A Hybrid Data Compression Scheme for Power Reduction in Wireless Sensors for IoT

This paper presents a novel data compression and transmission scheme for power reduction in Internet-of-Things (IoT) enabled wireless sensors. In the proposed scheme, data is compressed with both lossy and lossless techniques, so as to enable hybrid transmission mode, support adaptive data rate selection and save power in wireless transmission. Applying the method to electrocardiogram (ECG), the data is first compressed using a lossy compression technique with a high compression ratio (CR). The residual error between the original data and the decompressed lossy data is preserved using entropy coding, enabling a lossless restoration of the original data when required. Average CR of 2.1x and 7.8x were achieved for lossless and lossy compression respectively with MIT/BIH database. The power reduction is demonstrated using a Bluetooth transceiver and is found to be reduced to 18% for lossy and 53% for lossless transmission respectively. Options for hybrid transmission mode, adaptive rate selection and system level power reduction make the proposed scheme attractive for IoT wireless sensors in healthcare applications.