Adaptive Adjustment of Compressed Measurement for Energy-Efficient Data Gathering in WSNs
Zhipeng Nie, Dandan Wang, Donghao Wang, Nan Wang, Jiangwen Wan · 2016
In CS based data gathering method, a small number of measurements arouse a great deal of improvement in reducing energy consumption, and hold a low reconstruction accuracy. In addition, fixed number of measurements are not always work effectively since signals change over time in actual WSNs. Based on recent theoretical results for L1-L1 minimization, a novel adaptive measurement algorithm for compressed data gathering is proposed to calculate the number of measurements taken at each time on-the-fly. In the algorithm, an estimation model is established based on the Pearson correlation of sensor readings within one-hop neighbors, which provides side information for L1-L1 minimization. Compared with conventional data gathering schemes, the proposed method allows a dramatic reduction in the number of measurements or reconstruction error, which contributes to energy-efficient data collection process.