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In the past few years, lossy compression has been widely applied in the fieldof wireless sensor networks (WSN), where energy efficiency is a crucial concerndue to the constrained nature of the transmission devices. Often, the commonthinking among researchers and implementers is that compression is always agood choice, because the major source of energy consumption in a sensor nodecomes from the transmission of the data. Lossy compression is deemed a viablesolution as the imperfect reconstruction of the signal is often acceptable inWSN. In this paper, we thoroughly review a number of lossy compression methodsfrom the literature, and analyze their performance in terms of compressionefficiency, computational complexity and energy consumption. We consider twodifferent scenarios, namely, wireless and underwater communications, and showthat signal compression may or may not help in the reduction of the overallenergy consumption, depending on factors such as the compression algorithm, thesignal statistics and the hardware characteristics, i.e., micro-controller andtransmission technology. The lesson that we have learned, is that signalcompression may in fact provide some energy savings. However, its usage shouldbe carefully evaluated, as in quite a few cases processing and transmissioncosts are of the same order of magnitude, whereas, in some other cases, theformer may even dominate the latter. In this paper, we show quantitativecomparisons to assess these tradeoffs in the above mentioned scenarios.Finally, we provide formulas, obtained through numerical fittings, to gaugecomputational complexity, overall energy consumption and signal representationaccuracy for the best performing algorithms as a function of the most relevantsystem parameters.

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