Deepu Chacko John, Heng Chun-Huat, Lian Yong
IEEE Trans Biomed Circuits Syst. 2017 Apr;11(2):245-254. doi: 10.1109/TBCAS.2016.2591923. Epub 2016 Nov 7.
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 2.1 × and 7.8 × 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.
本文提出了一种用于降低物联网（IoT）无线传感器功耗的新型数据压缩与传输方案。在所提出的方案中，数据采用有损和无损技术进行压缩，以实现混合传输模式，支持自适应数据速率选择并在无线传输中节省功耗。将该方法应用于心电图（ECG）时，首先使用具有高压缩率（CR）的有损压缩技术对数据进行压缩。原始数据与解压缩后的有损数据之间的残余误差通过熵编码进行保留，以便在需要时能够无损恢复原始数据。使用麻省理工学院/波士顿儿童医院（MIT/BIH）数据库分别实现了无损压缩和有损压缩的平均压缩率为2.1倍和7.8倍。使用蓝牙收发器演示了功耗降低情况，发现有损传输的功耗降低到18%，无损传输的功耗降低到53%。混合传输模式、自适应速率选择和系统级功耗降低的选项使得所提出的方案对于医疗保健应用中的物联网无线传感器具有吸引力。
