Walsh transform with moving average filtering for data compression in wireless sensor networks

Abstract

Due to the peculiarity of wireless sensor networks (WSNs), where a group of sensors continuously transmit data to other sensors or to the fusion center, it is crucial to compress the transmitted data in order to save the consumed power, which is paramount in the case of portable devices. There exists several techniques for data compression such as discrete wavelet transform (DWT) based, which fails to achieve high compression ratio for an acceptable distortion ratio. In this paper, we explore exploiting Walsh transform with a moving average filtering (MAF) for data compression in WSNs. One application of WSN is wireless body sensor networks. We apply Walsh transform on real Electroencephalogram (EEG) data collected from patients. Furthermore, we compare our results to DWT and show the superiority of exploiting Walsh transform for data compression. We show that using MAF with Walsh transform enhances the compression ratio for up to 30% more than that achieved by DWT.