Distributed data aggregation algorithm based on lifting wavelet compression in wireless sensor networks
Songtao Guo, Ledan Cheng, Ying Wang, Defang Liu · International Journal of Sensor Networks · 2018
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.