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首页> 外文期刊>IEEE transactions on wireless communications >Adaptive Lossless Entropy Compressors for Tiny IoT Devices
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Adaptive Lossless Entropy Compressors for Tiny IoT Devices

机译：适用于微型物联网设备的自适应无损熵压缩器

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摘要

Internet of Things (IoT) devices are typically powered by small batteries with a limited capacity. Thus, saving power as much as possible becomes crucial to extend their lifetime and therefore to allow their use in real application domains. Since radio communication is in general the main cause of power consumption, one of the most used approaches to save energy is to limit the transmission/reception of data, for instance, by means of data compression. However, the IoT devices are also characterized by limited computational resources which impose the development of specifically designed algorithms. To this aim, we propose to endow the lossless compression algorithm (LEC), previously proposed by us in the context of wireless sensor networks, with two simple adaptation schemes relying on the novel concept of appropriately rotating the prefix-free tables. We tested the proposed schemes on several datasets collected in several real sensor network deployments by monitoring four different environmental phenomena, namely, air and surface temperatures, solar radiation and relative humidity. We show that the adaptation schemes can achieve significant compression efficiencies in all the datasets. Further, we compare such results with the ones obtained by LEC and, by means of a non-parametric multiple statistical test, we show that the performance improvements introduced by the adaptation schemes are statistically significant.
机译：物联网（IoT）设备通常由容量有限的小型电池供电。因此，尽可能节省功率对于延长其使用寿命并因此使其能够在实际应用领域中使用至关重要。由于无线电通信通常是功耗的主要原因，因此，最节能的方法之一是例如通过数据压缩来限制数据的发送/接收。但是，物联网设备的特征还在于计算资源有限，这迫使开发专门设计的算法。为此，我们建议赋予我们先前在无线传感器网络环境中提出的无损压缩算法（LEC），这两个简单的适应方案依赖于适当旋转无前缀表的新颖概念。我们通过监视四种不同的环境现象，即空气和地面温度，太阳辐射和相对湿度，在几个实际的传感器网络部署中收集的几个数据集上测试了提议的方案。我们表明，自适应方案可以在所有数据集中实现显着的压缩效率。此外，我们将这些结果与LEC获得的结果进行比较，并通过非参数多元统计检验，表明适应方案引入的性能改进具有统计学意义。

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