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