态变化并提高数据传输效率。考虑到传感器网络中节点测量值之间的相关性，与随机线性网络编码（Random Linear Network Coding，RLNC）方案中的编码操作与压缩感知（Compressed Sensing，CS）中随机投影操作之间的相似性，CNC方案将CS引入到NC中，通过对测量值数据包以及NC局部编码向量的设计，来解决传统NC译码存在的“全有或全无”问题。在汇聚节点收集到的数据包个数小于网络中源节点个数的情况下，CNC方案仍能以高概率精确重构感知数据。仿真结果表明，在合理的误差容许范围内重构测量值，所需的数据包个数仅为传统NC方案所需个数的一半，与传统NC技术相比，CNC方案将数据传输效率提升了20%以上。

关键词: 无线传感器网络, 压缩感知, 随机线性网络编码