To Compress or Not To Compress: Processing vs Transmission Tradeoffs for Energy Constrained Sensor Networking

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, subject to some application dependent maximum error tolerance. Nevertheless, this is seldom supported by quantitative evidence. 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 (i.e., wireless versus underwater). In addition, we consider recently proposed and lightweight algorithms such as Lightweight Temporal Compression (LTC) as well as more sophisticated FFT- or DCT-based schemes and show that the former are the best option in wireless settings, whereas the latter solutions are preferable for underwater networks. Finally, we provide formulas, obtained through numerical fittings, to gauge the computational complexity, the overall energy consumption and the signal representation accuracy of the best performing algorithms as a function of the most relevant system parameters.