Title: Distributed data aggregation algorithm based on lifting wavelet compression in wireless sensor networks

Authors: Defang Liu; Songtao Guo; Ledan Cheng; Ying Wang

Addresses: School of Chemistry and Chemical Engineering, Southwest University, Chongqing 400715, China ' National and Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology (Chongqing), College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China ' College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China ' School of Computer and Information Science, Southwest University, Chongqing, 400715, China

Abstract: Compressive sensing (CS) is one of the most promising recoverable data aggregation schemes, which can considerably reduce the amount of data transmitted. However, CS technique brings heavy aggregation burden on sensor nodes, which challenges their restricted available energy and computation capacity. In this paper, we focus on the energy-efficient data compression with the objective of recovering the original dataset. We first propose a dynamic clustering algorithm based on data spatial correlation (CDSC) to balance aggregation load. Furthermore, we propose a faster data compression approach based on eliminable lifting wavelet, which can eliminate spatial and temporal data redundancy. Also, it offers high fidelity recovery for the raw data. Extensive experiments demonstrate that our CDSC algorithm outperforms other methods on prolonging network lifetime and reducing the amount of data transmitted.

Keywords: data aggregation; spatial data correlation clustering; compressive sensing; wavelet compression; WSNs; wireless sensor networks.

DOI: 10.1504/IJSNET.2018.093980

International Journal of Sensor Networks, 2018 Vol.27 No.4, pp.227 - 238

Accepted: 08 Feb 2018
Published online: 10 Aug 2018 *

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