Abstract :
As the volumes of sensor data being accumulated are likely to soar, data compression has become essential in a wide range of sensor-data applications. This has led to a plethora of data compression techniques for sensor data, in particular model-based approaches have been spotlighted due to their significant compression performance. These methods, however, have never been compared and analyzed under the same setting, rendering a "right" choice of compression technique for a particular application very difficult. Addressing this problem, this paper presents a benchmark that offers a comprehensive empirical study on the performance comparison of the model-based compression techniques. Specifically, we reimplemented several state-of-the-art methods in a comparable manner, and measured various performance factors with our benchmark, including compression ratio, computation time, model maintenance cost, approximation quality, and robustness to noisy data. We then provide in-depth analysis of the benchmark results, obtained by using 11 different real data sets consisting of 346 heterogeneous sensor data signals. We believe that the findings from the benchmark will be able to serve as a practical guideline for applications that need to compress sensor data.
Keywords :
data compression; approximation quality; compression ratio; computation time; model based approaches; model maintenance cost; noisy data robustness; sensor data applications; sensor data compression; sensor data signals; sensor data volumes; state-of-the-art methods; Benchmark testing; Chebyshev approximation; Computational modeling; Data models; Piecewise linear approximation; Principal component analysis; Lossy compression; benchmark; sensor data;
