Please use this identifier to cite or link to this item: https://doi.org/10.1109/TBCAS.2016.2591923
Title: A Hybrid Data Compression Scheme for Power Reduction in Wireless Sensors for IoT
Authors: Deepu, Chacko John 
Heng, Chun Huat 
Lian, Yong 
Keywords: wireless sensors, Hybrid compression, internet-of-things, lossless, lossy, wearable devices
Issue Date: 7-Nov-2016
Publisher: Institute of Electrical and Electronics Engineers
Source: Deepu, Chacko John, Heng, Chun Huat, Lian, Yong (2016-11-07). A Hybrid Data Compression Scheme for Power Reduction in Wireless Sensors for IoT. IEEE Transactions on Biomedical Circuits and Systems PP (99) : 1-10. ScholarBank@NUS Repository. https://doi.org/10.1109/TBCAS.2016.2591923
Abstract: 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.
Source Title: IEEE Transactions on Biomedical Circuits and Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/130536
ISSN: 19324545
DOI: 10.1109/TBCAS.2016.2591923
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