Purpose

As the limited energy of wireless sensor networks (WSNs), energy-efficient data-gathering algorithms are required. This paper proposes a compressive data-gathering algorithm based on double sparse structure dictionary learning (DSSDL). The purpose of this paper is to reduce the energy consumption of WSNs.

Design/methodology/approach

The historical data is used to construct a sparse representation base. In the dictionary-learning stage, the sparse representation matrix is decomposed into the product of double sparse matrices. Then, in the update stage of the dictionary, the sparse representation matrix is orthogonalized and unitized. The finally obtained double sparse structure dictionary is applied to the compressive data gathering in WSNs.

Findings

The dictionary obtained by the proposed algorithm has better sparse representation ability. The experimental results show that, the sparse representation error can be reduced by at least 3.6% compared with other dictionaries. In addition, the better sparse representation ability makes the WSNs achieve less measurement times under the same accuracy of data gathering, which means more energy saving. According to the results of simulation, the proposed algorithm can reduce the energy consumption by at least 2.7% compared with other compressive data-gathering methods under the same data-gathering accuracy.

Originality/value

In this paper, the double sparse structure dictionary is introduced into the compressive data-gathering algorithm in WSNs. The experimental results indicate that the proposed algorithm has good performance on energy consumption and sparse representation.

中文翻译：

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无线传感器网络中一种新颖的基于双稀疏结构字典学习的压缩数据收集算法

目的

由于无线传感器网络（WSN）的能量有限，因此需要节能的数据收集算法。提出了一种基于双稀疏结构字典学习（DSSDL）的压缩数据收集算法。本文的目的是减少无线传感器网络的能耗。

设计/方法/方法

历史数据用于构建稀疏表示库。在字典学习阶段，将稀疏表示矩阵分解为双稀疏矩阵的乘积。然后，在字典的更新阶段，将稀疏表示矩阵正交化和单位化。将最终获得的双稀疏结构字典应用于WSN中的压缩数据收集。

发现

该算法获得的字典具有更好的稀疏表示能力。实验结果表明，与其他字典相比，稀疏表示误差可减少至少3.6％。此外，更好的稀疏表示能力使WSN在相同的数据收集精度下实现更少的测量时间，这意味着可以节省更多的能源。根据仿真结果，与其他压缩数据收集方法相比，该算法在相同的数据收集精度下，可将能耗降低至少2.7％。

创意/价值

本文将双稀疏结构字典引入到无线传感器网络的压缩数据收集算法中。实验结果表明，该算法在能量消耗和稀疏表示方面具有良好的性能。