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학술대회 Lossy Compression on IoT Big Data by Exploiting Spatiotemporal Correlation
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문애경, 김재영, Jialing Zhang, Seung Woo Son
International Conference on High Performance Extreme Computing (HPEC) 2017, pp.12-14
As the volume of data generated by various deployed IoT devices increases, storing and processing IoT big data becomes a huge challenge. While compression, especially lossy ones, can drastically reduce data volume, finding an optimal balance between the volume reduction and the information loss is not an easy task given that the data collected by diverse sensors exhibit different characteristics. Motivated by this, we present a feasibility analysis of lossy compression on agricultural sensor data by comparing fidelity of reconstructed data from various signal processing algorithms and temporal difference encoding. Specifically, we evaluated five real-world sensor data from weather stations as one of major IoT applications. Our experimental results indicate that Discrete Cosine Transform (DCT) and Fast Walsh-Hadamard Transform (FWHT) generate higher compression ratios than others. In terms of information loss, Lossy Delta Encoding (LDE) significantly outperforms others nonetheless. We also observe that, as compression factor is increased, error rates for all compression algorithms also increase. However, the impact of introduced error is much severe in DCT and FWHT while LDE was able to maintain a relatively lower error rate than other methods.
KSP 제안 키워드
Big Data, Compression Algorithm, Compression factor, Data Volume, Data collected, Delta encoding, Discrete cosine Transform, IOT applications, Information Loss, IoT Devices, Lossy Compression