Adaptive Sampling Allocation for Distributed Data Storage in Compressive CrowdSensing

Distributed data storage (DDS) can assist compressive crowdsensing (CCS) to solve the challenge of temporary data storage in the network. Block compressive sensing effectively addresses DDS of large spatiotemporal data from crowdsensing, allowing efficient reconstruction at low storage and computational costs. However, when the data is unevenly distributed over the sensing area, existing algorithms ignore the variability of information between blocks and still require the central server to uniformly collect samples from each block stored on the mobile device, leading to a reduction in overall reconstruction accuracy. To address this, we propose an adaptive sampling allocation strategy that deeply analyzes the statistical information of each block which can help the central server to collect the number of measurements for each block adaptively to improve the sampling quality. Additionally, we consider the correlation between blocks and use a global denoising strategy to further improve the reconstruction accuracy. Experimental results demonstrate that, compared to the state-of-the-art DDS-CCS algorithm, our proposed adaptive sampling allocation with a joint-denoising mechanism significantly improves the accuracy of the information-rich blocks that most affect the global accuracy, and hence the global reconstruction accuracy. which also remains robust to different block sizes and exhibits improved stability.