Sparse Sensor Placement for Interpolated Data Reconstruction Based on Iterative Four Subregions in Sensor Networks

Data acquisition in large areas has issues of cost and data loss. When sensors are sparse in the physical field, it is critical to study the deployment methods to improve the accuracy of reconstructed data set and the precision of the recovery of lost data. It is desirable to place sensors at optimal locations to achieve higher precision of recovery. In this paper, we present a sparse sensor placement scheme for data interpolation reconstruction based on iterative four subregions using fractal theory. The results of our experiments demonstrate that the precision of our algorithm is higher than that with random placement in dispersion degree, coverage rate, and reconstruction accuracy.

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