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

TL;DR

This paper evaluates the energy tradeoffs of lossy compression in wireless sensor networks, showing that its benefits depend on specific system parameters and that careful analysis is needed to determine when compression is advantageous.

Contribution

It provides a comprehensive analysis of lossy compression methods in WSNs, including performance evaluation, quantitative tradeoff assessments, and formulas for energy and complexity estimation.

Findings

Compression can save energy but depends on system factors.

Processing and transmission costs are often comparable.

Quantitative formulas help predict energy and accuracy outcomes.

Abstract

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.