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Data compression is a technique used to save energy in Wireless Sensor Networks by reducing the quantity of data transmitted and the number of transmission. Actually, the main cause of energy consumption in WSN is data transmission. There exist critical applications such as delay constrained activities in which the data have to arrive quickly to the Sink for rapid analysis. In this article, we explore the use of data compression algorithms for delay constrained applications by evaluating a recent data compression algorithm for WSN named K-RLE with optimal parameters on an ultra-low power microcontroller from TI MSP430 series. The relevance of the parameter K for the lossy algorithm K-RLE led us to propose and compare two methods to characterize K: the Standard deviation and the Allan deviation. The last one allow us to control the percentage of data modified. Experimental results show that data compression is an energy efficient technique which can also perform in certain cases the global data transfer time (compression plus transmission time) compared to direct transmission.