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The last two decades have seen tremendous growth in data collections becauseof the realization of recent technologies, including the internet of things(IoT), E-Health, industrial IoT 4.0, autonomous vehicles, etc. The challenge ofdata transmission and storage can be handled by utilizing state-of-the-art datacompression methods. Recent data compression methods are proposed using deeplearning methods, which perform better than conventional methods. However,these methods require a lot of data and resources for training. Furthermore, itis difficult to materialize these deep learning-based solutions on IoT devicesdue to the resource-constrained nature of IoT devices. In this paper, wepropose lightweight data compression methods based on data statistics anddeviation. The proposed method performs better than the deep learning method interms of compression ratio (CR). We simulate and compare the proposed datacompression methods for various time series signals, e.g., accelerometer, gassensor, gyroscope, electrical power consumption, etc. In particular, it isobserved that the proposed method achieves 250.8\%, 94.3\%, and 205\% higher CRthan the deep learning method for the GYS, Gactive, and ACM datasets,respectively. The code and data are available athttps://github.com/vidhi0206/data-compression .

Near Lossless Time Series Data Compression Methods using Statistics and Deviation