Adaptive Sensor Data Compression in IoT systems: Sensor data analytics based approach

Abstract

Sensor nodes are embodiment of IoT systems in microscopic level. As the volume of sensor data increases exponentially, data compression is essential for storage, transmission and in-network processing. The compression performance to realize significant gain in processing high volume sensor data cannot be attained by conventional lossy compression methods. In this paper, we propose ASDC (Adaptive Sensor Data Compression), an adaptive compression scheme that caters various sensor applications and achieve high performance gain. Our approach is to exhaustively analyze the sensor data and adapt the parameters of compression scheme to maximize compression gain while optimizing information loss. We apply robust statistics and information theoretic techniques to establish the adaptivity criteria. We experiment with large sets of heterogeneous sensor datasets to prove the efficacy. Nonlinear lossy compression (Chebyshev) is extensively considered as the standard technique as well as experimental result with frequency domain compression like Discrete Fourier Transform (DFT) is shown as future scope of further improvement.