压缩还是不压缩：能量受限传感器网络的处理与传输权衡
To Compress or Not To Compress: Processing vs Transmission Tradeoffs for Energy Constrained Sensor Networking
摘要：
在过去的几年中，有损压缩已被广泛应用于无线传感器网络（WSN）领域，由于传输设备的约束性质，能量效率是一个至关重要的问题。通常，研究人员和实施者的共同想法是，压缩始终是一个很好的选择，因为传感器节点中的主要能耗来源来自数据的传输。有损压缩被认为是一种可行的解决方案，因为在无线传感器网络中，信号的不完美重构通常是可以接受的。在本文中，我们全面回顾了一些有损压缩方法从文献中，并分析其性能的压缩效率，计算复杂性和能量消耗。我们考虑了两种不同的情况，即无线和水下通信，并表明信号压缩可能有助于或可能无助于降低整体能耗，这取决于压缩算法，信号统计和硬件特性等因素，即，微控制器和传输技术。我们学到的教训是，信号压缩实际上可以节省一些能量。然而，应仔细评估其使用情况，因为在相当多的情况下，处理和传输成本是同一数量级的，而在其他一些情况下，前者甚至可能超过后者。在本文中，我们展示了定量比较，以评估这些权衡在上述情况下。最后，我们提供的公式，通过数值拟合，以衡量计算复杂性，整体能耗和信号表示精度的最佳性能的算法作为最相关的系统参数的函数。
英文摘要：
In the past few years, lossy compression has been widely applied in the field of wireless sensor networks (WSN), where energy efficiency is a crucial concern due to the constrained nature of the transmission devices. Often, the common thinking among researchers and implementers is that compression is always a good choice, because the major source of energy consumption in a sensor node comes from the transmission of the data. Lossy compression is deemed a viable solution as the imperfect reconstruction of the signal is often acceptable in WSN. In this paper, we thoroughly review a number of lossy compression methods from the literature, and analyze their performance in terms of compression efficiency, computational complexity and energy consumption. We consider two different scenarios, namely, wireless and underwater communications, and show that signal compression may or may not help in the reduction of the overall energy consumption, depending on factors such as the compression algorithm, the signal statistics and the hardware characteristics, i.e., micro-controller and transmission technology. The lesson that we have learned, is that signal compression may in fact provide some energy savings. However, its usage should be carefully evaluated, as in quite a few cases processing and transmission costs are of the same order of magnitude, whereas, in some other cases, the former may even dominate the latter. In this paper, we show quantitative comparisons to assess these tradeoffs in the above mentioned scenarios. Finally, we provide formulas, obtained through numerical fittings, to gauge computational complexity, overall energy consumption and signal representation accuracy for the best performing algorithms as a function of the most relevant system parameters.
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