Wireless Sensor Networks (WSNs) are becoming important in today's technology in helping to monitor our surrounding. WSN s are widely used such as in the military, medical and industrial field. It is capable in monitoring, collecting and transmitting data to its main server wirelessly. However, wireless sensor node is powered by a limited energy supply such as by a small battery or an energy harvester that usually produce a small amount of energy. To extend the lifetime of the device, energy consumption must be reduced. Data transmission is known to consume the largest amount of energy in a sensor node. Thus, one method to reduce the energy used is by compressing the data before transmitting it. The Huffman algorithm have an average complexity with good compression data and an average bit per character which suits in compressing wireless sensor node data. The algorithm was design using the standard Huffman data compression technique. This study analyses the efficiency of the compression ratio for the Huffman algorithm when compressing data that are commonly used in wireless sensor node. The speed, power and
energy consumption for the architecture circuit of the algorithm were also analysed. From the experimental results, the Huffman algorithm gave good results for this type of data. The
compression ratio's efficiency for temperature is between 3.50 and 5.07, 5.51 and 7.47 for humidity, 4.74 and 10.00 for electrocardiograph and lastly for text, between 5.35 and 6.85
using MATLAB software. Based on simulation results using ModelSim and SYNOPSYS software on Huffman circuit architecture, this algorithm consumed 51394 clock cycles to completely compress 366 humidity data samples, which used 3.729mW of power consumption. Based on 20MHz clock frequency, this is equivalent to 9.5824j..l.J of energy consumption. The highest power consumption in the architecture is the Huffman Tree Generator which consumed 31.75% of the total power. This is followed by Huffman Code Generator and Frequency Calculator, which took 13.97% and 13.65% respectively.
