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A hierarchical adaptive spatio-temporal data compression scheme for wireless sensor networks

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Abstract

How to reduce the number of transmissions or prolong the lifetime of wireless sensor networks significantly has become a great challenge. Based on the spatio-temporal correlations of sensory data, in this paper, we propose a hierarchical adaptive spatio-temporal data compression (HASDC) scheme to address this issue. The proposed compression scheme explores the temporal correlation of original sensory data by employing the discrete cosine transform and adaptive threshold compression algorithm (ATCA). And then, the cluster head node explores the spatial correlation among the compressed temporal readings by utilizing discrete wavelet transform (DWT) and ATCA. The HASDC scheme obtains better recovery quality and compression ratio by combining data sorting, ATCA and spatio-temporal compression concept. At the same time, according to the correlation of sensory data and the adaptive threshold value, the HASDC scheme can adjust the compression ratio adaptively, thus it’s applicable to different physical scenarios. Finally, the simulation results confirm that the transformed coefficients are more concentrated than the ones without introducing DWT, and the proposed scheme outperforms other spatio-temporal schemes in terms of compression and recovery performances.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Nos. 61572262, 61771258), the Six Talented Eminence Foundation of Jiangsu Province (No. XYDXXJS-044), the Natural Science Foundation of Jiangsu Province (No. BK20151507), the 1311 Talents Plan of NUPT, the Scientific Research Foundation of NUPT (No. NY217057) and the Natural Science Foundation for Colleges and Universities in Jiangsu Province (No. 16KJB520034).

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Authors and Affiliations

  1. Key Lab of Broadband Wireless Communication and Sensor Network Technology of Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing, China

    Siguang Chen, Jincheng Liu, Kun Wang & Meng Wu

Authors
  1. Siguang Chen

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  2. Jincheng Liu

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  3. Kun Wang

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  4. Meng Wu

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Correspondence toSiguang Chen.

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