SHDC: An Smart Hybrid Data Compression Method in WBANs

Abstract

The Wireless Body Area Networks (WBANs) are a set of sensors on the human s body that are designed in very small sizes. These sensors are faced with the exchange of a huge amount of data, which causes them to run out of energy consumption. One of the most effective methods to save the sensors' energy is the use of data compression. However the problem of compression methods in WBANs is inadequate for compressing large amounts of duplicate data. In this regard, a new method called Smart Hybrid Data Compression (SHDC) is presented. SHDC method use Recurrent Neural Network (RNN) and Run Length Encoding (RLE) which involves four key steps: 1-create and fit the LSTM network, 2-prediction, 3-calculate error and 4-compressed by lossless procedure. The presented method has a very good performance in energy consumption, data volume reduction by removing unimportant and repetitive data in the exchange of information between sensors and sink, high accuracy in data prediction and achieving a high compression rate due to low error. The SHDC method is implemented on the data set received from body sensors including blood pressure systolic, blood pressure diastolic, heart rate, respiration and SPO2. In the last step the differences between predictions and original signals are coded by four lossless coding: RLE, Huffman, Limpel Ziv Welch (LZW) and Arithmetic. Three measurements of Compression Ratio (CR), Energy Remaining (ER) and Root Mean Square Error (RMSE) after data compression have been measured. Our results compared with state-of-art lossless coding methods. Results show the SHDC method with RNN and RLE has the highest energy saving on average of 98% by maintaining the appropriate compression ratio.