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Lightweight Time-Series Signal Compression Period Extraction and Multiresolution using Difference Sequences

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In the Internet of Things (IoT), connected devices generate a massive amount of data that need to be processed and transmitted to the data aggregator or edge device. The connected devices are resource-constrained in terms of memory, computation power, and energy. In this paper, we propose a novel transform using difference sequences. The proposed transform is multiplierless, which makes it very promising for resource-constrained IoT devices. Various properties of the difference sequences, such as orthogonality, linear independence, and circular shift, are studied in detail. These sequences are sparse and take values from the set {0,1,-1}, which make these sequences very efficient in computation. Applications of the proposed transform are shown for lossless compression, period extraction, and multiresolution using electrocardiogram, accelerometer, images, and photoplethysmography datasets. Furthermore, the proposed transform is compared with the state-of-the-art data compression transforms.
Original languageEnglish
JournalIEEE Internet of Things Journal
Pages (from-to)7043-7050
Number of pages8
Publication statusPublished - May 2022

    Research areas

  • Discrete derivatives, Internet of Things (IoT), Lossless compression, Multiresolution, Period estimation, Ramanujan sums, Resource-constrained

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