Enabling energy-efficient and lossy-aware data compression in wireless sensor networks by multi-objective evolutionary optimization
Enabling energy-efficient and lossy-aware data compression in wireless sensor networks by multi-objective evolutionary optimization
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DOI：
10.1016/j.ins.2010.01.027
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发表时间：
2010-05-15
影响因子：
8.1
通讯作者：
Vecchio, Massimo
中科院分区：
文献类型：
--
作者：
Marcelloni, Francesco;Vecchio, Massimo
无线传感器网络(WSN)的节点通常由容量有限的电池供电。因此，能量是无线传感器网络设计和部署中的主要限制因素。由于无线电通信通常是功率消耗的主要原因，因此文献中提出的提高能量效率的不同技术主要集中于限制数据的发送/接收，例如通过采用数据压缩和/或聚合。然而，传感器节点可用的资源有限，需要开发专门设计的算法。为此，我们提出了一种基于连续采样差值量化的差分脉冲编码调制方案的单节点有损压缩方法。由于量化过程参数的不同组合决定了压缩性能和信息损失之间的不同权衡，因此我们利用多目标进化算法来生成对应于不同最优权衡的这些参数的组合。因此，用户可以为特定应用选择具有最合适折衷的组合。我们在实际无线传感器网络收集的三个数据集上测试了我们的有损压缩方法。我们的结果表明，在重建误差可以忽略不计的情况下，我们的方法可以获得显著的压缩比。此外，我们还讨论了我们的方法在压缩比和复杂度方面如何优于LTC，LTC是一种专门设计用于嵌入传感器节点的有损压缩算法。(C)2010 Elsevier Inc.保留所有权利。
Nodes of wireless sensor networks (WSNs) are typically powered by batteries with a limited capacity. Thus, energy is a primary constraint in the design and deployment of WSNs. Since radio communication is in general the main cause of power consumption, the different techniques proposed in the literature to improve energy efficiency have mainly focused on limiting transmission/reception of data, for instance, by adopting data compression and/or aggregation. The limited resources available in a sensor node demand, however, the development of specifically designed algorithms. To this aim, we propose an approach to perform lossy compression on single node based on a differential pulse code modulation scheme with quantization of the differences between consecutive samples. Since different combinations of the quantization process parameters determine different trade-offs between compression performance and information loss, we exploit a multi-objective evolutionary algorithm to generate a set of combinations of these parameters corresponding to different optimal trade-offs. The user can therefore choose the combination with the most suitable trade-off for the specific application. We tested our lossy compression approach on three datasets collected by real WSNs. We show that our approach can achieve significant compression ratios despite negligible reconstruction errors. Further, we discuss how our approach outperforms LTC, a lossy compression algorithm purposely designed to be embedded in sensor nodes, in terms of compression rate and complexity. (C) 2010 Elsevier Inc. All rights reserved.