基于时空压缩网络编码的数据传输与恢复方法

Spatio-temporal compressed network coding based data communication and data recovery in wireless sensor networks

针对无线传感网络中数据传输耗能高和恢复精度受限的问题,提出了一种实用的基于时空压缩网络编码的数据传输和恢复方法。新方法充分利用了数据的时空相关性,采用了具有中间节点选择的压缩网络编码方法,最大可能地避免了数据的冗余传输;并利用KSVD思想,通过训练时间稀疏字典来获得最佳的时间观测矩阵,使得数据更好地被稀疏化,提高数据恢复的准确性。理论分析和仿真结果表明,新方法可以显著地降低能耗,并提高数据恢复的精度。

Aiming at the problem of high energy consumption on data transmission and limited recovery accuracy in wireless sensor networks, a practical spatio-temporal compressed network coding based data transmission and recovery method is proposed. The method makes full use of spatio-temporal correlation of data, and adopts the improved compressed network coding method to avoid the redundant of data communication as much as possible. By using KSVD, the optimal time measurement matrix can be obtained by training the sparse dictionary, which can make the data having a better sparsity to improve the accuracy of data recovery. Theoretical analysis and simulation results show that the proposed method can reduce the energy consumption and improve the accuracy of data recovery.