An Efficient Compression Method to Improve Energy Consumption in WBANs

One of the most essential services that allow the patient to remotely monitor and check vital signs of the patient to diagnose and treat the patient without physically attending the treatment center is wireless body area networks (WBANs). In WBANs, tiny sensors that charge limited batteries work together. These sensors can be placed on the human body or inside it. Because of limited energy consumption of these sensors, energy efficiency is one of the most critical challenges in WBANs. One way to improve and reduce energy consumption in these sensors is to compress the data. In this paper, a method is presented that is a modification of Huffman method. In this paper, for the first time in the field of Wireless Body Area Networks, Huffman method has been modified to be more compatible with the limitations of Wireless Body Area Networks. Then Modified Huffman method has been implemented on medical data and finally compared with previous methods presented in this field. The results in the diagrams show the Modified Huffman method performs better than the NIS and saves 1770 units of energy more than the NIS method. As a result, compression data using the Modified Huffman method stores 11.8% more energy than the NIS.