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

TY - JOUR

T1 - Lightweight Time-Series Signal Compression Period Extraction and Multiresolution using Difference Sequences

AU - Kuldeep, Gajraj

PY - 2022/5

Y1 - 2022/5

N2 - 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.

AB - 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.

KW - Discrete derivatives

KW - Internet of Things (IoT)

KW - Lossless compression

KW - Multiresolution

KW - Period estimation

KW - Ramanujan sums

KW - Resource-constrained

UR - http://www.scopus.com/inward/record.url?scp=85115687770&partnerID=8YFLogxK

U2 - 10.1109/JIOT.2021.3113951

DO - 10.1109/JIOT.2021.3113951

M3 - Journal article

SN - 2327-4662

VL - 9

SP - 7043

EP - 7050

JO - IEEE Internet of Things Journal

JF - IEEE Internet of Things Journal

IS - 9

ER -